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Patients with chronic illnesses account for three-quarters of health care costs in the United States. For many patients, chronic conditions such as diabetes, coronary heart disease, and neurological disorders significantly affect their health-related quality of life (HQoL). Recent advances in wearable and ambient sensors and mobile health (mHealth) technologies have enabled remote monitoring of patient health and their symptoms thereby enhancing effective self-management of chronic conditions and improvement in patient HQoL. This paper presents a comprehensive review of recent trends and challenges in designing smart and pervasive health systems for monitoring patients with chronic illnesses. Recent work in developing wearable sensors and mHealth technologies for managing a variety of chronic diseases such as coronary heart disease, hypertension, and Parkinson’s disease is described in detail. The article addresses various research gaps in security, energy optimization, scalability, and interoperability. The paper concludes with future research directions and recommendations in user centric design, closed loop systems, value based treatment, signal processing, and device level research for efficient design and adoption of smart and pervasive health systems.

The proliferation in the number of mobile devices and developments in cellular technology has led to an ever increasing demand for mobile data. The global bandwidth shortage facing wireless carriers today has motivated research for fifth generation (5G) cellular systems. In recent years, millimeter wave (mmW) frequencies between 30 and 300 GHz are being considered as a promising technology for 5G systems. Such systems can offer superior user experience by providing data rates that exceed one Gigabit per second and latencies lower than a millisecond. However, there is little research about cellular mmW propagation in densely populated urban environments. Understanding the radio channel is a primary requirement for optimal design of mmW systems. Radio propagation in mmW systems faces significant challenges due to rapidly varying channel conditions and intermittent connectivity. In this paper, we study the propagation of mmW spectrum in an urban environment. We use a statistical model to simulate an urban environment with diverse building distributions. We perform extensive simulations to analyze the path loss behavior for both line of sight (LOS) and non line of sight (NLOS) conditions for 28 GHZ and 73 GHZ mmW frequencies. We observe that the path loss approximates a logarithmic fit for both LOS and NLOS environments. Our simulations show that the omnidirectional free space path loss is approximately 30 dB higher for mmW systems compared to current 3G PP cellular systems. To address this challenge, we propose using highly directional horn antennas with beam forming for reducing the path loss.

This paper develops an analytical model for probabilistic area coverage in terms of the target detection probability. A decision fusion framework is utilized to infer the presence or absence of the target. Analytical results are derived for the target detection and false alarm probabilities in the presence of correlated sensor noise. The spatially correlated sensor observations are utilized to select a subset of sensors to meet the specified area coverage. Two new sensor selection schemes are proposed for maximizing information theoretic measures such as joint entropy. The sensor selection schemes are analyzed extensively based on simulations. The results show that the proposed sensor selection scheme outperforms two state-of-the-art sensor selection schemes: constrained random sensor selection and disjoint random sensor selection.

This paper investigates the probabilistic connectivity of wireless sensor networks in the presence of channel fading. Due to the stochastic nature of wireless channels in sensor networks, the geometric disk shaped model widely used for connectivity analysis of sensor networks can be misleading. In this paper, we develop a mathematical model to evaluate the probabilistic connectivity of sensor networks which incorporates the characteristics of wireless channels, multi-access interference, the network topology and the propagation environment. We present an analytical framework for the computation of node isolation probability and network connectivity under different channel fading models. We also analyze the connectivity of sensor networks in the presence of unreliable sensors. We present numerical and simulation results that compare different regular topologies in terms of several metrics such as node isolation probability, end-to-end connectivity, and network connectivity.

Smart grid uses bi directional flow of information to create a distributed and efficient energy delivery network. Some of the important objectives of a smart grid include improving energy efficiency, maximizing utility, reducing cost, and controlling emission. Smart grids use demand response as an effective strategy to address this challenge. Demand response uses real time scheduling to enable customers to modify their demand according to energy consumption costs. In this paper, we consider the problem of efficient scheduling of energy consumption of users in a smart grid. Efficient energy management involves tradeoffs between the cost associated with energy consumption and a utility function. The utility function can represent the living comfort of users or gross income of the utility company. The utility function is non decreasing with respect to total utilized power. Hence, it is important to understand the tradeoffs between energy consumption and utility. The main contribution of this work is the development of a multi-objective optimization framework for efficient energy scheduling in smart grids. A recently developed multi-objective evolutionary algorithm called the evolutionary multi-objective crowding algorithm (EMOCA) is adapted for simultaneously optimizing the energy cost and utility function subject to a constraint on the power generation capacity. Simulation results show that EMOCA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.

Wireless sensor networks consist of sensor nodes with sensing and com- munication capabilities. We focus on data-aggregation problems in energy- constrained sensor networks. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that net- work lifetime is enhanced. In this article we present a survey of data-aggre- gation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy. We conclude with possible future research directions.

Acoustic source localization using microphone arrays is widely used in videoconferencing and surveillance systems. However, it still remains a challenging task to develop efficient algorithms for accurate estimation of source location using distributed data processing. In this work, we propose a new algorithm for efficient localization of a speaker in noisy and reverberant environments such as videoconferencing. We propose a hybrid algorithm that combines generalized cross correlation based phase transform method (GCC-PHAT) and Tabu search to obtain a robust and accurate estimate of the speaker location. The Tabu Search algorithm iteratively improves the time difference of arrival (TDOA) estimate of GCC-PHAT by examining the neighboring solutions until a convergence in the TDOA value is obtained. Experiments were performed based on real world data recorded from a meeting room in the presence of noise such as computer and fans. Our results demonstrate that the proposed hybrid algorithm outperforms GCC-PHAT especially when the noise level is high. This shows the robustness of the proposed algorithm in noisy and realistic videoconferencing systems.

Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches generally focus on a single objective (ignoring the others), or combine multiple objectives into a single function to be optimized, to facilitate the application of classical optimization algorithms. This restricts their ability and constrains their usefulness to the network designer. A much more appropriate and natural approach is to address multiple objectives simultaneously, applying recently developed multi-objective evolutionary algorithms (MOEAs) in solving sensor network design problems. This chapter describes and illustrates this approach by modeling two sensor network design problems (mobile agent routing and sensor placement), as multi-objective optimization problems, developing the appropriate objective functions and discussing the tradeoffs between them. Simulation results using two recently developed MOEAs, viz., EMOCA (Rajagopalan, Mohan, Mehrotra, & Varshney, 2006) and NSGA-II (Deb, Pratap, Agarwal, & Meyarivan, 2000), show that these MOEAs successfully discover multiple solutions characterizing the tradeoffs between the objectives.

Many sensor network design problems are characterized by the need to optimize multiple objectives. However, existing techniques in sensor network design generally optimize only one objective while treating the others as constraints or convert the multi-objective optimization problem into a single objective optimization problem using weights associated with different objectives. The weighted sum approach is subjective and is incapable of obtaining multiple tradeoff solutions for non-convex optimization problems. A multi-objective optimization approach optimizes all objectives simultaneously and obtains multiple tradeoff solutions for non-convex problems without the need for a weight vector. This paper illustrates this approach by formulating and solving the sensor placement problem for energy efficient target detection as a multi-objective optimization problem.

A recent approach for data fusion in wireless sensor networks involves the use of mobile agents that selectively visit the sensors and incrementally fuse the data, thereby eliminating the unnecessary transmission of irrelevant or non-critical data. The order of sensors visited along the route determines the quality of the fused data and the communication cost. The computation of mobile agent routes involves tradeoffs between energy consumption, path loss, and detection accuracy. For instance, as the number of sensors in the route increases, the quality of fused data improves but the energy consumption and path loss increase. This paper models the mobile agent routing problem as a multi-objective optimization problem, maximizing the total detected signal energy while minimizing the energy consumption and path loss. A recently developed multi-objective evolutionary algorithm called the evolutionary multi-objective crowding algorithm (EMOCA) is employed for obtaining the mobile agent routes. The performance of EMOCA is compared with a recently proposed combinatorial optimization approach. Simulation results show that EMOCA outperforms the combinatorial optimization approach for different network sizes clearly demonstrating the advantage of a multi-objective optimization approach.

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

This article presents the kinematic model of an automatic guided vehicle (AGV) with an inclined steering column and an offset distance. The vehicle configuration considered here employs the front wheel as the driving and steering wheel. From the kinematic model, a criterion is derived to test the existence of inverse kinematic solution. The criterion is a function of the geometric parameters of the AGV and the steering angle of the wheel. From the criterion defined, conditions for the existence of inverse kinematic solution for steering column configurations with and without inclination and offset distance are obtained. The criterion also presents the range of steering angles for which the inverse kinematic solution exists. The criterion signifies the presence of a unique solution for the motion parameters of the wheel, i.e., the wheel speed, steering angle, and steering rate for desired values of the motion parameters of the AGV, i.e., the linear and angular velocities of the AGV. This information is needed for the guidance control of AGVs based upon a kinematic control scheme (dead-reckoning control) without using the absolute position referencing system to follow a predefined path at prescribed linear and angular velocities. © 1992 John Wiley & Sons, Inc.

For a distributed detection in a wireless sensor network. sensors arrive at decisions about the event of interest and send their decisions to the central fusion center. The fusion center combines the incoming sensor decisions and reaches a final decision about the absence or presence of the event. For binary sensor decisions, determination of the local sensor decision thresholds is crucial. In this paper, we evaluate the set of local sensor thresholds through multi-objective optimization where the probability of error and the total energy consumption of the network are optimized simultaneously. The optimal threshold sets are generated by using a mathematical programming Normal Boundary Intersection (NBI) method and a multi-objective evolutionary algorithm Non Dominating Sorting Genetic Algorithm (NSGA-II). Simulation results show that both NBI and NSGA-II successfully obtain a set of solutions reflecting the tradeoffs between the objectives.

This paper formulates and solves the sensor placement problem for efficient target localization in a sensor network. We illustrate our approach through a missile defense problem where the objective is the efficient localization of the missile using multiple sensors. We develop a mathematical framework for the localization of the missile using multiple sensors based on Cramer-Rao lower bound (CRLB) analysis. We derive the fisher information matrix to facilitate the evaluation of estimation accuracy. We develop an evolutionary algorithm for obtaining the sensor placements. Simulation results show that the evolutionary algorithm outperforms a greedy sensor placement algorithm and obtains sensor placements with very low estimation error.

Efficient localization of a ballistic missile is an important task in missile defense problems. This paper formulates and solves the sensor placement problem for efficient estimation of the missile location. The first part of this paper develops a mathematical framework for the localization of the missile using multiple sensors based on Cramer-Rao lower bound (CRLB) analysis. We derive the Fisher information matrix to facilitate the evaluation of estimation accuracy. The second part of the paper presents an evolutionary algorithm for obtaining the sensor placements. Simulation results show that the evolutionary algorithm outperforms a greedy sensor placement algorithm and obtains sensor placements with very low estimation error.

An approach for data fusion in wireless sensor networks involves the use of mobile agents that selectively visit the sensors and incrementally fuse the data, thereby eliminating the unnecessary transmission of irrelevant or non-critical data. The order of sensors visited along the route determines the quality of the fused data and the communication cost. We model the mobile agent routing problem as a multi-objective optimization problem, maximizing the total detected signal energy while minimizing the energy consumption and path loss. Simulation results show that this problem can be solved successfully using evolutionary multi-objective algorithms such as EMOCA and NSGA-II. This approach also enables choosing between two alternative routing algorithms, to determine which one results in higher detection accuracy.

High-performance computing has increasingly adopted the use of clustered Intel architecture-based servers. This increase in adoption has been largely fueled by a number of technological enhancements in the Intel architecture-based servers, primarily due to substantial improvement in the Intel processor and memory technology over the past few years. This paper compares the performance characteristics of three Dell PowerEdge (PE) servers that are based on three different Intel processor technologies. They are the PE1750 which is an IA-32 based Xeon system, PE1850 which uses the new 90nm technology Xeon processor at faster frequencies and the PE3250 which is an Itanium2 based system. BLAST (Basic Local Alignment Search Tool), a high performance computing application used in the field of biological research, is used as the workload for this study. The aim is to understand the performance impact of the different features associated with each processor/platform technology when running the BLAST workload.

Moving average filters are commonly used in industries for real-time processing of noisy data. Though they perform well in filtering out the noise, they introduce significant lag in the signal. The resulting peak value of the filtered signal at the operating point is likely to be lower due to averaging of higher and lower peak signals in the averaging interval. The generalized moving average smoothing filter by Golay-Savitzky preserves the higher moments and does not suffer from the limitations imposed by the conventional moving average filter. The smoothing strategy is derived from least squares fitting of a lower order polynomial to a number of consecutive points. Due to polynomial curve fitting as opposed to a line fitting in the case of conventional moving average filter, this filter preserves the higher frequency components of the signal and their line width. This paper presents a generalized causal moving average filter deduced using the concepts in Golay-Savitzky smoothing filter for real-time applications. Golay-Savitzky filter is non-causal, relies on the future data that is not available, hence not suitable for real-time applications. Further, the designed causal filter makes use of the filtered data as opposed to the original data in the case of Golay-Savitzky. This approach allows us to conduct frequency response studies to evaluate the quality and the applicability of the filter for various signals in the aircraft engines and other engineering applications. Frequency response studies cannot carried out using the Golay-Savitzky filter. This paper also investigates the performance of various polynomial orders in reproducing the signal from a noisy data. Some of the performance measures used are bandwidth, overshoots, and lags introduced by the filter. The mathematical technique to extract the signal and deduce the coefficients in off-line is also presented.

This paper reports an experimental study on the effect of high enthalpy flows on the aerodynamics of caret wings. The experiments were conducted in the super orbital expansion tunnel X3 at an equivalent flight of 9,25 Km/s. Mach number of 11.5. Reynolds number of 80000 and enthalpy of 43 MJ/Kg. An external three component stress wave balance was used for measuring the forces and moments on a caret wing which had a design Mach number of 8 which was production of a configuration used at lower enthalpy (Kipke). Due to the low Reynolds no. of the flow, a detached shock is observed instead of the attached shock with subsequent inboard shocks. Because of this, there is a considerable loss in the high pressure on the wind ward side and this results in a lower magnitude of lift pitching moment, L/D and higher drag. In contrast to this, Kipke had an attached shock and hence more lift, L/D, pitching moment and less drag for the same configuration at Mach no. of 10.9 and Re. No of 0 56 million. The data will be very useful for assessing waveriders at real conditions and for evaluation of CFD models.

This paper presents the design and implementation of a high performance vehicle controller based on parallel digital processing systems for automated vehicles. From the literature it has been observed that one of the main limiting factors of most automated vehicles rests on the available computing power. Most systems employ camera vision for guidance purposes. In some cases other sensors are used in combination with camera vision. The amount of information that has to be processed can overwhelm many processors. Solutions so far involved distributed processing, massively parallel processors, dedicated processors and mini computers. In most cases, these systems use specially designed processors, lacking standard interfacing, and as a result proprietary interface cards have to be built. This paper takes the alternate approach of designing a high performance controller using the parallel DSP systems, namely, the TMS320C40 processors with 275 MIPS and 50 MFLOPS. This controller processes data from a CCD camera which is focused onto a road segment containing a line that has suitable contrast with the road surface. The DSP based controller in a PC environment. Carries out the task of high level control while the low level servo control is assigned to dedicated motion controllers communicating with the DSP based controller through the PC bus. Results of image processing and timing requirements for various topologies are detailed.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a scheme that employs feedforward control in conjunction with a predictor-corrector scheme for guidance control of Automated Guided Vehicles (AGVs). The predictor-corrector scheme provides the desired values of steering parameters which depend on the geometry of the track and a driving criterion. The geometry of the track/road ahead of the vehicle is obtained by extrapolating the identified (estimated) geometry of the track/road traversed during the elapsed time interval. This real-time identification is carried out by fitting a curve to the path traversed by the vehicle. The coordinates of the path are provided by a transformation formulation which makes use of the motion parameters and a kinematic model of the vehicle. The driving criterion specifies the positioning of the AGV on the track. Several possible criteria are identified in the paper and mathematical formulations are presented for one such criterion. Results of off-line calculations using simulated track profiles and experimental data obtained using a prototype AGV while following various track profiles are provided for illustration.

Operating system software of a prototype autonomous vehicle is presented in this paper. The prototype Autonomous Vehicle employs binary camera vision for navigation and is differentially steered. The software is a top-down design, modular in architecture to facilitate software maintenance and continual enhancement. At this present stage of development, it contains several parameter definition files and close to 50 major sub functions/sub function groups besides the main supervisory module. The subroutines range from system validation (data acquisition in real-time) and equipment calibration, image data filtering and processing to the driving and steering of the vehicle. The control codes are developed using the Borland Turbo-C compiler.

A micro computer based multi-axis motion controller for servo control of d.c. motors for robotic applications is presented in this paper. The motion controller interface uses LM628 precision motion controller chips. The design presented in this paper facilitates control of four motors from a single PC board consisting of four LM628 chips. The motion controller board has provisions to issue commands to all or some chips simultaneously. The presence of LM628 chips relieves the host computer of the responsibility of feedback control and it is free to perform other tasks once the necessary commands are downloaded to the motion controller board. The motion controller chips perform the closed loop control to drive the motors to the desired positions and/or to run at the desired velocity. The motion controller board also has safety features to stop the motors in case of problems and provide self diagnostics. This motion controller is used to control a prototype Automatic Guided Vehicle (AGV) and a five axis robot. The performance of the motion controller board is observed to be satisfactory.