Classifications, applications, and design challenges of drones: A review

This work proposes a processor architecture for elliptic curves cryptosystems over fields GF(2 m ) This is a scalable architecture in terms of area and speed that exploits the abilities of reconfigurable hardware to deliver optimized circuitry for different elliptic curves and finite fields The main features of this architecture are the use of an optimized bit-parallel squarer, a digit-serial multiplier, and two programmable processors Through reconfiguration, the squarer and the multiplier architectures can be optimized for any field order or field polynomial The multiplier performance can also be scaled according to system's needs Our results show that implementations of this architecture executing the projective coordinates version of the Montgomery scalar multiplication algorithm can compute elliptic curve scalar multiplications with arbitrary points in 021 msec in the field GF(2 167 ) A result that is at least 19 times faster than documented hardware implementations and at least 37 times faster than documented software implementations

A high-performance reconfigurable elliptic curve processor for GF(2m)

Research Interests Security and privacy: Active authentication, biometrics template protection, biometrics recognition. Computer vision: Domain adaptation, dictionary learning, object recognition, activity recognition, shape representation. Machine learning: Dimensionality reduction, clustering, kernel methods, weakly-supervised learning. Signal/Image processing: Sparse representation, compressive sampling, synthetic aperture radar imaging, millimeter wave imaging.

Sparse and redundant representations for inverse problems and recognition

Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination, and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detection process. It is hard to determine whether a single pixel is skin or nonskin without considering the context. An efficient traditional hand-engineered skin color detection algorithm requires extensive work by domain experts. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have achieved great success in pixel-wise labeling tasks. However, CNN-based architectures are not sufficient for modeling the relationship between pixels and their neighbors. In this letter, we integrate recurrent neural networks (RNNs) layers into the fully convolutional neural networks (FCNs), and develop an end-to-end network for human skin detection. In particular, FCN layers capture generic local features, while RNN layers model the semantic contextual dependencies in images. Experimental results on the COMPAQ and ECU skin datasets validate the effectiveness of the proposed approach, where RNN layers enhance the discriminative power of skin detection in complex background situations.

Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

A review of recent developments in tin dioxide composites for gas sensing application

FPGA based fast and high-throughput 2-slow retiming 128-bit AES encryption algorithm

High-speed hardware architecture of scalar multiplication for binary elliptic curve cryptosystems

Effect of silver additive on electrical conductivity and methane sensitivity of SnO2

Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

/pdf/a-comprehensive-survey-on-human-skin-detection-3i02kesshw.pdf

A Comprehensive Survey on Human Skin Detection

In this study high-performance and high-speed field-programmable gate array (FPGA) implementations of polynomial basis Itoh–Tsujii inversion algorithm (ITA) over GF(2 m ) constructed by irreducible trinomials and pentanomials are presented. The proposed structures are designed by one field multiplier and k -times squarer blocks or exponentiation by 2 k , where k is a small positive integer. The k -times squarer blocks have an efficient tree structure with low critical path delay, and the multiplier is based on a proposed high-speed digit-serial architecture with minimum hardware resources. Furthermore, to reduce the computation time of ITA, the critical path of the circuit is broken to finer path using several registers. The computation times of the structure on Virtex-4 FPGA family are 0.262, 0.192 and 0.271 µs for GF(2163), GF(2193) and GF(2233), respectively. The comparison results with other implementations of the polynomial basis Itoh–Tsujii inversion algorithm verify the improvement in the proposed architecture in terms of speed and performance.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ifs.2015.0461

Sayed Masoud Sayedi

Papers

FPGA based fast and high-throughput 2-slow retiming 128-bit AES encryption algorithm

High-speed hardware architecture of scalar multiplication for binary elliptic curve cryptosystems

Effect of silver additive on electrical conductivity and methane sensitivity of SnO2

A Comprehensive Survey on Human Skin Detection

High-performance and high-speed implementation of polynomial basis Itoh–Tsujii inversion algorithm over GF(2 m )