A. El Gamal

Bio: A. El Gamal is an academic researcher from Stanford University. The author has contributed to research in topics: Pixel & Channel capacity. The author has an hindex of 45, co-authored 80 publications receiving 11849 citations.

CMOS image sensors

Abstract: In this article, we provide a basic introduction to CMOS image-sensor technology, design and performance limits and present recent developments and future directions in this area. We also discuss image-sensor operation and describe the most popular CMOS image-sensor architectures. We note the main non-idealities that limit CMOS image sensor performance, and specify several key performance measures. One of the most important advantages of CMOS image sensors over CCDs is the ability to integrate sensing with analog and digital processing down to the pixel level. Finally, we focus on recent developments and future research directions that are enabled by pixel-level processing, the applications of which promise to further improve CMOS image sensor performance and broaden their applicability beyond current markets.

Throughput-delay trade-off in wireless networks

Abstract: Gupta and Kumar (2000) introduced a random network model for studying the way throughput scales in a wireless network when the nodes are fixed, and showed that the throughput per source-destination pair is /spl otimes/(1//spl radic/nlogn). Grossglauser and Tse (2001) showed that when nodes are mobile it is possible to have a constant or /spl otimes/(1) throughput scaling per source-destination pair. The focus of this paper is on characterizing the delay and determining the throughput-delay trade-off in such fixed and mobile ad hoc networks. For the Gupta-Kumar fixed network model, we show that the optimal throughput-delay trade-off is given by D(n) = /spl otimes/(nT(n)), where T(n) and D(n) are the throughput and delay respectively. For the Grossglauser-Tse mobile network model, we show that the delay scales as /spl otimes/(n/sup 1/2//v(n)), where v(n) is the velocity of the mobile nodes. We then describe a scheme that achieves the optimal order of delay for any given throughput. The scheme varies (i) the number of hops, (ii) the transmission range and (iii) the degree of node mobility to achieve the optimal throughput-delay trade-off. The scheme produces a range of models that capture the Gupta-Kumar model at one extreme and the Grossglauser-Tse model at the other. In the course of our work, we recover previous results of Gupta and Kumar, and Grossglauser and Tse using simpler techniques, which might be of a separate interest.

Noisy Network Coding

Abstract: A noisy network coding scheme for communicating messages between multiple sources and destinations over a general noisy network is presented. For multi-message multicast networks, the scheme naturally generalizes network coding over noiseless networks by Ahlswede, Cai, Li, and Yeung, and compress-forward coding for the relay channel by Cover and El Gamal to discrete memoryless and Gaussian networks. The scheme also extends the results on coding for wireless relay networks and deterministic networks by Avestimehr, Diggavi, and Tse, and coding for wireless erasure networks by Dana, Gowaikar, Palanki, Hassibi, and Effros. The scheme involves lossy compression by the relay as in the compress-forward coding scheme for the relay channel. However, unlike previous compress-forward schemes in which independent messages are sent over multiple blocks, the same message is sent multiple times using independent codebooks as in the network coding scheme for cyclic networks. Furthermore, the relays do not use Wyner-Ziv binning as in previous compress-forward schemes, and each decoder performs simultaneous decoding of the received signals from all the blocks without uniquely decoding the compression indices. A consequence of this new scheme is that achievability is proved simply and more generally without resorting to time expansion to extend results for acyclic networks to networks with cycles. The noisy network coding scheme is then extended to general multi-message networks by combining it with decoding techniques for the interference channel. For the Gaussian multicast network, noisy network coding improves the previously established gap to the cutset bound. We also demonstrate through two popular Gaussian network examples that noisy network coding can outperform conventional compress-forward, amplify-forward, and hash-forward coding schemes.

A 10000 frames/s CMOS digital pixel sensor

Abstract: A 352/spl times/288 pixel CMOS image sensor chip with per-pixel single-slope ADC and dynamic memory in a standard digital 0.18-/spl mu/m CMOS process is described. The chip performs "snapshot" image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4/spl times/9.4 /spl mu/m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 /spl mu/V/e/sup -/. At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

Cooperative diversity in wireless networks: Efficient protocols and outage behavior

Abstract: We develop and analyze low-complexity cooperative diversity protocols that combat fading induced by multipath propagation in wireless networks. The underlying techniques exploit space diversity available through cooperating terminals' relaying signals for one another. We outline several strategies employed by the cooperating radios, including fixed relaying schemes such as amplify-and-forward and decode-and-forward, selection relaying schemes that adapt based upon channel measurements between the cooperating terminals, and incremental relaying schemes that adapt based upon limited feedback from the destination terminal. We develop performance characterizations in terms of outage events and associated outage probabilities, which measure robustness of the transmissions to fading, focusing on the high signal-to-noise ratio (SNR) regime. Except for fixed decode-and-forward, all of our cooperative diversity protocols are efficient in the sense that they achieve full diversity (i.e., second-order diversity in the case of two terminals), and, moreover, are close to optimum (within 1.5 dB) in certain regimes. Thus, using distributed antennas, we can provide the powerful benefits of space diversity without need for physical arrays, though at a loss of spectral efficiency due to half-duplex operation and possibly at the cost of additional receive hardware. Applicable to any wireless setting, including cellular or ad hoc networks-wherever space constraints preclude the use of physical arrays-the performance characterizations reveal that large power or energy savings result from the use of these protocols.

Wireless communications

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

User cooperation diversity. Part I. System description

Abstract: Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II (see ibid., p.1939-48) focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.

Enhancing Sparsity by Reweighted ℓ 1 Minimization

Abstract: It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained l1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms l1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted l1-minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed near-sparsity in overcomplete representations—not by reweighting the l1 norm of the coefficient sequence as is common, but by reweighting the l1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as Compressive Sensing.