Neural networks

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

http://ieeexplore.ieee.org/iel5/5/31324/01456901.pdf

Microelectronic circuits

With the rapid development of society and the economy, an increasing number of human activities have gradually destroyed the marine environment. Marine environment monitoring is a vital problem and has increasingly attracted a great deal of research and development attention. During the past decade, various marine environment monitoring systems have been developed. The traditional marine environment monitoring system using an oceanographic research vessel is expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as potentially promising alternatives for monitoring marine environments since they have a number of advantages such as unmanned operation, easy deployment, real-time monitoring, and relatively low cost. This paper provides a comprehensive review of the state-of-the-art technologies in the field of marine environment monitoring using wireless sensor networks. It first describes application areas, a common architecture of WSN-based oceanographic monitoring systems, a general architecture of an oceanographic sensor node, sensing parameters and sensors, and wireless communication technologies. Then, it presents a detailed review of some related projects, systems, techniques, approaches and algorithms. It also discusses challenges and opportunities in the research, development, and deployment of wireless sensor networks for marine environment monitoring.

Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey

Analogue IC Design: the Current Mode Approach

This paper presents the implementation of a wearable wireless sensor network aimed at monitoring harmful gases in industrial environments. The proposed solution is based on a customized wearable sensor node using a low-power low-rate wireless personal area network (LR-WPAN) communications protocol, which as a first approach measures CO2 concentration, and employs different low power strategies for appropriate energy handling which is essential to achieving long battery life. These wearables nodes are connected to a deployed static network and a web-based application allows data storage, remote control and monitoring of the complete network. Therefore, a complete and versatile remote web application with a locally implemented decision-making system is accomplished, which allows early detection of hazardous situations for exposed workers.

A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications

This brief presents an ultralow-power class-AB operational amplifier (OpAmp) designed in a low-cost 0.18- μm CMOS technology. The proposed circuit uses transistors biased in the subthreshold region for low-voltage low-power operation. For a 0.8-V single supply, this OpAmp has 51-dB open-loop gain, 57-kHz unity-gain frequency, 60° phase margin, and 65-dB common-mode rejection ratio for 8-pF loads with a power consumption of only 1.2 μW. Experimental results illustrate performances such as a 0.14-V/μs slew rate and a 750-mV linear output swing, demonstrating its correct functionality.

An Ultralow-Power Low-Voltage Class-AB Fully Differential OpAmp for Long-Life Autonomous Portable Equipment

Early detection and monitoring of forest fire with a wireless sensor network system

Presented is a 3 V single supply analogue lock-in amplifier designed for processing output sensor signals in highly noisy environments. The proposed amplifier uses square input signals instead of the usual sinusoidal signals, consequently allowing a straight interface with microcontroller-based sensing systems working with both resistive and capacitive transducers. Experimental results for noisy signals with DC voltage variation confirm the capability of the proposed solution to effectively recover information from signal-to-noise ratios down to -24 dB with errors below 6% and with an average power consumption of only 5 mW in full operation.

Lock-in amplifier for portable sensing systems

This paper presents a new first-order temperature compensated CMOS current reference. To achieve a compact architecture able to operate under low voltage with low power consumption, it is based on a self-biasing beta multiplier current generator. Compensation against temperature is achieved by using instead of an ordinary resistor two triode transistors in parallel, acting as a negative and a positive temperature coefficient resistor, that generate a proportional to absolute temperature and a complementary to absolute temperature current which can be directly added to attain a temperature compensated current. Programmability is included to adjust the temperature coefficient and the reference current magnitude over process variations. Results for a 0.18 μm CMOS implementation show that the proposed 500 nA reference operate with supplies down to 1.2 V accomplishing over a (-40 to +120°C) range temperature drifts below 120 ppm/°C.

Nicolás Medrano

Papers

A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications

An Ultralow-Power Low-Voltage Class-AB Fully Differential OpAmp for Long-Life Autonomous Portable Equipment

Early detection and monitoring of forest fire with a wireless sensor network system

Lock-in amplifier for portable sensing systems

Precision CMOS current reference with process and temperature compensation