Solve the 8-puzzle problem using A * algorithme. Input: Program reads start state and goal state and heuristic (N or S) from EightPuzzle.INP file.0 representing blank. There are 2 Heuristic: 1. N: Number of misplaced tiles 2. S: Sum of Manhattan distance of current location and target location. Format: The first line write type of heuristic (N or S). Next is the status of departing and landing status. Between 2 states of 1 line blank. See examples EightPuzzle.INP
标签： state algorithme Program problem
上传时间： 2017-08-12
上传用户：jjj0202
Analysis of GPS data frame The design of the following four main data collection format: GPGGA (location information), GPRMC (Recommended Minimum location information), GPVTG (ground speed information), PGRME (forecast error message)
标签： data collection following Analysis
上传时间： 2017-09-25
上传用户：onewq
The object detector described below has been initially proposed by P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a Dalal-Triggs detector that uses a single filter on histogram of oriented gradients (HOG) features to represent an object category. This detector uses a sliding window approach, where a filter is applied at all positions and scales of an image. The first innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a “root” filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated deformation models. The score of one of star models at a particular position and scale within an image is the score of the root filter at the given location plus the sum over parts of the maximum, over placements of that part, of the part filter score on its location minus a deformation cost easuring the deviation of the part from its ideal location relative to the root. Both root and part filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of a feature pyramid computed from the input image. Another improvement is a representation of the class of models by a mixture of star models. The score of a mixture model at a particular position and scale is the maximum over components, of the score of that component model at the given location.
标签： 计算机视觉
上传时间： 2015-03-15
上传用户：sb_zhang
Computational models are commonly used in engineering design and scientific discovery activities for simulating complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who want to develop an understanding and/or predictive capability for complex behaviors typically observed in the corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system parameters, such as size or location dimensions and material properties, are adjusted to improve the performance of a system, as defined by one or more system performance objectives. Such optimization or tuning of the virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling methods are often used in uncertainty quantification to calculate a distribution on system performance measures, and to understand which uncertain inputs contribute most to the variance of the outputs. A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased models. These capabilities generally lead to improved designs and system performance in earlier design stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product development costs. In addition to providing this practical environment for answering system performance questions, the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized methods and meta-algorithms
标签： Optimization and Uncertainty Quantification
上传时间： 2016-04-08
上传用户：huhu123456
This report presents a tutorial of fundamental array processing and beamforming theory relevant to microphone array speech processing. A microphone array consists of multiple microphones placed at different spatial locations. Built upon a knowledge of sound propagation principles, the multiple inputs can be manipulated to enhance or attenuate signals emanating from particular directions. In this way, microphone arrays provide a means of enhancing a desired signal in the presence of corrupting noise sources. Moreover, this enhancement is based purely on knowledge of the source location, and so microphone array techniques are applicable to a wide variety of noise types. Microphone arrays have great potential in practical applications of speech processing, due to their ability to provide both noise robustness and hands-free signal acquisition.
标签： Microphone array Tutorial Array Signal Processing
上传时间： 2016-06-12
上传用户：halias
asp实现限制一个ip只能访问一次的方法 <% '///////////////////////////////////////////////////// '// // '//作用：一个IP地址只允许访问本页一次 // '//引用：<!-- #include file="Check_Ip.asp" --> // '// // '///////////////////////////////////////////////////// 'Response.Charset = 936 '设置输出编码为简体中文 'Response.Buffer = false '关闭缓冲区 Dim Fso,ts,IpList,Cfs '设置Cookies函数 Function SetCookie() Response.Cookies("IsBrow") = "Brow" Response.Cookies("IsBrow").Expires = Date+365 End Function '记录IP地址函数 Function WriteIp(FileName, IpAddress) Set Fso = Server.CreateObject("Scripting.FileSystemObject") Set ts = Fso.OpenTextFile(Server.MapPath(FileName),8,true) ts.WriteLine IpAddress ts.Close Set ts = Nothing Set Fso = Nothing End Function '读取IP地址函数 Function ReadIpList(FileName) Set Fso = Server.CreateObject("Scripting.FileSystemObject") If Not Fso.FileExists(Server.MapPath(FileName)) Then CreateFile("Iplist.txt") Exit Function End If Set ts = Fso.OpenTextFile(Server.MapPath(FileName)) Iplist = ts.ReadAll ts.Close Set ts = Nothing Set Fso = Nothing ReadIpList = Iplist End Function '创建文件函数 Function CreateFile(FileName) Set Fso = Server.CreateObject("Scripting.FileSystemObject") Set Cfs = Fso.CreateTextFile(Server.MapPath(FileName)) Cfs.Close Set Cfs = Nothing Set Fso = Nothing End Function '关闭当前IE窗口函数(注:IE6下通过,其他浏览器未测试) Function CloseWindow() 'Response.Write "<script>window.location='javascript:window.opener=null;window.close();'</script>" Response.Redirect "http://www.baidu.com" End Function Ip = Request.ServerVariables("REMOTE_ADDR") '获取浏览者IP地址 Cookie = Request.Cookies("IsBrow") '获取当前Cookies 'Response.Write Cookie If Request.ServerVariables("HTTP_X_FORWARDED_FOR") <> "" Then Response.Write "本站不允许使用代理访问" Response.End() Else If Cookie = "Brow" Then CloseWindow() Else If Instr(ReadIpList("Iplist.txt"),Ip) <>0 Then CloseWindow() Else WriteIp "Iplist.txt" , Ip End If SetCookie() End If End If %>
上传时间： 2016-07-14
上传用户：helei0915
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
标签： 传感器网络
上传时间： 2016-11-27
上传用户：xxmluo
Recently millimeter-wave bands have been postu- lated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios
标签： Communications Millimeter Wave V2V
上传时间： 2020-05-23
上传用户：shancjb
Mobile communication devices like smart phones or tablet PCs enable us to consume information at every location and at every time. The rapid development of new applications and new services and the demand to access data in real time create an increasing throughput demand. The data have to be transmitted reliably to ensure the desired quality of service. Furthermore, an improved utilization of the bandwidth is desired to reduce the cost of transmission.
标签： Architectures Processing Baseband Signal for
上传时间： 2020-05-26
上传用户：shancjb
To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation.
标签： Large-scale Antenna Systems
上传时间： 2020-05-27
上传用户：shancjb
