数字电视多频道延时直播系统的设计和实现

基于DVB标准设计直播系统,通过视频服务器方式进行延时直播,通过软件方法从多节目复用流中提取出多个单独的节目传输流,从而支持多个频道同时进行直播.可与硬盘播出系统进行系统集成.初步实现系统基本功能,按照相关标准进行传输流的技术指标检测,并对测试结果进行分析.     

语音增强用于坦克驾驶舱内无线语音通信

讨论了一种基于传统谱相减算法的改进方法。利用语音的短时平稳性,通过先验幅度比来连续更新噪声谱的估计,从而代替复杂的VAD(话音活性检测)。计算机仿真结果表明,这种改进方法有效抑制了噪声干扰,语音得到了增强,在极大地提高信噪比的同时,将残留的音乐噪声和语音失真保持在人耳听觉容忍的范围以内,从而较好的保持了语音自然度。     

采用FPGA通过BT.656接口实现传输4路视频流的方法

提出一种使用FPGA(现场可编程门阵列)实现一个ITU-R BT.656并行硬件接口传输4路CIF格式视频流的方法。该方法说明了从视频处理器到FPGA传输4路CIF格式视频流的数据结构,利用该数据结构,一个ITU-R BT.656的硬件接口可传输4路不同的CIF格式的视频数据流。FP-GA将4路视频数据流分离、插值生成D1(720×576像素)格式的数据输出给视频处理器。这种方法提高了视频处理器的扩展性,增加了视频处理器输出视频的路数。     

一种UHF频段无线数据传输系统接入点的射频前端

介绍了UHF(特高频)频段无线数据传输系统中接入点的一种射频前端。该前端包括调制器、上变频器、功率放大器、低噪声放大器、下变频器、解调器、频率合成器、增益控制和收发切换开关等电路。介绍了射频前端的实现方案以及关键部件的设计与测试。制作完成的射频前端集成在一块1 5 0mm×1 0 0mm的4层印制电路版上。该前端最大输出功率可达2W,接收灵敏度优于-100 dBm,收发切换时延小于1.5μs,满足系统指标要求。     

A 2.4GHz Low Power ASK Transmitter for Wireless Capsule Endoscope Applications

提出了一种应用于无线内窥镜系统的2.4GHz低功耗ASK发射机.为了获得高的数据传输速率,采用了基于混频器的直接上变换发射机结构.为了节省功耗,提出了一种基于电流复用技术的伪差分堆栈结构的A类功放.低功耗发射机由两部分组成:基于恒幅度锁相环(PLL)的20MHz的ASK基带调制器和直接上变换的射频电路.该设计已经采用TSMC 0.25μm CMOS工艺实现并进行了验证.测试结果表明,发射数据速率为1Mbps时,发射机的输出功率为-23.217dBm.采用单2.5V的电源供电下,低功耗发射机消耗的电流约为3.17mA.     

目标基视频编码中的运动目标提取与跟踪新算法

自动、快速的视频目标提取与跟踪是目标基视频编码中的一项关键技术.本文提出一种运动目标提取与跟踪新算法.首先,根据多帧运动信息和高阶统计检测方法得到二值运动掩模图像,然后提出一种改进分水岭算法对运动区域及其周围部分进行分割.将二者所得结果进行投影运算,得到最终运动目标.最后提出一种运动目标跟踪新算法,能对目标进行有效的跟踪.实验结果说明了本文算法的有效性.     

A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category.     

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

In complex network environments, there always exist heterogeneous devices with different computational powers. In this work, we propose a novel scalable random linear network coding (RLNC) framework based on embedded fields, so as to endow heterogeneous receivers with different decoding capabilities. In this framework, the source linearly combines the original packets over embedded fields based on a precoding matrix and then encodes the precoded packets over GF(2) before transmission to the network. After justifying the arithmetic compatibility over different finite fields in the encoding process, we derive a sufficient and necessary condition for decodability over different fields. Moreover, we theoretically study the construction of an optimal precoding matrix in terms of decodability. The numerical analysis in classical wireless broadcast networks illustrates that the proposed scalable RLNC not only guarantees a better decoding compatibility over different fields compared with classical RLNC over a single field, but also outperforms Fulcrum RLNC in terms of a better decoding performance over GF(2). Moreover, we take the sparsity of the received binary coding vector into consideration, and demonstrate that for a large enough batch size, this sparsity does not affect the completion delay performance much in a wireless broadcast network.     

An Unsupervised Video Stabilization Algorithm Based on Key Point Detection

In recent years, video stabilization has improved significantly in simple scenes, but is not as effective as it could be in complex scenes. In this study, we built an unsupervised video stabilization model. In order to improve the accurate distribution of key points in the full frame, a DNN-based key-point detector was introduced to generate rich key points and optimize the key points and the optical flow in the largest area of the untextured region. Furthermore, for complex scenes with moving foreground targets, we used a foreground and background separation-based approach to obtain unstable motion trajectories, which were then smoothed. For the generated frames, adaptive cropping was conducted to completely remove the black edges while maintaining the maximum detail of the original frame. The results of public benchmark tests showed that this method resulted in less visual distortion than current state-of-the-art video stabilization methods, while retaining greater detail in the original stable frames and completely removing black edges. It also outperformed current stabilization models in terms of both quantitative and operational speed.     

基于多维度和多模态信息的视频描述方法

针对视频自动描述任务中的复杂信息表征问题,提出一种多维度和多模态视觉特征的提取和融合方法。首先通过迁移学习提取视频序列的静态和动态等多维度特征,并采用图像描述算法提取视频关键帧的语义信息,完成视频信息的特征表征;然后采用多层长短期记忆网络融合多维度和多模态信息,最终生成视频内容的语言描述。实验仿真表明,所提方法与目前已有方法相比,在视频自动描述任务中取得了较好的效果。