Video transmission over wireless networks

Compressed video bitstream transmissions over wireless networks are addressed in this work. We first consider error control and power allocation for transmitting wireless video over CDMA networks in conjunction with multiuser detection. We map a layered video bitstream to several CDMA fading channels and inject multiple source/parity layers into each of these channels at the transmitter. We formulate a combined optimization problem and give the optimal joint rate and power allocation for each of linear minimum mean-square error (MMSE) multiuser detector in the uplink and two types of blind linear MMSE detectors, i.e., the direct-matrix-inversion (DMI) blind detector and the subspace blind detector, in the downlink. We then present a multiple-channel video transmission scheme in wireless CDMA networks over multipath fading channels. For a given budget on the available bandwidth and total transmit power, the transmitter determines the optimal power allocations and the optimal transmission rates among multiple CDMA channels, as well as the optimal product channel code rate allocation. We also make use of results on the large-system CDMA performance for various multiuser receivers in multipath fading channels. We employ a fast joint source-channel coding algorithm to obtain the optimal product channel code structure. Finally, we propose an end-to-end architecture for multi-layer progressive video delivery over space-time differentially coded orthogonal frequency division multiplexing (STDC-OFDM) systems. We propose to use progressive joint source-channel coding to generate operational transmission distortion-power-rate (TD-PR) surfaces. By extending the rate-distortion function in source coding to the TD-PR surface in joint source-channel coding, our work can use the ??equal slope?? argument to effectively solve the transmission rate allocation problem as well as the transmission power allocation problem for multi-layer video transmission. It is demonstrated through simulations that as the wireless channel conditions change, these proposed schemes can scale the video streams and transport the scaled video streams to receivers with a smooth change of perceptual quality

Towards Adaptive Semantic Segmentation by Progressive Feature Refinement

As one of the fundamental tasks in computer vision, semantic segmentation plays an important role in real world applications. Although numerous deep learning models have made notable progress on several mainstream datasets with the rapid development of convolutional networks, they still encounter various challenges in practical scenarios. Unsupervised adaptive semantic segmentation aims to obtain a robust classifier trained with source domain data, which is able to maintain stable performance when deployed to a target domain with different data distribution. In this paper, we propose an innovative progressive feature refinement framework, along with domain adversarial learning to boost the transferability of segmentation networks. Specifically, we firstly align the multi-stage intermediate feature maps of source and target domain images, and then a domain classifier is adopted to discriminate the segmentation output. As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation. Experimental results verify the efficiency of our proposed method compared with state-of-the-art methods

KEWS: A Evaluation Method of Workload Simulation based on KPIs

For end-to-end performance testing, workload simulation is an important method to enhance the real workload while protecting user privacy. To ensure the effectiveness of the workload simulation, it is necessary to dynamically evaluate the similarity of system inner status using key performance indicators(KPIs), which provide a comprehensive record of the system status, between the simulated workload and real workload by injecting workload into the system. However, due to the characteristics of KPIs, including large data size, amplitude differences, phase shifts, non-smoothness, high dimension, and Large numerical span, it is unpractical to evaluation on the full volume of KPIs and is challenging to measure the similarity between KPIs. In this paper, we propose a similarity metric algorithm for KPIs, extend shape-based distance(ESBD), which describes both shape and intensity similarity. Around ESBD, a KPIs-based quality evaluation of workload simulation(KEWS) was proposed, which consists of four steps: KPIs preprocessing, KPIs screening, KPIs clustering, and KPIs evaluation. These techniques help mitigate the negative impact of the KPIs characteristics and give a comprehensive evaluation result. The experiments conducted on Hipstershop, an open-source microservices application, show the effectiveness of the ESBD and KEWS.Comment: in Chinese languag

LOAD PATH VISUALIZATION USING U* INDEX AND PRINCIPAL LOAD PATH DETERMINATION IN THIN-WALLED STRUCTURES

U* index is used to express the load transfer inside a structure from a global perspective. Typically, a load path is defined as the ridgeline of the U* contours. However, it is cumbersome to directly locate the load paths by numerical approaches. This paper presents a streamline method with the fourth-order Runge-Kutta algorithm to visualize the load paths in thin-walled structures. The load paths can be consistently plotted on the surfaces of two-dimensional plates or three-dimensional shells by path projection. A new concept of principal load path is also introduced by evaluating the importance of load paths using statistical means. The principal load path is conceived as the “spine” of the structure that transfers the greatest internal force. A case study of a simplified vehicle body is presented. It is found that the structural stiffness can be greatly improved by reinforcing the set of principal load paths, which gives engineers an important insight into the development of weight-efficient structures

ANALYSIS OF MULTI-CHANNEL TWO-DIMENSIONAL PROBABILITY CSMA AD HOC NETWORK PROTOCOL BASED THREE-WAY HANDSHAKE MECHANISM

In wireless Ad Hoc networks, large number and flexible mobility of terminals lead to the rarity of wireless channel resources. Also the hidden and exposed terminal problem exists in the Ad Hoc network which is the major factors restricting its development and applying. Considering these factors, this paper proposes a new CSMA protocol: multi-channel two-dimensional probability CSMA for wireless Ad Hoc network protocol based on three-way handshake mechanism, and analyzes the system throughput, delay of information packet, energy consumption and other properties under the control of the proposed protocol. By using the cycle analysis method, computer simulation results not only verify the theoretical analysis, but also show that the protocol has the optimum performance. The proposed protocol can not only reduce the collision probability of information packets to some extent, improving the channel utilization, reducing the waste of channel resources, but also achieve the balancing of load in a variety of wireless Ad Hoc network services, meeting the needs by different priorities with different QoS, and ensuring the systematic efficiency and fairness

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Microservice-based applications and cloud-native systems have been widely applied in large IT enterprises. The operation and management of microservice-based applications and cloud-native systems have become the focus of research. Essential and real workloads are the premise and basis of prominent research topics including performance testing, dynamic resource provisioning and scheduling, and AIOps. Due to the privacy restriction, the complexity and variety of workloads, and the requirements for reasonable intervention, it is difficult to copy or generate real workloads directly. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based application systems. First, LWS collects session logs and transforms them into grouped and well-organized sessions. Then LWS extracts the user behavior abstraction based on a relational model and the intervenable workload intensity by three methods from different perspectives. LWS combines the user behavior abstraction and the workload intensity for simulated workload generation and designs a domain-specific language for better execution. The experimental evaluation is performed on an open-source cloud-native application and a public real-world e-commerce workload. The experimental results show that the simulated workload generated by LWS is effective and intervenable