A Power-Efficient Bio-Potential Acquisition Device with DS-MDE Sensors for Long-Term Healthcare Monitoring Applications

This work describes a power-efficient bio-potential acquisition device for long-term healthcare applications that is implemented using novel microelectromechanical dry electrodes (MDE) and a low power bio-potential processing chip. Using micromachining technology, an attempt is also made to enhance the sensing reliability and stability by fabricating a diamond-shaped MDE (DS-MDE) that has a satisfactory self-stability capability and superior electric conductivity when attached onto skin without any extra skin tissue injury technology. To acquire differential bio-potentials such as ECG signals, the proposed processing chip fabricated in a standard CMOS process has a high common mode rejection ratio (C.M.R.R.) differential amplifier and a 12-bit analog-to-digital converter (ADC). Use of the proposed system and integrate simple peripheral commercial devices can obtain the ECG signal efficiently without additional skin tissue injury and ensure continuous monitoring more than 70 hours with a 400 mAh battery

Clustered two-dimensional mesh topology for large-scale network-on-chip architecture

Driven by the continuous scaling of Moore’s law, the number of processing cores in chip multiprocessors and systems-on-a-chip are expected to grow tremendously in the near future. Connecting the different components of a multiprocessor chip in a scalable and efficient way has become increasingly challenging. Current network-on-chip (NoC) topologies are adequate for small-size networks but are not optimized for large-scale networks. Transmitted packets inside a large NoC require longer route to reach their destinations, resulting in an increase in certain performance parameters such as latency and power consumption. Thus, it is necessary to develop a new topology appropriate for large-size NoCs. In this research, we proposed a cost-effective network topology for large-size NoCs that improves performance in terms of end-to-end latency. The topology, called RaMesh, consists of clusters of mesh networks. A routing algorithm suitable for this topology was also proposed. The RaMesh architecture together with mesh, torus, and clustered 2D-mesh were simulated using Noxim (NoC simulator), C for software NoC models, and Altera ModelSim for Verilog hardware models. Simulations were conducted under different network traffic and for a variety of network sizes. Experimental results showed that RaMesh performed better than equivalent 2D-mesh and torus topologies. RaMesh topology was also benchmarked against a clustered mesh topology. Average hop count in the proposed topology was at least 22.7% lower compared to the mesh and torus. Average latency was also decreased by at least 24.66% as compared to the mesh and torus. Finally, the saturation point for the proposed topology increased by at least 15% as compared to mesh and torus

An Overview of DRAM-Based Security Primitives

Recent developments have increased the demand for adequate security solutions, based on primitives that cannot be easily manipulated or altered, such as hardware-based primitives. Security primitives based on Dynamic Random Access Memory (DRAM) can provide cost-efficient and practical security solutions, especially for resource-constrained devices, such as hardware used in the Internet of Things (IoT), as DRAMs are an intrinsic part of most contemporary computer systems. In this work, we present a comprehensive overview of the literature regarding DRAM-based security primitives and an extended classification of it, based on a number of different criteria. In particular, first, we demonstrate the way in which DRAMs work and present the characteristics being exploited for the implementation of security primitives. Then, we introduce the primitives that can be implemented using DRAM, namely Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs), and present the applications of each of the two types of DRAM-based security primitives. We additionally proceed to assess the security such primitives can provide, by discussing potential attacks and defences, as well as the proposed security metrics. Subsequently, we also compare these primitives to other hardware-based security primitives, noting their advantages and shortcomings, and proceed to demonstrate their potential for commercial adoption. Finally, we analyse our classification methodology, by reviewing the criteria employed in our classification and examining their significance

Physical principles for scalable neural recoding

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices