Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithm

Synchronization and Control of Chaotic Spur Gear System Using Type-II Fuzzy Controller Optimized via Whale Optimization Algorithm

Interval type-II Fuzzy Inference System (FIS) assumes a crucial role in determining the coefficients of the PID controller, thereby augmenting the controller's flexibility. Controlling chaotic systems presents inherent challenges and difficulties due to their sensitivity to initial conditions and the intricate dynamics that require precise and adaptive control strategies. This paper offers an exhaustive exploration into the coordination and regulation of a chaotic spur gear system, employing a Type-II Fuzzy Controller. The initial control parameters of the PID controller undergo optimization using the Whale Optimization Algorithm (WOA) to increase the overall system performance. The adaptability and strength of the suggested control system are tested in various scenarios, covering diverse reference inputs and uncertainties. The investigation comprehensively assesses the operational efficacy of the formulated controller, contrasting its performance with other methodologies. The outcomes highlight the impressive efficiency of the suggested strategy, confirming its supremacy in attaining synchronization and control within the turbulent spur gear system under demanding circumstance

Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification

In this paper a novel cross-device text-independent speaker verification architecture is proposed. Majority of the state-of-the-art deep architectures that are used for speaker verification tasks consider Mel-frequency cepstral coefficients. In contrast, our proposed Siamese convolutional neural network architecture uses Mel-frequency spectrogram coefficients to benefit from the dependency of the adjacent spectro-temporal features. Moreover, although spectro-temporal features have proved to be highly reliable in speaker verification models, they only represent some aspects of short-term acoustic level traits of the speaker's voice. However, the human voice consists of several linguistic levels such as acoustic, lexicon, prosody, and phonetics, that can be utilized in speaker verification models. To compensate for these inherited shortcomings in spectro-temporal features, we propose to enhance the proposed Siamese convolutional neural network architecture by deploying a multilayer perceptron network to incorporate the prosodic, jitter, and shimmer features. The proposed end-to-end verification architecture performs feature extraction and verification simultaneously. This proposed architecture displays significant improvement over classical signal processing approaches and deep algorithms for forensic cross-device speaker verification.Comment: Accepted in 9th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2018

Polar Coding for Achieving the Capacity of Marginal Channels in Nonbinary-Input Setting

Achieving information-theoretic security using explicit coding scheme in which unlimited computational power for eavesdropper is assumed, is one of the main topics is security consideration. It is shown that polar codes are capacity achieving codes and have a low complexity in encoding and decoding. It has been proven that polar codes reach to secrecy capacity in the binary-input wiretap channels in symmetric settings for which the wiretapper's channel is degraded with respect to the main channel. The first task of this paper is to propose a coding scheme to achieve secrecy capacity in asymmetric nonbinary-input channels while keeping reliability and security conditions satisfied. Our assumption is that the wiretap channel is stochastically degraded with respect to the main channel and message distribution is unspecified. The main idea is to send information set over good channels for Bob and bad channels for Eve and send random symbols for channels that are good for both. In this scheme the frozen vector is defined over all possible choices using polar codes ensemble concept. We proved that there exists a frozen vector for which the coding scheme satisfies reliability and security conditions. It is further shown that uniform distribution of the message is the necessary condition for achieving secrecy capacity.Comment: Accepted to be published in "51th Conference on Information Sciences and Systems", Baltimore, Marylan

Synthesis and Studies of Potential Antifungal and Antibacterial Agents New Aryl Thiazolyl Mercury (II) Derivatives Compounds

Combination of mercaptothiazoles and mercury phenyl chloride synthesized some new compounds of thiazoles. Firstly some mercaptothiazoles with different sorts have been synthesized, and then synthesized compounds were reacted with different mercury phenyl chloride structures. At last, each of these synthesized compounds was purified. Consequently, these structures were recrystallized using oil ether. Forming product through chromatogram (TLC) and combination of Rf with other compound's Rf of were identified, and their purity percent was recognized. The 1H-NMR and other methods like FT-IR and mass spectroscopy have determined all of compounds. Obtained results of synthesized compounds showed that reactions were carried out with suitable speed and high yield

Investigation of the battery degradation impact on the energy management of a fuel cell hybrid electric vehicle

This paper studies the influence of battery degradation over the performance of a fuel cell hybrid electric vehicle (FCHEV). For this purpose, an optimized fuzzy strategy based on the costs of battery and fuel cell degradations as well as fuel consumption and battery recharging is employed. Simulations are done by two driving cycles for three scenarios based on battery state of health (SOH) and validity of feedback signal. Simulation results prove that battery aging has a considerable impact on the total cost of a FCHEV. Moreover, tuning of the EMS parameters according to the battery SOH decreases the defined cost