Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Gradient-based learning applied to document recognition

http://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/SensorNetworksSurvey.pdf

Wireless sensor networks: a survey

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

/pdf/an-application-specific-protocol-architecture-for-wireless-1s7y9fiv40.pdf

An application-specific protocol architecture for wireless microsensor networks

Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto

Wireless communications

https://www.cs.montana.edu/courses/csci566/Ref/CR-Survey.pdf

NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey

A method for calculating phase noise based on a particle diffusion model in the phase plane is introduced. This approach allows for the derivation of closed-form solutions, which can be used to see the explicit dependencies of phase noise on circuit component values. The theory is applied to the Van der Pol and Colpitts oscillators, and comparisons to simulations show close matching.

An equation-based method for phase noise analysis [oscillator examples]

IIB-1 noise measurements on buried-channel charge-coupled devices

In this paper, we consider wideband extensions of narrowband signal processing techniques for MIMO wireless transceivers. The extensions are applied and analyzed on commonly used wideband systems such as W-CDMA and OFDM. We focus on signal processing techniques for channel estimation and correction, including pilot-aided and decision-directed LMS-based adaptive estimation. Simulation study compares cases with one and two transmit/receive antennas and shows that BER of W-CDMA system consistently improves by exploiting multi-path diversity through additional antenna at the receiver, while additional transmit antenna is beneficial under specific channel conditions. BER performance in OFDM is more sensitive to the value of the channel's smallest eigen-value than to the eigen-spread. In most practical cases, therefore, the sub-channels with small eigen-values are not usable due to poor BER.

https://www.researchgate.net/profile/Dejan_Markovic3/publication/4155647_On_MIMO_signal_processing_for_adaptive_W-CDMA_and_OFDM_wireless_transceivers/links/53e2a0140cf216e8321e172c.pdf

On MIMO signal processing for adaptive W-CDMA and OFDM wireless transceivers

This chapter contains sections titled: Transition Density: A New Measure of Activity in Digital Circuits Estimation of Average Switching Activity in Combinational and Sequential Circuits Power Estimation Methods for Sequential Logic Circuits A Monte Carlo Approach for Power Estimation Stratified Random Sampling for Power Estimation A Survey of High-Level Power Estimation Techniques Activity-Sensitive Architectural Power Analysis Power Analysis of Embedded Software: A First Step Towards Software Power Minimization

Power Analysis Techniques

This chapter contains sections titled: Zig-Zag Path to Understanding A Low-Power Multiphase Circuit Technique Asymptotically Zero Energy Split-Level Charge Recovery Logic Low Power Digital Systems Based on Adiabatic Switching Principles Adiabatic Dynamic Logic

Robert W. Brodersen

Papers

An equation-based method for phase noise analysis [oscillator examples]

IIB-1 noise measurements on buried-channel charge-coupled devices

On MIMO signal processing for adaptive W-CDMA and OFDM wireless transceivers

Power Analysis Techniques

Adiabatic Logic Circuits