Hardware Trojan Horses have emerged as great threats to modern electronic design and manufacturing practices. Because of their inherent surreptitious nature, test vector generation to detect hardware Trojan horses is a difficult problem. Efficient online detection techniques can be more effective in detection of hardware Trojan horses. In this paper, we propose a low-overhead detection technique which inserts malicious logic detection circuitry at netlist sites chosen by an algorithm that employs an intelligent and accurate analysis of fault propagation through logic gates. Proactive system-level countermeasures can be activated on detection of malicious logic, thereby avoiding disastrous system failure. Experimental results on benchmark circuits show close to 100 percent HTH detection coverage when our proposed technique is employed, as well as acceptable overheads.

Biomedical signals such as electroencephalogram (EEG), electrocardiogram (ECG), intracranial pressure (ICP), and photoplethysmogram (PPG) encode complex dynamical patterns that reflect the adaptive control of physiological systems. Complexity analysis—through entropy, compression, and causality metrics—has emerged as a critical tool for early diagnosis, risk prediction, and continuous monitoring in clinical and wearable settings. This review presents a clinically grounded synthesis of key complexity measures, including Sample Entropy (SampEn), Permutation Entropy (PE), Lempel–Ziv Complexity (LZC), and Effort-to-Compress (ETC), as well as causal inference techniques like Granger Causality, Transfer Entropy, and Compression–Complexity Causality (CCC). Applications across neurological and cardiovascular pathologies—such as epilepsy and sudden unexpected death in epilepsy (SUDEP)—are examined alongside signal-specific preprocessing, interpretability, and statistical validation strategies. We further evaluate the feasibility of deploying these measures in real-time on constrained hardware platforms—microcontrollers, FPGAs, and RISC–V-based SoCs—highlighting their suitability for embedded health systems and remote diagnostics. The review also explores synergistic integration with explainable machine learning, showing how complexity-informed features can enhance both performance and interpretability. By bridging signal complexity with hardware-aware deployment, ML transparency, and clinical translation, this work aims to guide the development of scalable, interpretable, and secure biomedical analytics for next-generation digital health systems.

Due to the increasing size and complexity of system-on-chips (SoCs), new threats and vulnerabilities are emerging, mainly related to flaws at the system level. Due to the lack of decisive security requirements and properties from the perspective of the SoC designer, the system-level verification process, whose violation may lead to exploiting a hardware vulnerability, is not studied comprehensively. To enable more comprehensive verification of system-level properties, this paper presents a framework known as HUnTer (Hardware Underath Trigger) for identifying sets of instructions (sequences) at the processor unit (PU) that reveal the underlying hardware vulnerabilities. HUnTer automates (i) threat modeling, (ii) threat-based formal verification, (iii) generating counterexamples, and (iv) generating snippet code to exploit the vulnerability. Furthermore, the HUnTer framework defines a unique security coverage metric ( HUnT_Coverage ) to measure the performance and effectiveness of vulnerability exploits. To demonstrate the high effectiveness of the proposed framework, we conduct a wide variety of case studies using the HUnTer framework on RISC-V-based open-source SoC architecture and attains the security coverage of 86% as an average for 11 benchmarks of the Trust-Hub database.

Digital twins (DTs) are emerging as transformative tools for the monitoring, control, and optimization of photovoltaic (PV) systems. While prior studies have addressed aspects such as MPPT (Maximum Power Point Tracking), forecasting, and inverter control in isolation, a cohesive, system-level integration remains underexplored. This paper proposes a unified functional architecture for DTs in PV applications, encompassing real-time monitoring, signal processing, control synchronization, and AI-enabled forecasting. We systematically categorize the enabling technologies—including IoT platforms, edge–cloud orchestration, and cyber–physical system (CPS) integration—and analyze their roles across the DT lifecycle. Furthermore, case-driven evaluations illustrate practical implementation challenges, performance metrics, and deployment strategies. The proposed framework offers a scalable and interoperable blueprint to guide the development of intelligent DT infrastructures for next-generation solar energy systems.

Among the applications of Internet of Things (IoT) smart health care and management is a significant one. The wireless technologies and wearable sensors enable effective monitoring of the patients. This voluminous data from the wearable sensors can be processed, analyzed and classified using the machine learning algorithms. Many techniques are emerging for the biomedical data mining. The proposed method can be used for arrhythmia patient monitoring and classification. The arrhythmia patient can be continuously monitored using the wearable sensors and mobile application. This method benefits the patient as they have the freedom to be mobile and monitored in their usual environment. The data from electrocardiogram (ECG) sensors are categorized into various groups using machine learning algorithm. In this paper, a method for monitoring, visualizing the arrhythmia of patients and classification of the data for the hospitals in order to extend their patient care using the mobile application is proposed.