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Speaker Identification using Mel frequency Cepstral Co-efficients (MFCC) methods for Model Training -Speaker Identification using Mel frequency Cepstral Co-efficients (MFCC) methods for Model Training
Update : 2024-05-02 Size : 1024 Publisher : 卡布

DL : 0
聪慧科技有限公司一家大型的电子产品生成企业,其下有1W以上的员工。为了更好的管理公司的员工,需要开发一套人力资源管理系统。能够对公司的员工信息、人才信息、公司培训信息、考勤信息以及薪酬信息等等进行管理。-Technology Co., Ltd. a large intelligent electronic products to generate business, under which a 1W or more employees. In order to better manage the company' s employees, the need to develop a human resources management system. To the company' s employee information, personnel information, corporate training information, attendance information and salary information, etc. to manage.
Update : 2024-05-02 Size : 869376 Publisher : nzj

DL : 0
华为技术有限公司,硬件工程师培训手册,主要讲述硬件开发过程,硬件工程师应该具备哪些技能等-Huawei Technologies Co., Ltd., a hardware engineer training manual focuses on hardware development, hardware engineers should have what skills
Update : 2024-05-02 Size : 899072 Publisher : 冯海

The p erceptro n algo rithm ceases to update the para meters only when all the training images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of updates (mistakes ). We’ll sho w this in lectur e 2.-The p erceptro n algo rithm ceases to update the para meters only when all the training images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of updates (mistakes ). We’ll sho w this in lectur e 2.
Update : 2024-05-02 Size : 89088 Publisher : jacobjacobjacob17

深圳赛盛技术有限公司的培训案例精选,主要包括传导辐射。-Training in selected cases Sheng Technology Co., Ltd. Shenzhen tournament, including the conduction of radiation.
Update : 2024-05-02 Size : 471040 Publisher : fff

Program : Keypad 01 Description : Keypad input and show key value to DSP with shift display For : MCS-51 Microcontroller Training System Filename : lab1401.asm Environment : ASEM-51 Macro assembler Copyright (C) 2002-2005 Innovative Experiment Co.,Ltd. **************************************************************************** - Program : Keypad 01 Description : Keypad input and show key value to DSP with shift display For : MCS-51 Microcontroller Training System Filename : lab1401.asm Environment : ASEM-51 Macro assembler Copyright (C) 2002-2005 Innovative Experiment Co.,Ltd. ****************************************************************************
Update : 2024-05-02 Size : 35840 Publisher : Chhunhour.kh

奥瑞文oTraining在线培训系统是大连奥瑞文网络技术有限公司专门针对企业和政府机构设计的新一代网络学习平台, 根据用户实际使用情景研究透彻的基础上开发的系统,至今已经发展了5年。在众多用户的支持和鼓励下,竭力打造一款在功能和用户体验上达到完美平衡的产品。奥瑞文oTraining在线培训系统在承接传统教育的基础之上充分实现了E-learning的设计理念, 它为现代学习型组织提供了卓有成效的学习与培训方案, 能够通过在线学习、在线考试和在线评估的方式轻松完成针对员工制订的培训计划。-Orin the oTraining online training system is Dalian Aorui the Network Technology Co., Ltd. specialized in enterprises and government agencies to design a new generation of network learning platform, according to the user s actual scene is studied thoroughly based on the development of the system, has been developed for five years. With the support and encouragement of many users, trying to create a product in the function and user experience to achieve the perfect balance. Orin the oTraining online training system in the undertaking of traditional education based on the full realization of the e-learning design concept. It is modern learning organization provides effective learning and training project, can through online learning, online examination and assessment way easy to complete training plan for employees to develop.
Update : 2024-05-02 Size : 6198272 Publisher : empudn54

The following Matlab project contains the source code and Matlab examples used for anfis for 2 dof robot. in this program i am first creating a training data set by applying the angular values to the 2 dof DK model and then supplying the data to the anfis function the function DK47 is the direct kinematics model the function co-ordinates create the coordinate training data the program is very similar to the program in matlab product help but the problem is it takes to much time to train the FIS so please help. The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.-The following Matlab project contains the source code and Matlab examples used for anfis for 2 dof robot. in this program i am first creating a training data set by applying the angular values to the 2 dof DK model and then supplying the data to the anfis function the function DK47 is the direct kinematics model the function co-ordinates create the coordinate training data the program is very similar to the program in matlab product help but the problem is it takes to much time to train the FIS so please help. The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.
Update : 2024-05-02 Size : 3072 Publisher : sina

classifier.mat文件太大未上传。可运行一次main3生成 getFeatures.m 获取灰度共生矩阵相关特征 main-main6 训练 + 识别 predict.m 单独的分类程序 temp.m 单独的分类演示程序(classifier.mat file is too large to upload. Can run a main3 to generate getFeatures.m Get the grayscale co-occurrence matrix related features main-main6 Training + Recognition predict.m separate classifier temp.m separate classification demo)
Update : 2024-05-02 Size : 2670592 Publisher : adewalike

DL : 3
BCI_MI_CSP_DNN是一种基于matlab的运动图像脑电信号分类程序。 基于matlab深度学习工具箱编写了BCI_MI_CSP_DNN程序 本程序的原理基于CSP和DNN算法 这个程序的性能是基于BCI竞赛II数据集II 提出了一种基于深度学习的运动图像脑电信号分类方法。在预处理原始脑电图信号的基础上,采用共空间模型(CSP)方法提取脑电图特征矩阵,并将其输入深度神经网络(DNN)进行训练和分类。我们的工作在BCI Competition II Dataset III上进行了实验测试,提出了最佳的DNN框架,准确率达到83.6%。(In this study, our goal was to use deep learning methods to improve the classification performance of motor imagery EEG signals. Therefore, we propose a classification method based on deep learning for motor imagery EEG signals. Based on the pre-processed raw EEG signals, a co-space model (CSP) method is used to extract the EEG feature matrix, which is then fed to a deep neural network (DNN) for training and classification. Our work was tested experimentally on the BCI Competition II Dataset III dataset, and the best DNN framework was proposed, achieving an accuracy of 83.6%.)
Update : 2024-05-02 Size : 14833664 Publisher : 渔舟唱晚1
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