IEEE Projects 2015 For Embedded Systems

controllers that maximize the translational movements of a complex locomotion system. The algorithm learns an optimal action for each boundary condition instead of one for each discredited state-action pair of the system, as is typically done in machine learning. The system is modeled as a hybrid system because it contains both discrete and continuous dynamics. With this hy bridification of the system and with this abstraction of learning boundary-action pairs, the “curse of dimensionality” is mitigated. The effectiveness of this learning algorithm is demonstrated on both a simulated system and on a physical robotic system. In both cases, the algorithm is able to learn the hybrid control strategy that maximizes the forward translational movement of the system without the need for human involvement.

generation and loop closing method are proposed to build the environment map through autonomous exploration. Based on the derived map and the Markov localization method, the robot can then localize itself and navigate freely in the indoor environment. Experiments are performed on a recently built mobile robot system, and the results verify the effectiveness of the proposed methodology.

on the motion information from the Leap sensor, a predictive control approach is proposed to accurately track the human

motion by controlling the speed of the robot arm movement. Tracking experiments are conducted to track both simulated movement with varying speed and actual human hand movement. It is found that the proposed predictive controller is able to track human hand movement with satisfactory accuracy. A welding experiment has also been conducted to verify the effectiveness of the proposed remotely-controlled welding system. A foundation is

thus established to realize teleoperation and help transfer human knowledge to the welding robot.

inference system and a particle swarm algorithm are adopted for solving a nonlinear multivariable inverse PMV model so as to determine thermal comfort temperatures. In solving inverse PMV

models, the particle swarm algorithm is more accurate thanANFIS according to computational results. Based on the comfort temperature, this study utilizes feedforward–feedback control and digital

self-tuning control, respectively, to satisfy thermal comfort. The control methods are validated by experimental results. Compared with conventional fixed temperature settings, the present control methods effectively maintain the PMV value within the range of and energy is saved more than 30% in this study.

is validated through extensive experiments.

is thus established to explore the echanism and transformation of human welder’s intelligence into robotic welding systems.

facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have

addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic Algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed

algorithm.

Real-Time Industrial Networks

of inactive Ethernet links. In this paper, we address the application of EEE to Real-Time Ethernet (RTE) networks, the popular communication systems typically employed in factory automation, characterized by tight timing requirements. We start with a description of the EEE basics and, subsequently, focus on the introduction of EEE in the industrial communication scenario. Then, we specifically address the implementation of effective EEE strategies for some popular RTE networks. The analysis is carried out on configurations commonly deployed at low levels of factory automation systems. The obtained results show that

Considerable power savings can be achieved with very limited impact on network performance.

Low-Rank Tensor Decomposition

process monitoring techniques fail to fully utilize the information of color images due to their complex data characteristics including the high-dimensionality and correlation structure (i.e.,temporal, spatial and spectral correlation). This paper proposes a new image-based process monitoring approach that is capable of handling both grayscale and color images. The proposed approach models the high-dimensional structure of the image data with tensors and employs low-rank tensor decomposition techniques to extract important monitoring features monitored using multivariate control charts. In addition, this paper shows the analytical relationships between different low-rank tensor decomposition methods. The performance of the proposed method in quick detection of process changes is evaluated and compared with existing methods through extensive simulations and a case study in a steel tube manufacturing

process.

tasks of visual servoing and bimanual hybrid motion/force control. The industrial robot, consisting of a rotating torso and two seven degree-of-freedom arms, performs autonomous vision-

based target alignment of both arms with the aid of fiducial markers, two-handed grasping and force control, and robust

object manipulation in a tele-robotic framework. The operator uses hand motions to command the desired position for the object via Microsoft Kinect while the autonomous force controller

maintains a stable grasp. Gestures detected by the Kinect are also used to dictate different operation modes. We demonstrate the effectiveness of our approach using a variety of common objects with different sizes, shapes, weights, and surface compliances.

signals to detect apnea events by dividing the signals into equal-length segments, which may lead to incorrect apnea

event detection and weaken the performance of OSA diagnosis. This paper proposes an automatic-segmentation-based screening approach with the single channel of Electrocardiogram (ECG) signal for OSA subject diagnosis, and the main work of the proposed approach lies in three aspects: (i) an automatic signal segmentation algorithm is adopted for signal segmentation instead

of the equal-length segmentation rule; (ii) a local median filter is improved for reduction of the unexpected RR intervals before signal segmentation; (iii) the designed OSA severity index and additional admission information of OSA suspects are plugged into support vector machine (SVM) for OSA subject diagnosis. A real clinical example from PhysioNet database is provided

to validate the proposed approach and an average accuracy of 97.41% for subject diagnosis is obtained which demonstrates the effectiveness for OSA diagnosis.

biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on–off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc–air batteries as the power supply; the power consumption is 586.5 μW. As for the

radio frequency receiver and DSP, which are the receiving-end circuits

that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transformbased digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.

Wearable Wireless Ultrasonic Sensor Network

as medical information. However, many existing mobile health solutions focused on collecting patients’ data at their homes that do not include security among their main requirements. Aiming to tackle this issue, this paper presents Secour Health, a lightweight security framework focused on highly sensitive data collection applications. SecourHealth provides many security services for both stored and in-transit data, displaying interesting features such as tolerance to lack of connectivity (a common issue when promoting health in remote locations) and the ability to protect data even if the device is lost/stolen or shared by different data collection agents.

Together with the system’s description and analysis, we also show how SecourHealth can be integrated into a real data collection solution currently deployed in the city of Sao Paulo, Brazil.

Preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality.

Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of ophisticated approaches to handle

EEG artifacts.

aging populations. Enhancing compliance to prescribed medical regimens is an essential challenge to many systems, even those using smartphone technology. In this paper,we provide a technique to improve smartphone battery consumption and examine the effects of smartphone battery lifetime on compliance, in an attempt to enhance users’ adherence to remote monitoring systems. We deploy

WANDA-CVD, an RHM system for patients at risk of cardiovascular disease (CVD), using a wearable smartphone for detection of physical activity. We tested the battery optimization technique in an in-lab pilot study and validated its effects on compliance in the Women’s Heart Health Study. The battery optimization technique enhanced the battery lifetime by 192% on average, resulting in a 53% increase in compliance in the study. A system like WANDA-CVD can help increase smartphone battery lifetime for RHM systems monitoring physical activity.

system be interfaced with different sensors or actuators. A mobile external unit controls the operation of the implantable chip and reads the sensor’s data. A locator system is proposed to align the mobile unit with the implant unit for the efficient magnetic power transfer. The location of the implant is detected with 6 mm resolution from the rectified voltage level at the implanted side. The rectified voltage level is fedback to the mobile unit to

adjust the magnetic field strength and maximize the efficiency of the remote powering system. The sensor’s data are transmitted by using a free running oscillator modulated with on-off key

scheme. To tolerate large data carrier drifts, a custom designed receiver is implemented for the mobile unit. The circuits have been fabricated in 0.18 um CMOS technology. The remote powering

link is optimized to deliver power at 13.56 MHz. On chip voltage regulator creates 1.8 V from a 0.9 V reference voltage to supply the sensor/actuator blocks. The implantable chip dissipates 595 W and requires 1.48 V for start up.

signals allows enhanced signal coverage.

the performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days,

three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated “gold standard” (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen’s Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53 ± 4.20 mmHg and 1.46 ± 5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.

in Mental Health

technologies to patients with mental disorders and physiciansmanaging

such diseases. The system, hereinafter called the PSYCHE system, ismainly comprised of a comfortable t-shirtwith embedded sensors, such as textile electrodes, to monitor electro cardiogramheart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a

smartphone-based interactive platform for electronicmood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions

from a pathological mood state (HM, DP, or MX—where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients’ clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders.

or poorly controlled hypertension. Telehealth is now being deployed widely in both rural and urban settings, and in this paper, we discuss the contribution made by biomedical instrumentation, user interfaces, and automated risk stratification algorithms in developing

a clinical diagnostic quality longitudinal health record at home. We identify technical challenges in the acquisition of highquality biometric signals fromunsupervised patients at home, identify new technical solutions and user interfaces, and propose new

measurement modalities and signal processing techniques for increasing

the quality and value of vital signs monitoring at home. We also discuss use of vital signs data for the automated risk stratification of patients, so that clinical resources can be targeted to those most at risk of unscheduled admission to hospital. New research is also proposed to integrate primary care, hospital, personal genomic, and telehealth electronic health records, and apply predictive analytics and data mining for enhancing clinical decision support.

system embeddedwith sensing and actuating functions.We present the design, the implementation, and the evaluation of the prototype. Clinical tests at M´axima Medical Center in the Netherlands show that among the nine of ten infants who showed discomfort following diaper change, a shorter recovery time to baseline skin conductance analgesimeter values could be measured when the maternal heartbeat vibration in the Mimo was switched ON and in seven of these ten a shorter crying time was measured.

approach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its

accuracy and the overall system power consumption. We demonstrate

that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting94%of all seizure events and only 10% of background EEG.

of high-risk group for neurodevelopment delay in preterm infants. Preterm infants must coordinate the motor patterns of sucking, swallowing, and respiration skillfully to avoid choking, aspiration,

oxygen desaturation, bradycardia, or apnea episodes. However, up to now, the judgment and classification severity in preterm infants are mostly subjective and phasic evaluations. Directly monitoring

the coordination of sucking–swallowing–breathing during oral feeding simultaneously is difficult for preterm infants. In this study, we proposed a wireless oral-feeding monitoring system for preterm infants to quantitatively monitor the sucking pressure via a designed sucking pressure sensing device, swallowing activity

via a microphone to detect swallowing sound, and diaphragmatic breathing movement via surface electro myogram. Moreover, a sucking–swallowing–breathing detection algorithm is also proposed to evaluate the events of sucking–swallowing–breathing activities. Furthermore, verification of the accuracy and rationality of oral-feeding parameters with clinical findings including sucking,

swallowing, and breathing in term and preterm infants had proved the practicality and value of the proposed system.

living. In these applications, the robots are expected to perform a range of natural body motions, which can be either preprogrammed or learnt from human demonstration. This paper proposes a strategy for imitating dynamic walking gait for a humanoid robot by formulating the problem as an optimization process. The human motion data are recorded with an inertial sensor-based motion tracking system (Biomotion+). Joint angle trajectories are obtained from the transformation of the estimated posture. Key locomotion frames corresponding to gait events are chosen from the trajectories. Due to differences in joint structures of the human and robot, the joint angles at these frames need to be optimized to satisfy the physical constraints of the robot while preserving

robot stability. Interpolation among the optimized angles is needed to generate continuous angle trajectories. The method is validated using a NAO humanoid robot, with results demonstrating the ffectiveness

of the proposed strategy for dynamic walking.

Transmitter and Receiver

where both the practical transmitter and receiver, whose hardware circuits consume non-zero power when active, are powered solely by the energy harvested from external sources. An energy cooperation save-then-transmit (EC-ST) scheme is proposed: both the transmitter and receiver go into sleep mode to save energy for a proportion of time while their passive energy harvester units collect energy for later operations, and then become active to communicate for the remaining time proportion (referred to as the active-ratio), during which energy is allowed to flow between the transmitter and receiver.We first consider additive white Gaussian noise one-way channels with two-way energy transfer under a deterministic energy arrival rate. In this case, the optimal activeratio and the energy cooperation power are obtained in closed form to achieve the maximum throughput. Next, for Rayleigh block fading channels with a stochastic energy arrival rate, we

find the optimal energy cooperation power for minimizing the outage probability. Finally, numerical and simulation results are

presented to validate the analytical findings.

mobile phones. A speaker verification function protects private information and separates the user’s voice from that of people nearby who are also speaking. A CELP-based speaker verification method is used to match the audio stream

by comparing the trajectories of continuous phonemes. Experimental evaluation of the speaker verification method demonstrated the effectiveness of the proposed verification method

Power Capacitor

Then, the load on each power system bus with respect to EV wireless charging is obtained using the stationary distribution of the queuing network. An economic dispatch problem is formulated to incorporate the EV wireless charging

demand, and the LMP of each power system bus is obtained. Furthermore, a pricing mechanism based on the

LMP variations of power system buses is investigated to enhance the social welfare. The performance of our proposed analytical model is verified by a realistic road traffic simulator (SUMO) based on a 3-bus test system and an IEEE 30-bus test system, respectively. Simulation results indicate that our proposed analytical model can accurately provide an estimation of the LMP variations due to EV wireless charging.

optimization software CPLEX.

video surveillance at level crossing (LC) environments. In this paper we explore the possibility of implementing a smart video surveillance security system that is tuned toward detecting and evaluating abnormal situations induced by users (pedestrians, vehicle drivers, and unattended objects) in LCs. This intelligent security system starts by detecting, separating, and tracking moving objects shot in the LC. Then, a hiddenMarkov model is developed to estimate ideal trajectories, allowing the detected targets to discard dangerous situations. After that, the level of risk of each target is instantly estimated by using the Dempster–Shafer data fusion technique. The proposed analysis allows for also recognizing hazard scenarios. The video surveillance system is connected to a communication system (the Wireless Access for Vehicular Environment), which takes the information on the dynamic status of the LC (safe or presence of a dangerous situation) and sends it to users approaching the LC. Four hazard scenarios are tested and evaluated with different real video image sequences: presence of the obstacle in the LC, presence of the stopped vehicles line, vehicle zigzagging between two closed half barriers, and pedestrian crossing the LC area.

connected to a resonance converter. The study carries out the system operation, electromagnetic radiation, and testing. It is shown that the presence of the chassis leads to a double resonance and has a strong influence on the radiated fields.

brain activity measured by electroencephalographic (EEG) signals.

Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects.

During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver’s needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

and improve their passenger service. Particularly in the case of disruptions, when traditional route choice models may not be valid, this is an advantage. This paper proposes a method for deducing the chosen route of passengers based on smart card data and validates this method on a real-life data set. The method deduces the correct route for about 95% of the journeys per day in our validation sample, and also in the case of disruptions. Moreover, it is shown how this method can be used to analyze and evaluate passenger service by a case study based on a real-life data

set of Netherlands Railways, which is the largest passenger railway operator in the Netherlands.

to exceptional road conditions, such as highway workzones, because such unusual events can alter previously known traffic rules and road geometry. In this paper, we present a set of computer vision methods that recognize, through identification of workzone signs, the bounds of a highway workzone and temporary changes in highway driving environments. Through testing using video data about highway workzones recorded under various weather conditions, our approach was able to perfectly identify the boundaries of workzones and robustly detect a majority of driving condition changes. In addition to these tests, we evaluated, using a mock workzone setup, the usefulness of our workzone recognition systems’ outputs for safe-guarding a self-driving car.

when it is finished with its current request. An urban environment approximated by Singapore is then analyzed through simulation. Charging requests are simulated through a trip generation model based on Singapore. In such a realistic environment, an updated practical NJN service strategy is proposed. For an MP system in an urban environment such as Singapore, there exists an optimal battery capacity with a threshold battery charge rate. Similarly, the battery swap capacity of an MS system does not need to be large for the system to perform.

the interdependency between traffic and infrastructure, significant advantages can be expected if the monitoring system for traffic can be integrated with that for infrastructure. In October 2011, a

wireless sensing network (WSN) was installed on Virginia State Route 114. Since then, Virginia Polytechnic Institute and State University (Blacksburg, VA, USA) has been developing an integrated monitoring system of pavements and traffic. This paper presents the achievement of this study: a prototype system that can monitor pavement conditions and collect traffic information simultaneously using the same embedded sensing network. In the sensing network, the sensors are ranged in longitudinal and transverse groups, and the compositions, characteristics, and functionalities of the two arrangements are introduced. The two monitoring patterns of the system, i.e., continuous monitoring and periodic testing, are explained, and the data processing methods in the two patterns are illustrated. The whole system is presented combining hardware and software, and a flowchart is used to

clarify its component and function modules. This system has the potential to achieve comprehensive monitoring functions for both traffic and infrastructure based on the embedded sensing network. This system is still under study, and needs to be consummated with various loading and environmental conditions taken into consideration. Once finished, this system will be valuable for the Intelligent Transportation System in the future.

such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.

Fleet of Electric Vehicles

to address pollution problems and fuel shortages. Transportation agencies in various countries, along with several standardization organizations, have proposed different types of energy sources (such as hydrogen, biodiesel, electric, and hybrid technologies) as alternatives to fossil fuel to achieve a more ecofriendly and sustainable environment. However, to achieve this goal, there are significant challenges that still need to be addressed. We present a survey on sustainable transportation systems that aim to reduce pollution and greenhouse gas emissions. We describe the architectural components of a future sustainable means of transportation, and we review current solutions, projects, and standardization fforts related to green transportation with particular focus on electric vehicles.We also highlight the main issues that still need to be addressed to achieve a green transportation management system. To address these issues, we present an integrated architecture for sustainable transportation management systems.

guaranteed communication. Techniques are introduced for merging estimates of a target’s position fromvehicles that regain contact after long periods of time, and a fully distributed team-planning algorithm is proposed, which utilizes limited shared information as it becomes available. The proposed data fusion techniques are

shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search.Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open water and harbor environments. The results demonstrate that the proposed distributed

coordination techniques provide performance competitive with full

communication.

for Recharging in Persistent Tasks

approach is to discretize the 3-D UAV flight trajectories into sets of projected charging points on the ground, thus allowing the problem to be abstracted onto a partitioned graph. Solutions consist of charging robot paths that collectively charge each of the UAVs. The problem is solved by first formulating the rendezvous planning problem to recharge each UAV once using both an integer linear program and a transformation to the Travelling Salesman Problem. The methods are then leveraged to plan recurring rendezvous’

over longer horizons using fixed horizon and receding horizon strategies. Simulation results using realistic vehicle and battery models demonstrate the feasibility and robustness of the proposed approach.

error below 7.5% in estimating the load torque from the system parameters. Both the strategies proposed for closed-loop control were effective in regulating the load speed with a relative error below 2%of the desired steady-state value. However, the load-side closed-loop control approach was more precise and allowed the system to transmit larger values of torque, showing, at the same time, less dependence from the angular velocity. In particular, an average value of 1.5 mN·m can be transferred at 7 cm, increasing up to 13.5 mN·m as the separation distance is reduced down to 2 cm. Given the constraints in diameter and volume for a surgical instrument, the proposed approach allows for transferring a larger amount of mechanical power than what would be possible to achieve by embedding commercial dc motors.

manual welders, when they are performing tungsten inert gas (TIG) welding interactively with the KUKA lightweight robot arm (LWR). The results show that hand impedance differs across professional

and novice welders. The welding torch is attached to the KUKA LWR, which is admittance controlled via a force sensor to give the feeling of a free floating mass at its end-effector. The subjects perform TIG welding on 1.5-mm-thick stainless steel plates by manipulating the torch. Impedance is measured by introducing

external force disturbances and fitting a mass–damper–spring model to human hand reactions. The quality of welding is measured using the variance of the position signals above 0.1 Hz. Professional welders demonstrate less variance and, in general, apply larger hand impedance (larger damping and stiffness) than the

novice welders. The variance of position during nominal welding is minimal for both professional and novice welders in the direction perpendicular to the welding line in the plane of the plate, which is the most important direction for the quality of the weld. For both professional and novice welders, the mass and damping values are

largest in this direction compared with the other two directions. Professional welders demonstrate larger damping than the novice welders in this direction.

the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing

the overall cost of maintenance. This paper introduces a selfevolving maintenance scheduler framework for maintenance management of wind turbines.  urthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.

and heart rate with surrounding temperature and humidity, has been developed. The developed system can also analyze the stress level corresponding to thermal humidity index. The IEEE802.15.4 and IEEE1451.2 standards-based sensor module has been developed successfully. The zigbee device and PIC18F4550 microcontroller are used in the implementation of sensor module. The graphical user interface (GUI) is  mplemented in LabVIEW 9 according to the IEEE1451.1 standard. The real-time monitoring of physiological and behavioral parameters can be present on the GUI PC. The device is very helpful for inexpensive health care of livestock. A prototype model is developed and tested with high accuracy results.

of the sensor was improved by optimizing its performance parameters. A high-temperature-resistant material was used, and an embedded structure design was introduced to enable the sensor to operate in high-temperature environments. A series LC resonant circuit containing a fixed inductance coil and variable capacitance that varies with temperature was embedded in an alumina ceramic substrate using high-temperature cofired ceramic technology. The temperature in the high-temperature environment was detected wirelessly via the frequency diversity of the sensor. Furthermore, the experimental results showed that the sensor can measure temperatures ranging from room temperature to 1000 °C, and the average sensitivity of the sensor is ∼2 KHz/°C.

monitoring (SHM) system has been always desirable for wind turbines. A real-time nondestructive SHM technique based on multisensor data fusion is proposed in this paper. The objective is to critically analyze and evaluate the feasibility of the proposed technique to identify and localize damages in wind turbine blades. The structural properties of the turbine blade before and after damage are investigated through different sets of finiteelement method simulations. Based on the obtained results, it is shown that information from smart sensors, measuring strains, and vibrations data, distributed over the turbine blades can be used to assist in more accurate damage detection and overall understanding of the health condition of blades. Data fusion technique is proposed to combine these two diagnostic tools to improve the detection system that provides a more robust reading with reduced false alarms.

cities. This paper proposes a new algorithm for parking occupancy detection based on the use of anisotropic magnetoresistive sensors. Parking occupancy detection is abstracted as binary pattern recognition problem. According to the status of the parking space, the recognition result contains two categories: 1) vacant and 2) occupied. The feature extraction method of the parking magnetic signal is proposed. In addition, the classification criteria are derived based on the distance discriminate analysis method. Eighty-two sensor nodes are deployed on the roadside parking spaces. By running the system for six months, we observed that the accuracy rate of the proposed parking occupancy detection algorithm is better than 98%.

Behavior Monitoring

nominal ferrite-less configuration. The use of angled ferrite rods further improved power transfer, where the medium-ferriteangled (4MFA) configuration is best. Experiments show the maximum power collected by 4MFA was 113 mW, when parallel to the primary coil, and 28 mW when 60° to the primary coil.

detection during the hemodialysis treatment. The design includes

a photointerrupter, a Bluetooth 4 wireless module, power, and alert components. The validation results show that it only needs a very small amount of blood (0.01 ml), and takes 1.6 s to detect a blood leakage. Furthermore, the lifetime of the battery on this device is longer than the currently available commercial products. It can continuously give out an alert for 18-h long and continuously monitor up to 41 h. In addition, the transmission

range of Bluetooth wireless signal can be extended to 23 m. As long as the patients wear this bracelet blood leakage detector

during the hemodialysis therapy and affix the absorbent material onto the junction of fistula, any blood leakage can be detected. As the absorbent material is placed at the light sensing position of the  hotointerrupter, which causes the received light intensity to change during blood leakage. Once a blood leakage occurs, the absorbent material absorbs the blood due to the capillary action and triggers the alarm system. A warning light will also be activated, and a leakage occurrence is transmitted to the healthcare stations alarming healthcare workers via the Bluetooth wireless. The healthcare workers can take appropriate action immediately to prevent any risks to the patients during hemodialysis therapy. The proposed blood leakage monitoring system can improve the current medical approach for the hemodialysis therapy.

localization of robots. The sensor is low power, lightweight, low cost, and designed to be applicable across many robotic platforms, including microaerial vehicles. The proposed sensor design requires only two robots to perform relative measurements of each other and achieves a measurement accuracy of 0.96-cm Root-Mean-Square Error (RMSE) for range and 0.3° RMSE for bearing. The sensor nodes are scalable and can be configured using either Star or Mesh protocols with a maximum of 10-Hz update rates over a detection range of 9 m. The correspondence issue of having multiple robots is resolved using time division multiple access methods where different time slots are used by each sensor node. These features are verified by multiple experimental evaluations on a multirobot team with both ground and aerial agents. The proposed approach allows multirobot localization in scenarios where supportive positioning services such as GPS are unavailable. As a result, even basic robots, which lack powerful simultaneous localization and mapping capabilities, will be capable of autonomous navigation by accessing the positional information provided by the sensor network.

and Stolen Vehicle Detection

as expected.

processing techniques that use pipe vibration signatures to non-intrusively identify water consumption at the appliance level. The method requires as little as one easily installed vibration

sensor. This method provides a no-fuss retrofit solution for detecting

the operation of a building’s water consuming appliances. In addition, flow rate is nonintrusively obtained from a conventional water meter via a new, high sensitivity strap-on magnetic sensor. Together, these two sensors track load operating schedule and water consumption in a building, demonstrated here at three different field test sites.

for the health and comfort of individuals, especially considering that people currently spend >80% of their time indoors. The primary aim of this paper is to design a low-power ZigBee sensor network and internode data reception control framework to use in the real-time acquisition and communication of data

concerning air pollutant levels from VOCs. The network consists of end device sensors with photoionization detectors, routers that propagate the network over long distances, and a coordinator that communicates with a computer. The design is based on the ATmega16 microcontroller and the Atmel RF230 ZigBee module,

which are used to effectively process communication data with low power consumption. Priority is given to power consumption and sensing efficiency, which are achieved by incorporating various smart tasking and power management protocols. The measured data are displayed on a computer monitor through a graphical user interface. The preliminary experimental results demonstrate that the wireless sensor network system can monitor VOC concentrations with a high level of accuracy and is thus suitable for automated environmental monitoring. Both good indoor air quality and energy conservation can be achieved by integrating the VOC monitoring system proposed in this paper with the residential integrated ventilation controller.

presents an interoperable smart lighting solution that combines heterogeneous lighting technologies enabling intelligent functions. The system can shift light intensity to increase visual comfort,

and it is oriented toward human centric lighting studies. Moreover, this system follows the guidelines defined by the

ISO/IEC/IEEE 21451 standards and ZigBee Light Link and also, it includes an additional transducer signal treatment service for artificial intelligence algorithms. Finally, a representational state transfer application allows us to test the interoperability and visualize energy savings in an office room.

to evaluate the benefit of dimming for this light technology. Measurements of total power consumption over a continuous six months period (winter to summer) of a busy office were acquired to verify the performance and the power savings across several weather conditions scenarios. The proposed smart lighting system reduces total power consumption in the application scenario by 55% during a six month period and up to 69% in spring months. These figures take also into account individual user preferences.

Monitoring: A Review

chip. The sensor key chip is fabricated in 0.18-μm CMOS process. In passive mode, the maximum wireless sensitivity of

on-chip sensor is −15.1/−11.2 dBm for reading and sensing operation, respectively, and the temperature sensing error is −1 °C/0.8 °C over operating range from −20 °C to 50 °C. It achieves a reading/sensing distance of over 9.5/6 m with 4-W effective isotropic radiated power (EIRP) by the commercial interrogator. In semi-passive mode, the temperature and humidity sensing distance of off-chip sensors is 2.7 m.

distributed self-gesture and artwork recognition, and does not need fixed cameras nor radio-frequency identifications sensors. We propose the use of dense trajectories sampled around the hand region to perform self-gesture recognition, understanding the way a user naturally interacts with an artwork, and demonstrate that our approach can benefit from distributed training. We test our algorithms on publicly available data sets and we extend our experiments to both virtual and real museum scenarios, where our method shows robustness when challenged with real-world data. Furthermore, we run an extensive performance analysis on our ARM-based wearable device.