A portable medical system using real-time streaming transport over 3G wireless networks

The paper proposes architecture for the software implementation of a multimedia, tele-medicine system. It considers video streaming from both video servers in hospitals and webcams localized to patients. It also considers transmission of vital bio-signs, such as heart rates and blood pressure, etc. All these data are transmitted over a 3G-wireless communication system to various client devices (hand-held devices, such as PDAs) used by physicians and nurses. Our video codec is a software implementation of the MPEG-4 standard, compression rate at about 1/24 sizes suited for available transmitting bandwidth. Moreover, our design, which also integrates the processing of heart sounds, supports a 44.1 KHz sampling rate and a 16-bit representation required about 11 kbps bandwidth. At the same time, in the streaming process, we propose a congestion control scheme to reduce packet losses.     

An Effective Algorithm for Automatic Detection and Exact Demarcation of Breath Sounds in Speech and Song Signals

Automatic detection of predefined events in speech and audio signals is a challenging and promising subject in signal processing. One important application of such detection is removal or suppression of unwanted sounds in audio recordings, for instance in the professional music industry, where the demand for quality is very high. Breath sounds, which are present in most song recordings and often degrade the aesthetic quality of the voice, are an example of such unwanted sounds. Another example is bad pronunciation of certain phonemes. In this paper, we present an automatic algorithm for accurate detection of breaths in speech or song signals. The algorithm is based on a template matching approach, and consists of three phases. In the first phase, a template is constructed from mel frequency cepstral coefficients (MFCCs) matrices of several breath examples and their singular value decompositions, to capture the characteristics of a typical breath event. Next, in the initial processing phase, each short-time frame is compared to the breath template, and marked as breathy or nonbreathy according to predefined thresholds. Finally, an edge detection algorithm, based on various time-domain and frequency-domain parameters, is applied to demarcate the exact boundaries of each breath event and to eliminate possible false detections. Evaluation of the algorithm on a database of speech and songs containing several hundred breath sounds yielded a correct identification rate of 98% with a specificity of 96%     

基于李雅普诺夫优化和深度强化学习的多任务端边迁移

针对多终端、多边缘服务器场景下异构工业任务的端边协同处理问题,提出一种基于李雅普诺夫优化和深度强化学习的多任务端边迁移算法.首先,以联合优化任务迁移决策、迁移比例和传输功率为目标,充分考虑计算频率、传输功率、长期能耗和任务截止期等约束,构建系统长期平均开销最小化问题;由于问题中长期目标及约束中变量在不同时隙相互耦合,难以求解,基于李雅普诺夫优化理论,将长期平均开销最小化问题解耦为独立时隙的策略优化问题;通过马尔可夫决策过程建模,并采用双层竞争深度神经网络架构,提出基于深度强化学习的多任务迁移算法.实验结果表明,所提算法能够稳定收敛,并在长期能耗约束和任务截止期要求下有效降低系统长期平均开销.     

Classification of asthmatic breath sounds: preliminary results of the classifying capacity of human examiners versus artificial neural networks.

For continuous monitoring of the respiratory condition of patients, e.g., at the intensive care unit, computer assistance is required. Existing mechanical devices, such as the peak expiratory flow meter, provide only with incidental measurements. Moreover, such methods require cooperation of the patient, which at, e.g., the ICU is usually not possible. The evaluation of complicated phenomena such as asthmatic respiratory sounds may be accomplished by use of artificial neural networks. To investigate the merit of artificial neural networks, the capacities of neural networks and human examiners to classify breath sounds were compared in this study. Breath sounds were in vivo recorded from 50 school-age children with asthma and from 10 controls. Sound intervals with a duration of 20 seconds were randomly sampled from asthmatics during exacerbation, asthmatics in remission, and controls. The samples were digitized and related to peak expiratory flow. From each interval, two full breath cycles were selected. Of each selected breath cycle, a Fourier power spectrum was calculated. The so-obtained set of spectral vectors was classified by means of artificial neural networks. Humans evaluated graphic displays of the spectra. Human examiners could not clearly discriminate between the three groups by inspecting the spectrograms. Classification by self-classifying neural networks confirmed the existence of at least three classes; however, discrimination of 11 classes seemed more appropriate. Good results were obtained with supervised networks: as much as 95% of the training vectors could be classified correctly, and 43% of the test vectors. The three patient groups, as discriminated in advance, do not correspond with three sharply separated sets of spectrograms. More than three classes seem to be present. Humans cannot take up the spectral complexity and showed negative classification results. Artificial neural networks, however, are able to handle classification tasks and show positive results.     

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).     

基于Web服务的分布式文件系统模型

文章研究了面向服务架构的高性能网络海量文件存储系统。文中采用具有高可扩展性的Web服务体系,将Internet上大量分散的文件服务器组织成一个逻辑整体,形成一个遍布Internet的大规模分布式系统,各文件服务器相互协作,共同对外提供文件服务。文中探讨了分布式体系结构中的高扩展性能和提高广域网范围文件访问性能的方法。     

智能家居中的边缘计算

近年来,智能音箱、扫地机器人已经成为很多用户生活中不可或缺的一部分.随着物联网技术的发展,越来越多的智能设备走进家庭场景,让用户的生活变得更加便捷和舒适.当种类繁多、功能细分的智能设备通过网络进行连接和控制时,为了解决网络延时、数据安全等诸多问题,基于边缘计算的智能家居成为未来趋势.探讨智能家居场景中的边缘计算,介绍围绕感知、通信和计算3个方向所展开的研究.在感知方面,关注边缘节点的泛在感知能力,介绍在非接触式呼吸监测上取得的进展;在通信方面,研究无线感知和无线通信的融合设计,在有限的频谱资源上兼顾感知和通信;在计算方面,关注基于边缘节点的个性化机器学习,在不泄露用户数据的前提下建立个性化机器学习模型.     

Computerized management of patient care in a complex, controlled clinical trial in the intensive care unit

Acute respiratory distress syndrome (ARDS) is often not responsive to conventional supportive therapy and the mortality rate may exceed 90%. A new form of supportive care, extracorporeal carbon dioxide removal (ECCO2R), has shown a dramatic increase in survival (48%). A controlled clinical trial of the new ECCO2R therapy versus conventional continuous positive pressure ventilation (CPPV) is being initiated. Detailed care protocols have been developed by 'expert' critical care physicians for the management of patients. Using a blackboard control architecture, the protocols have been implemented on an existing hospital information system and will direct patient care and help manage the controlled clinical trial. Therapeutic instructions are automatically generated by the computer from data input by physicians, nurses, respiratory therapists, and the laboratory. Preliminary results show that the computerized protocol system can direct therapy for acutely ill patients.     

Dynamic energy-aware sensor configuration in multi-application monitoring systems

A typical pervasive monitoring system like a smart building depends on an infrastructure composed of hundreds of heterogeneous wireless sensor devices. Managing the energy consumption of these devices poses a challenging problem that affects the overall efficiency and usability. Existing approaches for sensor energy consumption typically assume a single monitoring application to consume sensor data and a static configuration for sensor devices. In this paper, we focus on a multi-application context with dynamic requirements and multi-modal sensor devices. We present 3SoSM, an approach to optimize interactions between application requirements and wireless sensor environment in real-time. It relies on an energy-aware dynamic configuration of sensor devices to lower energy consumption while fulfilling application requirements. To bind together sensor configuration and dynamic management of data streams, we design a sustainable multi-application monitoring system architecture for pervasive environments that collects application requirements for sensor data streams and optimizes them into sensor configurations. To demonstrate the effectiveness of our approach, a set of experiments are designed in the context of smart buildings. We comparatively evaluate our approach to show how dynamic sensor configuration for multiple monitoring applications indeed outperforms the mainstream duty-cycling method.     

基于区块链的边缘计算IIoT架构研究

在智能制造系统中,工业物联网通过先进的管理技术将制造设备互连,实现了信息的实时传输、设备的范在化感知和数据的快速分析处理。但是由于制造设备的异构性、物联网网关（IoT网关）数据分析能力的有限性、制造设备的存储力低下,设备和数据的低安全性等缺陷严重阻碍了智能制造的发展。BEIIoT架构从制造企业的实际生产过程与应用角度出发,将区块链技术与边缘计算相结合,通过对服务器进行P2P组网以实现对设备去中心化管理;通过对边缘设备进行服务化封装,增强设备的安全性与实时分析能力,降低设备的异构性;使用DAG双链式数据存储结构,提高数据的冗余度与安全性,实现生产线数据的异步并发备份存储。BEIIoT架构为智能制造的实施提供了体系支持。     

Disconnection prediction in mobile ad hoc networks for supporting cooperative work

We're investigating a specific pervasive architecture that can maintain continuous connections among MANET devices. We're targeting this architecture for computer-supported-cooperative-work (CSCW) and workflow management applications that would constitute the coordination layer. The basic problem of such an architecture is, how do you predict possible disconnections of devices, to let the coordination layer appropriately address connection anomalies? To solve this problem, we've developed a technique for predicting disconnections in MANETS. This technique serves as the basic layer of an innovative pervasive architecture for cooperative work and activity coordination in MANETS. We believe that in emergency scenarios, our proposed pervasive architecture can provide more effective coordination among team members.     

基于深度图像的非接触式呼吸检测算法研究

呼吸是人的基本生命活动,监测呼吸可以得知呼吸道和胸廓运动的生理、病理学状态,对某些呼吸系统疾病的诊断有重要的参考价值;提出了一种非接触式呼吸监测方法:对红外视频流中的每帧胸腹部区域数据进行降维,计算所有胸腹部区域数据的方差,将一定时间段内的方差序列进行低通滤波;最后根据方差序列可以获得该段时间内的呼吸频率和呼吸暂停时间;提出的非接触式呼吸检测算法在不影响被监测者正常睡眠活动的情况下,可以准确获取呼吸频率与其他相关参数,为健康监测和相关疾病的诊断提供了数据支持;日常家居场景的实验中,检测到的呼吸次数与实际完全一致,并且与实际胸腹部起伏变化基本同步,较好的保证了结果的准确性。     

非入侵式睡眠呼吸监测系统的设计与实现

根据养老院、医院等特殊区域人群的睡眠呼吸监护需求,设计了非入侵式柔性压感睡眠呼吸监测系统。系统通过硬件电路设计,采集人体睡眠时的呼吸信号,并进行消噪、去趋势等预处理。在硬件终端中通过呼吸信号的幅度和周期的特征区分呼吸类别,并实时判断是否发生了呼吸暂停,记录暂停的时刻与持续时长,并将数据通过蓝牙传至手机,在手机APP上可绘制实时波形,手机把数据上传至云平台。PC端软件可从云平台获取数据,绘制拟合呼吸信号曲线,判定记录睡眠数据。经实验测试,系统判定呼吸次数与实际基本一致,并可准确判断呼吸暂停情况,满足长程实现睡眠呼吸监测的要求。     

Context-aware service roaming for heterogeneous embedded devices over cloud

Cloud computing advocates a promising paradigm that facilitates the access within heterogeneous services, platforms, and end users. However, platforms (or host servers) have confined to devices which require a considerable computing resources. In this case, solutions concerning the efficient use of pervasive devices with constrained resources become an open issue. This study investigates the seamless connection between embedded devices and cloud resources to enhance the capability of computing and furthermore provide context-aware services. A method for wireless program dissemination and boot loading is proposed to transfer necessary information and resources between service and target device(s). The experiment results on time delay and energy cost demonstrate the feasibility and performance.     

协同移动边缘计算分层资源配置机制

针对车联网（IoV）中存在大量的车辆卸载任务计算需求,而本地端边缘服务器运算能力有限的问题,提出一种移动边缘计算分层协同资源配置机制（HRAM）。所提算法以多层式的架构合理分配与有效利用移动边缘计算（MEC）服务器的运算资源,减少不同MEC服务器之间的数据多跳转发时延,并优化卸载任务请求时延。首先构建IoV边缘计算系统模型、通信模型、决策模型和计算模型;然后利用层次分析法（AHP）进行多因素综合考虑以确定卸载任务迁移的目标服务器;最后提出动态权值的任务路由策略,调用整体网络的通信能力以缩短卸载任务的请求时延。仿真实验结果表明,HRAM算法相较于任务卸载单层式资源分配（RATAOS）算法和任务卸载多层式资源分配（RATOM）算法,分别降低了40.16%和19.01%的卸载任务请求时延;且所提算法在满足卸载任务最大可容忍时延的前提下,能够满足更多卸载任务的计算需求。     

移动边缘计算中的端到端任务分配算法

为了提高移动应用程序的运行效率,移动边缘计算将部分任务从终端设备迁移到边缘云中计算来缩减应用程序的运行时间和终端设备的能耗。针对应用程序所需的总代价即能耗和时间两个目标进行了研究,提出一个移动边缘计算模型和基于贪心策略的快速算法（HGA）;构造了一个结合贪心策略的粒子群（HPSO）算法,进一步优化HGA的解。实验结果表明,与传统所有任务只在一个设备上执行和尽可能上传云端执行两种策略相比,提出的HGA总代价分别优化28.5%和9.1%;与HGA相比,HPSO算法总代价减少12.3%;即所提算法能有效减少系统的总代价,更加满足用户需求。     

淤地坝监测场景下边缘计算协作式任务调度方法

针对目前我国西北地区淤地坝实时监测问题,研究了淤地坝监测与预警任务的调度方法.为避免淤地坝坝体隐患发现不及时,提高预警系统的时效性,本文考虑了任务卸载至边缘服务器的平均等待时间,提出了一种淤地坝监测场景下边缘计算协作式任务调度方法.根据任务计算量、边缘服务器计算能力等信息建立计算任务完成时间模型,然后采用模拟退火算法优化计算任务卸载位置,设计了一种多个边缘计算服务器相互协作的任务调度策略.实验结果表明,该方法有效降低了监测任务的计算时间,提高了监测预警的时效性.     

融合LSD算法与深度学习的开关状态检测方法

针对工业复杂环境下设备维保成本高、视觉检测落地周期长等问题,并根据建筑信息模型（Building Information Modeling,BIM）具有与现实场景空间一致,场景视角灵活可调以及可以模拟各类光照条件等优点,提出一种在BIM环境下融合LSD（Line Segment Detector）直线检测与深度学习的设备开关状态检测方法。通过检测图像直线段信息,并基于开关盒边沿特征对直线段进行筛选,实现在图像中框定开关盒位置生成图像数据集,进而输入到YOLOv3（You Only Look Once version3）网络训练生成深度学习模型。将深度学习网络框架部署到边缘设备,在边缘侧对真实环境下开关盒工作状态进行检测。实验结果表明,该方法能够在短时间内实现BIM环境下识别检测机柜设备上的开关盒工作状态,并对真实环境下开关盒工作状态检测具有良好适应性。     

Policy-based Diffserv on Internet servers: the AIX approach

The Internet infrastructure must evolve from best-effort service to meet the needs of different customers and applications. With Internet traffic differentiation, service providers can support a range of offerings, such as loss or delay bounds and network bandwidth allocation, to meet different performance requirements. The differentiated services (Diffserv) architecture provides a scalable approach, in which network access (or edge) devices aggregate traffic flows onto provisioned pipes that traverse a streamlined network core. We have identified the key requirements for provisioning Diffserv functions on Internet servers. Based on these requirements, we have implemented, and deployed, a policy-based architecture on IBM's AIX operating system that provides Diffserv services to both QoS-aware and -unaware applications     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.