List of Titles

SPEED: A deep learning assisted privacy-preserved framework for intelligent transportation systems

- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza

Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4376-4384
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Description: Roadside cameras in an Intelligent Transportation System (ITS) are used for various purposes, e.g., monitoring the speed of vehicles, violations of laws, and detection of suspicious activities in parking lots, streets, and side roads. These cameras generate big multimedia data, and as a result, the ITS faces challenges like data management, redundancy, and privacy breaching in end-to-end communication. To solve these challenges, we propose a framework, called SPEED, based on a multi-level edge computing architecture and machine learning algorithms. In this framework, data captured by end-devices, e.g., smart cameras, is distributed among multiple Level-One Edge Devices (LOEDs) to deal with data management issue and minimize packet drop due to buffer overflowing on end-devices and LOEDs. The data is forwarded from LOEDs to Level-Two Edge Devices (LTEDs) in a compressed sensed format. The LTEDs use an online Least-Squares Support-Vector Machines (LS-SVMs) model to determine distribution characteristics and index values of compressed sensed data to preserve its privacy during transmission between LTEDs and High-Level Edge Devices (HLEDs). The HLEDs estimate the redundancy in forwarded data using a deep learning architecture, i.e., a Convolutional Neural Network (CNN). The CNN is used to detect the presence of moving objects in the forwarded data. If a movement is detected, the data is forwarded to cloud servers for further analysis otherwise discarded. Experimental results show that the use of a multi-level edge computing architecture helps in managing the generated data. The machine learning algorithms help in addressing issues like data redundancy and privacy-preserving in end-to-end communication. © 2000-2011 IEEE.

PAWN: a payload‐based mutual authentication scheme for wireless sensor networks

- Jan, Mian, Nanda, Priyadarsi, Usman, Muhammad, He, Xiangjian

Authors: Jan, Mian , Nanda, Priyadarsi , Usman, Muhammad , He, Xiangjian
Date: 2017
Type: Text , Journal article
Relation: Concurrency and computation Vol. 29, no. 17 (2017), p. e3986-n/a
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Description: Summary Wireless sensor networks (WSNs) consist of resource‐starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery‐powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy‐scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource‐constrained nature of sensor nodes, inaccessible human terrains, and error‐prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload‐based mutual authentication scheme for a cluster‐based hierarchical WSN. The proposed scheme, also known as payload‐based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes.

A distributed and anonymous data collection framework based on multilevel edge computing architecture

- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza, Xu, Min, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza , Xu, Min , He, Xiangjian , Chen, Jinjun
Date: 2020
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
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Description: Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.

An AI-enabled lightweight data fusion and load optimization approach for internet of things

- Jan, Mian, Zakarya, Muhammad, Khan, Muhammad, Mastorakis, Spyridon, Balasubramanian, Venki

Authors: Jan, Mian , Zakarya, Muhammad , Khan, Muhammad , Mastorakis, Spyridon , Balasubramanian, Venki
Date: 2021
Type: Text , Journal article
Relation: Future Generation Computer Systems Vol. 122, no. (2021), p. 40-51
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Description: In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications. © 2021 Elsevier B.V. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.

Adaptive capacity task offloading in multi-hop D2D-based social industrial IoT

- Ibrar, Muhammad, Wang, Lei, Akbar, Aamir, Jan, Mian, Balasubramanian, Venki, Muntean, Gabriel-Miro, Shah, Nadir

Authors: Ibrar, Muhammad , Wang, Lei , Akbar, Aamir , Jan, Mian , Balasubramanian, Venki , Muntean, Gabriel-Miro , Shah, Nadir
Date: 2023
Type: Text , Journal article
Relation: IEEE Transactions on Network Science and Engineering Vol. 10, no. 5 (2023), p. 2843-2852
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Description: Traditional communication technologies such as cellular networks are facing problems to support high service quality when used for time-critical applications in an Industrial Internet-of-Things (IIoT) context, including real-time data transmission, route dependability, and scalability. To address these problems, device-to-device (D2D) communications based on social relationships can be used, which allow for task-offloading: resource-rich devices share unused computing resources with resource constraint devices. However, unbalanced task offloading in Social IIoT (SIIoT) might actually degrade the overall system performance, which is not desirable. In this paper, we propose an adaptive capacity task offloading solution for D2D-based social industrial IoT (ToSIIoT) which considers devices utilization ratio and strength of social relationships in order to improve resource utilization, increase QoS and achieve better task completion rate. The proposed approach consists of three aspects: social-aware relay selection in a multi-hop D2D communication context, choice of a resource-rich SIIoT device for task offloading, and adaptive redistribution of tasks. The paper proposes heuristic algorithms, as finding optimal solutions to the problems are NP-hard. Extensive experimental results show that the proposed ToSIIoT performs better than existing approaches in terms of utilization ratio, QoS violation, average execution delay, and task completion ratio. © 2013 IEEE.

A survey on big multimedia data processing and management in smart cities

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2019
Type: Text , Journal article
Relation: ACM computing surveys Vol. 52, no. 3 (2019), p. 1-29
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Description: Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.

Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks

- Jan, Syed, Khan, Rahim, Khan, Fazlullah, Jan, Mian, Balasubramanian, Venki

Authors: Jan, Syed , Khan, Rahim , Khan, Fazlullah , Jan, Mian , Balasubramanian, Venki
Date: 2021
Type: Text , Journal article
Relation: Computer Communications Vol. 174, no. (2021), p. 101-108
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Description: In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station. © 2021 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**

A survey on representation learning efforts in cybersecurity domain

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2020
Type: Text , Journal article
Relation: ACM computing surveys Vol. 52, no. 6 (2020), p. 1-28
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Description: In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.

An improved congestion-controlled routing protocol for IoT applications in extreme environments

- Adil, Muhammad, Usman, Muhammad, Jan, Mian, Abulkasim, Hussein, Farouk, Ahmed, Jin, Zhanpeng

Authors: Adil, Muhammad , Usman, Muhammad , Jan, Mian , Abulkasim, Hussein , Farouk, Ahmed , Jin, Zhanpeng
Date: 2024
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 11, no. 3 (2024), p. 3757-3767
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Description: The Internet of Things (IoT) has shown its presence in applications that require monitoring extreme environments, such as wildfires, military operations, and coastal areas, among others. In these applications, the IoT nodes are deployed in hazardous terrains where humanistic access is hard or not possible. Hence, to ensure reliable data transmission in these applications, novel routing protocols need to be designed due to the multihop nature of communication possessed by the deployed nodes. Currently, most of the routing protocols utilized by IoT nodes follow traditional approaches, which creates congestion and contention in the network. As a result, the network performance is degraded in terms of various communication metrics. To address this problem and improve the communication statistics in extreme environments, we propose a deep- Q -learning-enable-destination-sequenced distance-vector (DQL-DSDV) framework. DQL-DSDV focuses on selecting the next hop during communication. Initially, the DSDV protocol updates routing information for connected nodes. This information is subsequently utilized by the deep- Q -learning (DQL) algorithm to compute the next hop count. This computation is based on reward functions, known as Q-values, which are conceptualized as the distance between connected nodes by taking into account the traffic flow. These distinguishing operational features of DQL and DSDV ensure that DQL-DSDV minimizes the packet lost ratio, congestion, end-to-end delay, and communication cost with improved Quality of Service (QoS). During simulations, we observed significant improvement in these performance metrics, in the presence of the existing schemes. Despite that, we checked the computation complexity of the proposed approach with existing protocols, which demonstrated noteworthy outcomes just like the other metrics. © 2014 IEEE.

A comprehensive survey of security threats and their mitigation techniques for next‐generation SDN controllers

- Han, Tao, Jan, Syed, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian, Khan, Rahim, Xu, Yongzhao

Authors: Han, Tao , Jan, Syed , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Rahim , Xu, Yongzhao
Date: 2020
Type: Text , Journal article
Relation: Concurrency and computation Vol. 32, no. 16 (2020), p. n/a
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Description: Summary Software Defined Network (SDN) and Network Virtualization (NV) are emerged paradigms that simplified the control and management of the next generation networks, most importantly, Internet of Things (IoT), Cloud Computing, and Cyber‐Physical Systems. The Internet of Things (IoT) includes a diverse range of a vast collection of heterogeneous devices that require interoperable communication, scalable platforms, and security provisioning. Security provisioning to an SDN‐based IoT network poses a real security challenge leading to various serious security threats due to the connection of various heterogeneous devices having a wide range of access protocols. Furthermore, the logical centralized controlled intelligence of the SDN architecture represents a plethora of security challenges due to its single point of failure. It may throw the entire network into chaos and thus expose it to various known and unknown security threats and attacks. Security of SDN controlled IoT environment is still in infancy and thus remains the prime research agenda for both the industry and academia. This paper comprehensively reviews the current state‐of‐the‐art security threats, vulnerabilities, and issues at the control plane. Moreover, this paper contributes by presenting a detailed classification of various security attacks on the control layer. A comprehensive state‐of‐the‐art review of the latest mitigation techniques for various security breaches is also presented. Finally, this paper presents future research directions and challenges for further investigation down the line.

Performance evaluation of high definition video streaming over mobile ad hoc networks

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Alam, Muhammad

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Alam, Muhammad
Date: 2018
Type: Text , Journal article
Relation: Signal processing Vol. 148, no. (2018), p. 303-313
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Description: A unique combinational approach of QoS and QoE for multimedia traffic in MANETs is proposed in this paper.•A feedback-based multi-path transmission strategy is adopted to minimize the data loss during HD video streaming.•An EC technique based on multi-threading-based parallel processing is used to recover the lost video frames.•We use multiple objective evaluation metrics to test the performance of the EC technique on recovered video frames.•Subjective analysis is combined with objective evaluation using subjective scores and statistical analysis to maintain QoE. Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data.

A Joint framework for QoS and QoE for video transmission over wireless multimedia sensor networks

- Usman, Muhammad, Ning, Yang, Jan, Mian, Xiangjian, He, Min, Xu, Kin-Man, Lam

Authors: Usman, Muhammad , Ning, Yang , Jan, Mian , Xiangjian, He , Min, Xu , Kin-Man, Lam
Date: 2018
Type: Text , Journal article
Relation: IEEE transactions on mobile computing Vol. 17, no. 4 (2018), p. 746-759
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Description: With the emergence of Wireless Multimedia Sensor Networks (WMSNs), the distribution of multimedia contents have now become a reality. Without proper management, the transmission of multimedia data over WMSNs affects the performance of networks due to excessive packet-drop. The existing studies on Quality of Service (QoS) mostly deal with simple Wireless Sensor Networks (WSNs) and as such do not account for an increasing number of sensor nodes and an increasing volume of data. In this paper, we propose a novel framework to support QoS in WMSNs along with a light-weight Error Concealment (EC) scheme. The EC schemes play a vital role to enhance Quality of Experience (QoE) by maintaining an acceptable quality at the receiving ends. The main objectives of the proposed framework are to maximize the network throughput and to cover-up the effects produced by dropped video packets. To control the data-rate, Scalable High efficiency Video Coding (SHVC) is applied at multimedia sensor nodes with variable Quantization Parameters (QPs). Multi-path routing is exploited to support real-time video transmission. Experimental results show that the proposed framework can efficiently adjust large volumes of video data under certain network distortions and can effectively conceal lost video frames by producing better objective measurements.

Data sharing in secure multimedia wireless sensor networks

- Usman, Muhammad, Jan, Mian, Xiangjian, He, Nanda, Priyadarsi

Authors: Usman, Muhammad , Jan, Mian , Xiangjian, He , Nanda, Priyadarsi
Date: 2016
Type: Text , Conference proceedings
Relation: 2016 IEEE Trustcom/BigDataSE/ISPA;Tianjin, China; 23-26 August 2016 p. 590-597
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Description: The use of Multimedia Wireless Sensor Networks (MWSNs) is becoming common nowadays with a rapid growth in communication facilities. Similar to any other WSNs, these networks face various challenges while providing security, trust and privacy for user data. Provisioning of the aforementioned services become an uphill task especially while dealing with real-time streaming data. These networks operates with resource-constrained sensor nodes for days, months and even years depending on the nature of an application. The resource-constrained nature of these networks makes it difficult for the nodes to tackle real-time data in mission-critical applications such as military surveillance, forest fire monitoring, health-care and industrial automation. For a secured MWSN, the transmission and processing of streaming data needs to be explored deeply. The conventional data authentication schemes are not suitable for MWSNs due to the limitations imposed on sensor nodes in terms of battery power, computation, available bandwidth and storage. In this paper, we propose a novel quality-driven clustering-based technique for authenticating streaming data in MWSNs. Nodes with maximum energy are selected as Cluster Heads (CHs). The CHs collect data from member nodes and forward it to the Base Station (BS), thus preventing member nodes with low energy from dying soon and increasing life span of the underlying network. The proposed approach not only authenticates the streaming data but also maintains the quality of transmitted data. The proposed data authentication scheme coupled with an Error Concealment technique provides an energy-efficient and distortion-free real-time data streaming. The proposed scheme is compared with an unsupervised resources scenario. The simulation results demonstrate better network lifetime along with 21.34 dB gain in Peak Signal-to-Noise Ratio (PSNR) of received video data streams.

SAMS: A seamless and authorized multimedia streaming framework for wmsn-based iomt

- Jan, Mian, Usman, Muhammad, He, Xiangjian, Ur Rehman, Ateeq

Authors: Jan, Mian , Usman, Muhammad , He, Xiangjian , Ur Rehman, Ateeq
Date: 2019
Type: Text , Journal article
Relation: IEEE internet of things journal Vol. 6, no. 2 (2019), p. 1576-1583
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Description: An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.

P2DCA: A Privacy-preserving-based data collection and analysis framework for IoMT applications

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2019
Type: Text , Journal article
Relation: IEEE journal on selected areas in communications Vol. 37, no. 6 (2019), p. 1222-1230
Full Text: false
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Description: The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

Time-frequency filter bank: A simple approach for audio and music separation

- Yang, Ning, Usman, Muhammad, He, Xiangjian, Jan, Mian, Zhang, Liming

Authors: Yang, Ning , Usman, Muhammad , He, Xiangjian , Jan, Mian , Zhang, Liming
Date: 2017
Type: Text , Journal article
Relation: IEEE access Vol. 5, no. (2017), p. 27114-27125
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Description: Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.

Error concealment for cloud-based and scalable video coding of hd videos

- Usman, Muhammad, He, Xiangjian, Lam, Kin-Man, Xu, Min, Bokhari, Syed, Chen, Jinjun, Jan, Mian

Authors: Usman, Muhammad , He, Xiangjian , Lam, Kin-Man , Xu, Min , Bokhari, Syed , Chen, Jinjun , Jan, Mian
Date: 2019
Type: Text , Journal article
Relation: IEEE transactions on cloud computing Vol. 7, no. 4 (2019), p. 975-987
Full Text: false
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Description: The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.

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