Lane Keeping Assistance and Adaptive Cruise Control in Advanced Driving Assistance Systems (ADAS) requires road parameters for better control of the vehicle. Therefore, in this paper we present the Iterative Least Squares with Optimization (ILSO) method based on a monocular front camera which estimates the horizontal, vertical parameters, lateral slope and the camera’s distances to the left/right line as road parameters and longitudinal range. The ILSO algorithm is smartly divided into two steps LS and optimization depending on their estimation capabilities. First of all, the horizontal and vertical parameters, lateral slope, and distances to the left and right lines are estimated by Least Squares (LS). Then, the longitudinal range is estimated by convex optimization. LS and convex optimization are executed in an alternating manner. ILSO is in advance prior to changes on roads and a simple technique due to its analytical solution which does not require complex geometric calculations and does not need a match between left and right lines. In a simulation environment, given the noisy road lines, ILSO outperforms covariance based and multilayer perceptron (MLP) based methods under different noise variances. In addition, ILSO provides plausible numerical results in both virtual and real road images.

Task engagement is a key factor in sustaining patients’ participation in rehabilitation. Adaptive task difficulty level adjustment techniques are designed to determine the appropriate exercise difficulty level in where subjects are appropriately challenged and engaged without causing any distress. Such adaptive difficulty adjustment within rehabilitation tasks has the potential to individualise training. In this study, the authors have compared two dynamic difficulty level adjustment algorithms, partially ordered set master (POSM) and increment/decrement one level (IDOL), those change the difficulty levels for each individual adaptively based on his/her performance. These two algorithms are integrated into the robot‐assisted rehabilitation system, RehabRoby, and their functionality is explored via a small user study with 20 healthy subjects. The subjects were asked to perform a computer‐based fruit picker game using RehabRoby under these two algorithms. The impacts of the adaptation algorithms are evaluated in terms of engagement of the subjects by looking at their physiological signals, performance (score), and survey results. Experiments show that although POSM on the average suggests easier difficulty levels to the subjects than IDOL, the subjects experience a wider range of difficulty levels in POSM that may help them to become more engaged in the game, which can also be observed by lower skin temperatures of the subjects.

Engagement is a key factor in gaming. Especially, in gamification applications, users’ engagement levels have to be assessed in order to determine the usability of the developed games. The authors first present computer vision‐based game design for physical exercise. All games are played with gesture controls. The authors conduct user studies in order to evaluate the perception of the games using a game engagement questionnaire. Participants state that the games are interesting and they want to play them again. Next, as a use case, the authors integrate one of these games into a robot‐assisted rehabilitation system. The authors perform additional user studies by employing self‐assessment manikin to assess the difficulty levels that can range from boredom to excitement. The authors observe that with the increasing difficulty level, users’ arousal increases. Additionally, the authors perform psychophysiological signal analysis of the participants during the execution of the game under two distinctive difficulty levels. The authors derive features from the signals obtained from blood volume pulse (BVP), skin conductance, and skin temperature sensors. As a result of analysis of variance and sequential forward selection, the authors find that changes in the temperature and frequency content of BVP provide useful information to estimate the players’ engagement.

We employ Permutation Trellis Code (PTC) based multi-level Frequency Shift Keying signaling to mitigate the impact of Primary Users (PUs) on the performance of Secondary Users (SUs) in Cognitive Radio Networks (CRNs). The PUs are assumed to be dynamic in that they appear intermittently and stay active for an unknown duration. Our approach is based on the use of PTC combined with multi-level FSK modulation so that an SU can improve its data rate by increasing its transmission bandwidth while operating at low power and not creating destructive interference for PUs. We evaluate system performance by obtaining an approximation for the actual Bit Error Rate (BER) using properties of the Viterbi decoder and carry out a thorough performance analysis in terms of BER and throughput. The results show that the proposed coded system achieves i) robustness by ensuring that SUs have stable throughput in the presence of heavy PU interference and ii) improved resiliency of SU links to interference in the presence of multiple dynamic PUs.

In this paper, we address the constrained resource allocation problems arising in the context of spectrum sharing in cognitive radio networks utilizing a multi-dimensional formulation. Given the activity of the primary users (PUs), we consider multiple objectives and constraints, viz., sum rate, fairness, number of active secondary users (SUs), power consumption, and quality of service requirements (of both PUs and SUs). The three dimensions for the optimization task are the assignment of power, frequency, and antenna directionality to various SUs. Efficient heuristic algorithms are developed for five variations of the NP-hard optimization problems. Solution quality tradeoffs are shown for three algorithms, viz., convex relaxation with tree pruning, convex relaxation with gradual removal, and a genetic algorithm (GA); results show that the GA provides a reasonable balance between solution quality and computational effort. The multi-objective problems are solved using a modification of the NSGA-II evolutionary algorithm, obtaining a set of Pareto-optimal solutions under computational constraints.

We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor selection is studied. Specifically, we adopt an alternative conditional posterior Cramér-Rao lower bound (C-PCRLB) as the optimization criterion for sensor selection, where the trace of the conditional Fisher information matrix is maximized subject to an energy constraint. We demonstrate that the proposed sensor selection problem can be transformed into the problem of maximizing a convex quadratic function over a bounded polyhedron. This optimization problem is NP-hard in nature, and thus we employ a linearization method and a bilinear programming approach to obtain locally optimal sensor schedules in a computationally efficient manner.

In this paper, we consider the problem of estimating a spatially varying field in a wireless sensor network, where resource constraints limit the number of sensors selected in the network that provide their measurements for field estimation. Based on a one-to-one correspondence between the selected sensors and the nonzero elements of Kriging weights, we propose a sparsity-promoting ordinary Kriging approach where we minimize the Kriging error variance while penalizing the number of nonzero Kriging weights. This yields a combinatorial optimization problem, which is intractable in general. To solve the proposed non-convex optimization problem, we employ the alternating direction method of multipliers (ADMM) and the reweighted ℓ1minimization method, respectively. Numerical results are provided to illustrate the effectiveness of our proposed approaches that provide a balance between the estimation accuracy and the number of selected sensors.

In this paper, we study an unsupervised learning problem where the aim is to cluster the emotional state (excitedness, boredom, or stress) using the biofeedback sensor data while subjects perform tasks under different difficulty levels on the robot assisted rehabilitation system-RehabRoby. The dimension of the training vectors has been reduced by using the Principal Component Analysis (PCA) algorithm after collecting the biofeedback sensor measurements from different subjects under different task difficulty levels to better visualize the sensor data. The reduced dimension vectors are fed into a K-means clustering algorithm. Numerical results have been given to demonstrate that for each training vector, the emotional state decided by the clustering algorithm is consistent with the subjects declaration of his/her emotional state obtained via surveys after performing the task.

In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1relaxation of the ℓ0norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

In this paper, we propose a market based dynamic bit allocation scheme for target tracking in energy constrained wireless sensor networks using quantized data. We model the dynamic bit allocation problem as a market based policy where the fusion center is the customer and sensors are the producers of the market. The fusion center releases the energy to purchase m-bit measurements from sensors in such a way that the trace of the posterior Cramér-Rao lower bound (PCRLB) on the mean squared error (MSE) is minimized. Sensors then compete to purchase the energy released from the fusion center and produce their m-bit quantized measurements which maximize their profit. Simulation results show that the market based dynamic bit allocation scheme achieves tracking performance close to the case where all the sensors report their most accurate information to the fusion center while the market based dynamic bit allocation scheme releases energy which is significantly less than the energy required to transmit all sensor data to the fusion center.

In this letter, we study the problem of target tracking based on energy readings of sensors. We minimize the estimation error by using an extended Kalman filter (EKF). The Kalman gain matrix is obtained as the solution to an optimization problem in which a sparsity-promoting penalty function is added to the objective. The added term penalizes the number of nonzero columns of the Kalman gain matrix, which corresponds to the number of active sensors. By using a sparse Kalman gain matrix only a few sensors send their measurements to the fusion center, thereby saving energy. Simulation results show that an EKF with a sparse Kalman gain matrix can achieve tracking performance that is very close to that of the classical EKF, where all sensors transmit to the fusion center.

We analyze the potential for using multiple transmission dimensions in a cognitive radio network (CRN) in terms of the probability of successful communications. We consider a random CRN where the users are randomly distributed in an area of interest. The users can be mobile. We derive the successful communication probability (SCP) with respect to transmit power for different density of primary users and secondary users by including different transmission dimensionalities such as time, frequency and antenna directionality. It is shown that using multiple transmission dimensions improves the SCP significantly. The advantage of using directional antennas for spatial reuse and increased range is also shown in terms of improved SCP.

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.