This article proposes a tunable true random number generator (TTRNG) based on stochastic magnetic tunnel junction (MTJ) switching in the subcritical current regime. The proposed design consists of three parts. The first part is a write/read circuit featuring a static write circuit and an energy-efficient circuit optimized for read operations. The second finds the probability generation array (PGA) similar to the longest common subsequence (LCS) problem. However, the algorithms presented so far for the LCS have not been responsive to finding the Desired-PGA as it must look for the common subsequence among one million sequences, each sequence being 259 long. Hence, the convergence of those algorithms is impossible in this problem. Accordingly, we propose an algorithm to find a common subsequence of length 24, each defining a logic function. We also propose a controller to generate arbitrary probabilities with a zero steady-state error. These parts, especially PGA, make the design highly robust to the process variations. Notably, the proposed design passes the National Institute of Standards and Technology test. The proposed design also reduces energy dissipation compared to previous designs. Also, as it does not require precise sizing, the FinFET technology is appropriately used to design the proposed approach.

Unfit drivers are the cause of tens of thousands of incidents on the roads which lead to injuries and deaths. Therefore, it is very important to take preventive measures against such incidents. One of the unfit driving conditions is driving while being drowsy. Using image processing techniques, drowsiness of the driver could be detected and hence such incidents could be prevented. In this work, inspired by how images are processed by the human visual system, an enhancement for driver's drowsiness detection is suggested. Furthermore, to improve the robustness of the drowsiness detection system, the mechanism for using energy levels in frames is changed. Lastly, a better decision making process is proposed. To measure the merit of the system, it is applied to a set of drivers' data. Test results show that using the proposed system, success rate of the drowsiness detection system is 90%.

As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method, fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements. The results of the proposed method are evaluated using Dice similarity and Accuracy indices. Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.

Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells. The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature, pressure, and flow rate. To overcome this problem, fast and delay-free systems are necessary for predicting control signals. In this paper, we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance. This is done through an input air pressure control signal. The proposed artificial neural network was constructed based on a back propagation network. A fuel cell nonlinear model, with and without feed forward control, was investigated and compared under random current variations. Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage. Also, less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.

We develop a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (superclasses) to train a set of binary classifiers. The output of the classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results. 1

The error correcting output coding (ECOC) approach to classifier design decomposes a multi-class problem into a set of complementary two-class problems. We show how to apply the ECOC concept to automatic face verification, which is inherently a two-class problem. The output of the binary classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. We propose two different combining strategies as the matching score for face verification. The first uses the first order Minkowski metric, and requires a threshold to be set. The second is a kernel-based method and has no parameters to set. The proposed method exhibits better performance on the well known XM2VTS data set compared with previous reported results.

Error correcting output coding (ECOC), an information theoretic concept, seems an attractive idea for improving the performance of automatic classifiers, particularly for problems that involve large number of classes. In this paper, we look at the conditions necessary for reduction of error in this framework and introduce a new version of ECOC. To show the error reduction procedure and compare the new algorithm with traditional one, we use an artificial benchmark on which we are able to control the rate of noise to investigate the behaviour of system in different parts of input space, as well as a few real problems.