Abstract The detection of Hardware Trojans (HTs) in electronic components is critical to ensuring the security and integrity of electronic systems, as these malicious modifications can leak sensitive information, alter device functionality, or completely disable the device. This study introduces a novel detection method combining deep learning techniques with dual-channel image transformations to identify HTs via Side-Channel Analysis (SCA). Specifically, our approach converts side-channel time series signals, such as power consumption, electromagnetic emissions, and timing data, into dual-channel, image-like representations using Gramian Angular Field (GAF) and reshaping techniques. These transformations allow convolutional neural networks (CNNs), known for their effectiveness in image analysis, to capture subtle and complex anomalous patterns indicative of HTs. We rigorously evaluated our dual-channel methodology using publicly available Advanced Encryption Standard (AES) datasets for HTs provided by TrustHub and IEEE Dataport. Experimental results demonstrate that the proposed approach achieves superior accuracy compared to existing methods, particularly in challenging datasets. Additionally, we assessed the robustness of our model by introducing varying noise levels to simulate real-world operational perturbations such as process variations, aging, and voltage fluctuations. The proposed method maintains high detection accuracy and demonstrates enhanced resilience under noisy conditions, underscoring its practical applicability and effectiveness in detecting sophisticated HT threats.

Educational humanoid robots have started to find there place in institutions for children with special needs and Autism Spectrum Disorder (ASD) children therapy centers. Most of the humanoid robots are expensive, or come with specific software or method for controlling the robot. This gives limits to what can be done with the robot and the software or method used may not allow expansion of the robot functionalities or possible applications. This paper presents an of system integration example with custom-built graphical toolkit used in controlling a simple toy like robot, the robot (RoboSapien), for a variety of customizable functions and applications. The main focus is on the activities and games that can be used with this platform in order to customize enhance the functionalities of the robot to suit the therapy process for the child under consideration. The mimicking mode of the robot, where the robot mimics the behavior of the user, has been found to be very promising for catching attention, making children involve in an activity, and retain their interest for longer period of time. Several activities have been developed and tested with children having ASD and have shown remarkable improvement in concentration spans, focus, and willingness to follow instructions.

Electrocardiogram (ECG) based health diagnosis of cardiac diseases has been a saturated area of research and almost any known heart-condition can be detected and diagnosed by doctors in the hospital setting. However, these approaches fall extremely short when attempting to design an automatic detection system to do the same. The situation becomes even more difficult when the measurement system is being designed for a ubiquitous application in which the patient is not confined to the hospital and the device is attached to him/her externally while the person is involved in daily chores. This paper presents the classification technique for one such system which is being built by the same team. Hence the presented work covers the initial findings related to some of the cardiac conditions that can be monitored in the ubiquitous scenario. This detection system produces warning signals that can be conveyed to the concerned healthcare personnel if signs of critical cardiac conditions begin to show. Due to the compact nature of such systems, the detection and classification techniques have to be extremely simple in order to be stored in the small memory of the microcontroller of the ubiquitous system. The paper presents one such technique that is a combination of digital filters and Fuzzy classifications implemented at look-up table level in order to preserve the simplicity of the system.

In this paper, a customized Fuzzy Inference System is presented to classify the corrosion and distinguish it from the geometric defects or normal state of the steel pipes used in gas/petroleum industry. The presented strategy is hybrid in the sense that it utilizes both the soft computing as well as conventional parametric modeling through Hinfinoptimization methods. An experimental strategy is first outlined through which the necessary data is collected as A-scan which are the ultrasonic echoes pulses in ID. Then, using empirical modeling approach a parametric transfer function is obtained for each pulse. In this respect, each A-scan is treated as an output from a defining function when a pure metal's A-scan is used as its input. Three defining states are considered in the paper; healthy, corroded, and defective, corresponding to the healthy or very much less corroded metal, corroded metal, and metal with any artificial or other defects, respectively. Impulse responses for each of these parametric models are plotted and human heuristics is then utilized in coming up with a set of quantitative features that can be used in distinguishing these classes. This feature set is then supplied to the Fuzzy Inference system as input to be used in distinguishing various classes under study. The main contribution of this work is to elaborate the fact that corrosion modeling provides easier approach in classifying the A-scans better rather than the raw A-scan data which is more prone to noise errors and more dependent on the measuring device's parameters.

In this paper, two algorithms have been presented to enhance the infrared (IR) images. Using the autoregressive moving average model structure and H(infinity) optimal bounds, the image pixels are mapped from the IR pixel space into normal optical image space, thus enhancing the IR image for improved visual quality. Although H(infinity)-based system identification algorithms are very common now, they are not quite suitable for real-time applications owing to their complexity. However, many variants of such algorithms are possible that can overcome this constraint. Two such algorithms have been developed and implemented in this paper. Theoretical and algorithmic results show remarkable enhancement in the acquired images. This will help in enhancing the visual quality of IR images for surveillance applications.

Efficiency of haemodialysis in end-stage renal disease (ESRD) is determined by calculating adequacy. The adequacy of dialysis and its measurement have been debated over the past 20 years by authorities concerned about how much of this life-sustaining treatment is appropriate for patients with ESRD. Currently, the minimum dose of dialysis (Kt/V) is assessed by computerised calculation of urea kinetics. Although fairly standard, it is still an approximate method due to the various assumptions made in the development of the final parametric model. In this paper, a new algorithm approach is presented for determining Kt/V using generalised regression neural networks (GRNN) and this research has shown it to be very promising.

This article presents a new algorithm for blind deconvolution and consequently enhancement for ultrasonic C-scan images. A modified two-dimensional (2-D) H/sub /spl infin// state estimation technique has been utilized that deconvolves the effect of unknown blur in the image and enhances the important edges. A new state-space model, based on redundant representation of constrained 2-D convolution sum, has been developed for the image so that it can be utilized in the H/sub /spl infin// state estimation framework. Satisfactory results have been obtained for real images. The same procedure can also be applied to any other image where localized enhancement can be helpful.

A new approach is presented to use higher order statistics (HOS) to deconvolve the effects of blurring in ultrasonic C-scans. The quantization effects of the mechanical scan-grid and the conversion of the 1D A-scan into one pixel on the image cause blurring. The proposed approach is completely blind to the source or the type of distortion and the formulation is purely two-dimensional. When the blurring function is modeled as an AR process, the image is restored recursively with the application of the inverse filter based on the AR estimate. A significant improvement in the image quality has been demonstrated. In particular, the edges are detected more prominently than in the original image.