The 4.0 kbit/s speech codec described in this paper is based on a Frequency Domain Interpolative (FDI) coding technique, which belongs to the class of prototype waveform Interpolation (PWI) coding techniques. The codec also has an integrated voice activity detector (VAD) and a noise reduction capability. The input signal is subjected to LPC analysis and the prediction residual is separated into a slowly evolving waveform (SEW) and a rapidly evolving waveform (REW) components. The SEW magnitude component is quantized using a hierarchical predictive vector quantization approach. The REW magnitude is quantized using a gain and a sub-band based shape. SEW and REW phases are derived at the decoder using a phase model, based on a transmitted measure of voice periodicity. The spectral (LSP) parameters are quantized using a combination of scalar and vector quantizers. The 4.0 kbits/s coder has an algorithmic delay of 60 ms and an estimated floating point complexity of 21.5 MIPS. The performance of this coder has been evaluated using in-house MOS tests under various conditions such as background noise. channel errors, self-tandem. and DTX mode of operation, and has been shown to be statistically equivalent to ITU-T (3.729 8 kbps codec across all conditions tested.
标签： frequency-domain interpolation performance Design kbit_s speech coder based and of
上传时间： 2018-04-08
上传用户：kilohorse
This book provides the essential design techniques for radio systems that operate at frequencies of 3 MHz to 100 GHz and which will be employed in the telecommunication service. We may also call these wireless systems, wireless being synonymous with radio, Telecommunications is a vibrant indus- try, particularly on the ‘‘radio side of the house.’’ The major supporter of this upsurge in radio has been the IEEE and its 802 committees. We now devote Ž . an entire chapter to wireless LANs WLANs detailed in IEEE 802.11. We also now have subsections on IEEE 802.15, 802.16, 802.20 and the wireless Ž . Ž metropolitan area network WMAN . WiFi, WiMax,, and UWB ultra wide- . band are described where these comparatively new radio specialties are demonstrating spectacular growth.
标签： Telecommunication Design System Radio for
上传时间： 2020-06-01
上传用户：shancjb
In 2001, Orange, a UK mobile network operator, announced the “Orange at Home” project, a smart house incorporating the latest technology wizardry built some 20 miles north of London. It was intended to be more than a mere showcase, with plans for real families to move in and live with the smart home. My then research establishment, the Digital World Research Centre at the University of Surrey, was commissioned to study how these families reacted to their new home, and to report lessons for the future development of smart homes and smart home technologies.
上传时间： 2020-06-06
上传用户：shancjb
Over the past few decades there has been an exponential growth in service robots and smart home technologies, which has led to the development of exciting new products in our daily lives. Service robots can be used to provide domestic aid for the elderly and disabled, serving various functions ranging from cleaning to enter- tainment. Service robots are divided by functions, such as personal robots, field robots, security robots, healthcare robots, medical robots, rehabilitation robots and entertainment robots. A smart home appears “intelligent” because its embedded computers can monitor so many aspects of the daily lives of householders. For example, the refrigerator may be able to monitor its contents, suggest healthy alter- natives and order groceries. Also, the smart home system may be able to clean the house and water the plants.
标签： Robotics Service Digital within Home the
上传时间： 2020-06-06
上传用户：shancjb
这是我在做大学教授期间推荐给我学生的一本书，非常好，适合入门学习。《python深度学习》由Keras之父、现任Google人工智能研究员的弗朗索瓦•肖莱（François Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，包括计算机视觉、自然语言处理、产生式模型等应用。书中包含30多个代码示例，步骤讲解详细透彻。作者在github公布了代码，代码几乎囊括了本书所有知识点。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。但是有一个小小的遗憾：代码的解释和注释是全英文的，即使英文水平较好的朋友看起来也很吃力。本人认为，这本书和代码是初学者入门深度学习及Keras最好的工具。作者在github公布了代码，本人参照书本，对全部代码做了中文解释和注释，并下载了代码所需要的一些数据集（尤其是“猫狗大战”数据集），并对其中一些图像进行了本地化，代码全部测试通过。（请按照文件顺序运行，代码前后有部分关联）。以下代码包含了全书约80%左右的知识点，代码目录：2.1: A first look at a neural network（ 初识神经网络）3.5: Classifying movie reviews（电影评论分类：二分类问题）3.6: Classifying newswires（新闻分类：多分类问题 ）3.7: Predicting house prices（预测房价：回归问题）4.4: Underfitting and overfitting（ 过拟合与欠拟合）5.1: Introduction to convnets（卷积神经网络简介）5.2: Using convnets with small datasets（在小型数据集上从头开始训练一个卷积网络）5.3: Using a pre-trained convnet（使用预训练的卷积神经网络）5.4: Visualizing what convnets learn（卷积神经网络的可视化）
上传时间： 2022-01-30
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