Bilateral Waveform Similarity Overlap-and-Add Based Packet Loss Concealment for Voice over IP

This paper invested a bilateral waveform similarity overlap-and-add algorithm for voice packet lost. Since Packet lost will cause the semantic misunderstanding, it has become one of the most essential problems in speech communication. This investment is based on waveform similarity measure using overlap-and-Add algorithm and provides the bilateral information to enhance the speech signal reconstruction. Traditionally, it has been improved that waveform similarity overlap-and-add (WSOLA) technique is an effective algorithm to deal with packet loss concealment (PLC) for real-time time communication. WSOLA algorithm is widely applied to deal with the length adaptation and packet loss concealment of speech signal. Time scale modification of audio signal is one of the most essential research topics in data communication, especially in voice of IP (VoIP). Herein, the proposed the bilateral WSOLA (BWSOLA) that is derived from WSOLA. Instead of only exploitation one direction speech data, the proposed method will reconstruct the lost voice data according to the preceding and cascading data. The related algorithms have been developed to achieve the optimal reconstructing estimation. The experimental results show that the quality of the reconstructed speech signal of the bilateral WSOLA is much better compared to the standard WSOLA and GWSOLA on different packet loss rate and length using the metrics PESQ and MOS. The significant improvement is obtained by bilateral information and proposed method. The proposed bilateral waveform similarity overlap-and-add (BWSOLA) outperforms the traditional approaches especially in the long duration data loss

Screening interacting factors in a wireless network testbed using locating arrays

Wireless systems exhibit a wide range of configurable parameters (factors), each with a number of values (levels), that may influence performance. Exhaustively analyzing all factor interactions is typically not feasible in experimental systems due to the large design space. We propose a method for determining which factors play a significant role in wireless network performance with multiple performance metrics (response variables). Such screening can be used to reduce the set of factors in subsequent experimental testing, whether for modelling or optimization. Our method accounts for pairwise interactions between the factors when deciding significance, because interactions play a significant role in real-world systems. We utilize locating arrays to design the experiment because they guarantee that each pairwise interaction impacts a distinct set of tests. We formulate the analysis as a problem in compressive sensing that we solve using a variation of orthogonal matching pursuit, together with statistical methods to determine which factors are significant. We evaluate the method using data collected from the w-iLab.t Zwijnaarde wireless network testbed and construct a new experiment based on the first analysis to validate the results. We find that the analysis exhibits robustness to noise and to missing data

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications

Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network. Ensuring realistic conditions for music performance, however, constitutes a significant engineering challenge due to extremely strict requirements in terms of audio quality and, most importantly, network delay. To minimize the end-to-end delay experienced by the musicians, typical implementations of NMP applications use un-compressed, bidirectional audio streams and leverage UDP as transport protocol. Being connection less and unreliable,audio packets transmitted via UDP which become lost in transit are not re-transmitted and thus cause glitches in the receiver audio playout. This article describes a technique for predicting lost packet content in real-time using a deep learning approach. The ability of concealing errors in real time can help mitigate audio impairments caused by packet losses, thus improving the quality of audio playout in real-world scenarios.Comment: 8 pages, 2 figure

First Responders' Localization and Health Monitoring During Rescue Operations

Currently, first responders’ coordination and decision-making during res-cue, firefighting or police operations is performed via radio/GSM channels with some support of video streaming. In unknown premises, officers have no global situational awareness on operation status, which reduces coordination efficiency and increases decision making mistakes. This paper pro-poses a solution enabling the situational awareness by introducing an integrated operation workflow for actors localization and health monitoring. The solution will provide global situational awareness to both coordinators and actors, thereby increasing efficiency of coordination, reducing mistakes in decision making and diminishing risks of unexpected situations to appear. This will result in faster operation progress, lower number of human casualties and financial losses and, the most important, saved human lives in calamity situations

Trading Virtual Legacies (Management of Tradition from Alexandria to Internet)

Will the reconstructed library of Alexandria prevent a forthcoming clash of civilizations? Inventing and re-inventing traditions requires total quality management and multiple networking in shifting alliances in the information space. Stock exchange of cultural forms has long abandoned the golden standards of Enlightenment and follows a theory of cultural relativity and an international political economy of attention.Virtual legacies;cultural relativity;detraditionalization;political economy of attention;re-enchantment

Point Cloud in the Air

Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission