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Computer Science
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Dynamic positioning of beacon vehicles for cooperative underwater navigation

Autonomous Underwater Vehicles (AUVs) are used for an ever increasing range of applications due to the maturing of the technology. Due to the absence of the GPS signal underwater, the correct estimation of its position is a challenge for submerged vehicles. One promising strategy to mitigate this problem is to use a group of AUVs where one or more assume the role of a beacon vehicle which has a very accurate position estimate due to an expensive navigation suite or frequent surfacings. These beacon vehicles broadcast their position and the remaining survey vehicles can use this position information and intra-vehicle ranges to update their position estimate. The effectiveness of this approach strongly depends on the geometry between the beacon vehicles and the survey vehicles. The trajectories of the beacon vehicles should thus be planned with the goal to minimize the position uncertainty of the survey vehicles. We propose a distributed algorithm which dynamically computes the locally optimal position for a beacon vehicle using only information obtained from broadcast communication of the survey vehicles. It does not need prior information about the survey vehicles' trajectory and can be used for any group size of beacon and survey vehicles.

I. Introduction
Autonomous Underwater Vehicles (AUVs) navigating underwater face significant localization challenges when compared to aerial or ground vehicles. High frequency electromagnetic waves such as Global Positioning System (GPS) signals do not penetrate the water more than a few millimeters. For the same reason other localization methods based on optical systems such as laser range finders or cameras cannot be employed except in a few niche applications. AUVs thus rely mostly on deadreckoning navigation using proprioceptive sensors which inadvertently accrue a position drift which grows without bound. The most common methods which provide The work presented in this paper was carried out at MIT and EPFL. At MIT it was funded by Office of Naval Research grants N00014-97-1-0202, N00014-05-1-0255, N00014-02-C-0210 and N00014-07-1-1102. At EPFL it was funded by the Spin Due to the limited range of these systems (< 10 km) and the time required to deploy them they are normally used in applications where the are of operations is only a few km 2 . In addition to the restrictions submerged vehicles face with respect to navigation, communication is also restricted to slow (≈ 1 kBytes/s), acoustic channels, where the limited bandwidth only allows for only one vehicle transmitting at a time.
As AUV technology matures AUV operations shift from deploying a single vehicle to deploying a larger group of vehicles. Deploying several AUVs is beneficial for many applications, particularly those which require a large area to be surveyed or redundancy is key. In addition to these benefits a group of AUVs provide the opportunity to implement Cooperative Navigation (CN). The underlying idea of CN is that in group of vehicles where the individual members operate sufficiently close together the individual vehicles can exchange navigation information and thereby improve their own position estimate. This requires that each vehicle is outfitted with an acoustic modem for vehicle-to-vehicle communication.
In addition the modem provides intra-vehicle ranges through one-way or two-way travel time measurements. If in such a group a single vehicle has a more accurate position estimate than the other members, the position estimate of all vehicles within communication range can be improved.
One possible CN strategy has no vehicles which are designated navigation aids and the approach simply relies on the fact that some vehicles may have a better navigation estimate than others. As all vehicles usually broadcast their position estimate to provide a telemetry feedback to the surface operator this approach does not require any additional hardware or communication bandwidth, but only software which incorporates the overheard telemetry packages.
For applications however, such as precision surveys, where navigation accuracy is key the concept of designated Communication and Navigation Aids. The concept of dedicated CNAs was first proposed in [1] for a mine-hunting scenario with the underlying idea that a very small number of CNA (one or two) with a very accurate estimate of their positions could be used to provide a much larger group of Search, Classify and Map SCM-vehicles with navigation information. These SCM-vehicles would be equipped with a special sonar payload to detect buried or free-floating mines. The CNA would be either surface crafts with a permanent access to GPS or an AUV with a very accurate (and expensive) navigation suite. To maintain a bounded uncertainty on their position estimates, these CNA would move at a very shallow depth and surface for a GPS fix whenever necessary. The SCM outfitted with much simpler (and cheaper) navigation sensors would be able to maintain a bounded uncertainty on their position estimates without surfacing over the entire duration of the mission.
The sole mission objective of the CNA is to minimize the overall uncertainty of the SCM vehicles. To accomplish this, their first objective is to maintain a very good estimate about their own position, as in CN any uncertainty in the CNA's position directly translates into an uncertainty in the SCM's position. In addition, the relative position between the CNA and SCM will also strongly affect the position uncertainty of the SCM as shown in [2]. Therefore the second objective of the CNA is to adjust its position such that the CNA-SCM geometry is optimal for CN. This paper proposes an algorithm which attempts to determine a locally optimal position of the CNAs based only on the information available to them through the CNAs' sensors and broadcast messages from the SCMs. No a priori knowledge of the SCMs' trajectory is required. It also does not need any information about the number of CNAs and SCMs. As a result CNA or SCM vehicles which are temporarily outside the communication range will automatically be removed from the optimization and added back once communication is reestablished. This property makes our approach robust against the strong variations of the acoustic communication channel. If additional information is available however several algorithms such as the one proposed in [3] and [4] exist which can provide a globally optimal trajectory.

II. Related work
One particular strength of our method is that it does not need the path of the AUVs to be known a priori. If however such information is available, the approach presented by Chitre [3] and Tan [4] can provide more optimized trajectories.
The problem of selecting an action for an agent, in our case the speed and course of our CNA, in a situation in which several objectives have to be satisfied has been the subject of extensive research [5] and [6]. These methods typically switch between satisfying the different goals individually or perform averaging which does not necessarily lead to the optimal solution. Dias et al. provide a good survey about market-based methods [7].
Benjamin developed the IVP-method which can compute an optimal solution for a set of piece-wise linear objective functions [8]. This implementation was tested in several different scenarios and has demonstrated an Autonomous Surface Craft successfully reaching a waypoint while observing the "rules of the road" [9] and tracking Fig. 1. A set of 5 vehicles performing CN using an EKF. Each vehicle i maintains the distribution over its state (red) through a mean vector µ i and the associated covariance matrix P i . This information, along with the unique ID is broadcasted to other vehicles. Here a broadcast from vehicle 2 is received by vehicle 1.
underwater targets with a towed array while ensuring that its maneuvers would not damage the array [10].
While not addressing the problem of dynamically positioning the beacon, several papers deal with the specific case of one or more submerged vehicles navigating of a single beacon. They also address the influence intravehicle geometry on the effectiveness of the approach [11], [12].

III. Cooperative Navigation
When applying the EKF to solve the problem of CN, we assume that all n vehicles of the set of participating vehicles V i = {1, . . . , i, . . . , n} maintain a vector which consists of the mean vector that contains the estimate of their position at time k, as well as P i the covariance matrix describing the uncertainty associated with that estimate.

A. Prediction
Whenever vehicle i = 1 obtains proprioceptive measurements u 1 (k) from its dead-reckoning sensors, µ 1 (k) and P 1 (k) are propagated (µ and P denote the state after the predict step, but before the update step) where Q 1 (k + 1) is a matrix where the elements contain the variances of the motion model which is modeled as zero-free Gaussian noise and G 1 (k + 1) is the Jacobian containing the partial derivatives of g.

B. Update
If vehicle 1 receives a broadcast from vehicle 2 at k that contains µ 2 (l) and P 2 (l) together with an intra-vehicle range measurement r 1,2 (k), it uses this information to update its estimate of its own position as follows: First, it computes what the predicted range z 1,2 (k) between the two vehicles would be, based on their estimated position.
The difference between the predicted measurement and the measured distance z 1,2 (k) − r 1,2 (k) represents the innovation.
The covariance matrix of vehicle 1 and vehicle 2 are combined into Note that P 1 (k + 1) and P 2 (k + 1) are assumed to be independent (P 1,2 (k + 1) is diagonal). This is not generally true and if the non-zero off-diagonal elements of P 1,2 (k + 1) are ignored, the EKF can become overconfident and diverge. As keeping track of these elements in CN is very difficult, however, [13] proposes a method which keeps P 1 (k + 1) and P 2 (k + 1) independent. We compute the Jacobian H 1,2 (k + 1) that contains the derivatives of the range measurement with respect to the position of vehicle 1 and 2 (time index k omitted on matrix components). Using the residual covariance and the variance S 1,2 (k + 1) = H 1,2 (k + 1)P 1,2 (k + 1)H T 1,2 (k + 1) + σ 2 r and σ r associated with the exteroceptive (range) sensor we compute the Kalman gain that represents a weighting factor for how much the measurement will affect the updated position. Using the innovation z 1,2 (k) − r 1,2 (k) and the Kalman gain, the updated combined position estimate is µ 1,2 (k + 1) = µ 1 (k + 1), µ 2 (k + 1) = µ 1,2 (k + 1) + K 1,2 (k + 1) z 1,2 (k) − r 1,2 (k) (5) and the combined covariance is from which we can extract the updated position estimate µ 1 (k + 1) and the updated covariance P 1 (k + 1) for vehicle 1. Note that we also obtain an updated estimate for the position and covariance of vehicle 2 P 2 (k +1) and µ 2 (k + 1).

IV. Algorithm
Our algorithm computes the optimal future position of a CNA such that a position-information broadcast from this position by the CNA will reduce the combined position uncertainty of all AUVs by the largest amount. The algorithm is decentralized and as such only incorporates information which is locally available or overheard through the acoustic channel. Using decentralized algorithms is a key requirement in the underwater domain as the reliable communication channel to a single controller, as required by centralized topologies, is not available. As we do only use locally available information and in particular don't have any knowledge about the future SCM positions (beyond actual course and speed) we are not able to compute a globally optimal trajectory. For the remainder of this paper "optimal" thus refers to a local optimum within the set of locations which can be reached by the CNA at that time.
The metric which is minimized in this version of the algorithm is the sum of the trace differences between the prior and posterior covariances of the AUV's position estimates. This metric assumes that the navigation algorithm running on all vehicles is an EKF as described in the previous section. The algorithm however can accommodate other Bayes filters and any state representation by modifying line 6 in algorithm 2 and line 6 in algorithm 3 accordingly. Also, the metric which is minimized can be changed to other metrics by modifying line 5 in algorithm 4. The following assumptions are made by the adaptive positioning algorithm:

A. Vehicles
There are two groups of vehicles. A group of AUVs, A, which carry out a mission and a group of CNA, C, which serve as moving navigation beacons. Optimizing the relative position between CNA and an AUV is entirely left to the CNA as it is assumed that each AUV's track is solely controlled by its mission objective. No CNA needs to be aware a priori of all members of the set of participating AUVs and CNAs. The sets A and C can be updated dynamically.

1) Communication:
Each member of A and C shall be outfitted with an acoustic modem for data transmission and intra-vehicle ranging. As only one vehicle can transmit at any given time, there will be a schedule S which assigns a time slot during which a vehicle (CNA or AUV) can broadcast a status message. The schedule S is, either, provided to all vehicles before the mission starts or, in the case of a central communications controller which initializes communication through polling, the vehicles "learn" the schedule as they overhear polling requests. It is assumed that the schedule is repetitive and does not change over a longer period of time such that predictions about the time of future transmissions are possible once S is known. Each entry in S consists of a vehicle identification number, i, and a broadcast time, t b i , which is relative to the start of the schedule. When a vehicle i broadcasts, its transmission m i not only contains the actual distribution over its pose estimate x i , but also its course θ i and speed v i or even a short description of the upcoming mission plan. This information fits into a typical modem packet with the size of ≈ 40 bytes. This will enable every other vehicle overhearing this message to compute a short-time prediction of the vehicle's future position. The message also contains a unique vehicle identification number i. Each vehicle also stores the predicted positions of CNAs and AUVs in the according entries in A or C.
2) Sensors: Optionally, the CNA may have available to them a sensor table N which contains a set of tuples, in which each tuple n i ∈ N contains information about the i-th sensor's capabilities. If this information is available to the CNA it can also carry out short-term predictions about the future position and uncertainties of the AUV and CNA.
The adaptive positioning algorithm consists of four modules (Algorithm 1, 2, 3 and 4), which are run on each CNA individually when the appropriate conditions are met. Algorithm 2 and 3 both call the function algorithm 4 which computes the optimal CNA position for a given setup of CNAs and AUVs.
Algorithm 1 is run whenever the CNA receives a broadcast from an AUV.
Algorithm 2 is run whenever the CNA receives a broadcast from another CNA.
Algorithm 3 is run whenever the schedule S indicates that the CNA should broadcast.
Algorithm 4 is a function which computes an optimal future CNA position when the position and associated uncertainties of all CNAs and AUVs have been predicted for this time.

B. Message Reception from an AUV (Algorithm 1)
When a CN receives a broadcast from an AUV, a j , it decodes the message (line 3) and uses it to update its estimate of the future positions and associated uncertainties of a j up to the next time t b i (line 4) at which the CNA is scheduled to broadcast. It achieves this by forward projection using a j 's actual position course and speed (line 5) and the information about a j 's sensor quality which is retrieved from N i (j). If the received message m A j (t 0 ) from a j contains a description of its short term mission plan an even more accurate prediction can be made. For the scenario we use to illustrate the algorithm, all predictions are based on available course and speed information. The functions g(·) and h(·) in line 5 also use the information locally stored in C i so as to consider the message broadcasts from all other CNA which occur between the current time (t 0 ) and t b i and how they will affect the AUV's position estimate at the time t b i . The updated information about a j is stored in Algorithm 1 Executed on CNA whenever a message from an AUV is received.
end if 8: end loop

C. Message Reception from Another CNA (Algorithm 2)
When a message is received from CNA c j it shall contain a more recent estimate of the CNA's state estimate x C j , the associated uncertainty P C j as well as the actual course and speed (estimates) v C j and θ C j (line 3). The algorithm then locally emulates the effect that that specific broadcast would have had on the positioning estimate of all AUVs assuming that all AUVs received the message. This is carried out as follows: Firstly, it fetches the predicted position, x A k , and uncertainty estimate, P A k , for the actual time t 0 for each AUV in A i from its AUV table (line 5). It then updates the position and uncertainty of each AUV using the Kalman state update (5) and the uncertainties using the Kalman covariance update (6) (line 6) and then stores the the resultant estimate back into the table A i (k) (line 7).
Algorithm 2 then duplicates the decision making process taking place at CNA c j . Using the communications schedule S i (j), it computes the point in time, t b j , at which CNA c j will broadcast again (line 9). Calling the function compute opt CNA position (algorithm 4) with the actual position of c j obtained from m C j (t 0 ) and our Algorithm 2 Executed on a CNA whenever a message from another CNA is received.
if message m C j received from CNA c j ∈ C i then 3: for all a k ∈ A i do 5: 10: end if 14: end loop local knowledge of the future positions of the AUVs and the CNAs, we can compute the optimal position x C j opt (t b j ) for c j (line 11). If all information transmitted through the acoustic modems was received by all vehicles, then CNA c i and c j will have the same positioning information available and x C j opt (t b j ), computed locally by c j , should be the same location computed by c i . If not all values were equally shared, c i and c j will compute different values, but in the absence of any other information x C j opt (t b j ) is the best prediction for c j 's position at t b j . Additionally we use the table entry for c j 's sensor noise characteristics N i (j) to predict the future position uncertainty at x C j opt (t b j ) (line 11). The new estimate about c j 's future positions is updated in C i (j, t b j ) (line 12).

D. CNA broadcast (Algorithm 3)
When the actual time, t 0 , matches its scheduled broadcast time, t b i , CNA c i first broadcasts a message m C i (t 0 ) containing its actual position estimate x C i , associated covariance P C i as well as its actual course θ C i and speed v C i (line 3) in a similar manner to that of algorithm 2. First, the effect that this CNA's position broadcast would have on each AUV is modeled, in which it is assumed that each received the latest broadcast m C i (t 0 ) (line 5, 6 and 7). Then using the schedule S i the next broadcast time t b i is computed (line 9). At this time all available information about the positions of each CNA and AUV at t b i (from A i (t b j ) and C i (t b j )) is used to determine the optimal position, x C i opt (t b i ) at which the CNA's next Algorithm 3 Executed on a CNA whenever it is scheduled to broadcast.
for all a k ∈ A i do 5: end for 9: 10: end if 14: end loop broadcast should take place (line 10). The position uncertainty accumulated up to x C i opt (t b i ) is predicted based on the actual position and uncertainty, as well as the future position and the sensor noise N i (line 11). All updated information is stored in C i (i, t b j ) (line 12).

E. Determining the Optimal CNA Position (Algorithm 4)
This function computes the optimal CNA position for a desired time, t b i , assuming that the predicted position of all other CNAs in C i and the positions for all AUVs in A i are available.
As we showed in [14] that there is no closed form solution to find the optimal beacon point, we chose a brute-force approach. The function first computes a grid of discrete positions M which could possibly be reached by the CNA before the next broadcast (line 1). The number of grid positions in M depends on the maximum speed of the vehicle, v max , the time between now (t 0 ) and the next broadcast t b i and the spacing of the grid points. As the runtime of the function is linearly dependent on the number of grid points, the grid spacing can be varied depending on v max , t b i and the available CPU cycles. For each grid point, x C p in M , we now compute by how much the overall position uncertainty would be reduced if it would broadcast from this point at t b i . It does this by fetching the position x A k (t b i ) for each AUV a k (line 4) and computing the difference between the trace of the prior P A k (t b i ) and posterior covariance matrix P A k (t b i ), assuming a Kalman update (6) by c i from position x C p . The trace differences for all AUVs are summed up and Algorithm 4 Compute the optimal position x C opt for a CNA c i for a predicted time t b i . It assumes that the position and uncertainties for all other vehicles (CNAs and AUVs) are given by A i and C i .
for all a k ∈ A i do 4: stored in K (line 5). K has the same size as M . After the total achievable improvement has been computed for all x C p (t b i ), we determine the largest entry in K. The position which maps to this entry is the optimal position x C p opt to which the CNA should move so as to maximally reduce the uncertainty of the AUV set (line 8).

V. Results
To test this adaptive positioning algorithm we simulate two scenarios. The first scenario (  A. One AUV, one CNA Figure 2 shows the simulation results for the most basic possible CN setup, one CNA and one AUV. Every 60 seconds the CNA broadcasts its position and then computes the optimal position for the next broadcast. As there are no other CNA present, the CNA only needs to take the effect of its own updates and the vehicles' sensor performance into account. The top plot, at t=20 s, shows the situation directly after the mission commenced. The CNA has just broadcast its position and the position it predicts for the AUV at the next broadcast which is marked with red "+". The semi-transparent circle with radius r = ∆t · v max = 60 s · 2 m/s = 120 m marks all positions which the CNA could reach at maximum speed. Our algorithm discretizes this circle into grid points with 5 m spacing. It then computes, for each grid point, the position uncertainty which the AUV would have after a hypothetical update broadcast by the CNA from this grid position. The difference between the prior and posterior trace of the AUV's position estimate is represented by the color of the semi-transparent circle. Positions marked blue would lead to a very small decrease in overall uncertainty and positions marked red to a very high overall decrease. The mapping between the absolute value of K(p) and the color is scaled, each time the circle is plotted, to span the maximum color space. Thus we cannot provide a legend which maps colors to absolute values for K(p). The position which corresponds to the maximum of that difference is selected as the future position for he CNA.
As the AUV has a high variance in its heading direction it accumulates the highest uncertainty in the direction perpendicular to the direction it is traveling in. As shown by Zhou and Roumeliotis in [15], the biggest decrease in the trace of the covariance can be achieved if the beacon vehicle is somewhere along the semi-major axis of the AUV's covariance ellipse. Bruteforce computation confirms this, by highly favoring positions perpendicular to the direction in which the AUV is traveling, illustrated in dark red, for the first update. At t=72 s (middle plot) the CNA has reached its planned position. The AUV has reached its predicted position and the CNA has transmitted its message and computed a new optimal broadcast position for its new message. As the previous broadcast, at t=70 s, strongly reduced the error in the north-south direction, the along-track error will dominate the position uncertainty and the optimal position is in line with the vehicle traveling. The bottom plot, at t=320 s, shows the vehicles after the fifth broadcast. At this stage a "saw-tooth" pattern has been established, in which the CNA oscillates between the two relative positions (top and middle plot). Due to the much larger distances that the CNA has to travel in this scenario, compared to those of the AUV, the distance between the CNA and the AUV slowly increases, as reaching the optimal relative position is the CNA's only goal. Future versions of the algorithm will enforce a minimum distance between the vehicles.

B. Two AUV, Two CNA
A more complex CN-scenario is shown in figure 3. Here, two CNA try to jointly optimize their trajectory to improve the position uncertainty for two AUV. All four vehicles start at the same position and both CNA broadcast their position every 30 s. After CNA 1 broadcasts its first message, at t=10 s, it determines that the position marked by the blue "+" is the optimal position for its next broadcast. Meanwhile CNA 2 waits until its first broadcast, at t=40 s, and then determines its optimal position for its next broadcast at t=100 s (cyan "+"). When computing the trace difference represented by the semi-transparent circle in the middle plot (the corresponding circle for CNA 1 is not shown as they would overlap), CNA 2 takes the effects of the broadcast from CNA 1 at t=70 s into account, as otherwise it would also head for the optimal position previously computed by CNA 1, leading to a redundant update. Shortly after CNA 2 reaches its computed position, all four vehicles achieve the stable position of a quadrilateral which is maintained throughout the mission (bottom plot).
The "one AUV, one CNA scenario" depicted in figure 2 shows how optimizing the trajectory for the shortterm optimal broadcast position alone can lead to a sub-optimal long-term solution as the distance between the vehicles constantly grows until the distance is too long for transmission. Therefore we would also like the dynamic positioning of our CNA to be influenced by other objectives such as maintaining a minimum distance to all vehicles. If the acoustic propagation conditions are known, choosing the broadcast position such that the transmission loss to all vehicles is minimized could be another possible objective. Fusing multiple objectives is beyond the scope of this paper. However the output of our algorithm could provide an input into the IVP method proposed in [10] which would carry out the fusion.

VI. Conclusions
In this paper we propose an algorithm which allows dedicated mobile navigation beacons (CNAs) to optimally position themselves in order to best serve a group of submerged vehicles carrying out a mission (SCMs). The algorithm does not require any a priori knowledge about the vehicle's path and only uses information available from overheard broadcasts and proprioceptive sensors. It is completely distributed and can dynamically adapt to a change in the number of CNAs and SCMs. As the required update rates are low, the computational load of the algorithm is negligible. Simulations for two scenarios show that stable CNA trajectories can emerge.
Future versions of the algorithm will improve the resilience towards loss of communication by including link quality information. By optimizing the trajectory for improved intra-vehicle communication, we also enable the CNA to better serve in its secondary role as communications hub or relay.


Neural Network Approach for Eye Detection

Driving support systems, such as car navigation systems are becoming common and they support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness based on eye-blink count and eye directed instruction controlhelps the driver to prevent from collision caused by drowsy driving. Eye detection and tracking under various conditions such as illumination, background, face alignment and facial expression makes the problem complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently. In the proposed algorithm, first the neural Network is trained to reject the non-eye regionbased on images with features of eyes and the images with features of non-eye using Gabor filter and Support Vector Machines to reduce the dimension and classify efficiently. In the algorithm, first the face is segmented using L*a*btransform color space, then eyes are detected using HSV and Neural Network approach. The algorithm is tested on nearly 100 images of different persons under different conditions and the results are satisfactory with success rate of 98%.The Neural Network is trained with 50 non-eye images and 50 eye images with different angles using Gabor filter. This paper is a part of research work on"Development of Non-Intrusive system for real-time Monitoring and Prediction of Driver Fatigue and drowsiness"project sponsored by Department of Science&Technology, Govt. of India, New Delhi at Vignan Institute of Technology and Sciences, Vignan Hills, Hyderabad.

INTRODUCTION
Recognition of human faces and various features out of still images or image sequences is an actively developing research field. There are many applications with systems coping with problem face and eye detection such as face identification for security purpose. Due to variation in illumination, background, face alignment and facial expression the problem becomes complex.Face and eye recognition is an actively developing research field and one of the most successful applicationsof image analysis and understanding. There is large numberof commercial, securities, gaze and human interaction detection, fatigue detection for intelligent vehicle systems, video conferencing and vision assisted user interface and forensic applications requiringthe use of face recognition technologies. These applicationsinclude face reconstruction, content-based image databasemanagement and multimedia communication. Early eye detection algorithms used simple geometric models, template matching etc.,but the eye detection process has now matured into a science ofsophisticated mathematical representations and matchingprocesses.Major advancements and initiatives in the past ten to fifteenyears have propelled face recognition technology into thespotlight. Eye detection and localization have playedan important role in face recognition over the years. Even greater concern is the structure of image of an eye varies considerably with distance and camera resolution. The motivation of this work is to detect eyes for real time monitoring and prediction of driver fatigue and drowsiness. The following section summarizes various techniques that have been utilized in the field of eye detection research.

RELATED WORK
A lot of research work has been published in the field of eye detection so far. Various techniques have been proposed using template matching, IR based approaches, feature based approach, Skin detection method, Hough transform method, Eigen space method or combination of these for eye detection.
In the template matching method, segments of input image are compared with previously stored images, to evaluate the similarity using correlation values. The problem with template matching is it cannot deal with eye variations in scale, expression, rotation and illumination. A method of deformable templates is proposed by Yuille et al. This provides an advantage of finding some extra features of an eye like its shape and size at the same time. But the success rate of this method depends on the templates.The most common approach employed to achieve eye detection in real-time [1,2,3,4] is by using infrared lighting to capture the physiological properties of eyes and an appearance-based model to represent the eye patterns. The appearance-based approach detects eyes based on the intensity distribution of the eyes by exploiting the differences in appearance of eyes from the rest of the face. This method requires a significant number of training data to enumerate all possible appearances of eyes i.e. representing the eyes of different subjects, under different face orientations, and different illumination conditions.Vezhnevets et al. [5] focus on several landmark points (eye corners, iris border points), from which the approximate eyelid contours are estimated. The iris center and radius is detected by looking for a circle separating dark iris and bright sclera. The upper eyelid points are found using on the observation that eye border pixels are significantly darker than surrounding skin and sclera. The detected eye boundary points are filtered to remove outliers and a polynomial curve is fitted to the remaining boundary points. The lower lid is estimated from the known iris and eye corners. In Skin detection method, the detection of the skin region is very important in eye detection.
The skin region helps determining the approximate eye position and eliminates a large number of false eye candidates. There are various color spaces available to detect skin region such as HSV, RGB, YCbCr, and NTSC spaces to increase the efficiency of eye detection. The data was obtained for all the color space components, and was fitted to a Gaussian curve and peak value determines the color of skin. Pentland et al. [7] proposed an Eigen space method for eye and face detection. If the training database is variable with respect to appearance, orientation, and illumination, then this method provides better performance than simple template matching. But the performance of this method is closely related to the training set used and this method also requires normalized sets of training and test images with respect to size and orientation. Another popular eye detection method is obtained by using the Hough transform. This method is based on the shape feature of an iris and is often used for binary valley or edge maps. Hough transform is a general technique for identifying the locations and orientations of certain types of features in a digital image and used to isolate features of a particular shape within an image. Lam and Yuen [6] noted that the Hough transform is robust to noise, and can resist to a certain degree if occlusion and boundary effects. Akihiko Torii and Atsushi Imiya [7] proposed a randomized Hough transform based method for the detection of great circles on a sphere. Cheng Z. and Lin Y [8] proposed a new efficient method to detect ellipses in gray-scale images, called Restricted Randomized Hough transform. The key of this method is restricting the scope of selected points when detecting ellipses by prior image processing from which the information of curves can be obtained. Yip et al. [9] presented a technique aimed at improving the efficiency and reducing the memory size of the accumulator array of circle detection using Hough transform. The drawback of this approach is that the performance depends on threshold values used for binary conversionof the valleys.
Various other methods that have been adopted for eye detection include wavelets, principal component analysis, fuzzy logic, evolutionary computation and hidden markov models. Huang and Wechsler [10] perform the task of eye detection by using optimal wavelet packets for eye representation and radial basis functions for subsequent classification of facial areas into eye and non-eye regions. Filters based on Gabor wavelets to detect eyes in gray level images are used in [11]. Talmi et al. and Pentland et al. [12,13] use principal component analysis to describe and represent the general characteristics of human eyes with only very few dimensions. In [7] Eigeneyes are calculated by applying Karhunen-Loeve-Transformation to represent the major characteristics of human eyes and are stored as reference patterns for the localization of human eyes in video images. Given a new input image of the same size, it is projected into the eigeneye space. The produced vector describes the similarity of this new image to the eigeneyes. If similarity measure (the Euclidean distance between the mean adjusted input image and its projection onto the eigeneye space) smaller than a threshold, the new image is classified as an eye region. Hjelms and Wroldsen [14] utilize Gabor Filters and PCA for eye detection.Li et al. [15] construct a fuzzy template which is based on the piecewise boundary. A judgment of eye or noneye is made according to the similarity between the input image and eye template. In the template, the eyelid is constructed by a region of adjacent segments along the piecewise boundary. Each segment in the fuzzy template is filled with the darkest intensity value within this segment. This makes the method have invariance to slight change of eye images in size and shape and it gives the method high robustness to different illumination and resolution for input images, by increasing the contrast between eyelid region and its adjacent regions. The proposed algorithm uses a neural network to scan an input window of pixels across the image, where each gray value in the input window serves as an input for the neural network. The neural network is then trained to give a high response when the input window is centered on the eye. After scanning the entireimage, the position with the highest response then reveals the center position of the eye in theimage. In order to allow applicability in far more general situations, i.e. covering a large variety of eye appearances, allowing rotation and scaling, and allowing different lighting conditions the eyes are located by locating micro-features, rather than entire eyes and the neural network responses are post-processed using Gabor filter which exploits the geometrical information about the micro-features.

PROPOSED ALGORITHM
The proposed algorithm is divided in two stages -training and detection stage. The steps of preprocessing with Gabor filter, and the classification usingNeural Network are executed in both

Database
The VITS and GATV face database is used for both stages. It is formed by 10 people with different facial expressions, hair styles, and illumination conditions and without glasses, adding up to 200 grayscale images of different sizes. In the present work, total 100 images are used for training and nearly 100 images for testing.

Training methodology
To train the neural network in first stage a large number of eye and non-eye images are needed. Nearly 50 eyes cropped from different face examples were gathered from face databases at VITS and from GATV face database. The images contained eyes of various sizes, orientations, positions, open and close. These eye images were used to normalize each eye to the same scale, orientation, and position, as follows: 1. Initialize F F, a vector which will be the average positions of each labeled feature over all the faces, with the feature locations in the first face.
2. The feature coordinates in F F are rotated, translated, and scaled, so that the average locations of the eyes will appear at predetermined locations in a 32x20 pixel window.
Forty eye examples are generated for the training set from each original image, by randomly rotating the images (about their center points) up to 10 0 scaling between 90% and 110%, translating up to half a pixel, and mirroring.
Practically any image can serve as a non-eye example because the space of non-eye images ismuch larger than the space of eye images. However, collecting a "representative" set of non-eye is difficult. Instead of collecting the images before training is started, the images are collectedduring training, in the following manner, adapted from [13] 1. Create an initial set of non-eye images by generating 50 random images. Apply the preprocessing steps to each of these images. 2. Train a neural network to produce an output of 1 for the eye images, and 0 for the non-eye images. 3.

Gabor filter
Two-dimensional Gabor functions were proposed by Daugman(1) to model the spatial summation properties (of the receptive fields) of simple cells in the visual cortex. They are widely used in image processing, computer vision, neuroscience and psychophysics. visualize Gabor functions, use a Gabor filter for edge detection and extraction of texture features, simulate simple and complex cells (visual cortex), simulate non-classical receptive field inhibition or surround suppression and use it for object contour detection, and explain certain visual perception effects. The training images are applied to following two-dimensional Gabor function: ݔ ′ = x cosθ + y sinθ ݕ ′ = -x sinθ + y cosθ The Gabor function for the specified values of the parameters "wavelength", "orientation", "phase offset", "aspect ratio", and "bandwidth" will be calculated and displayed as an intensity map image in the output window. (Light and dark gray colors correspond to positive and negative function values, respectively.) The image in the output window has the same size as the input image.
The training data set obtained using Gabor Filter kernel by changing various parameters of Gabor function such as Wavelength, orientation, rotation angle are shown in figure 4 below

Neural network
The network will receive the 960 real values as a 960-pixel input image (Image size ~ 32 x 20). It will then be required to identify the eye by responding with a output vector [25]. The output vectors represent a eye or non-eye. To operate correctly the network should respond with a 1 if eye is presented to the network else output vector should be 0 [25]. In addition, the network should be able to handle non-eye. In practice the network will not receive a perfect image of eye which represented by vector as input.

Architecture of neural network
The neural network needs 960 (p1,p2,p3,…..pm) inputs and output layer to identify the eyes. The network is a two-layer log-sigmoid/log-sigmoid network [26], [27]. The log-sigmoid transfer function was picked because its output range (0 to 1) is perfect for learning to output Boolean values [25]. The hidden layer has 200 neurons [25]. This number was picked by guesswork and experience [25]. If the network has trouble learning, then neurons can be added to this layer [25], [28]. The network is trained to output a 1 for correct detection and 0 for non-eye detection. However, non-eye input images may result in the network not creating perfect 1's and 0's. After the network has been trained the output will be passed through the competitive transfer function.

Test methodology
In the test stage the reliability of the neural network is measured by testing the network with hundreds of input images under different conditions. In the present work an algorithm is developed for segmenting eyes from the given input images. The proposed algorithm is shown in below figure. The algorithm, deals with face extraction using L*a*b Transform color space model by removing the skin region. The eyes are detected by eliminating unwanted regions using HSV Color Space and applying Morphological operation to remove unwanted areas. The proposed algorithm is shown below. is an effective feature in face detection. Several different color spaces have been utilized to label pixels as skin including RGB [16], normalized RGB [17], HSV [24], [18], [19], [20], YCbCr [21], YIQ [22], XYZ [23].
The method proposed in this paper involves skin detection to eliminate background components followed by eye detection. Eliminating the skin region helps in determining the approximate eye position by eliminating a large number of false eye candidates. More elaboration was to be made on the skin color algorithms.RGB color space is the most commonly used basis for color descriptions. However each of the coordinates red, green, and blue, does not necessarily provide relevant information about whether a particular image has skin or not as it is subject to luminance effects from the intensity of the image. The Lab color model is used in order to improve color representation. It is a three-dimensional color space in which color differences perceived to be equally large also have equal distances between them. The Lab color model is applied to segment the face. In the proposed algorithm the Lab color model is applied subsequent to applying HSV color space, to get the exact eye region.
The HSV provides color information similar to humans think of colors. "Hue" describes the basic pure color of the image and "saturation" gives the manner by which this pure color (hue) is diluted by white light, and "Value" provides an achromatic notion of the intensity of the color. As proposed in the paper [29] the first two parameters, H and S will provide discriminating information regarding skin. As reported [29] the value of H shall be between 0.01 and 0.1 i.e 0.01 < H < 0.1 → skin It may be noted that, this does not remove all the skin in the image but small portions of it remains in the image. As Morphological operations for any image is considered as an advantage. This left out skin areas can be effectively removed using Morphological operations. Finally eyes are segmented from the extracted face using HSV color model and Morphological operations as shown in figure below.The features extracted from the above algorithm are submitted to classification by the already trained SVM. After executing this stage, the results are evaluated using the measurements sensitivity, specificity and accuracy. Sensitivity is defined by TP/(TP + FN), specificity is defined by TN/(TN + FP), and accuracy is defined by (TP + TN)/(TP + TN + FP + FN), where TN is true-negative, FN is false-negative, FP is false-positive, and TP is truepositive.

EXPERIMENTAL RESULTS AND DISCUSSION
The proposed algorithm is tested onVITS and GATV database. The algorithm is tested on 100 images in the stage of automatic extraction of the region of the eyes. Figure 6 shows successfully detected eye images. On the other hand, in Figure 7 we have examples of images for which the detection failed. We observed that most of the errors occurred due to the position of the face, especially when the face was turning aside, eye closure and distance.The efficiency of proposed algorithm is evaluated on100 images on which the detection of the region of eyes succeeded. The result of the test is with a sensitivity rate of 88.6%, specificity of 95.2% and accuracy of 89.2%. Figure 7shows eye regions which were classified as non-eye. It is noticed that the error occurred in the images whose eyes regions weredarkened, becoming similar to eyebrow, hair or background regions.

CONCLUSION
Eye detection has become an important issue for different applications. Eyes shall be segmented after eliminating all other portions like background, skin and other human parts in image. Some of the techniques employed by different researches include template matching, Eigenvectors & Hough Transform. We proposed altogether a different technique that employs HSV as well as Lab color spaces for removing all unwanted pixels in the imageexcept eyes and SVM classify the left region in the image as eye or non-eye. Experiments were conducted using 100 images of different head persons, obtained from the standard databases available [24] and VITS database created during the research work. The success rate of the proposed algorithm is 98%, the 2% failure in results is due to head movement, background and due to improper training or neural network.The efficiency of proposed algorithm can also be evaluatedbased on Sensitivity, Specificity and Accuracy , the calculated values are88.6%, 95.2% and 89.2% respectively. The authors continue to work on the subject for further improvements.


The Diagnosis of Traumatic Brain Injury on the Battlefield

The conflicts in Iraq and Afghanistan have placed an increased awareness on traumatic brain injury (TBI). Various publications have estimated the incidence of TBI for our deployed servicemen, however all have been based on extrapolations of data sets or subjective evaluations due to our current method of diagnosing a TBI. Therefore it has been difficult to get an accurate rate and severity of deployment related TBIs, or the incidence of multiple TBIs our service members are experiencing. As such, there is a critical need to develop a rapid objective method to diagnose TBI on the battlefield. Because of the austere environment of the combat theater the ideal diagnostic platform faces numerous logistical constraints not encountered in civilian trauma centers. Consequently, a simple blood test to diagnosis TBI represents a viable option for the military. This perspective will provide information on some of the current options for TBI biomarkers, detail concerning battlefield constraints, and a possible acquisition strategy for the military. The end result is a non-invasive TBI diagnostic platform capable of providing much needed advances in objective triage capabilities and improved clinical management of in-Theater TBI.

THE TBI PROBLEM
In the early years of the wars in Afghanistan (Operation Enduring Freedom, OEF) and Iraq (Operation Iraqi Freedom, OIF), there appeared to be an increase in the numbers of causalities sustaining a traumatic brain injury (TBI). By early 2005 TBI was being called the "signature wound of war in Iraq" as cases of soldiers suffering from TBI were appearing in National newspapers, such as USA Today (Okie, 2005;Zoroya, 2005). The increased awareness and emphasis of TBI in the military population spurned a movement to identify and collect data on the incidence of TBI in our deployed forces.
The incidence of TBI in the deployed forces varies depending upon the type of information collected. One initial study examined a cohort of casualties that were wounded in OEF/OIF from 2001 to 2005. The study reported that approximately 30% of wounds were to the head and neck area with 8% of the total attributed as head wounds (Owens et al., 2008). However, this study was based on casualties treated for wounds, and excluded those that were returned to duty within 72 h, thus potentially missing TBI cases that were mild and did not accompany an open wound. The RAND Corporation's "Invisible Wounds of War" collected data from April 2007 to January 2008, part of which included a telephone survey of 1,965 previously deployed persons. From this survey they reported that 19.5% of the previously deployed persons suffered from a"probable TBI."They further estimated that of the 1.64 million deployed service members (at that time), 19.5% of them, or 320,000 have suffered a TBI (Tanielian and Jaycox, 2008). However, data collected from the Armed Forces Health Surveillance Center show that by the end of 2008 there had only been approximately 130,000 clinically confirmed cases of TBI. In addition, the number of TBI diagnosis rose sharply between the years of 2005 thru 2009, specifically in the cases of mild TBI (mTBI; http://www.health.mil/Research/TBI_Numbers.aspx). Is this rise in TBI a real rise, or an increase in TBI awareness and improved vigilance for detecting TBI? Clearly there appears to be a real challenge on getting an accurate estimate of the actual incidence of TBI experienced by our deployed Forces.

DIAGNOSING TBI
One of the issues creating this challenge lies in the current limitations in the diagnosis of TBI, specifically for the military. In general, the diagnosis of TBI relies on a clinician to accurately interpret a patient's signs and symptoms of injury, often through some type of self-report from the patient and possibly corroborated by a witness. In the case of a penetrating brain injury, the signs and symptoms are more straightforward. However, with closed-head injuries, especially mTBIs/concussions, the symptoms are often not as straightforward or clear. Typically the initial evaluation of a possible TBI captures the patients Glasgow Coma Scale (GCS), the length of loss of consciousness, alteration of consciousness/mental state, and/or post-traumatic amnesia (Harrington et al., 1993;Meyer et al., 2010). If certain symptoms are present the clinician can further evaluate the injury by image analysis of the brain via a computed tomography (CT) scan or even magnetic resonance imaging (MRI), both of which have their own limitations in the ability to detect TBI, especially mTBI (Chastain et al., 2009;Mondello et al., 2011a;Prabhu, 2011). Besides the image analysis, most of the tools to aid in the diagnosis of TBI are subjective and as stated above involve self-reporting. Hence, there are no true objective measures available to determine that the brain indeed has been injured, thus the ability to diagnose closed-head TBI, especially those of the mild-moderate severity, is at best limited by available methods. However, the ability to diagnosis a TBI can also be limited by the setting.
The civilian environment and the military (deployed) environment are vastly different. In a civilian setting, if someone experiences a head injury from a motor vehicle accident, fall, or a sporting event, the event is often witnessed; they are quickly transported to the local medical facility and promptly evaluated by a medical professional, sometimes with access to the latest imaging technology. In the military setting, there is often some type of battle taking place either before, during, or after, when someone experiences a head injury. By its very nature the event is dangerous, and often chaotic and loud, occurring in remote locations and possibly lasting hours before casualties can be evaluated by a clinician. In addition, the military casualty can and often will experience multiple serious injuries (Kelly et al., 2008;Owens et al., 2008) and the evaluation of a potential head injury can take a back seat to life saving interventions, possibly missing the treatment window for the TBI. Further, evacuation priorities and logistics may impact the ability to evaluate all suspected mTBI cases to the same extent as a civilian setting as the ability to image the brain is only located at specific locations in the theater of operations. Clearly the military setting creates another layer of difficulty and complexity in the ability to diagnose TBI.

ON THE BATTLEFIELD
Due to the complexities of war and of TBI, it has been extremely difficult to get a handle on the rate of TBIs experienced during a deployment, not to mention the severity of TBIs or the incidence of multiple TBIs our service members are actually experiencing, or not experiencing. One of the major reasons is the lack of a method to objectively determine if the brain has been injured. In 2006 the Military Acute Concussion Evaluation (MACE) was add to the list of tools to screen casualties (Meyer et al., 2010). While the MACE has been well integrated into the military medical evaluation, it still relies on subjective recall of the events, may be affected by fatigue as other neuropsychological tests and has shown low sensitivity when administered greater than 12 h (Coldren et al., 2010). Thus there remains a gap for the ability to objectively measure brain injury with a method that is not impacted by other factors such as extra-cranial injury (i.e., polytrauma), stress, fatigue, or battlefield conditions. Due to the logistic constraints faced on a battle field, any method involving a piece of equipment, must meet additional requirements sometimes not faced in civilian medical care. The battlefield is often a very austere environment. The most restrictive environments are often isolated locations where power supply is lacking or being provided by a generator which must sustain all of the electrical needs for the deployed force. Since units often need to remain quickly mobile, the footprint of the unit is limited, so space, and weight of equipment is a priority concern. In addition, if medical refrigeration exists, it is typically small and in high demand and there is little hope of having specialized reagents such as deionized water. Further consideration is given to the shelf-life of a piece of equipment and its ability to sustain high altitude, large temperature, and/or humidity fluctuations and a considerably dusty and dirty environment. Taking all of these issues into consideration, the ideal method for the diagnosis of TBI would be quick, simple, easy to obtain, not rely on self-report of symptoms, and be portable. In addition, the results of the test should be able to differentiate the severity of injury and in a most ideal world, be predictive of some level of clinical outcome.

OBJECTIVE DIAGNOSTIC TEST FOR TBI
In recent years there have been a number of technologies under development to objectively aid in the diagnosis of TBI (Marion et al., 2011). Some of these include advances in MRI (Kumar et al., 2010;Prabhu, 2011), quantitative electroencephalogram (EEG; Nuwer et al., 2005), visual tracking (Maruta et al., 2010), and serum based biomarkers of brain injury . Using the logistical constraints mentioned above and the need for the test to not be confounded by conditions of deployment (sleep deprivation, stress, fatigue, etc.), one of the more promising options is the development of a simple blood test to detect brain specific proteins after a TBI. Blood based tests have been successful in the diagnosis of other disease conditions such as cardiac disease and cancer, so it is possible biomarkers could be identified for TBI as well.
The TBI community has been actively engaged in the discovery of biomarkers for TBI in the last decade. A number of review articles have captured the pros and cons of various potential markers Mondello et al., 2011a). Some of the more promising candidates have been tested in human clinical trials of TBI patients, most of them in severe TBI Liliang et al., 2010;Mondello et al., 2010Mondello et al., , 2011bMondello et al., , 2012aVos et al., 2010;Brophy et al., 2011;Stein et al., 2011Stein et al., , 2012Gong et al., 2012). A number of proteins of interest are markers that are not solely found in the brain (i.e., cytokines, growth factors, interleukins; Hergenroeder et al., 2010;Stein et al., 2011;Gong et al., 2012). While these may provide clinical utility in situations of head trauma with no confounding injuries, this is typically not the case in the military combat causality. However, a number of markers of interest are more brain specific, to include S100B, glial fibrillary acidic protein (GFAP), Ubiquitin C-terminal hydrolase L1 (UCH-L1), Neuron Specific Enolase (NSE), spectrin breakdown products (SBDP), and Tau (Siman et al., 2009;Liliang et al., 2010;Mondello et al., 2010Mondello et al., , 2011bMondello et al., , 2012aVos et al., 2010;Brophy et al., 2011;Stein et al., 2012).
Most of the clinical trials have been conducted with severe TBI patients, but a few have also included moderate and mTBI patients as well and show promise (Honda et al., 2010;Papa et al., 2012a,b;Topolovec-Vranic et al., 2011;Egea-Guerrero et al., 2012). In an early study of S100B in mTBI subjects, S100B serum levels were significantly different between mTBI patients and uninjured controls (Nygren De Boussard et al., 2004). However, S100B is also increased in other extra-cranial injuries, making less than ideal in the military environment (Savola et al., 2004). Promising recent studies include a study on moderate-mTBI (GCS ≤ 12), that demonstrated that the levels of GFAP in patient serum were able to significantly differentiate not only between TBI patients and uninjured controls, but also between TBI patients and trauma controls (trauma injury without head injury). Further, when TBI groups were dichotomized into traditional groups of mTBI (GCS 13-15) and moderate TBI (GCS 9-12), the level of serum GFAP were significantly different between groups (Papa et al., 2012a). Similar results were demonstrated with serum levels of UCH-L1 differentiating between mTBI, moderate TBI, normal, and trauma controls . Another study recently characterized the differences in areas under the curve (AUC) for sensitivity and specificity of GFAP, S100B, and NSE in TBI patients (GCS range 5-14) determined by positive CT findings. GFAP performed the best with sensitivity set to 100% and the corresponding specificity was 88.9% on day one compared to S100B with a specificity of 27.8% and NSE with a specificity of 22.2% on day one (Honda et al., 2010).

TBI BIOMARKER PLATFORMS FOR THE FIELD
Although recent trials have indicated that it is possible to detect TBI biomarkers in the serum of injured patients, all of the studies have taken place in civilian centers and were able to use a research laboratory platform for analysis. Typically this platform is a standard enzyme-linked immunosorbent assay (ELISA) which has a number of limitations for use in a combat environment. First, the typical assay requires multiple pieces of equipment (reader, incubator, and automated washing machine), refrigeration of reagents, and deionized water. Second, the assay can be lengthy to run, taking as little as 4 h and as many as 24 h. Third, the standard assay plate consists of 96 wells, which could equate to wasting two-thirds of a plate if there is only one sample to assay. However, when we consider the needs for our military applications the assay in an ELISA platform is indeed the most mature in development and testing of clinical samples. Similarly this platform could potentially have greater sensitivity (lower limits of detection) and serve as a benchtop in a reference laboratory. In addition, porting the standard ELISA assay onto an automated benchtop platform could cut down on some of the size and weight of the system.
On the other end of the spectrum would be the development of a hand-held device that performs similar to a TBI "pregnancy test." Such hand-held systems would be lightweight, portable, and require little logistical support. The assay time and waste would likely be reduced as well. However, these devices do not exist today for the TBI biomarkers that have been examined in clinical studies; therefore they are the least mature in development and Food and Drug Administration (FDA) approval timeline. In addition, it is possible that this platform could suffer in the limit of detection.
As mentioned above, the development of a device platform for the military environment presents unique challenges with different restrictions upon instrumentation at each level of care. The initial level of combat casualty care is located close to the point of injury such as a Battalion Aid Station (BAS). This level of care is the most remote and has the highest logistical constraints concerning power, refrigeration, and footprint. In addition, this level of care does not have much capacity, if any, to hold patients for treatment and evaluation. The most robust level of care on the battlefield is located at a combat support hospital (CSH) or a field hospital. It includes specialist diagnostic resources, specialist surgical and medical capabilities, and operational stress management teams. The holding capacity is sufficient to allow diagnosis, treatment, and holding of those patients who can receive total treatment and be returned to duty. However, it is still a deployed environment faced with the same challenges of reduced and restricted footprint, especially for power and refrigeration. For the highest level of definitive care, patients are evacuated back to fixed facility hospitals such as Landstuhl Regional Medical Center or Walter Reed National Military Medical Center. These hospitals operate as any civilian medical facility with few, if any, logistical constraints.

MILITARY ACQUISITION APPROACH
Given (1) the importance of providing an objective test to aid in the diagnose of TBI, (2) the level of maturity of platforms to evaluate TBI biomarkers, and (3) the unique abilities and constraints on each level of possible medical care, the best approach to solve this problem may be an phased acquisition approach (Mondello et al., 2011a; Figure 1). The first phase of development/fielding should focus on the technology that is the most mature, and potentially fieldable, not only in terms of platform logistics but also in clinical testing and familiarity with the FDA. Therefore, Phase I could focus on an automated benchtop system suitable for use in a fixed medical facility. Although this system is the most complex and logistically intensive, it is the most similar to the standard research ELISA that have been employed in previous testing of clinical samples. In addition, it could possibly have the highest level of sensitivity for detection of biomarkers and ultimately serve as a reference platform for confirmatory testing (higher diagnostic specificity). Phase II could be a small single assay point-of-care system, such as a physician's office or a field hospital. Phase II systems would ideally have the ability to screen for the presence of mild-moderate TBI and differentiate the level of severity for all TBIs. Ideally Phase II systems would be more portable than Phase I systems, have the availability of battery operation and require less refrigeration of reagents. Phase III systems could be a miniaturized hand-held system, most suitable for use by emergency medical personnel or personnel deployed in the remote and more austere locations on the battlefield, such as a BAS. These systems would ideally use whole blood from a finger stick to screen for the presence of TBI. They would use little power supply and require no refrigeration. Currently, Phase III systems are the least mature in development and testing for TBI biomarkers, but would provide the most use for screening of casualties (highest diagnostic sensitivity).
If all three Phases of systems were developed and deployed, one could image a scenario that starts with screening casualties for the presence of TBI biomarkers immediately after an event. Screening tests can typically have higher diagnostic sensitivity than diagnostic specificity (Ruan et al., 2009; i.e., more false positives than false negatives). Using a screening test designed in this manner will ensure you do not miss a casualty with a possible TBI, however you will have more people identified as having a TBI that could later be ruled out. The casualty could be evacuated back to the next higher level of care that can provide a more robust confirmatory test for the TBI biomarkers. At the highest level of care, the test could be more confirmatory and have a higher diagnostic specificity than sensitivity (lower number of false positive) and essentially rule in those with TBI and determine which casualties were false positives for the screening test. In addition, TBI biomarker assays at the highest level of care could also be used to possibly determine what type of injury has occurred and monitor if the injury is getting worse over time or better with treatment (Mondello et al., 2011a). This scenario also fits well with the current military levels of medical care on the battlefield. The biomarker assay screen (Phase I/II) could be completed during the initial assessment after www.frontiersin.org FIGURE 1 | Potential placement of TBI diagnostic assay systems. Each potential device has advantages and disadvantages that constrain its use in the military medical system. Solid lines represent ideal placement of each system, dashed lines represent possible placement of each system depending on logistical support. All device pictures are representative examples of commercially available devices, but do not indicate the use of TBI biomarkers on each device. Pictured benchtop device is Dynex Technologies DS2™and DSX™(http://www.dynextechnologies.com), pictured point-of-care device is Abbott Laboratories ISTAT ® (http://www.abbottpointofcare.com). injury, where the other screening test, the MACE is performed. The biomarker assay confirmatory test (Phase III) could be performed at a field hospital where other more extensive diagnostic tools are available, such as a CT.

SUMMARY
Overall the current methods to diagnosis a TBI could be improved with the development and addition of a non-invasive, objective test for the presence of TBI. Current research and development in the field of biomarkers give hope the development of such an objective diagnostic test. However, military conditions contain logistical constraints which may require different platforms for different levels of care. Development of this non-invasive TBI diagnostic platform applicable to all levels of military care would provide much needed advances in objective triage capabilities and improved clinical management of in-Theater TBI. The ability to objectively determine the occurrence of an initial mTBI/concussion and the incidence of multiple mTBIs on our front lines of defense is critical to the success of our military operations, and to the long term health of our warfighters. Likewise, improved TBI diagnosis will also significantly advance the management of civilian patients.


extfieldsofstudy:
Computer Science
Medicine

text:
The language of gene ontology: a Zipf’s law analysis

Background Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipf’s law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language. Results Annotations from the Gene Ontology Annotation project were found to follow Zipf’s law. Surprisingly, the measured power law exponents were consistently different between annotation captured using the three GO sub-ontologies in the corpora (function, process and component). On filtering the corpora using GO evidence codes we found that the value of the measured power law exponent responded in a predictable way as a function of the evidence codes used to support the annotation. Conclusions Techniques from computational linguistics can provide new insights into the annotation process. GO annotations show similar statistical behaviours to those seen in natural language with measured exponents that provide a signal which correlates with the nature of the evidence codes used to support the annotations, suggesting that the measured exponent might provide a signal regarding the information content of the annotation.

Background
The gene ontology and annotation The Gene Ontology (GO) is used extensively in biology. It provides a structured set of concepts that can be used to describe genes and gene products. These concepts are divided into three separate sub-ontologies focused on molecular function (MF), biological process (BP) and cellular component (CC) [1]. The GO has now been used to annotate many of the standard databases of genes and gene products. This annotation helps to integrate biological resources across various experimental organisms and different data bases [2][3][4]. The power of the GO annotation is that it allows unambiguous communication of knowledge among biologists as to the functionality of gene products, at the same time as making the biological knowledge computer-comprehensible [3,4]. GO annotation is undertaken either manually, automatically, or by some combination of both [4]. The GO Consortium provide codes that indicate the evidence to support the association between a specific GO term and gene product (for example through sequence similarity or direct experimental support). Evidence codes should not be directly used as a measure of the annotation quality [5]; they can, however, help inform the level of belief a user might have in the GO terms assigned [6].
A number of studies have attempted to address issues of annotation quality, for example by looking at the consistency of coding between different annotators [7]. Another study introduced an Annotation Confidence Scoring system for comparing the annotation of genes and gene products to those found in a reference genome set [8]. Others have used the GO evidence codes and term depth in the GO to provide evidence of quality [9]. There is some evidence that sources annotated through manual curation are of higher quality than those annotated automatically [10] as they are the result of the combined effort of many scientists [11]. None of these methods, however, has addressed the core question of how effective the annotations are in conveying meaning to a wider biological audience. We therefore need methods that determine the extent to which annotation is meeting user requirements. Unfortunately, we have very few ways of judging whether the set of annotations produced to describe a collection of genes/gene products in a database works effectively in communicating knowledge between the annotator and the end user of those annotations.

Language and the principle of least effort
The GO provides a vocabulary used by annotators to encode information regarding gene product function, information that the wider community then need to decode. The annotation associated with a gene product can be thought of as a sentence made up of words from GO.
It has long been known that natural languages show power-law behaviour. For example Zipf's law states that for any sufficiently large corpus word frequency is approximately inversely proportional to word rank (in which words are ordered by their frequency within the text, the most common ranked first). Indeed, Zipf's law is considered as the statistical characteristic of human language [12,13], and as a wider property of many different complex systems [14]. This pattern has even been observed in a number of extinct and undeciphered languages such as Meroitic [15], and in the mysterious encrypted 15th century Voynich manuscript [16].
If N(r) is the number of words in a text with rank r then Zipf's law can be expressed as: where α is the Zipf's law exponent. There are a number of different ways in which this behaviour can be represented mathematically -power law behaviour, Zipf's law, Pareto's law -that can be demonstrated to be equivalent [17]. For example, if P (ƒ) is the proportion of words in a text with frequency ƒ then the power law can also be expressed as: It is straightforward to show that β and α are related by: For typical single author sources in English β is about 2 [18][19][20]. There can, however, be variations around this value. For example, in the speech of young children β is around 1.6 [21] whereas β > 2 has been found in sets of nouns taken from single author texts [22]. Almost all texts analysed have values of β in the range [1.6-2.4] [23]. Zipf further argued that the power law behaviour arose from a principle of "least effort" in communication. A communication process can be thought of as having three components; a speaker, a listener and a message. The principle of "least effort" examines the work required from the speaker and the listener in communicating a message [12,24].
Similarly, we can view annotation as a process of communication. Consider the process of annotating the cellular location of the gene product integrin alpha8. The simplest annotation for the speaker (annotator) to produce is a frequently used (and ambiguous) term such as "cell" (GO:0002623). Such an annotation would, however, push greater effort on to the person using the annotationthe listener. The listener's job is easiest if the term used is clear and unambiguous, for example "integrin complex" (GO:0008305). This, however, requires significant effort from the speaker in identifying such rarely used GO terms.

Zipf's law and the gene ontology
In this paper we have applied methods of computational linguistics to large repositories of GO annotation data from a number of complete published genomes. The objectives are to determine the extent to which: GO annotation from complete genomes show power law behaviour; the exponent of the power law provides insights into the nature of the underlying annotation; computational linguistic analysis provide insights into the annotation process.
To do this we have retrieved genome annotations from the Gene Ontology Annotation (GOA) project. In particular, the GOA data can be regarded as a goldstandard annotation set, with a significant portion that has been extensively curated by human experts.

Methods
Gene Ontology identifiers and evidence codes were retrieved from each of the genome projects covered by the Gene Ontology Annotation (GOA) project (the version published in October 2009). Table 1 shows the data sets that were obtained and the total number of annotations and distinct number of GO identifiers included in each.
The mouse and human GOA data sets were then further sub-divided using GO evidence codes to produce eight new data sets with different levels of support from the evidence codes. A set of high confidence (HC) data were derived chosen by selecting annotations labeled with at least one of the IDA (Inferred from Direct Assay); IPI (Inferred from Physical Interaction); IMP (Inferred from Mutant Phenotype ); TAS (traceable Author Statement); EXP (Inferred from experiment); IC (Inferred by Curator); IEP (Inferred for Expression Pattern) or IGI (Inferred from Genetic Interaction) evidence codes. A set of low confidence (LC) data were derived by selecting annotations labeled with IEA (Inferred from Electronic Annotation) evidence codes. These data sets are characterized and described in Table 2.
It can be difficult to calculate an accurate exponent for the Zipf's law exponent if the data are presented in the form of a frequency vs rank graph, particularly as the data for high rank (low frequency) terms are often noisy.
By representing the data in the form of a Pareto distribution it is possible to measure the exponent much more accurately [17]. This is because the Pareto distribution is expressed in terms of the cumulative distribution frequency: where the distribution shape parameter k can be converted to the Zipf's law exponent α via: and to the power law exponent β as below: The cumulative frequency graph is well defined for all values of x, and removes the problem of noise in the low frequency terms [17].
The data on the GO identifier frequencies were therefore analysed using the Matlab packages plfit, plplot and, plpva (version 1.0.10 published in January 2010) developed by Clauset and Shalizi [25]. These packages attempt to fit a power law model to the empirical data (represented as a Pareto distribution) and determine the extent to which the data can be effectively modeled using a power law. These tools provide two statistics describing the data. The first is a P-value that is used to determine the extent to which the power law model is appropriate. If the P-value is greater than 0.1 we can regard the power law to be a plausible model of our data. The second statistic produced is β, the exponent of the power law.

Annotation and power law behaviour
Some of the most frequently used terms in the annotation data are some of the most generic (low term depth). For example the term GO:0005515 (protein binding) is typically one of the top two most frequent terms in all  the MF data analysed and is only two levels down from the root of the molecular function sub-ontology. The top 25% of the most commonly used GO terms for human molecular function have an average depth of 4.6, compared with an average depth of 6.4 for the 25% least commonly used terms. A similar pattern is repeated for all the sub-ontologies in all species examined in this paper (data not shown). This difference is significant (p < 0.001 in a paired t-test), demonstrating that the most commonly used terms are typically less specific (higher in the ontology) than those which are used less frequently (deeper in the ontology). Figure 1 shows the log-log plots of cumulative frequency vs. term rank (Pareto plots) for data from the human GOA. It can be seen from these figures that there is strong support for a power law model for these data for the annotations from all three sub-ontologies, as demonstrated in the P-values returned from the fitting software. Table 3 shows the results obtained for the GOA datasets as defined in Table 1. In all cases the data are welldescribed by a power law, with exponents in the range observed for language (1.6 < β < 2.4). By examining the results we can see that there are differences between the exponents measured for the BP sub-ontology compared with the CC and MF ontologies; the mean value of β for the GO BP sub-ontology is 2.13, for the MF subontology is 1.81, and for the CC sub-ontology is 1.71. The difference between the mean values from the BP compared with the CC and MF ontologies is significant (p < 0.001). There is no significant difference between the exponents measured for the CC and MF sub-ontologies. One interesting anomaly is the value of the exponent measured for the biological process sub-ontology of D. rerio at 1.88 compared with the mean of 2.13.
The analysis was then repeated for the data-sets obtained from the mouse and human GOA data sets divided into high and low confidence evidence codes (the statistics for which are shown in Table 2). These results are shown in Table 4.
Again there is a clear trend visible in these results, with the low confidence data showing consistently lower exponents than the full data set, with the highest exponents being measured for the filtered high confidence data. A paired t-test analysis of data measured from the high confidence and low confidence data supports the fact that the difference in exponents between these data sets is significant (p = 0.01). It is also interesting to note that two of the annotation data sets with lower values of β have P-values < 0.1, i.e. cannot be effectively represented by a power law.
Using the data from Tables 1 and 3 it is possible to examine β as a function of both the total and distinct number of GO identifiers in each genomic annotation dataset. There is no clear correlation between the size of the data set and the power law exponent (Figure 2). This analysis includes data from a wide range of species data sets from the Ensembl database in addition to the GOA datasets.

Discussion
We have used computational linguistics methods to examine a range of gene annotation data sets used to populate genome resources. In almost all cases these data sets obey Zipf's law, with exponents typical of those for human languages ( Table 3). The This supports the hypothesis that the GO annotation can be thought of as a language, and that we can think of annotation as a form of communication process with the characteristics of a natural language. This then provides us with a framework in which to look at the effectiveness of the communication process using power law. For example, we have observed a real and significant difference in the power law exponents measured for annotation using the biological process sub-ontology (β % 2.1) compared with that using the molecular function and cellular component sub-ontologies (β % 1.8).
The measured exponent changes in a predictable and significant way as a function of the evidence codes that have been used to support annotation, but not as a function of the size of the annotation available ( Figure 2). However, it is not clear that the absolute value of the exponent can be interpreted as a quality measure; for example, we would not want to state that the BP annotations are of higher quality than those done with the MF and CC ontologies. We therefore need to look more deeply into the linkage between the exponent and information transfer. For example, some insights can be drawn from work in statistical mechanics approaches to understanding the behaviour of language [26]. In this work it is hypothesised that the exponent β is proportional to the "temperature" of the communication system, where temperature is to be interpreted as a "willingness to communicate". This would therefore imply that the increase we see in the value of β as a function of the annotation source (Table 4) reflects an increasing effort in the communication process. Indeed, this observation has been made previously in a number of studies of human language, in which the value of the exponent has been somewhat controversially linked to communication effectiveness [23,24,27,28]. Similarly, there is a large literature (e.g. [29]) which debates the interpretations that can legitimately me be made of the Zipf's law exponent in linguistics and the extent to which these variations provide insights into communication, whether in whistles between dolphins [30], the nature of the schizophrenic brain [31] or language in children [32]. In particular, much of this analysis has investigated the ways in which differences in language use, communication effectiveness or brain structure are reflected in the measured exponent.
An inference that might therefore be drawn as regards the differences in exponents between the various GO sub-ontologies could therefore be that the information conveyed by BP is fundamentally more complex than that described by the other two sub-ontologies, capturing the processes in which the molecule is involved, rather than a simple molecular function description, or a location in which the activity takes place. That is, we simply have more to say about process than we do about function and cellular location; the biology is more complex in processes. This might intrinsically require more "willingness to communicate" than is needed to describe aspects of molecular function or cellular component. An anomaly in this analysis is the observed low exponent for the D. rerio BP sub-ontology, from which we might infer that the information content captured in the annotation for biological processes in this model species is lower than that from the other model organisms (as reflected in the significantly smaller number of published papers on D. rerio compared to those of the other model species listed).
One key difference between this analysis and that more generally used in computational linguistics is in Figure 1 The cumulative distribution function Pr(x) plotted as a function of frequency (x) for GO gene annotations contained within Human GOA. The straight line shows the region of the plots for which a power law was found to provide a good model of the data [25]. 1(a) Annotation from the biological process sub-ontology, 1(b) annotation from the molecular function sub-ontology, and 1 (c) annotation from the cellular component sub-ontology The measured power law exponents, β, (were 2.04, 1.83, and 1.73 respectively. For all graphs p-value > 0.55, suggesting that the power law does provide a plausible model of the data. the variation of word length. In the GO annotation all words have the same length (the GO Identifier) whereas in natural languages word lengths can vary. A recent paper [33] has revisited one of Zipf's original observations that word length correlates inversely with frequency [34]. The key finding was that the correlation between word length and information content was better than that between word length and frequency. The analysis presented here, in the rather more controlled environment of genome annotation, has the potential to throw new light on this long-running debate in computational linguistics, as we can separate out the effects of word length and focus specifically on the information content and frequency of terms.
In principle we also believe that the straightforward computational linguistics methods we have applied to GO data in this paper should be more widely applicable to any situation in which data are described using terms from an ontology; for example, medical patient data described using terms from SNOMED-CT [35]. Indeed, we have recently observed very similar Zipf's law behaviour in a large corpus of primary care general practice data describing patients in Salford (UK) (data not shown).

Conclusions
In this paper we have demonstrated that computational linguistics, in the form of Zipf's law, provides a powerful and innovative framework in which to examine GO annotation. As hypothesised, the GO annotation does follow Zipf's law and there is some evidence that the exponent does provide information on the nature of the annotation; for example, it responds in a predictable way as a function of the evidence codes used to support the annotation. An unexpected finding is that the power law exponent of data described using the process subontology is significantly different to that measured for data in the function and component ontologies. We do not know whether this difference is some fundamental feature of the structure of the GO sub-ontologies, the nature of the biology being communicated, or whether it reflects thought processes in the annotation teams. Such Table 3 Results obtained from the power law analysis of each of the data sets characterized in Table 2 Species Ontology GOA  β is the power law exponent and P-value is a statistic used to determine how good a model the power law is of the data. Statistically significant values are denoted in bold. The GO evidence codes used to define the high confidence (HC) and low confidence (LC) data sets are described in the materials and methods.  Figure 2 The power law exponent, β, as a function of the total number of distinct GO identifiers in each of the GO sub-ontologies referenced in table 4 as well as a number of other species datasets taken from Ensembl.