Analytical constant-modulus algorithm, to separate linear combinations of CM sourcesThe algorithm
is robust in the presence of noise, and is tested on measured data,
collected from an experimental set-up.
SDP, Service Delivery Platform, is more for telecom operators who want to manage the Data Service better delivered to the end device users by bridging with back-end content providers. Operators rely on the content provider to create & distribute data content to different types of devices. This is different from the open world in the internet communication. Operators must control who can access what content based on his rate plans. Also, based the content access results, the process will be recorded as the transaction records based on which billing statements can be generated to collected the money and shared by operators and content providers. I am working on the conceptual architecture level and the real implementation is very complicated due to too many types of service from different content providers to different types of devices based on the different types of the rate plans.
SDP, Service Delivery Platform, is more for telecom operators who want to manage the Data Service better delivered to the end device users by bridging with back-end content providers. Operators rely on the content provider to create & distribute data content to different types of devices. This is different from the open world in the internet communication. Operators must control who can access what content based on his rate plans. Also, based the content access results, the process will be recorded as the transaction records based on which billing statements can be generated to collected the money and shared by operators and content providers. I am working on the conceptual architecture level and the real implementation is very complicated due to too many types of service from different content providers to different types of devices based on the different types of the rate plans.
JLAB is a set of Matlab functions I have written or co-written over the past fifteen years for the purpose of analyzing data. It consists of four hundred m-files spanning thirty thousand lines of code. JLAB includes functions ranging in complexity from one-line aliases to high-level algorithms for certain specialized tasks. These have been collected together and made publicly available for you to use, modify, and --- subject to certain very reasonable constraints --- to redistribute.
Some of the highlights are: a suite of functions for the rapid manipulation of multi-component, potentially multi-dimensional datasets a systematic way of dealing with datasets having components of non-uniform length tools for fine-tuning figures using compact, straightforward statements and specialized functions for spectral and time / frequency analysis, including advanced wavelet algorithms developed by myself and collaborators.
NEA1803 51單片機與12864 由串口中斷收取數據 顯示經度緯度 高度 速度 時間 使用衛星數-GPS development NEA1803 51 SCM and 12864 collected by the serial interrupt data show a high degree of longitude latitude speed time-use satellite
有多徑信道、多普勒頻移，瑞利、RICE(萊斯)信道等仿真，QPSK調制和解調等，交織編碼。程序經過本人測試，絕對可用，并附上本人測試說明和仿真圖像結果-I collected information on 2, how-path channel, Doppler frequency shift, Rayleigh, RICE (Rice) channel, such as simulation, QPSK modulation and demodulation, etc., Interleaved Coded. After I tested the procedure is absolutely available, along with my test images and simulation results indicate.
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.
The term “ smart grid ” defi nes a self - healing network equipped with dynamic optimiza-
tion techniques that use real - time measurements to minimize network losses, maintain
voltage levels, increase reliability, and improve asset management. The operational data
collected by the smart grid and its sub - systems will allow system operators to rapidly
identify the best strategy to secure against attacks, vulnerability, and so on, caused by
various contingencies. However, the smart grid fi rst depends upon identifying and
researching key performance measures, designing and testing appropriate tools, and
developing the proper education curriculum to equip current and future personnel with
the knowledge and skills for deployment of this highly advanced system.
General paradigm in solving a computer vision problem is to represent a raw image
using a more informative vector called feature vector and train a classifier on top of
feature vectors collected from training set. From classification perspective, there are
several off-the-shelf methods such as gradient boosting, random forest and support
vector machines that are able to accurately model nonlinear decision boundaries.
Hence, solving a computer vision problem mainly depends on the feature extraction
algorithm
Much has been written concerning the manner in which healthcare is changing, with
a particular emphasis on how very large quantities of data are now being routinely
collected during the routine care of patients. The use of machine learning meth-
ods to turn these ever-growing quantities of data into interventions that can improve
patient outcomes seems as if it should be an obvious path to take. However, the
field of machine learning in healthcare is still in its infancy. This book, kindly
supported by the Institution of Engineering andTechnology, aims to provide a “snap-
shot” of the state of current research at the interface between machine learning and
healthcare.
