다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

The Internet of Things for Health Care: A Comprehensive Survey

A first course in stochastic processes

Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable [3]. Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. [4]) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection [1]. Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime [2]. This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: fstann@usc.edu, johnh@isi.edu. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.

RMST: Reliable Data Transport in Sensor Networks

Photoacoustic imaging is a promising method for visualizing biological tissues with optical absorbers. This article provides an overview of the rapidly developing field of photoacoustic imaging. Photoacoustics, the physical basis of photoacoustic imaging, is analyzed briefly. The merits of photoacoustic technology, compared with optical imaging and ultrasonic imaging, are described. Various imaging techniques are also discussed, including scanning tomography, computed tomography and original detection of photoacoustic imaging. Finally, some biomedical applications of photoacoustic imaging are summarized.

Photoacoustic imaging in biomedicine

Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has on average 83% prolonged lifetime at a lower cost compared to previously proposed network in the agriculture field.

/pdf/a-low-power-iot-network-for-smart-agriculture-2vxj794lsz.pdf

A low power IoT network for smart agriculture

Classification of medical data is one of the most challenging pattern recognition problems. As stated in literature a single classifier is unable to solve all medical image classification problems due to high sensitivity to noise and other imperfections like data imbalance. So, several individual classifiers have been studied to solve the different types of classification problems arising in medical datasets but all have proven to be useful on some specific datasets. Hence, in this paper, we propose a generic multi-level classification approach for medical datasets using sparsity based dictionary learning and support vector machine approaches. The proposed technique demonstrates the following advantages: 1) gives better performance of classification accuracy over all datasets 2) solves imbalanced data problems 3) needs no fusion and ensemble methods in multi-level classification. The results presented on the 5 standard UCI medical datasets demonstrate that the efficacy of the proposed multi-level classification technique.

Multi-level classification: A generic classification method for medical datasets

In the remote health care monitoring applications, the collected medical data from bio-medical sensors should be transmitted to the nearest gateway for further processing. Transmission of data contributes to a significant amount of power consumption by the transmitter and increase in the network traffic. In this paper we propose a low complex rule engine based health care data acquisition and smart transmission system architecture, which uses IEEE 802.15.4 standard for transferring data to the gateway. The power consumed and the network traffic generated by the device can be reduced by event based transmission rather than continuous transmission of data. We developed two different rule engines: static rule engine and adaptive rule engine, which decides whether to transmit the collected data based on the important features extracted from the data, thereby achieving power saving. In this paper, ECG data acquisition and transmission architecture is considered. The metrics used for performance analysis are the amount of power saving and reduction in network traffic. It is shown that the proposed rule engine gives a significant reduction in energy consumption and network traffic generated.

https://www.iith.ac.in/~raji/Cpapers/8.pdf

Adaptive rule engine based IoT enabled remote health care data acquisition and smart transmission system

Purpose: Ultrasound scanning has been widely used for preliminary diagnosis as it is non-invasive and has good scope for the doctors to analyze many diseases. Due to lack of trained radiologists in remote areas, tele-radiology is used to diagnose the scanned ultrasound data. Availability of online radiographers and having communication facility for the portable ultrasound are issues in tele-radiology for using ultrasound scanning in remote health-care. In these situations, Computer Aided Diagnosis (CAD) will be beneficial in diagnosing the patients with minimal manual intervention. Methods: We proposed FPGA based CAD algorithm for abnormality detection of kidney in ultrasound images. The proposed algorithm works in the following way: as a pre-processing, an ultrasound image is denoised and region of interest of kidney in ultrasound image is segmented. Intensity histogram features and Haralick features are extracted from the segmented kidney region. Based on extracted features, the classification algorithm is implemented in two stages. In first stage, a Look Up Table (LUT) based approach is used to differentiate between normal and abnormal kidney images. In second stage, after confirming the abnormality, Support Vector Machine (SVM) with Multi-Layer Perceptron (MLP) classifier trained with extracted features is used to further classify the presence of stone or cyst in kidney. The proposed algorithm is implemented on a FPGA based Xilinx Kintex-7 board. Results: The proposed algorithm gave an accuracy of 98.14%, sensitivity of 100% and specificity of 96.82% in detecting the exact abnormality present in kidney ultrasound images. Conclusion: The proposed algorithm and its hardware implementation will be beneficial for diagnosing the kidney in absence of radiologists and internet connectivity.

Computer Aided Abnormality Detection for Kidney on FPGA Based IoT Enabled Portable Ultrasound Imaging System

Smart grid technology is one of the recent developments in the area of electric power systems that aid the use of non-conventional sources of energy in parallel with the conventional sources of energy. Monitoring and control of smart grids is essential for its efficient and effective functioning. In this paper, we propose an architecture for monitoring power in smart grid applications using wireless sensor network (WSN) technology. A prototype power sensing module is designed and developed to calculate the power for any kind of loads. Using WSN technology, the monitored power is communicated to the sink at periodic intervals. Multi hop wireless mesh network is set up using IRIS motes to enhance the communication between the power sensing nodes and the sink. The data collected is a rich source of repository for data analysis and modelling. A number of smart actions and applications, such as power theft detection, energy efficient building design, smart automation systems and smart metering can evolve out of the proposed model. A novel Power theft detection algorithm is proposed and simulated in this paper. The system is also scaled using GSM technology to extend the range of communication. Load monitoring can aid distributed architecture in smart grids with automated technology to switch between the non-conventional source of energy and the grid.

/pdf/wsn-based-power-monitoring-in-smart-grids-1qpw2d6bip.pdf

Pachamuthu Rajalakshmi

Papers

A low power IoT network for smart agriculture

Multi-level classification: A generic classification method for medical datasets

Adaptive rule engine based IoT enabled remote health care data acquisition and smart transmission system

Computer Aided Abnormality Detection for Kidney on FPGA Based IoT Enabled Portable Ultrasound Imaging System

WSN based power monitoring in smart grids