Compute Classical detection threshold for radar detection under additive Gaussian white noise criterion and specified false alarm probability.
標簽： detection Classical threshold additive
上傳時間： 2013-12-09
上傳用戶：hwl453472107
Compute Classical CFAR binary detection threshold for radar detection under additive Gaussian white noise criterion and specified false alarm probability.
標簽： detection Classical threshold additive
上傳時間： 2013-12-22
上傳用戶：希醬大魔王
Homogeneous Partitioning of the Surveillance Volume discusses the implementation of the first of three sequentially complementary approaches for increasing the probability of target detection within at least some of the cells of the surveillance volume for a spatially nonGaussian or Gaussian “noise” environment that is temporally Gaussian. This approach, identified in the Preface as Approach A, partitions the surveillance volume into homogeneous contiguous subdivisions.
標簽： Receivers Adaptive Antennas and
上傳時間： 2020-05-26
上傳用戶：shancjb
In this paper, we consider the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE).
標簽： Estimation Millimeter Adaptive Approach Channel Systems Rate MIMO Wave for
上傳時間： 2020-05-26
上傳用戶：shancjb
Performance analysis belongs to the domain of applied mathematics. The major domain of application in this book concerns telecommunications sys- tems and networks. We will mainly use stochastic analysis and probability theory to address problems in the performance evaluation of telecommuni- cations systems and networks. The first chapter will provide a motivation and a statement of several problems.
標簽： Communications Performance Analysis Networks of
上傳時間： 2020-05-31
上傳用戶：shancjb
This paper presents a Hidden Markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden Markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.
標簽： Telecommunications Processing Signal for
上傳時間： 2020-06-01
上傳用戶：shancjb
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. The subtitle of this book is “A Modern Approach.” The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common frame- work, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable.
標簽： A-Modern-Approach Intelligence
上傳時間： 2020-06-10
上傳用戶：shancjb
This edition of Digital Image Processing is a major revision of the book. As in the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992, 2002, and 2008 editions by Gonzalez and Woods, this sixth-generation edition was prepared with students and instructors in mind. The principal objectives of the book continue to be to provide an introduction to basic concepts and methodologies applicable to digital image processing, and to develop a foundation that can be used as the basis for further study and research in this field. To achieve these objectives, we focused again on material that we believe is fundamental and whose scope of application is not limited to the solution of specialized problems. The mathematical complexity of the book remains at a level well within the grasp of college seniors and first-year graduate students who have introductory preparation in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. The book website provides tutorials to support readers needing a review of this background material
標簽： Processing Digital Image
上傳時間： 2021-02-20
上傳用戶：
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