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Register a new userA State of Art Review on Wireless Power Transmission Approaches for Implantable Medical Devices
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Added by
Mohammad Haerinia
Publication date
2020
fields
Electronic Engineering
and research's language is
English
Authors
Mohammad Haerinia
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Wireless power transmission (WPT) is a critical technology that provides a secure alternative mechanism for wireless power and communication with implantable medical devices. WPT approaches for implantable medical devices have been utilized based on applications. For instance, the inductive coupling tactic is mostly employed for transmission of energy to neuro-stimulators, and the ultrasonic method is used for deep-seated implants. This article provides a study concentrating on popular WPT techniques for implantable medical devices (IMDs) including inductive coupling, microwave, ultrasound, and hybrid WPT systems consisting of two approaches combined. Moreover, an overview of the major works is analyzed with a comparison of their major design elements, operating frequency, distance, efficiency, and harvested power.
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These days, the development of smart cities, specifically in location-aware, latency-sensitive, and security-crucial applications (such as emergency fire events, patient health monitoring, or real-time manufacturing) heavily depends on a more advance computing paradigms that can address these requirements. In this regard, fog computing, a robust cloud computing complement, plays a preponderant role by virtue of locating closer to the end-devices. Nonetheless, utilized approaches in smart cities are frequently cloud-based, which causes not only the security and time-sensitive services to suffer but also its flexibility and reliability to be restricted. So as to obviate the limitations of cloud and other related computing paradigms such as edge computing, this paper proposes a systematic literature review (SLR) for the state-of-the-art fog-based approaches in smart cities. Furthermore, according to the content of the reviewed researches, a taxonomy is proposed, falls into three classes, including service-based, resource-based, and application-based. This SLR also investigates the evaluation factors, used tools, evaluation methods, merits, and demerits of each class. Types of proposed algorithms in each class are mentioned as well. Above all else, by taking various perspectives into account, comprehensive and distinctive open issues and challenges are provided via classifying future trends and issues into practical sub-classes.
Implantable and wearable medical devices (IWMDs) are widely used for the monitoring and therapy of an increasing range of medical conditions. Improvements in medical devices, enabled by advances in low-power processors, more complex firmware, and wireless connectivity, have greatly improved therapeutic outcomes and patients quality-of-life. However, security attacks, malfunctions and sometimes user errors have raised great concerns regarding the safety of IWMDs. In this work, we present a HW/SW (Hardware/Software) framework for improving the safety of IWMDs, wherein a set of safety rules and a rule check mechanism are used to monitor both the extrinsic state (the patients physiological parameters sensed by the IWMD) and the internal state of the IWMD (I/O activities of the microcontroller) to infer unsafe operations that may be triggered by user errors, software bugs, or security attacks. We discuss how this approach can be realized in the context of a artificial pancreas with wireless connectivity and implement a prototype to demonstrate its effectiveness in improving safety at modest overheads.
The advent of various wireless technologies have revolutionized the communication infrastructure and consequently changed the entire world into a global village. Use of wireless technology has also been made for transmission of electric power wirelessly. It increases the portability of power systems and integrates the communication technologies and electric power to the same platform. This paper presents a comprehensive review and detailed analysis of various techniques used for wireless power transmission. Feasibility, implementations, operations, results and comparison among different methods have also been covered in order to identify the favorable and economical method for low power and small distance applications.
Wireless signals contain transmitter specific features, which can be used to verify the identity of transmitters and assist in implementing an authentication and authorization system. Most recently, there has been wide interest in using deep learning for transmitter identification. However, the existing deep learning work has posed the problem as closed set classification, where a neural network classifies among a finite set of known transmitters. No matter how large this set is, it will not include all transmitters that exist. Malicious transmitters outside this closed set, once within communications range, can jeopardize the system security. In this paper, we propose a deep learning approach for transmitter authorization based on open set recognition. Our proposed approach identifies a set of authorized transmitters, while rejecting any other unseen transmitters by recognizing their signals as outliers. We propose three approaches for this problem and show their ability to reject signals from unauthorized transmitters on a dataset of WiFi captures. We consider the structure of training data needed, and we show that the accuracy improves by having signals from known unauthorized transmitters in the training set.
Novel low-power wireless technologies and IoT applications open the door to the Industrial Internet of Things (IIoT). In this new paradigm, Wireless Sensor Networks (WSNs) must fulfil, despite energy and transmission power limitations, the challenging communication requirements of advanced manufacturing processes and technologies. In industrial networks, this is possible thanks to the availability of network infrastructure and the presence of a network coordinator that efficiently allocates the available radio resources. In this work, we consider a WSN that simultaneously transmits measurements of Networked Control Systems (NCSs) dynamics to remote state estimators over a shared packet-erasure channel. We develop a minimum transmission power control (TPC) policy for the coordination of the wireless medium by formulating an infinite horizon Markov decision process (MDP) optimization problem. We compute the policy using an approximate value iteration algorithm and provide an extensive evaluation of its parameters in different interference scenarios and NCSs dynamics. The evaluation results present a comprehensive characterization of the algorithms performance, proving that it can flexibly adapt to arbitrary use cases.
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