Scalable Multiple Description Coding and Distributed Video Streaming over 3G Mobile Networks

In this thesis, a novel Scalable Multiple Description Coding (SMDC) framework is proposed. To address the bandwidth fluctuation, packet loss and heterogeneity problems in the wireless networks and further enhance the error resilience tools in Moving Pictures Experts Group 4 (MPEG-4), the joint design of layered coding (LC) and multiple description coding (MDC) is explored. It leverages a proposed distributed multimedia delivery mobile network (D-MDMN) to provide path diversity to combat streaming video outage due to handoff in Universal Mobile Telecommunications System (UMTS). The corresponding intra-RAN (Radio Access Network) handoff and inter-RAN handoff procedures in D-MDMN are studied in details, which employ the principle of video stream re-establishing to replace the principle of data forwarding in UMTS. Furthermore, a new IP (Internet Protocol) Differentiated Services (DiffServ) video marking algorithm is proposed to support the unequal error protection (UEP) of LC components of SMDC. Performance evaluation is carried through simulation using OPNET Modeler 9. 0. Simulation results show that the proposed handoff procedures in D-MDMN have better performance in terms of handoff latency, end-to-end delay and handoff scalability than that in UMTS. Performance evaluation of our proposed IP DiffServ video marking algorithm is also undertaken, which shows that it is more suitable for video streaming in IP mobile networks compared with the previously proposed DiffServ video marking algorithm (DVMA)

Association rules analysis on patterns of multimorbidity in adults: based on the National Health and Nutrition Examination Surveys database

Objective To explore the prevalence and patterns of multimorbidity in population with different genders and age ranges.Design A cross-sectional study.Setting National Health and Nutrition Examination Surveys database.Participants 12 576 patients.Primary and secondary outcome measures The prevalence and patterns of multimorbidity.Results High cholesterol had the highest prevalence in all population (33.4 (95% CI: 32.0 to 34.9)) and males. In females <65 years, the most prevalent disease was sleep disorder (32.1 (95% CI: 29.6 to 34.5)) while in females ≥65 years, hypertension was the most prevalent disease (63.9 (95% CI: 59.9 to 67.9)). Hypertension and high cholesterol were associated with the highest support (occur together most frequently) in all population regardless of genders. Hypertension displayed the highest betweenness centrality (mediating role in the network) followed by high cholesterol and arthritis in all population. For males aged <65 years, hypertension and high cholesterol presented the highest betweenness centrality. In males ≥65 years, hypertension, high cholesterol and arthritis were the top three diseases of degree centrality (direct association with other conditions). As for females ≥65 years, hypertension showed the highest betweenness centrality followed by high cholesterol and arthritis. The associations of hypertension, arthritis and one other item with high cholesterol presented the highest support in all population. In males, the associations of depression, hypertension with sleep disorders had the highest lift (the chance of co-occurrence of the conditions and significant association). Among females, the associations of depression, arthritis with sleep disorders had the highest lift.Conclusion Hypertension and high cholesterol were prevalent in all population, regardless of females and males. Hypertension and high cholesterol, arthritis and hypertension, and diabetes and hypertension were more likely to coexist. The findings of this study might help make plans for the management and primary care of people with one or more diseases

Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning

Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack events than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning

DeepTPA-Net: A Deep Triple Attention Network for sEMG-Based Hand Gesture Recognition

The use of hand gestures for human-computer interaction (HCI) has gained popularity due to its ability to provide natural and intuitive communication in human dialogues. Hand gesture recognition (HGR) using surface electromyography (sEMG) signals is more reliable and user-friendly than traditional computer vision-based methods. This study proposes a deep network named DeepTPA-Net that utilizes multi-channel sEMG signals to recognize hand gestures. DeepTPA-Net employs a ResNet50 network as an automated feature extractor and a novel triple attention (3Attn) block that connects spatial, temporal, and channel attention modules in parallel to signify important features for HGR. We evaluated the performance of DeepTPA-Net using five publicly available benchmark sEMG hand gesture datasets, including CapgMyo DB-a, Csl-hdemg, NinaPro DB1, NinaPro DB2, and SeNic. The effectiveness of the proposed 3Attn block for HGR is demonstrated through a performance comparison with other attention mechanisms. We compared the performance of DeepTPA-Net with various baseline models, including its variations and other existing methods. The results show that DeepTPA-Net significantly outperforms the baseline models for all five benchmark datasets, indicating the superiority of DeepTPA-Net for sEMG-based HGR

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