J. Bartram

Bio: J. Bartram is an academic researcher from Raytheon. The author has contributed to research in topics: Modular design & Digital image processing. The author has an hindex of 2, co-authored 2 publications receiving 75 citations.

Digital Communication Systems

Abstract: DIGITAL COMMUNICATION ADVANTAGES AND DISADVANTAGES TOTALECER. INTRODUCTION TO DIGITAL COMMUNICATIONS DIGITAL. INTRODUCTION TO EECS II DIGITAL COMMUNICATION SYSTEMS. PRINCIPLES OF DIGITAL COMMUNICATION SHARIF UNIVERSITY OF. SIMON S HAYKIN DIGITAL COMMUNICATION SYSTEMS WILEY 2013. DIGITAL COMMUNICATION SYSTEMS. DIGITAL COMMUNICATION SYSTEMS NPTEL. DIGITAL COMMUNICATION SYSTEM MODULATION BANDWIDTH. DIGITAL COMMUNICATION SYSTEMS FREE EBOOKS DOWNLOAD. COMMUNICATION SYSTEMS ANALOG VS DIGITAL WIKIBOOKS OPEN. DIGITAL COMMUNICATION SYSTEMS WILEY COM. COMMUNICATION SYSTEMS II. DIGITAL COMMUNICATION DEFINITION OF DIGITAL. MODERN DIGITAL AND ANALOG COMMUNICATION THE AMAZON COM. DIGITAL COMMUNICATION SYSTEMS MCQ SERIES. DIGITAL COMMUNICATIONS FUNDAMENTALS AND APPLICATIONS. DIGITAL COMMUNICATION SYSTEMS 1ST EDITION AMAZON COM. DIGITAL COMMUNICATIONS SYSTEMS MASTER’S DEGREE. DIGITAL COMMUNICATION SYSTEM SLIDESHARE. DIGITAL COMMUNICATION SYSTEMS GOOGLE BOOKS. ANALOG AND DIGITAL COMMUNICATION SYSTEMS BY MARTIN S RODEN. WILEY DIGITAL COMMUNICATION SYSTEMS 1ST EDITION SIMON. DIGITAL COMMUNICATION SYSTEM MANAGEMENT STUDY GUIDE. DIGITAL MODULATION IN COMMUNICATIONS SYSTEMS – AN INTRODUCTION. DATA TRANSMISSION WIKIPEDIA. DIGITAL COMMUNICATION SYSTEMS EBOOK BY SIMON HAYKIN. DESIGN OF A DIGITAL COMMUNICATION SYSTEM SIGNAL PROCESSING. DIGITAL COMMUNICATIONS J S CHITODE GOOGLE BOOKS. INTRODUCTIONTOCOMMUNICATIONSYSTEMS UC SANTA BARBARA. INTRODUCTION TO DIGITAL COMMUNICATION MIT OPENCOURSEWARE. INTRODUCTION TO DIGITAL COMMUNICATIONS SYSTEM. DCSL DIGITAL COMMUNICATION SYSTEMS LIMITED. TYPES OF DIGITAL COMMUNICATION TECHWALLA COM. TELECOMMUNICATION WIKIPEDIA. DIGITAL COMMUNICATION SYSTEMS UNIVERSITY OF OTTAWA. DIGITAL COMMUNICATION BY SIMON HAYKIN PDF 4ED TOTALECER. FUNDAMENTALS OF DIGITAL COMMUNICATIONS SYSTEMS 1 1. COMMUNICATION SYSTEMS ADVANTAGES OF DIGITAL TRANSMISSION. 3 INTRODUCTION TO DIGITAL COMMUNICATION SYSTEMS YOUTUBE. DIGITAL COMMUNICATION SYSTEMS SLIDESHARE. THEORY AND DESIGN OF DIGITAL COMMUNICATION SYSTEMS. ELEMENTS OF A DIGITAL COMMUNICATION SYSTEM. DIGITAL COMMUNICATION SYSTEMS UOTECHNOLOGY EDU IQ. ELEMENTS OF DIGITAL COMMUNICATION SYSTEMS. DIGITAL AND ANALOG COMMUNICATION SYSTEMS. DIGITAL AMP ANALOG COMMUNICATION SYSTEMS 8TH EDITION PDF. DIGITAL COMMUNICATION SYSTEM QUESTIONS AND ANSWERS. DIGITAL COMMUNICATIONS TRAINING SYSTEMS LAB VOLT. DIGITAL COMMUNICATION TUTORIAL. EE2EE2 4 COMMUNICATION SYSTEMS4 COMMUNICATION SYSTEMS

Fifth generation digital sonar signal processing

Abstract: This paper discusses the evolutionary development, which has taken place over the last decade, in digital sonar systems architecture with the application of first, second, and third generation computers as system controllers for sonar systems. It is the opinion of the authors that, with the arrival of microprocessors, the system controller tasks in real time digital sonars will diminish. We present, as the "fourth generation," the present systems which still have a relatively large CPU, assisted by an array of microprocessors under their control for several subtasks which can be handled, more efficiently, locally in the systems. The "fifth generation" concept is postulated as a further development of this concept. A distributed processing scheme is presented in which the processing elements are actually highly functionally distributed themselves at the lowest level of architecture; consequently, the user views them as uniprocessors within the tightly coupled network. This approach should result in relatively high throughput utilizing a fairly small repertoire of modular hardware components and requiring minimal software effort by implementing, via firmware, very high level macros. This concept allows adaptive system architecture for the various advanced sonar data processing requirements for multielement linear, spatial, or blanket type array systems postulated for the future.

Cooperative Diversity in Wireless Networks

Abstract: Transmit Diversity is an effective methodology for improving the quality and reliability of a wireless network by reducing the effects of fading. As majority of the wireless devices (i.e. mobile handsets, etc) are limited to only one antenna, especially due to hardware constraints, size and cost factors; cooperative communication can be utilized in order to generate transmit diversity [1]. This enables single antenna wireless devices to share their antennas during transmission in such a manner that creates a virtual MIMO (multiple-input and multiple-output) system [2] [3]. In this paper, we will analyze the recent developments and trends in this promising area of wireless Ad hoc networks. The article will also discuss various main cooperative signaling methods and will also observe their performance.

Practical fusion of quantized measurements via particle filtering

Abstract: Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that by an intelligent quantization directly of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin."; In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. We show results that indicate a compander/particle-filter combination is a natural fit, and specifically that quite good performance is achievable with only 2-3 bits per dimension per observation. The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.

Optimal binary communication with nonequal probabilities

Abstract: Optimal signal energies are derived for optimal binary digital communication systems with arbitrary signal probabilities and correlation with both coherent and noncoherent detection. The resulting bit-error probability (BEP) is computed and compared with the BEP of the same systems with equal signal energies. One of the conclusions is that for the coherent system with nonnegative correlation, and for the noncoherent system with arbitrary correlation, the optimal signals are on-off keying (OOK), i.e., the signal with probability p/spl les/0.5 has energy E/p, while the second signal has zero energy, where E is the average signal energy. The proposed system is also better than a system with source coding and equiprobable signals.

A stochastic model for beaconless IEEE 802.15.4 MAC operation

Abstract: IEEE 802.15.4 is a popular choice for MAC/PHY protocols in low power and low data rate wireless sensor networks. In this paper, we develop a stochastic model for the beaconless operation of IEEE 802.15.4 MAC protocol. Given the number of nodes competing for channel access and their packet generation rates, the model predicts the packet loss probability and the packet latency. We compared the model predictions with NS2 simulation results and found an excellent match between the two for a wide range of the packet generation rates and the number of competing nodes in the network.