Network Bandwidth Requirements Research Articles

Combining federated learning and control: A survey

AbstractThis survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real‐world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state‐of‐the‐art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi‐agent decision‐making systems.

Security of Magnetic Resonance Medical Images using Region-based Lossless Image Compression in Healthcare Information Systems

Purpose The purpose of region-based medical image compression is to optimize the compression process by focusing on specific regions of interest within medical images. Unlike traditional compression methods that treat the entire image uniformly, region-based compression techniques identify and prioritize certain areas or regions within the image that are deemed more diagnostically significant or relevant. By allocating more resources to compressing these critical regions while reducing compression in less important areas, region-based compression methods aim to achieve higher compression efficiency while preserving diagnostic quality. This approach is particularly valuable in medical imaging, where accurate representation of anatomical structures or pathological findings is paramount for clinical diagnosis and decision-making. Region-based compression can help reduce storage requirements, transmission bandwidth, and processing time without compromising the diagnostic integrity of medical images, thereby facilitating more efficient healthcare delivery and telemedicine applications. Methods In this study, we utilized distortion-limiting compression techniques to optimize the compression process for specific regions within medical images. We employed lossless scalable RBC (Region-Based Compression) using Discrete Wavelet Transform (DWT) for Digital Imaging and Communication in Medicine (DICOM) images. The initial step involved medical image pre-processing, followed by segmentation to separate the image into regions of interest (ROI) and non-ROI. Compression techniques were then applied to reduce network bandwidth and storage requirements. Fractal lossy compression was employed for the non-ROI portion, while context-tree weighting lossless compression was proposed for the ROI portion, effectively compressing the image while rejecting noisy background elements. During decompression, the original medical image can be reconstructed using the reverse process. This approach optimizes storage and transmission efficiency while preserving diagnostic integrity in medical imaging applications. Results The experiment involved testing various medical images, and the proposed method outperformed previous techniques in terms of results. According to the findings, the improvement in Peak Signal-to-Noise Ratio (PSNR) over current techniques reached up to 24.23 dB compared to the Joint Photographic Experts Group (JPEG). Additionally, it achieved up to 12.22 dB improvement compared to other transform approaches. These significant enhancements prompted the development of a web and mobile platform for compressing and sending medical images, particularly microscopic ones, in real time. Conclusion This research focuses on employing wavelet transform techniques to compress the Region of Interest (ROI) within medical images. This ROI-based compression approach is particularly valuable as it retains essential diagnostic information while reducing the overall file size. Such a technique holds significant promise for telemedicine systems, especially in rural regions where network resources may be limited or constrained. By selectively compressing the most diagnostically relevant areas of medical images, this approach ensures that critical information is preserved while optimizing data transmission and storage efficiency. This can ultimately enhance access to medical imaging services and facilitate remote diagnosis and treatment in underserved areas with limited network infrastructure.

Deep learning‐based spectrum sharing in next generation multi‐operator cellular networks

SummaryOwing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi‐operator spectrum sharing. While spectrum sensing can be useful in spectrum‐sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing‐based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum‐sharing mechanism leveraging a spectrum coordinator (SC) in a multi‐operator spectrum‐sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL‐based spectrum‐sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL‐based spectrum‐sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi‐operator spectrum sharing.

Enhancing Efficiency and Fine-Grained Control in Redactable Blockchains with EPBCHF

Blockchain technology has presented a promising decentralized paradigm to preclude trusted thirdparties' dominancy. It is a transparent and distributed ledger initially designed for digital cryptocurrencies whilecurrently extended to serve various industries. However, Blockchain immutability presents challenges, as it can bemisused for storing illicit content, violating privacy regulations, and limiting data management flexibility. PolicyBased Chameleon Hash Function (PBCH) has transformed blockchain rewriting contents concept via permittingmodifiers to amend certain transaction since they possessed fundamental privileges satisfying certain access policy.However, PBCHF suffers from efficiency issues due to its reliance on Chameleon Hash ephemeral Trapdoor (CHET)and Attribute-Based Encryption (ABE), significantly impacting overall efficiency. We propose the Efficient PolicyBased Chameleon (EPBCHF) construction by replacing CHET with Chameleon-Hashes by Dual Long-TermTrapdoors (CHDLTT) to address these challenges. Additionally, we introduce an enhanced encryption schemeresilient against chosen-ciphertext attacks (CCA) without compromising overall efficiency. Modelling EPBCHFproves practical instantiation accompanied by rigorous security proofs. Our construction provides a fine-grainedredactable blockchain in comparison to the currently proposed solutions. The evaluated results confirm that theproposed EPBCHF is scalable and efficient due to having the ability to handle unlimited transaction volumesadditionally, data is efficiently processed without further overhead meanwhile data size consistency reflects a robustmemory management due to predicted memory size, network bandwidth and storage requirement for future growththereby, EPBCHF is proven to be reliable and scalable.

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.

Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling

Optical networks satisfy high bandwidth and low latency requirements for telecommunication networks and data center interconnection. To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system performance such as quality of transmission. However, a comprehensive measurement dataset is required for ML to accurately predict an EDFA’s wavelength-dependent gain. We present an open dataset consisting of 202,752 gain spectrum measurements collected from 16 commercial-grade reconfigurable optical add–drop multiplexer (ROADM) booster and pre-amplifier EDFAs under varying gain settings and diverse channel-loading configurations over 2,785 hours in total, with a total dataset size of 3.1 GB. With this EDFA dataset, we implemented component-level deep-neural-network-based EDFA models and use transfer learning (TL) to transfer the EDFA model among 16 ROADM EDFAs, which achieve less than 0.18/0.24 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target training set. We also showed that TL reduces the EDFA data collection requirements on a new gain setting or a different type of EDFA on the same ROADM.

Edge computing-based Generative Adversarial Network for photo design style transfer using conditional entropy distance

In recent years, cell phone photography systems have made significant advancements due to their widespread adoption. However, processing the large amount of data generated by these systems on the cloud often leads to high network latency and bandwidth requirements, limiting computational efficiency. To address this challenge, this study introduces a novel design style transfer method for generating high-quality stylized photos in cell phone photography systems using Generative Adversarial Networks (GANs) with an edge computing-based approach. The proposed method aims to enhance the visual appeal of cell phone images by mimicking the artistic style of paintings through abstraction and emphasis on key features. To achieve this objective, a conditional GAN is trained on meticulously labeled data pairs that identify crucial regions in images. This approach offers an efficient solution for computationally intensive tasks by utilizing inference on edge computing nodes. This effectively reduces network latency and bandwidth requirements while improving computational efficiency. Additionally, a penalty is introduced using conditional entropy distance to ensure consistent entropy levels across different photo samples, resulting in higher quality generated photos. Experimental results demonstrate that the proposed CEDGAN model achieves a detection accuracy ranging from 95% to 98%, with consistent results, thus laying a solid foundation for effective photo enhancement. When collecting important portions of materials, CEDGAN maintains a detection accuracy of around 90% to 95% without any significant difference in overall performance. As a result, this method opens up new possibilities for cell phone photography enthusiasts who desire to effortlessly create artistic and stylized photos using edge computing-based GANs. It offers a practical solution for combining technological innovation with creativity in photography.

Research on cloud-edge AC Power Monitoring Systems based on MODBUS

With the development of the Internet of Things and automation technology in the industry, the types of equipment in the industrial field are increasing, and the data parameters of the industrial process are gradually becoming complex. It puts forward higher requirements for the system perception layer’s real-time performance, reliability, and maintainability. The original data acquisition architecture is not easy to ensure the reliable transmission and real-time processing of massive industrial data. In order to solve these problems, this paper designs an AC Power Monitoring System. It proposes a cloud edge computing framework based on MODBUS: First, the data is collected and sent to the edge server by MODBUS through the RS485 bus. Second, combined with the advantages of the powerful service capability of cloud computing resources and the low transmission delay of edge computing, the edge side and cloud side will work together. Finally, the functions of data reading, uploading and sending, correction, configuration, and fault alarm are realized. The field operation shows that using a cloud edge computing framework can reduce the processing pressure of cloud computing, reduce network latency and lower requirements for network bandwidth and make the system efficient and safe for collecting and analyzing big data. The cooperation of MODBUS protocol enhances the reliability and practicability of communication. The cloud edge computing system based on MODBUS is easy to design and maintain and is suitable for many monitoring systems, which have high promotion value.

IoT-Enabled Motion Control: Architectural Design Challenges and Solutions

Ever-increasing demands for highly-efficient customized manufacturing are driving the development of Industry 4.0. Reconfigurable manufacturing systems based on modular, convertible, and interoperable equipment present a key enabler of the fourth industrial revolution. Besides suitable mechanical design, control of these smart manufacturing resources should facilitate Internet of Things (IoT) integration and reconfigurability. Existing numerical control kernels (NCK)—the major control component for motion control—hinder rapid reconfiguration due to the complexity of their monolithic centralized structure. On the other hand, reconfigurability is naturally promoted by the distributed control paradigm, as proposed by the industrial IoT (IIoT) concept; hence, in this article, we investigate design challenges in distributing the conventional centralized NCK designs used for control of computerized numerical control systems. We introduce an architecture where each axis module is augmented with a networked, IIoT-enabled low-level controller (LLC) that performs local control and exposes a network interface for communication with other LLCs toward executing the desired process. These smart manufacturing resources communicate with an edge-based high-level controller (HLC) that provides the trajectory specification over the network and schedules manufacturing tasks. We investigate real-time and network bandwidth requirements of different mappings of the NCK layers to the LLCs and the HLC, providing design-time tradeoffs for implementing IoT-ready, distributed motion control. We demonstrate feasibility of our approach using industry-grade single-axis robots and low-cost IoT microcontrollers, and show that minimal accuracy impairment is introduced compared to the centralized setup based on ISO 230 and ISO 10791-7 standards.

Method for 3D Images Transformation and Streaming over Low Bandwidth Communication Networks

The article describes an approach to streaming 3D human images over communication networks allowing to significantly reduce network bandwidth requirements by transmitting not the image itself, but data on the movement of control points. The subject of the study is 3D moving images of a person data on the movement of their control points. The main results of the work: a system of three applications that allows to record data about object movements, convert it into coordinates of key points, transfer it over communication networks and play it in the form of movements of a 3D object in an augmented reality application. The practical significance of the work lies in the possibility of using the developed applications for streaming moving 3D objects over low bandwidth networks. The application can be used in remote education or other areas.

An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning

Nowadays, Virtual Reality is becoming more and more popular, and 360 video is a very important part of the system. 360 video transmission over the Internet faces many difficulties due to its large size. Therefore, to reduce the network bandwidth requirement of 360-degree video, Viewport Adaptive Streaming (VAS) was proposed. An important issue in VAS is how to estimate future user viewing direction. In this paper, we propose an algorithm called GLVP (GRU-LSTM-based-Viewport-Prediction) to estimate the typical view for the VAS system. The results show that our method can improve viewport estimation from 9.5% to near 20%compared with other methods.

Dynamic event-triggered trajectory tracking control for underactuated marine surface vessels with positive minimum inter-event time guarantees

This paper proposes a dynamic event-triggered trajectory tracking control scheme for underactuated marine surface vessels (MSVs) with positive minimum inter-event time (MIET) guarantees. Under the presented scheme, control inputs only need to transmit to actuators at the designed event triggering time. Additionally, the maximum signal transmission frequency can be prior known by designers. To facilitate the design of the above control architecture, an unified second-ordered tracking error dynamic system is firstly derived. Then, based on the transformed error dynamic system, an event-based adaptive control law is developed. To counteract external disturbances and unmodeled dynamics, the minimum-learning-parameter (MLP) based neural approximator is constructed to enhance system robustness. Finally, by introducing internal dynamic variables, a dynamic event-triggered mechanism (DETM) is established to manage communications between the control module and actuators. Compared with existing event-triggered methods, the proposed event-driven method is dynamic and more flexible to satisfy network bandwidth requirements. Besides, MIETs of the presented DETM are also proved to be strictly positive and computable from control parameters. Theoretical analysis and simulation results verify the validity and effectiveness of the proposed DETM based control scheme.

User Gaze-Driven Adaptation of Omnidirectional Video Delivery Using Spatial Tiling and Scalable Video Encoding

Omnidirectional video is becoming increasingly popular among viewers, but its delivery requires considerable amount of network bandwidth. Today’s streaming services are transmitting the full spatial angle of omnidirectional videos, although most of the transmitted content is not utilized. Due to both limited bandwidth availability and its dynamic fluctuations, adaptive delivery solutions play a key role in supporting high user quality streaming of omnidirectional videos. This paper describes research which extends the MPEG-DASH Spatial Relationship Description by adding scalable video encoding to spatial tiling. It proposes a novel tile-layering based gaze adaptation algorithm for omnidirectional video delivery and employs it in conjunction with multiple tiling schemes. The benefits of the proposed algorithm with diverse tiling schemes are evaluated objectively in terms of bandwidth savings and adaptation latency. The results show a reduction of network bandwidth requirements to about 30% of the original bandwidth value with a low processing latency of 70.87ms.

Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner.

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people’s negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented.

Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence.

Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.

IS-HBase: An In-Storage Computing Optimized HBase with I/O Offloading and Self-Adaptive Caching in Compute-Storage Disaggregated Infrastructure

Active storage devices and in-storage computing are proposed and developed in recent years to effectively reduce the amount of required data traffic and to improve the overall application performance. They are especially preferred in the compute-storage disaggregated infrastructure. In both techniques, a simple computing module is added to storage devices/servers such that some stored data can be processed in the storage devices/servers before being transmitted to application servers. This can reduce the required network bandwidth and offload certain computing requirements from application servers to storage devices/servers. However, several challenges exist when designing an in-storage computing- based architecture for applications. These include what computing functions need to be offloaded, how to design the protocol between in-storage modules and application servers, and how to deal with the caching issue in application servers. HBase is an important and widely used distributed Key-Value Store. It stores and indexes key-value pairs in large files in a storage system like HDFS. However, its performance especially read performance, is impacted by the heavy traffics between HBase RegionServers and storage servers in the compute-storage disaggregated infrastructure when the available network bandwidth is limited. We propose an I n- S torage-based HBase architecture, called IS-HBase , to improve the overall performance and to address the aforementioned challenges. First, IS-HBase executes a data pre-processing module ( I n- S torage S can N er, called ISSN ) for some read queries and returns the requested key-value pairs to RegionServers instead of returning data blocks in HFile. IS-HBase carries out compactions in storage servers to reduce the large amount of data being transmitted through the network and thus the compaction execution time is effectively reduced. Second, a set of new protocols is proposed to address the communication and coordination between HBase RegionServers at computing nodes and ISSNs at storage nodes. Third, a new self-adaptive caching scheme is proposed to better serve the read queries with fewer I/O operations and less network traffic. According to our experiments, the IS-HBase can reduce up to 97% network traffic for read queries and the throughput (queries per second) is significantly less affected by the fluctuation of available network bandwidth. The execution time of compaction in IS-HBase is only about 6.31% – 41.84% of the execution time of legacy HBase. In general, IS-HBase demonstrates the potential of adopting in-storage computing for other data-intensive distributed applications to significantly improve performance in compute-storage disaggregated infrastructure.

A pilot study towards a smart-health framework to collect and analyze biomarkers with low-cost and flexible wearables

Artificial intelligence-enabled applications on edge devices have the potential to revolutionize disease detection and monitoring with smart health (sHealth) applications. The major challenges are user-friendly and flexible sensors for seamless collection of physiological data for long hours, online and on-device processing of sensitive medical data to facilitate privacy protection, reliable extraction of disease-related biomarkers, and implementation of lightweight artificially intelligent algorithms for inference at the edge without degrading the system performance. In this pilot project, we conducted a yearlong field study with 9 participants conducting 480 data collection sessions in the “living lab” environment. We used smartphones as the edge computing device and implemented pre-trained machine learning algorithms in the smartphone app for computing disease-related Events-of-Interest (EoI). We considered real-time data processing on the smartphone itself without sharing raw data with the cloud or any other computing facility to minimize privacy concerns, and network bandwidth requirements. We used a commercial smart band and a custom-designed zero-power inkjet-printed sensor for physiological sensing and capturing health biomarkers such as heart rate variability (HRV) and core body temperature. The extracted HRV feature values are within the 95% confidence interval of normative values. On top of that, the extracted HRV shows some informative trends i.e. hammock pattern for healthy subjects which may be helpful in subsequent research studies. Moreover, we used core body temperature with user-reported outcomes for estimating flu-related symptoms severity and visualizing the spatiotemporal trend in a cloud-server to facilitate personalized as well as community-wide health monitoring. Inference at the edge provided a data reduction of 3 order while the runtime latency, power consumption, memory requirement, and storage size of the smartphone app were 500 ms, 51.90 mAH, 9.4 MB, and 2.4 MB, respectively. Our developed framework of sHealth enables automated community-wide monitoring of symptoms severity in addition to personalized monitoring which paves the way for early monitoring of a disease outbreak for a smart and connected community.

Multi-node Acceleration for Large-scale GCNs

Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-scale GCNs call for a multi-node acceleration system (MultiAccSys) like TPU-Pod for large-scale neural networks. In this work, we aim to scale up single-node GCN accelerators to accelerate GCNs on large-scale graphs. We first identify the communication pattern and challenges of multi-node acceleration for GCNs on large-scale graphs. We observe that (1) coarse-grained communication patterns exist in the execution of GCNs in MultiAccSys, which introduces massive amount of redundant network transmissions and off-chip memory accesses; (2) overall, the acceleration of GCNs in MultiAccSys is bandwidth-bound and latency-tolerant. Guided by these two observations, we then propose MultiGCN, the first MultiAccSys for large-scale GCNs that trades network latency for network bandwidth. Specifically, by leveraging the network latency tolerance, we <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> propose a topology-aware multicast mechanism with a one <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">put</monospace> per <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multicast</monospace> message-passing model to reduce transmissions and alleviate network bandwidth requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , we introduce a scatter-based round execution mechanism which cooperates with the multicast mechanism and reduces redundant off-chip memory accesses. Compared to the baseline MultiAccSys, MultiGCN achieves 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 12\times$</tex-math></inline-formula> speedup using only 28% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 68% energy, while reducing 32% transmissions and 73% off-chip memory accesses on average. It not only achieves 2.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 8\times$</tex-math></inline-formula> speedup over the state-of-the-art multi-GPU solution, but also scales to large-scale graphs as opposed to single-node GCN accelerators.

Output Feedback Sliding Mode Control of Markovian Jump Systems and Its Application to Switched Boost Converter

This paper presents a sliding mode dynamic output feedback controller design for Markovian jump systems under a communication network. For the inaccessible states and uncertain parts of Markovian jump systems, a novel integral-type sliding mode output feedback controller combined with system states and dynamic controller states is proposed. An event-triggered mechanism is introduced to reduce network bandwidth and resources requirements. The augmented dynamic system and complete control framework are presented together. The reachability of the novel integral-type sliding mode is proved. Then, based on delay-dependent Lyapunov functions, free-weighting matrices, and a singular value decomposition approach, a less conservative sufficient condition is proposed to ensure exponential stability with a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{ L}_{\infty}$ </tex-math></inline-formula> disturbance performance. Finally, a numerical and a DC-DC switched boost converter circuit simulations that verify the feasibility and effectiveness of the proposed controller are presented.

Abstract 9423: Virtual Reality (VR) Guided Telerobotic Cardiac Catheter Ablation is Feasible Over Low Bandwidth Network Connection

Introduction: Telerobotic operation in which a remote surgeon performs an operation over distance has long held the promise of providing a more equitable distribution of healthcare in rural and underserved regions of the world. One of the technological limitations that has hampered the adoption of telerobotic surgery is the need for a broadband connection to transmit high-resolution video of the operative field. Hypothesis: We hypothesized that we could develop a low-bandwidth system for telerobotic cardiac catheter ablation that deploys a virtual reality (VR)-based surgical guidance system to allow remote operation without the need for high-resolution video transmission. Methods: We assembled a portable pre-clinical telerobotic catheter ablation system from commercial-grade components by 3 rd party vendors. Raw sensor data from the catheter ablation system requiring a bitrate of less than 100kb/sec was streamed in real-time over distance using a standard internet connection. We used a proprietary service (DOPL Technologies, Seattle, WA) and an Oculus Quest (Oculus, Inc. Menlo Park, CA) to generate a shared, virtual operative field and operating room environment. No video data was transmitted. The Institutional Animal Care and Use Committee of the University of Washington (Seattle, WA) approved the study performed on a 14-week (27 kg) Yorkshire pig. The operator was located 6 miles from the OR at the time of the operation. Results: A successful proof of concept telerobotic cardiac catheter ablation in an animal model was performed without video transmission. The remote surgeon applied radiofrequency energy lesions in the animal’s cavotricuspid isthmus region. The visual depiction of the operative environment was successfully constructed in virtual reality from sensor data generated from the animal subject and surgical instruments. The remote operator conducted the ablation while interacting in a shared virtual OR environment with members of the surgical team present in the physical operating room and three remote observers. Conclusions: Telerobotic cardiac catheter ablation is feasible without video transmission. This approach could reduce network bandwidth requirements for telerobotic operations in rural areas.