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Author

Robert W. Brodersen

Bio: Robert W. Brodersen is an academic researcher from University of California, Berkeley. The author has contributed to research in topic(s): CMOS & Signal processing. The author has an hindex of 68, co-authored 256 publication(s) receiving 28632 citation(s). Previous affiliations of Robert W. Brodersen include University of Hong Kong & Texas Instruments.
Papers
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Proceedings ArticleDOI
07 Nov 2004
TL;DR: To improve radio sensitivity of the sensing function through processing gain, three digital signal processing techniques are investigated: matched filtering, energy detection and cyclostationary feature detection.
Abstract: There are new system implementation challenges involved in the design of cognitive radios, which have both the ability to sense the spectral environment and the flexibility to adapt transmission parameters to maximize system capacity while coexisting with legacy wireless networks. The critical design problem is the need to process multigigahertz wide bandwidth and reliably detect presence of primary users. This places severe requirements on sensitivity, linearity and dynamic range of the circuitry in the RF front-end. To improve radio sensitivity of the sensing function through processing gain we investigated three digital signal processing techniques: matched filtering, energy detection and cyclostationary feature detection. Our analysis shows that cyclostationary feature detection has advantages due to its ability to differentiate modulated signals, interference and noise in low signal to noise ratios. In addition, to further improve the sensing reliability, the advantage of a MAC protocol that exploits cooperation among many cognitive users is investigated.

2,761 citations


Journal ArticleDOI
Abstract: Motivated by emerging battery-operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits while maintaining computational throughput. Techniques for low-power operation are shown which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. An architecturally based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations. This optimum is achieved by trading increased silicon area for reduced power consumption. >

2,651 citations


Journal Article
TL;DR: An architecturally based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations, and is achieved by trading increased silicon area for reduced power consumption.
Abstract: Motivated by emerging battery-operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits while maintaining computational throughput Techniques for low-power operation are shown which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations An architecturally based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations This optimum is achieved by trading increased silicon area for reduced power consumption >

2,293 citations


Proceedings ArticleDOI
11 Dec 2006
TL;DR: This work proposes light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios and shows that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection.
Abstract: Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.

1,546 citations


Book
30 Jun 1995
TL;DR: The Hierarchy of Limits of Power J.D. Stratakos, et al., and Low Power Programmable Computation coauthored with M.B. Srivastava, provide a review of the main approaches to Voltage Scaling Approaches.
Abstract: 1. Introduction. 2. Hierarchy of Limits of Power J.D. Meindl. 3. Sources of Power Consumption. 4. Voltage Scaling Approaches. 5. DC Power Supply Design in Portable Systems coauthored with A.J. Stratakos, et al. 6. Adiabatic Switching L. Svensson. 7. Minimizing Switched Capacitance. 8. Computer Aided Design Tools. 9. A Portable Multimedia Terminal. 10. Low Power Programmable Computation coauthored with M.B. Srivastava. 11. Conclusions. Subject Index.

1,017 citations


Cited by
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Journal ArticleDOI
Yann LeCun1, Léon Bottou2, Léon Bottou3, Yoshua Bengio3  +3 moreInstitutions (5)
01 Jan 1998
Abstract: 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.

34,930 citations


Journal ArticleDOI
TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
Abstract: This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

17,354 citations


Journal ArticleDOI
TL;DR: This work develops and analyzes 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.
Abstract: 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.

9,655 citations


Proceedings Article
01 Jan 2005
TL;DR: This book aims to provide a chronology of key events and individuals involved in the development of microelectronics technology over the past 50 years and some of the individuals involved have been identified and named.
Abstract: 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

7,279 citations


Journal ArticleDOI
TL;DR: The novel functionalities and current research challenges of the xG networks are explained in detail, and a brief overview of the cognitive radio technology is provided and the xg network architecture is introduced.
Abstract: Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as NeXt Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitive radio networks. The term xG networks is used throughout the paper. The novel functionalities and current research challenges of the xG networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the xG network architecture is introduced. Moreover, the xG network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. The influence of these functions on the performance of the upper layer protocols such as routing and transport are investigated and open research issues in these areas are also outlined. Finally, the cross-layer design challenges in xG networks are discussed.

6,471 citations


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Performance
Metrics

Author's H-index: 68

No. of papers from the Author in previous years
YearPapers
20191
20181
20161
20151
20123
20111