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[Program doc] kalmanexpri DL : 0: The Kalman filter is a set of mathematical equations that provides an efficient computational [recursive] means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown.
Date : 2025-12-29 Size : 1kb User : vinodh kumar
[Program doc] 4 DL : 0: Invariant Wideband Spectrum Sensing Under Unknown Variances
Date : 2025-12-29 Size : 347kb User : phillis
[Program doc] Protocols-for-Energy-Efficient-and-QoS-Aware-Comm DL : 0: --- 2008, Protocols for Energy Efficient and QoS Aware Communication in Cluster-based Wireless Sensor Networks, Unknown, Philosophy
Date : 2025-12-29 Size : 2.32mb User : mamad
[Program doc] A-Bayesian-Approach DL : 0: In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth’s crust.We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution.We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data.
Date : 2025-12-29 Size : 3.2mb User : 张洋
[Program doc] capacity-of-frequency-selection-channel DL : 0: With the higher and higher of transmitting rate, the signal bandwidth iswider and wider, and the p ractical channel p resents the frequency selective fading character. In this paper, a multi - input multi - output (M IMO) channelmodel under frequency selective fading is constructed according to the character of frequency selective fading channel. The frequency and time domain methods are used to discussM IMO channel capacity due to the known and unknown channel state information under the frequency selective fa2 ding. The simulation results show thatM IMO system can imp rove the channel capacity remarkably but no exchanging signal bandwidth under the circumstances of the same launching power and transmitting band2 width, and the numbers of antennas and the quantity of SNR have the different effect on the channel capaci2 ty. The conclusion offers the basis of how to imp rove M IMO channel capacity under the circumstances of frequency selective fading.
Date : 2025-12-29 Size : 501kb User : Yuan
[Program doc] 2.rar DL : 0: 收集了包括中国电信、中国移动、中国联通、长城宽带、聚友宽带等 ISP 的最新准确 IP 地址数据。包括最全的网吧数据。希望能够通过大家的共同努力打造一个没有未知数据，没有错误数据的QQ IP。IP数据库每 5天更新一次，请大家定期更新最新的IP数据库！因为IP地址数据是民间收集的，电信也会不时的更改IP段，所以有点遗漏、错误是难免的。,Collection of accurate and up-to-date, including China Telecom, China Mobile, China Unicom, Great Wall Broadband, Juyou broadband ISP IP address data. Including all cafes data. I hope that through our concerted efforts to build an unknown data, there is no error data QQ IP. IP database is updated once every five days, regularly updated with the latest IP database! IP address data is collected folk, Telecom will from time to time change the IP segment, so I am a bit omissions, errors are inevitable.
Date : 2025-12-29 Size : 4.13mb User : repair
[Program doc] Image-reconstruction_CS DL : 0: 合稀疏贝叶斯学习(SBL)和可压缩传感理论(CS)，给出一种在噪声测量条件下重建可压缩图像的方法。该方法将cS理论中图像重建过程看作一个线性回归问题，而待重建的图像是该回归模型巾的未知权值参数；利用sBL方法对权值赋予确定的先验条件概率分布用以限制模型的复杂度，并引入超参数- Hop sparse Bayesian learning ( SBL ) and compressible sensing theory ( CS ) , give a compressible image reconstruction in the noise measurement conditions . The method of the CS theory image reconstruction process as a linear regression problem , the image to be reconstructed is unknown weighting parameters of the regression model towel SBL method to determine the weights given a priori probability distribution to limit the complexity of the model and the introduction of the hyper-parameters
Date : 2025-12-29 Size : 399kb User : lili
[Program doc] FIND-LAN-IP-TOOL DL : 0: 非常好用的查找内网IPde工具，可以快速准确的查处内网的未知IP。-Very easy to find within the network IPde tool that can quickly and accurately in the investigation of the unknown network IP.
Date : 2025-12-29 Size : 1.11mb User : 秦建息
[Program doc] A-mobility-based-cluster-formation-algorithm-for- DL : 0: In this paper, we propose a learning automatabased weighted cluster formation algorithm calledMCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster-head.
Date : 2025-12-29 Size : 510kb User : ShAzZ
[Program doc] paper2 DL : 0: A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels
Date : 2025-12-29 Size : 321kb User : li
[Program doc] paper1 DL : 0: 关于LTE的文章，希望大家喜欢。 Belief-propagation-based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels-Belief-propagation-based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels
Date : 2025-12-29 Size : 215kb User : xiaozhang
[Program doc] shuizhunwangpingcha DL : 0: 读取水准网数据，分已知点高程，未知点点号，还有观测值，平差-Read leveling network data, sub-point elevation is known, unknown point number, as well as observations
Date : 2025-12-29 Size : 261kb User : 林凌
[Program doc] Channel-equalization DL : 0: CHANNEL EQUALIZATION AND BLIND DECONVOLUTION Blind deconvolution is the process of unravelling two unknown signals that have been convolved. An important application of blind deconvolution is in blind equalization for restoration of a signal distorted in transmission through a communication channel. Blind equalization has a wide range of applications, for example in digital telecommunications for removal of intersymbol interference, in speech recognition for removal of the effects of microphones and channels, in deblurring of distorted images, in dereverberation of acoustic recordings, in seismic data analysis, etc.
Date : 2025-12-29 Size : 272kb User : JRB