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EMBEDDED SYSTEM 2017

Category Archives

Hazards in the installation and maintenance of solar panels

Abstract:
Solar panels or modules are becoming popular in residential and some commercial sites for providing either the primary power source or a backup source of electricity. The advantages seem obvious and on the surface they seem to be a safe option. Photovoltaic power systems do have many benefits, but there are also safety issues that come into play with them during their installation, maintenance and during emergency situations, such as during a fire. This paper discusses a quick overview of photovoltaic power system construction and then some of the safety issues that may be present.


Study of sufficient number of optimal tilt angle adjustment to maximize residential solar panels yield

Abstract:
In this paper a comprehensive study is carried out on the optimal number of intervals for adjusting the optimal tilt angle of a solar panel. The tilt angle of solar panels is important for capturing solar radiation and it depends on the path of the sun in the location of the solar panel. A Bee Algorithm is used to compute the optimal tilt angle for a given period. The main goal is to show instead of using a tracking system, which is costly for residential usage, manual adjustment of the tilt angle of a solar panel for a certain number of times during a year is sufficient to receive most of the solar radiation. This study is performed for different locations across the US with different latitudes and longitudes. The optimal number of intervals for each location is computed and the effects of the changes in the longitude and latitude are investigated.


Environmental monitoring in grain granary based on embedded system

Abstract:
In order to solve the disadvantages of the traditional grain condition monitoring system, the development and implementation of a novel digital environmental monitoring solution for grain granary based on S3C6410X microcontroller and ARM-Linux embedded operating system is presented in this paper. The granary monitoring system proposes three-layer architecture based on distributed monitoring system, including application layer, data transmission layer and data acquisition layer. Furthermore, the design processes of the embedded environmental monitoring platform for grain granary, especially including its hardware structure and software components, are introduced in detail. The final experimental results show that it satisfies the real-time monitoring requirements of the granary environmental parameters such as the grain temperature, humidity, video image and other important sensor information.


A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes

Abstract:
The development of the Internet of Things (IoT) has made extraordinary progress in recent years in both academic and industrial fields. There are quite a few smart home systems that have been developed by major companies to achieve home automation. However, the nature of smart homes inevitably raises security and privacy concerns. In this paper, we propose an improved energy-efficient, secure, and privacy-preserving communication protocol for the smart home systems. In our proposed scheme, data transmissions within the smart home system are secured by a symmetric encryption scheme with secret keys being generated by chaotic systems. Meanwhile, we incorporate Message Authentication Codes (MAC) to our scheme to guarantee data integrity and authenticity. We also provide detailed security analysis and performance evaluation in comparison with our previous work in terms of computational complexity, memory cost, and communication overhead.


Security Vulnerabilities of Internet of Things: A Case Study of the Smart Plug System

Abstract:
With the rapid development of the Internet of Things (IoT), more and more small devices are connected into the Internet for monitoring and control purposes. One such type of devices, smart plugs, have been extensively deployed worldwide in millions of homes for home automation. These smart plugs, however, would pose serious security problems if their vulnerabilities were not carefully investigated. Indeed, we discovered that some popular smart home plugs have severe security vulnerabilities which could be fixed but unfortunately are left open. In this paper, we case study a smart plug system of a known brand by exploiting its communication protocols and successfully launching four attacks: device scanning attack, brute force attack, spoofing attack, and firmware attack. Our real-world experimental results show that we can obtain the authentication credentials from the users by performing these attacks. We also present guidelines for securing smart plugs.


A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

Abstract:
With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system.Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for realtime IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics


A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Abstract:
Emerging technologies, such as the Internet of Things, smart applications, smart grids, and machine-to-machine networks stimulate the deployment of autonomous, self-configuring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints, and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.


Machine-to-Machine Communications in Ultra-Dense Networks — A Survey

Abstract:
To achieve 1000-fold capacity increase in 5G wireless communications, ultra-dense network (UDN) is believed to be one of the key enabling technologies. Most of the previous research activities on UDNs were based very much on human-to-human (H2H) communications. However, to provide ubiquitous Internet of Things (IoT) services, machine-to-machine (M2M) communications will play a critical role in 5G systems. As the number of machine-oriented connections increases, it is expected that supporting M2M communications is an essential requirement in all future UDNs. In this paper, we aim to bridge the gaps between M2M communications and UDNs, which were commonly considered as two separate issues in the literature. The paper begins with a brief introduction on M2M communications and UDNs, and then will discuss the issues on the roles of M2M communications in future UDNs. We will identify different ways to implement M2M communications in the UDNs from the perspectives of layered architecture, including physical (PHY), media access control (MAC), network, and application layers. Other two important issues, i.e., security and network virtualization, will also be addressed. Before the end of this paper, we will give a summary on identified research topics for future studies.


Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks

Abstract:
The communication in the Smart Home Internet of Things (SH-IoT) comprising various electronic devices and sensors is very sensitive and crucial. In addition, the key requirements of the SH-IoT include channel security, handover support, mobility management, and consistent data rates. Proxy mobile IPv6 (PMIPv6) is considered as one of the core solutions to handle extreme mobility; however, the default PMIPv6 cannot ensure performance enhancement in SH-IoT scenarios, i.e., Route Optimization (RO). The existing security protocols for PMIPv6 cannot support secure RO for smart home IoT services, where mobile nodes (MNs) communicate with home IoT devices not belonging to their domain. Motivated by this, a secure protocol is proposed, which uses trust between PMIPv6 domain and smart home to ensure security as well as performance over the path between MNs and home IoT devices. The proposed protocol includes steps for secure RO and handover management, where mutual authentication, key exchange, perfect forward secrecy, and privacy are supported. The correctness of the proposed protocol is formally analyzed using BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, network simulations are conducted to evaluate the performance efficiency of the proposed protocol. The results show that the proposed approach is capable of providing secure transmission by resolving the RO problem in PMIPv6 along with the reduction in handover latency, end to end delay and packet loss, and enhancement in throughput and transmission rate even during the handover phase.


On-line impedance monitoring of transformer based on inductive coupling approach

Abstract:
An on-line impedance monitoring method of a transformer is proposed based on two-probe inductive coupling approach. The theory behind the method is described and validated experimentally. As it does not require direct electrical contact with the transformer powered by high voltage power supply, it can be easily installed on site and most importantly, it eliminates the potential electrical hazards imposed on the personnel who set up the measurement instrumentation.


Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon

Abstract:
In this paper, we propose a robust and accurate indoor localization and tracking system using smartphone built-in inertial measurement unit (IMU) sensors, WiFi received signal strength measurements and opportunistic iBeacon corrections based on particle filter. We utilize Pedestrian Dead Reckoning (PDR) approach which leverages smartphone equipped accelerometers, gyroscope and magnetometer to estimate the walking distance and direction of user. The position estimated by WiFi fingerprinting based approach is fused with PDR to reduce its drifting error. Since the number of WiFi routers is usually limited for localization in large-scale indoor environment, we employ the emerging iBeacon technology to occasionally correct the drifting error of PDR in poor WiFi coverage area. Extensive experiments have been conducted and verified the superiority of the proposed system in terms of localization accuracy and robustness.


Indoor localization framework with WiFi fingerprinting

Abstract:
Indoor localization through WiFi fingerprinting requires a large number of fine-grained data samples. This study presents a data acquisition and indoor localization framework that collects crowd-sourced WiFi received signal strength data in a metropolitan high-rise building and predicts location through WiFi fingerprinting. The framework consists of a server and an Android application and was tested at NYIT for data collection for two weeks in December 2016. The dataset was preprocessed and analyzed through linear support vector machine to test location prediction accuracy. Various feature selection schemes were compared for their location prediction accuracy. We show that a small subset of features suffices to provide high location prediction accuracy. The average location prediction accuracy increases from 83% to 100% when time features are considered comparing to using only spatial features.


Security and Privacy Preservation Scheme of Face Identification and Resolution Framework Using Fog Computing in Internet of Things

Abstract:
Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication and storage capabilities. Therefore, we have proposed the fog computing based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing based framework. In this paper, we propose a security and privacy preservation scheme to solve above issues. We give an outline of the fog computing based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the CIA (confidentiality, integrity, availability) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.


Electromagnetic pollution measurement in the system rooms of a university

Abstract:
Low frequency electromagnetic pollution is mostly caused by the transmission lines at 50 Hz in Turkey. Limit values for base stations and mobile phones are commonly known but there are also limit values for extremely low frequency (ELF) band. In this paper low frequency electromagnetic waves in system rooms of Mus Alparslan University are studied. Measured electromagnetic pollution compared with limit values and possible precautions examined.


Wearable sensors for analyzing personal exposure to air pollution

Abstract:
This paper describes hardware developments in a wearable air quality sensor assembly for analyzing personal exposure to air pollution. According to the World Health Organization, exposure to air pollution is now the largest single environmental health risk globally, leading to approximately 7 million deaths in 2012 alone. Understanding personal exposure from traditional stationary and spatially disperse monitoring stations is challenging, particularly in urban environments with multiple pollutant sources and complex transport dynamics. Portable air quality sensors have the potential to fill in the gap left by traditional air pollution monitoring. Air pollution sensor technology is decreasing in cost and size, meaning it is now tenable to use low-cost portable air pollution sensors. The data collected by the wearable air quality sensor assembly, EnviroSensor 2.0, includes measurements of ozone, particulate matter, temperature, humidity, latitude, and longitude.


Agricultural crop monitoring using IOT – a study

Abstract:
The Internet of things (IOT) is remodeling the agriculture enabling the farmers with the wide range of techniques such as precision and sustainable agriculture to face challenges in the field. IOT technology helps in collecting information about conditions like weather, moisture, temperature and fertility of soil, Crop online monitoring enables detection of weed, level of water, pest detection, animal intrusion in to the field, crop growth, agriculture. IOT leverages farmers to get connected to his farm from anywhere and anytime. Wireless sensor networks are used for monitoring the farm conditions and micro controllers are used to control and automate the farm processes. To view remotely the conditions in the form of image and video, wireless cameras have been used. A smart phone empowers farmer to keep updated with the ongoing conditions of his agricultural land using IOT at any time and any part of the world. IOT technology can reduce the cost and enhance the productivity of traditional farming.


Home automation and personalization through individual location determination

Abstract:
The focus of this project is to develop a prototype to demonstrate the utility of individualized location determination for home automation. While current home automation systems provide localization at a GPS level, they do not identify users’ locations within a building. The smart home technology market is growing rapidly and this feature can differentiate a product line by adding unique capabilities for the consumer. The objective for this system is to use individualized location determination to improve lifestyle areas in the home in passive and non-intrusive ways. Being passive is important in that users should not have to take extra steps (e.g., pushing a button when they enter a room) as they move throughout their house. Being non-intrusive is important because users should not have to wear anything extra (e.g., a special armband) or have personal information scanned (e.g., facial recognition camera). The system will use Bluetooth Low Energy (BLE) to identify and track users’ movements throughout a house, where the BLE signal of an individual will be associated with a smartphone or fitness wearable that they normally carry with them. A unique aspect of this project is the implementation of a flipped BLE architecture, which is implemented with a Texas Instruments development board that acts as a beacon to identify users based on their BLE signals from their smartphones and wearables. This architecture is “flipped” because most BLE beacons rely on a smartphone to “see” the beacons whereas the beacons in this system are “seeing” the smartphones. After identifying BLE devices in proximity to the beacon, the prototype system will record readings on the beacon locally, store data in an SQL database, and clean and process data through a PHP script. Different use cases for the BLE system within a house were considered. The final prototype will focus on a Smart Thermostat application which automatically adjusts where a thermostat reads the indoor temperature based on the location of the users. Results include a fully functioning prototype that can be used to demonstrate feasibility of the home automation use cases. Test results from the prototype include using a factorial experiment to measure the effect of distance and obstacles on the signal strength readings as well as performance on the system through a range of scenarios.


Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks

Abstract:
The communication in the Smart Home Internet of Things (SH-IoT) comprising various electronic devices and sensors is very sensitive and crucial. In addition, the key requirements of the SH-IoT include channel security, handover support, mobility management, and consistent data rates. Proxy mobile IPv6 (PMIPv6) is considered as one of the core solutions to handle extreme mobility; however, the default PMIPv6 cannot ensure performance enhancement in SH-IoT scenarios, i.e., Route Optimization (RO). The existing security protocols for PMIPv6 cannot support secure RO for smart home IoT services, where mobile nodes (MNs) communicate with home IoT devices not belonging to their domain. Motivated by this, a secure protocol is proposed, which uses trust between PMIPv6 domain and smart home to ensure security as well as performance over the path between MNs and home IoT devices. The proposed protocol includes steps for secure RO and handover management, where mutual authentication, key exchange, perfect forward secrecy, and privacy are supported. The correctness of the proposed protocol is formally analyzed using BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, network simulations are conducted to evaluate the performance efficiency of the proposed protocol. The results show that the proposed approach is capable of providing secure transmission by resolving the RO problem in PMIPv6 along with the reduction in handover latency, end to end delay and packet loss, and enhancement in throughput and transmission rate even during the handover phase.


Based on MEMS sensors man-machine interface for mechatronic objects control

Abstract:
The article describes the development of a system for wireless control of mechatronic objects and anthropomorphic robots. The main attention is paid to the man-machine interface on the basis of MEMS sensors. Given the structure of the developed system with a description of the main blocks. It describes the communication interfaces used. Substantiates the benefits of the proposed technical solutions.


Signal Processing in Cyber-Physical MEMS Sensors: Inertial Measurement and Navigation Systems

Abstract:
Motivated by industry needs, this paper focuses on statistical models, descriptive probabilistic data analysis and data-prescriptive signal processing in smart inertial sensors. These multimode sensors combine physical and cyber components such as solid-state and micromachined motion sensing elements, processing and interfacing integrated circuits, middleware, software, etc. We develop consistent algorithms and tools based upon cross-cutting engineering science along with substantiation and validation. Fundamental, applied and experimental results are reported. Our multidisciplinary findings advance fundamental knowledge and enhance transformative technologies. We empower synergetic system-level integration of diverse device physics with descriptive, predictive and prescriptive analyses. This paper contributes to design and deployment of next generation of smart sensors which utilize front-end microelectronic, microelectromechanical system and processing technologies.


Monolithic Multi-Sensor Design With Resonator-Based MEMS Structures

Abstract:
In this paper, we demonstrated a resonator-based MEMS architecture for multi-sensor SOC applications. A newly developed 0.18 μm 1P6M CMOS ASIC/MEMS process was adopted to integrate MEMS sensor and circuits monolithically. By using resonators as the building blocks, multiple MEMS sensors including environmental temperature sensor, ambient pressure sensor, accelerometer as well as gyro sensor can be monolithically implemented with the readout circuits by the single standard ASIC/MEMS process without off-fab pre/post processes. The proposed architecture enables compact and innovative sentient-assisted SOC design for the emerging IOT applications.


Optical self-excitation and detection for inertial MEMS Sensors

Abstract:
We report on the development of an optical self-excitation and detection method to allow for high stability operation of inertial MEMS sensors. A single constant amplitude laser beam is used to both drive and optically interrogate the resonant frequency of a MEMS resonator. A parametric study of x,y,z position of the laser spot location, laser power, and laser wavelength with respect to frequency stability of the MEMS sensor is presented. A frequency bias instability of 3 ppb at 1 s without any calibration and a frequency white noise of 2.4 ppb/√Hz for a MEMS resonator with resonant frequency of 68.28 kHz and quality factor of 4,600 have been measured.


Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant

Abstract:
Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.


State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Abstract:
Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-ofthe- art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.


An SDR based channel sounding technique for embedded systems

Abstract:
This paper presents a low cost OFDM based channel sounding technique which can be implemented on a low power embedded system. The technique is first used to measure the channel of an indoor environment and is validated by comparison with a VNA and a ray-tracing simulator. We then show its usage in two interesting situations. Finally, we conclude the paper by giving the advantages and drawbacks of the technique and propose some solutions for improvement.


Current Challenges for Visible Light Communications Usage in Vehicle Applications: A Survey

Abstract:
In the context of an increasing interest toward reducing the number of traffic accidents and of associated victims, communication-based vehicle safety applications have emerged as one of the best solutions to enhance road safety. In this area, visible light communications (VLC) have a great potential for applications due to their relatively simple design for basic functioning, efficiency and large geographical distribution. This article addresses the issues related to the VLC usage in vehicular communication applications, being the first extensive survey dedicated to this topic. Although VLC has been the focus of an intensive research during the last few years, the technology is still in its infancy and requires continuous efforts to overcome the current challenges, especially in outdoor applications, such as the automotive communications. This article is aimed at providing an overview of several research directions that could transform VLC into a reliable component of the transportation infrastructure. The main challenges are identified and the status of the accomplishments in each direction is presented, helping one to understand what has been done, where the technology stands and what is still missing. The challenges for VLC usage in vehicle applications addressed by this survey are: 1) increasing the robustness to noise; 2) increasing the communication range; 3) enhancing mobility; 4) performing distance measurements and visible light positioning; 5) increasing data rate; 6) developing parallel VLC and 7) developing heterogeneous dedicated short range communications (DSRC) and VLC networks. Addressing and solving these challenges lead to the perspective of fully demonstrating the high potential of VLC and therefore to enable the VLC usage in road safety applications. This article also proposes several future research directions for the automotive VLC applications and offers a brief review on the associated standardization activities.


Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges

Abstract:
This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.


Congestion Detection and Propagation in Urban Areas Using Histogram Models

Abstract:
Rapid growth of urbanization makes the roadways exacerbate many problems like traffic congestion, road accidents and passenger discomfort. Many actions have been taken globally to solve or reduce this impact but still the congestion problem seems to be persistent globally. In this paper, we propose a new histogram-based model for congestion detection. We subsequently extended our model with the base platform concept and use Intelligent Transportation System (ITS) technologies to provide a novel rerouting strategy. The proposed model enables the microscopic visualization of the traffic patterns for every individual lane and predicts the congestion in priori and takes actions proactively. The rerouting strategy used in our approach provides a novel solution to the congestion problem by taking precaution measures prior to the critical point of congestion occurrence. The proposed algorithm is compared with various existing algorithms and the simulation results show that the proposed model addresses the congestion problem effectively and provides better solution compared to existing algorithms.


Evaluating Secrecy Outage of Physical Layer Security in Large-Scale MIMO Wireless Communications for Cyber-Physical Systems

Abstract:
Large-scale multiple input multiple output (MIMO) wireless system is regarded as a solution to provide high speed connection for exponentially increasing wireless subscriptions for emerging cyber-physical systems (CPS) and Internet-of-Things (IoT). In order to realize its full potential, there are several challenges to be addressed to achieve high secrecy rate or data rate. In this paper, we analyze outage probability for secrecy rate in MIMO wireless systems in the presence of eavesdroppers and jammers for CPS devices. Our proposed approach takes into account the impact of jammers while finding the best response to minimize the jamming/interfering effect (or to enhance the secrecy rate) and the impact of eavesdropper in secrecy rates of the users. We present formal analysis for secrecy outage probability and interception probability considering Rayleigh fading scenario. The performance is evaluated by using numerical results obtained from Monte Carlo simulations. Numerical results indicate that the system performance is improved significantly when the users adapt their transmit vectors based on their observed interference values. Furthermore, the secrecy outage probability increases with power of jammer and the secrecy capacity decreases when jammer power increases. We observed that the proposed approach outperforms the other existing approaches.


Based on MEMS sensors man-machine interface for mechatronic objects control

Abstract:
The article describes the development of a system for wireless control of mechatronic objects and anthropomorphic robots. The main attention is paid to the man-machine interface on the basis of MEMS sensors. Given the structure of the developed system with a description of the main blocks. It describes the communication interfaces used. Substantiates the benefits of the proposed technical solutions.


Signal Processing in Cyber-Physical MEMS Sensors: Inertial Measurement and Navigation Systems

Abstract:
Motivated by industry needs, this paper focuses on statistical models, descriptive probabilistic data analysis and data-prescriptive signal processing in smart inertial sensors. These multimode sensors combine physical and cyber components such as solid-state and micromachined motion sensing elements, processing and interfacing integrated circuits, middleware, software, etc. We develop consistent algorithms and tools based upon cross-cutting engineering science along with substantiation and validation. Fundamental, applied and experimental results are reported. Our multidisciplinary findings advance fundamental knowledge and enhance transformative technologies. We empower synergetic system-level integration of diverse device physics with descriptive, predictive and prescriptive analyses. This paper contributes to design and deployment of next generation of smart sensors which utilize front-end microelectronic, microelectromechanical system and processing technologies.


State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Abstract:
Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-ofthe- art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.


A smart meter design and implementation using ZigBee based Wireless Sensor Network in Smart Grid Sign In or Purchase

Abstract:
In this paper Wireless Sensor Home Area Network (WSHAN) with ZigBee interfaced smart meter is designed and implemented. Because of the increasing demands on electricity, traditional electric grid needs to be replaced with intelligent, robust, reliable and costly effective smart grid applications. Wireless Sensor Networks (WSN) has a critical role to set up a reliable and costly effective smart electric power grid applications. Our system measures energy usage, logs data real time and shows time of use (TOU) values. The system also controls any device connected to power outputs. While powering on and off, zero-cross of AC signal is detected to calculate phase shift. The smart meter provides correct power usage and transmits data with ZigBee to PC (Personal Computer). The user monitors the power information and remotely controls the system.


Managing robot kinematics based on Arduino controllers using a Unity system

Abstract:
This paper contains investigation on managing robot kinematics based on Arduino controllers using a Unity system. The developed system allows to perform different operations using the specially developed software to perform movement of the robotic arm and to define position objects into the certain positions.


Efficient visual obstacle avoidance for robotic mower

Abstract:
We present a novel method for robotic mower’s lawn obstacle avoidance as well as motion control, which is based on the Gabor texture classification and drivable region search methods. In our approach, a camera is applied to obtain real-time image streams of lawn scenes, Gabor filters are then applied for extracting robust texture features. Based on the compressed features, a SVM model is trained and used to perform the grass texture classification task. The maximum connected drivable region is computed via a breadth-first search method (BFS) according to the classification results. By restricting the BFS method in a polygon window area, we accelerate the region search step by 64% compared with searching the whole image. The whole program is built on the Robot Operating System (ROS) for the purpose of expansion. Experiments have been performed on the robotic mower under complex scenes, including various obstacles, seasons and lighting conditions. The obstacle avoidance rate tested is 97.7% in a 75 m*40 m area, which proves the efficiency and superiority of our proposed method.


Bioinspired Ciliary Force Sensor for Robotic Platforms

Abstract:
The detection of small forces is of great interest in any robotic application that involves interaction with the environment (e.g., objects manipulation, physical human-robot interaction, minimally invasive surgery), since it allows the robot to detect the contacts early on and to act accordingly. In this letter, we present a sensor design inspired by the ciliary structure frequently found in nature, consisting of an array of permanently magnetized cylinders (cilia) patterned over a giant magnetoresistance sensor (GMR). When these cylinders are deformed in shape due to applied forces, the stray magnetic field variation will change the GMR sensor resistivity, thus enabling the electrical measurement of the applied force. In this letter, we present two 3 mm × 3 mm prototypes composed of an array of five cilia with 1 mm of height and 120 and 200 μm of diameter for each prototype. A minimum force of 333 μN was measured. A simulation model for determining the magnetized cylinders average stray magnetic field is also presented.


Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems

Abstract:
Closed kinematic chains are created whenever multiple robot arms concurrently manipulate a single object. The closed-chain constraint, when coupled with robot joint limits, dramatically changes the connectivity of the configuration space. We propose a regrasping move, termed “IK-switch,” which allows efficiently bridging components of the configuration space that are otherwise mutually disconnected. This move, combined with several other developments, such as a method to stabilize the manipulated object using the environment, a new tree structure, and a compliant control scheme, enables us to address complex closed-chain manipulation tasks, such as flipping a chair frame, which is otherwise impossible to realize using existing multi-arm planning methods.


Low-cost robotic assessment of visuo-motor deficits in Alzheimer’s disease

Abstract:
A low-cost robotic interface was used to assess the visuo-motor performance of patients with Alzheimer’s disease (AD). Twenty AD patients and twenty age-matched controls participated in the study. The battery of tests included simple reaction times, position tracking and stabilization tasks performed with both hands. The regularity, velocity, visual and haptic feedback were manipulated to vary movement complexity. Reaction times and movement tracking error were analyzed. Results show a marked group effect on a subset of conditions, in particular when the patients could not rely on the visual feedback of hand movement. The visuo-motor performance correlated with measures of global cognitive functioning (MMSE) and with different memory-related abilities. Our results support the hypothesis that the ability to recall and use visuo-spatial associations might underlie impairment in complex motor behavior that has been reported in AD patients. Importantly, the patients had preserved learning effects across sessions, which might relate to visuo-motor deficits being less evident in everyday life and clinical assessments. This robotic assessment, lasting less than an hour, provides detailed information about the integrity of visuo-motor abilities. The data can aid understanding of the complex pattern of deficits that characterizes this pervasive disease.


Robotic assistant for Mobility-Impaired Patients (RAMP)

Abstract:
There are currently 10 million American who need mobility assistance leading to an increase of 40 percent in demand for registered nurses by 2020 while the supply of registered nurses will only increase by 6 percent. This supply-demand gap can be partially addressed by a robot assistant to help an individual with limited mobility performs daily tasks. A detailed analysis of the tasks performed by a Mobility Impaired Patient was conducted and used to establish requirements for the robot: (i) to relocate items on behalf of the user, (ii) go to remote locations and relay video images to the user. Based on these requirements a robot was designed and constructed. Design alternatives are evaluated for designing the system to ensure the safety, cost, and energy efficiency. Design alternatives of RAMP are broken down into three stages; 1) base, 2) elevation, and 3) frame. The simulation was made in AutoCAD before building the model. After comparing and analyzing all alternatives, Omni wheels, leadscrew, and alumni frame have the best result due to their efficiency, safety, and cost-effectiveness. The results of verification tests show that the robot can be controlled on a straight line with maximum deviation errors of +/0.05 m. The robot can be stopped at a location in the visual line of sight within a maximum deviation of +/0.05 m. Beyond the visual line of sight, the RAMP can be controlled in a straight-line and stopped with a maximum deviation of 0.05 m from the center and 0.07 m from an object respectively. In future tests, we hope to verify that the robot can lift and carry items weight no more than 1.5 kg, open doors with lever handles and can receive images with the delay of 10 secs. The manufacturing cost of RAMP is $4000 per robot and the expected break-even point would be reached in 17 months.


Modeling and simulation of a moving robotic arm mounted on wheelchair

Abstract:
Wheelchairs equipped with fixed positioned robotic arms is proposed to help paralyzed people. However, such a configuration makes it hard for the users to orient the wheel chair such that the task in need lies within the arm workspace. In this work a robotic arm that slides over a track mounted over a wheelchair is proposed to attain much wider workspace, at no extra complexity of arm design. Proposed design also reliefs the need to orient the wheelchair for extra user comfort. This work represents the Kinematic equations, Dh-parameters and dynamic modeling & simulation of an RRR robotic arm moving along a path built onto a wheelchair including the 3D workspace of the system. Preliminary results show robotic arm can achieve a very dexterous workspace at no extra cost in terms of arm torques yet offering a great comfort for wheelchair users.


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