High-Linearity Self-Biased CMOS Current Buffer

A highly linear fully self-biased class AB current buffer designed in a standard 0.18 mu m CMOS process with 1.8 V power supply is presented in this paper. It is a simple structure that, with a static power consumption of 48 mu W, features an input resistance as low as 89 Omega, high accuracy in the input-output current ratio and total harmonic distortion (THD) figures lower than -60 dB at 30 mu A amplitude signal and 1 kHz frequency. Robustness was proved through Monte Carlo and corner simulations, and finally validated through experimental measurements, showing that the proposed configuration is a suitable choice for high performance low voltage low power applications

1.0 v-0.18 µm CMOS tunable low pass filters with 73 db dr for on-chip sensing acquisition systems

This paper presents a new approach based on the use of a Current Steering (CS) technique for the design of fully integrated Gm–C Low Pass Filters (LPF) with sub-Hz to kHz tunable cut-off frequencies and an enhanced power-area-dynamic range trade-off. The proposed approach has been experimentally validated by two different first-order single-ended LPFs designed in a 0.18 µm CMOS technology powered by a 1.0 V single supply: a folded-OTA based LPF and a mirrored-OTA based LPF. The first one exhibits a constant power consumption of 180 nW at 100 nA bias current with an active area of 0.00135 mm2 and a tunable cutoff frequency that spans over 4 orders of magnitude (~100 mHz–152 Hz @ CL = 50 pF) preserving dynamic figures greater than 78 dB. The second one exhibits a power consumption of 1.75 µW at 500 nA with an active area of 0.0137 mm2 and a tunable cutoff frequency that spans over 5 orders of magnitude (~80 mHz–~1.2 kHz @ CL = 50 pF) preserving a dynamic range greater than 73 dB. Compared with previously reported filters, this proposal is a competitive solution while satisfying the low-voltage low-power on-chip constraints, becoming a preferable choice for general-purpose reconfigurable front-end sensor interfaces

A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications

Symmetry in Chaotic Systems and Circuits

Symmetry can play an important role in the field of nonlinear systems and especially in the design of nonlinear circuits that produce chaos. Therefore, this Special Issue, titled “Symmetry in Chaotic Systems and Circuits”, presents the latest scientific advances in nonlinear chaotic systems and circuits that introduce various kinds of symmetries. Applications of chaotic systems and circuits with symmetries, or with a deliberate lack of symmetry, are also presented in this Special Issue. The volume contains 14 published papers from authors around the world. This reflects the high impact of this Special Issue

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial