AI-Driven Functional Safety Framework for Real-Time Biomedical Signal Processing Systems

Md. Abdul Kadir, Mossadaque Azmain Awon — Wed, 15 Jul 2026 00:00:00 +0000

This work investigates a software functional safety framework for biomedical signal processing using advanced deep learning techniques to achieve reliable, accurate, and low-latency operation in real-time healthcare monitoring systems. Functional safety is a critical requirement in biomedical sensor applications, where incorrect signal interpretation or delayed responses may compromise patient safety. The proposed framework integrates safety-aware signal processing with deep learning-based classification to enhance both performance and dependability. Experiments were conducted using TensorFlow 2.15 on an Intel i9 processor with an NVIDIA RTX 4090 GPU under Ubuntu 22.04. Biomedical signals were sampled at 500 Hz and evaluated using Accuracy, Latency, and F1-score metrics. The proposed method was compared against conventional Digital Signal Processing (DSP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) approaches. Experimental results demonstrate that the proposed framework achieves the highest classification accuracy, outperforming DSP (88.4%), CNN (93.2%), and LSTM (94.1%). Furthermore, the proposed approach exhibits the lowest processing latency of 6 ms, compared with 28 ms, 18 ms, and 12 ms for DSP, CNN, and LSTM methods, respectively. These improvements indicate that the framework effectively balances computational efficiency and predictive performance while satisfying real-time operational requirements. The incorporation of functional safety principles enhances system robustness by reducing the likelihood of hazardous failures and improving confidence in decision-making processes. The findings highlight the suitability of the proposed framework for deployment in safety-critical biomedical applications, including patient monitoring, wearable healthcare devices, and intelligent diagnostic systems. Overall, the study demonstrates that combining software functional safety mechanisms with deep learning can significantly improve the reliability, responsiveness, and accuracy of biomedical signal processing systems.

Neural Network Architectures: Taxonomy of Design Principles, Evolution, and Emerging Frontiers

Yohana Belay Sitotaw, Nigussie Mamushet Degeif, Belay Sitotaw Goshu — Mon, 05 Oct 2026 00:00:00 +0000

Neural network architectures have undergone a remarkable transformation over the past seven decades, evolving from the single-layer perceptron to billion-parameter foundation models defining the state of the art across vision, language, and multimodal reasoning. The proliferation of architectures, however, has outpaced theoretical understanding of underlying performance mechanisms, resulting in design decisions driven largely by empirical benchmarking rather than principled reasoning. This review presents a unified taxonomy of neural network architectures organized around four core design principles: locality, memory, attention, and hierarchy, supplemented by two secondary efficiency principles: parameter sharing and sparsity. The four core principles provide a stable explanatory framework for characterizing representational capabilities, whereas parameter sharing and sparsity function as cross-cutting efficiency mechanisms that regulate computational cost. Nine major architectural families are systematically examined: multilayer perceptrons, convolutional neural networks, recurrent networks and gated variants, autoencoders, generative adversarial networks, transformers, graph neural networks, diffusion models, and state space models. Analysis focuses on the mechanisms through which each architectural family instantiates the core principles, the resulting inductive biases, and associated computational and representational trade-offs. A systematic literature review synthesizes comparative evidence concerning parameter complexity, data efficiency, scalability, and representative benchmark performance. Cross-cutting analysis further examines trade-offs involving inductive biases, scaling laws, emergent behaviors, hybrid and modular architectures, computational efficiency, interpretability, and robustness. Emerging research frontiers, including state space models, neural architecture search, biologically inspired computing, neuro-symbolic integration, and generalist foundation models, are subsequently reviewed alongside open problems in theoretical understanding, architecture design, evaluation methodology, and sustainability. The analysis identifies four core design principles as persistent architectural invariants across successive eras of neural network development, while parameter sharing and sparsity operate primarily as efficiency-oriented modifiers. A principle-centered organization of neural network architectures provides a conceptual framework for understanding architectural evolution, a decision guide for architecture selection, and a research agenda supporting principle-driven rather than benchmark-driven design.

International Journal of Neural Systems and Applications

AI-Driven Functional Safety Framework for Real-Time Biomedical Signal Processing Systems

Neural Network Architectures: Taxonomy of Design Principles, Evolution, and Emerging Frontiers