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Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper.

This work proposes a residue number system (RNS) based hardware architecture of an elliptic curve cryptography (ECC) processor over prime field. The processor computes point multiplication in Jacobian coordinates by a combined architecture of point doubling and point addition. An optimized modular reduction architecture is also presented that achieves low area by dividing the RNS moduli in small groups and processes the groups one by one. The proposed ECC processor is generic and supports any random curve over F p 256 . The implementation on Virtex-7 and Virtex-6 FPGAs shows comparable performance to the state-of-the-art binary and RNS based ECC processors.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.

Fingerprints are the oldest and most widely used form of biometric identification. Many researchers have addressed the fingerprint classification problem and significant progress has been made in designing automatic fingerprint identification systems (AFIS) over the past two decades. However, some design factors such as lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, poor image acquisition, low contrast images create bottlenecks in achieving the desired performance. Noticeable among them is the fact that digitally acquired fingerprint images are rarely of perfect quality to be used directly with AFIS; one important step is fingerprint enhancement. Conventional fingerprint enhancement methods, such as Gabor and anisotropic filters, do fill the holes and gaps in ridge lines but lack the necessary capability to tackle scar lines. For scar lines, an explicit filling process is proposed that is a mix of Fourier and spatial domain strategies. The proposed method is to make use of the Fourier domain directional field to trace an appropriate candidate for the scar pixels to be replaced with. The necessary components of the process are locating scars, estimating directional field, and the filling strategy. This process can act as front-end to the subsequent Gabor and anisotropic diffusion filtering. The simulation results for synthetic, as well as real fingerprints, show improved performance regarding better extraction of genuine minutia points. HighlightsWe Model a new proposed method to make use of the Fourier domain directional field to trace an appropriate candidate for the scar pixels to be replaced with.The necessary components of the process are locating scars, finding the directional field, and the filling strategy.The strategy relies on the fact that in these linear scars, the ridge/valley pattern is still intact across the scar region.Using this information, the scar boundary is filled with appropriate normal region pixels using the local orientation field.This process can act as front-end to the subsequent Gabor and anisotropic diffusion filtering.

Digital image processing is widely used in fast and high-performance applications. The high speed and functional requirements of such applications, however, lead to increased power consumption. Hence, finding a way to solve the power-performance issues is of great importance. In this paper, we present the power-performance enhancement of a two-dimensional (2D) discrete wavelet transform (DWT) image processor using the residue number system (RNS) and the static voltage scaling (SVS) scheme. The aim of this paper is to investigate the effects of the RNS and SVS scheme on the proposed image processor. The original contributions of the proposed design include a low-complexity hardware architecture of the RNS-based filter banks, optimized transposition units and exploiting the SVS scheme to reduce the power consumption. The multiplierless scheme of the RNS-based filter banks and the binary-coded number format are used to save on hardware complexity, while modular arithmetics and 6-bit dyadic fraction filter coefficients are applied to improve the system performance. The bi-orthogonal discrete wavelet transform CDF97 is chosen to compress the images due to its multi-resolution features and its ability to localize finite signals. The proposed design has been synthesized using the generic library SAED 90 nmEDK with the Synopsys Design Compiler (DC).

Coherence-enhancing diffusion filtering is a striking application of the anisotropic diffusion in image processing. The technique deals with the problem of completion of interrupted lines and enhancement of flow-like features in fingerprint images. However, an anisotropic diffusion process is an iterated process, initializes with a poor quality image, and converges at the end towards a completely blurred image, with no structure surviving at the end. In anisotropic diffusion, one important question is how to find boundary between the under-smoothing and over-smoothing regions of the anisotropic diffusion process. The entropy change is found to be one such measure to describe that boundary adequately and thus provides a reasonable stopping rule for anisotropic diffusion. Numerical experiments with test pattern images confirm the desirable qualities of gap-closing and flow-enhancing qualities, along with the identification of frontier of useful smoothing. The proposed scheme is evaluated with the help of simulated images, and compared with other state of the art schemes using an objective criterion.

The objective of this paper is to design a model which can integrate a range sensor with real time image processing. An FPGA provides physical solutions for most image processing problems and offers a new hardware acceleration opportunity. In this paper we implemented image normalization along with edge detection on real time images and then integrated the range sensor. The model is validated using Spartan-3 FPGA.

The main task of a fingerprint image enhancement is to enhance the image in such a way that it not only remove the noise but also enhance the reliable minutiae points. For this purpose, in this paper we propose a multi-scale decimation-free directional filter bank method for reliable orientation estimation. This reliable orientation is used in coherence enhancement diffusion and in Gabor filter based enhancement, which overcomes the drawbacks of these two methods. Experimental results show that the proposed method not only enhances the images but also facilitates the minutiae algorithm, by enhancing the true minutiae points.