8,686 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Southern Adventist University Undergraduate Catalog 2022-2023

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp

    Optical Networks and Interconnects

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    The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity

    Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges

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    The growing landscape of emerging wireless applications is a key driver toward the development of novel wireless system designs. Such a design can be based on the metaverse that uses a virtual model of the physical world systems along with other schemes/technologies (e.g., optimization theory, machine learning, and blockchain). A metaverse using a virtual model performs proactive intelligent analytics prior to a user request for efficient management of the wireless system resources. Additionally, a metaverse will enable self-sustainability to operate wireless systems with the least possible intervention from network operators. Although the metaverse can offer many benefits, it faces some challenges as well. Therefore, in this tutorial, we discuss the role of a metaverse in enabling wireless applications. We present an overview, key enablers, design aspects (i.e., metaverse for wireless and wireless for metaverse), and a novel high-level architecture of metaverse-based wireless systems. We discuss metaverse management, reliability, and security of the metaverse-based system. Furthermore, we discuss recent advances and standardization of metaverse-enabled wireless system. Finally, we outline open challenges and present possible solutions

    Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss

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    Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizing only a subset of antennas in the BS. In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements. To address the computational complexity of the combinatorial optimization problem, we propose an algorithm called neural antenna muting (NAM) with an asymmetric custom loss function. NAM is a classification neural network trained in a supervised manner. The classification error in this scheme leads to either sub-optimal energy consumption or lower quality of service (QoS) for the communication link. We control the classification error probability distribution by designing an asymmetric loss function such that the erroneous classification outputs are more likely to result in fulfilling the QoS requirements. Furthermore, we present three heuristic algorithms and compare them with the NAM. Using a 3GPP compliant system-level simulator, we show that NAM achieves ∼73%\sim73\% energy saving compared to the full antenna configuration in the BS with ∼95%\sim95\% reliability in achieving the user throughput requirements while being around 1000×1000\times and 24×24\times less computationally intensive than the greedy heuristic algorithm and the fixed column antenna muting algorithm, respectively.Comment: Submitted to IEEE Transactions on Vehicular Technolog

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Work and occupation in French and English mental hospitals, c.1918-1939

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    This open access book demonstrates that, while occupation has been used to treat the mentally disordered since the early nineteenth century, approaches to its use have varied across different countries and in different time periods. Comparing how occupation was used in French and English mental institutions between 1918 and 1939, one hundred years after the heyday of moral therapy, the book is an essential read for those researching the history of mental health and medicine more generally. It provides an overview of the legislation, management structures and financial conditions that affected mental institutions in France and England, and contributed to their differing responses to the new theories of occupational therapy emerging from the USA and Germany during the interwar period. -- Provided by publisher

    An Internet of Things (IoT) based wide-area Wireless Sensor Network (WSN) platform with mobility support.

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    Wide-area remote monitoring applications use cellular networks or satellite links to transfer sensor data to the central storage. Remote monitoring applications uses Wireless Sensor Networks (WSNs) to accommodate more Sensor Nodes (SNs) and for better management. Internet of Things (IoT) network connects the WSN with the data storage and other application specific services using the existing internet infrastructure. Both cellular networks, such as the Narrow-Band IoT (NB-IoT), and satellite links will not be suitable for point-to-point connections of the SNs due to their lack of coverage, high cost, and energy requirement. Low Power Wireless Area Network (LPWAN) is used to interconnect all the SNs and accumulate the data to a single point, called Gateway, before sending it to the IoT network. WSN implements clustering of the SNs to increase the network coverage and utilizes multiple wireless links between the repeater nodes (called hops) to reach the gateway at a longer distance. Clustered WSN can cover up to a few km using the LPWAN technologies such as Zigbee using multiple hops. Each Zigbee link can be from 200 m to 500 m long. Other LPWAN technologies, such as LoRa, can facilitate an extended range from 1km to 15km. However, the LoRa will not be suitable for the clustered WSN due to its long Time on Air (TOA) which will introduce data transmission delay and become severe with the increase of hop count. Besides, a sensor node will need to increase the antenna height to achieve the long-range benefit of Lora using a single link (hop) instead of using multiple hops to cover the same range. With the increased WSN coverage area, remote monitoring applications such as smart farming may require mobile sensor nodes. This research focuses on the challenges to overcome LoRa’s limitations (long TOA and antenna height) and accommodation of mobility in a high-density and wide-area WSN for future remote monitoring applications. Hence, this research proposes lightweight communication protocols and networking algorithms using LoRa to achieve mobility, energy efficiency and wider coverage of up to a few hundred km for the WSN. This thesis is divided into four parts. It presents two data transmission protocols for LoRa to achieve a higher data rate and wider network coverage, one networking algorithm for wide-area WSN and a channel synchronization algorithm to improve the data rate of LoRa links. Part one presents a lightweight data transmission protocol for LoRa using a mobile data accumulator (called data sink) to increase the monitoring coverage area and data transmission energy efficiency. The proposed Lightweight Dynamic Auto Reconfigurable Protocol (LDAP) utilizes direct or single hop to transmit data from the SNs using one of them as the repeater node. Wide-area remote monitoring applications such as Water Quality Monitoring (WQM) can acquire data from geographically distributed water resources using LDAP, and a mobile Data Sink (DS) mounted on an Unmanned Aerial Vehicle (UAV). The proposed LDAP can acquire data from a minimum of 147 SNs covering 128 km in one direction reducing the DS requirement down to 5% comparing other WSNs using Zigbee for the same coverage area with static DS. Applications like smart farming and environmental monitoring may require mobile sensor nodes (SN) and data sinks (DS). The WSNs for these applications will require real-time network management algorithms and routing protocols for the dynamic WSN with mobility that is not feasible using static WSN technologies. This part proposes a lightweight clustering algorithm for the dynamic WSN (with mobility) utilizing the proposed LDAP to form clusters in real-time during the data accumulation by the mobile DS. The proposed Lightweight Dynamic Clustering Algorithm (LDCA) can form real-time clusters consisting of mobile or stationary SNs using mobile DS or static GW. WSN using LoRa and LDCA increases network capacity and coverage area reducing the required number of DS. It also reduces clustering energy to 33% and shows clustering efficiency of up to 98% for single-hop clustering covering 100 SNs. LoRa is not suitable for a clustered WSN with multiple hops due to its long TOA, depending on the LoRa link configurations (bandwidth and spreading factor). This research proposes a channel synchronization algorithm to improve the data rate of the LoRa link by combining multiple LoRa radio channels in a single logical channel. This increased data rate will enhance the capacity of the clusters in the WSN supporting faster clustering with mobile sensor nodes and data sink. Along with the LDCA, the proposed Lightweight Synchronization Algorithm for Quasi-orthogonal LoRa channels (LSAQ) facilitating multi-hop data transfer increases WSN capacity and coverage area. This research investigates quasi-orthogonality features of LoRa in terms of radio channel frequency, spreading factor (SF) and bandwidth. It derived mathematical models to obtain the optimal LoRa parameters for parallel data transmission using multiple SFs and developed a synchronization algorithm for LSAQ. The proposed LSAQ achieves up to a 46% improvement in network capacity and 58% in data rate compared with the WSN using the traditional LoRa Medium Access Control (MAC) layer protocols. Besides the high-density clustered WSN, remote monitoring applications like plant phenotyping may require transferring image or high-volume data using LoRa links. Wireless data transmission protocols used for high-volume data transmission using the link with a low data rate (like LoRa) requiring multiple packets create a significant amount of packet overload. Besides, the reliability of these data transmission protocols is highly dependent on acknowledgement (ACK) messages creating extra load on overall data transmission and hence reducing the application-specific effective data rate (goodput). This research proposes an application layer protocol to improve the goodput while transferring an image or sequential data over the LoRa links in the WSN. It uses dynamic acknowledgement (DACK) protocol for the LoRa physical layer to reduce the ACK message overhead. DACK uses end-of-transmission ACK messaging and transmits multiple packets as a block. It retransmits missing packets after receiving the ACK message at the end of multiple blocks. The goodput depends on the block size and the number of lossy packets that need to be retransmitted. It shows that the DACK LoRa can reduce the total ACK time 10 to 30 times comparing stop-wait protocol and ten times comparing multi-packet ACK protocol. The focused wide-area WSN and mobility requires different matrices to be evaluated. The performance evaluation matrices used for the static WSN do not consider the mobility and the related parameters, such as clustering efficiency in the network and hence cannot evaluate the performance of the proposed wide-area WSN platform supporting mobility. Therefore, new, and modified performance matrices are proposed to measure dynamic performance. It can measure the real-time clustering performance using the mobile data sink and sensor nodes, the cluster size, the coverage area of the WSN and more. All required hardware and software design, dimensioning, and performance evaluation models are also presented

    Performance modelling for scalable deep learning

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    Performance modelling for scalable deep learning is very important to quantify the efficiency of large parallel workloads. Performance models are used to obtain run-time estimates by modelling various aspects of an application on a target system. Designing performance models requires comprehensive analysis in order to build accurate models. Limitations of current performance models include poor explainability in the computation time of the internal processes of a neural network model and limited applicability to particular architectures. Existing performance models in deep learning have been proposed, which are broadly categorized into two methodologies: analytical modelling and empirical modelling. Analytical modelling utilizes a transparent approach that involves converting the internal mechanisms of the model or applications into a mathematical model that corresponds to the goals of the system. Empirical modelling predicts outcomes based on observation and experimentation, characterizes algorithm performance using sample data, and is a good alternative to analytical modelling. However, both these approaches have limitations, such as poor explainability in the computation time of the internal processes of a neural network model and poor generalisation. To address these issues, hybridization of the analytical and empirical approaches has been applied, leading to the development of a novel generic performance model that provides a general expression of a deep neural network framework in a distributed environment, allowing for accurate performance analysis and prediction. The contributions can be summarized as follows: In the initial study, a comprehensive literature review led to the development of a performance model based on synchronous stochastic gradient descent (S-SGD) for analysing the execution time performance of deep learning frameworks in a multi-GPU environment. This model’s evaluation involved three deep learning models (Convolutional Neural Networks (CNN), Autoencoder (AE), and Multilayer Perceptron (MLP)), implemented in three popular deep learning frameworks (MXNet, Chainer, and TensorFlow) respectively, with a focus on following an analytical approach. Additionally, a generic expression for the performance model was formulated, considering intrinsic parameters and extrinsic scaling factors that impact computing time in a distributed environment. This formulation involved a global optimization problem with a cost function dependent on unknown constants within the generic expression. Differential evolution was utilized to identify the best fitting values, matching experimentally determined computation times. Furthermore, to enhance the accuracy and stability of the performance model, regularization techniques were applied. Lastly, the proposed generic performance model underwent experimental evaluation in a real-world application. The results of this evaluation provided valuable insights into the influence of hyperparameters on performance, demonstrating the robustness and applicability of the performance model in understanding and optimizing model behavior
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