There is a need for fast, accurate, contactless, and continuous vital sign measurements to provide insights into overall health and to enhance medical care in hospitals and other healthcare facilities. Remote Photoplethysmotraphy (rPPG) is a contactless method that can enable quick and non-invasive monitoring of vital signs such as heart rate (HR) and respiration rate (RR). There have been a growing number of studies creating and testing different signal decomposition techniques with rPPG for frequency-based vital measurement of HR and RR; however, there are still numerous shortcomings. Methods including Dynamic Mode Decomposition (DMD) and Variational Mode Decomposition (VMD) are sparsely discussed with respect to rPPG, and have yet to be applied to rPPG for RR estimation. Additionally, there are no comprehensive analyses on these techniques for rPPG, including DMD, VMD, and other techniques such as Ensemble Empirical Mode Decomposition (EEMD) and Fast Fourier Transform (FFT) which are more established in the rPPG space. This study provides a comparative analysis on EEMD, VMD, DMD, and FFT for rPPG HR and RR estimation. DMD outperformed the other methods in HR estimation with an MAE and RMSE of 3.08 and 5.05 beats per minute (bpm), respectively. For RR estimation, VMD performed the best with an MAE and RMSE of 3.36 and 4.08 breaths per minute (bpm), respectively. These results highlight the importance of developing new rPPG methods with DMD and VMD for HR and RR estimation.

We consider in this article battery state of power (SoP) estimation, in particular, we propose two algorithms for predicting voltage corresponding to a future current profile that is known to be demanded by the battery load. The proposed algorithms belong to the class of data-driven methods and are based on the Gaussian Process Regression (GPR) framework. In comparison to conventional model-based approaches, data-driven approaches circumvent the issue of observability of SoP from measurements, especially pronounced in batteries with flat open circuit voltage (OCV) characteristic. In addition, the GPR framework admits accurate modeling of a fairly complicated battery dynamics using training data. Finally, the considered setup enables a relatively easy access to training data whenever the necessity for retraining arises, such as due to battery aging. The proposed algorithms aim to handle diverse battery operating conditions involving smooth and abruptly changing voltage/current measurements with both relatively small and large training datasets. The algorithms are tested on two such datasets, and the measured prediction performance and computation time verify their viability for real-time industrial use. We conclude with a number of possible directions for future research.

This paper presents an application of Gaussian process regression (GPR) to estimate a state of charge (SoC) of Lithium-ion (Li-ion) batteries with different kernel functions. One of the practical advantages of using GPR is that uncertainties in the estimates can be quantified, which enables reliability assessment of the SoC estimate. The inputs of GPR are voltage, current and temperature measurements of the battery and the output is an estimate of SoC. First, training is performed in which optimal hyperparameters of a kernel function are determined to model data properties. Then, the battery SoC is estimated online based on the trained model. The kernel function is the key element in the GPR model since it encodes the prior assumptions about the properties of the function being modeled. Therefore, the impact of kernel function selection on the estimation performance is analyzed using both simulated data and experimental data collected from a LiMn2O4/hard-carbon battery with a nominal capacity of 4.93Ah operating under constant charge and discharge currents.

A voltage/current transient typically caused by islanding and switching operations is treated as an adverse phenomenon that degrades power quality, and it may cause damage to electrical equipment. Therefore, a reliable system should effectively detect and monitor a transient disturbance. In this paper, the transient detection problem is formulated as a binary hypothesis test: normal signal (null) vs. transient (alternative). The sampled data is described by a sinusoid under the null hypothesis, while a sum of damped sinusoids is utilized to model the alternative one. As no prior knowledge is imposed on complex amplitudes, frequencies, or damping factors in signal modeling, the general likelihood ratio test (GLRT) is employed to fulfill the task. To reduce computational complexity, the maximum likelihood estimator is replaced by ESPRIT for parameter estimation. Probability of detection of 0.98 is achieved at a SNR of 27dB and probability of false alarm of 0.0005.

This letter develops a fast detection algorithm for voltage and phase unbalance in three phase power systems. It is suitable for real time applications since the required observation length is one cycle. It is shown to successfully detect small unbalance conditions at low SNRs. Its detection performance is shown to outperform traditional detectors that rely on changes in only a subset of positive, negative and zero sequence voltages. Unbalance detection is formulated as a hypothesis test under a framework of detection theory and solved by applying a generalized likelihood ratio test (GLRT). We first obtain an approximate maximum likelihood estimate (MLE) of the system frequency and then use it to substitute the true unknown frequency in the GLRT. A closed form expression is provided to detect unbalance conditions. Theoretical derivations are supported by simulations.

This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the persymmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.

ABSTRACT In this paper, theoretical lower bounds on performance of linear least‐squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least‐squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators. Copyright © 2010 John Wiley & Sons, Ltd.

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals,${\bf R}$, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal,${\bf R}_{0}$, is assumed to be equal to$\lambda{\bf R}$, i.e.,${\bf R}_{0}=\lambda{\bf R}$. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor$\lambda$between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

This work proposes a low-complexity space-time adaptive processing (STAP) algorithm for sensing applications built on a moving platform in the presence of strong clutters. The proposed algorithm achieves low-complexity computation via two steps. First, it utilizes improved fast approximated power iteration methods to compress the data into a much smaller subspace. To further reduce the computational complexity, a progressive singular value decomposition (SVD) approach is employed to update the inverse of the covariance matrix of the compressed data. As a result, the proposed low-complexity STAP algorithm can achieve order-of-magnitude computational complexity reduction as compared to conventional STAP algorithms. Simulation results are shown to confirm the validity of the proposed algorithm.

This paper presents a novel technique for multi-hop localization by using mobility (MLM) in mixed line-of-sight/non-line-of-sight (LOS/NLOS) environments. In MLM, nodes equipped with ultra-wideband (UWB) radio first move randomly for collecting time-of-arrival (TOA) measurements and then apply a modified biased Kalman filter (MBKF) designed for mitigating NLOS errors. NLOS bias in the measurements is further mitigated by using the shortest path distance (SPD) selection. Node positions are initialized by using multidimensional scaling (MDS) and then estimated by using iterative trilateration accompanied by an error accumulation management. Our simulation results demonstrate that the positioning accuracy of MLM outperforms previous methods in obstructed building environments with a limited anchor heard (AH).

Two-way time-of-arrival (TW-ToA) is a widely used ranging protocol that can provide the distance between two devices without time synchronization. One drawback of the TW-ToA is poor positioning accuracy in the absence of a sufficient number of reference ranging devices. Also, for a self- positioning system with a limited battery life, it might be necessary to limit the number of transmissions while satisfying accuracy constraints. In this paper, a cooperative positioning protocol is studied, which can improve positioning accuracy compared to the conventional TW-ToA based positioning systems and also facilitate positioning with fewer packet transmissions; hence, it can prolong battery life on average. The maximum likelihood estimator is obtained for the cooperative technique and the limits on the positioning accuracy are quantified in terms of the Cramer-Rao lower bound (CRLB). Simulation results are provided in order to show performance improvements.

IEEE 802.15.4 specification for MAC and PHY offers a standard for general purpose wireless sensor networks. The TG4e of IEEE 802.15 is currently engaged in defining a specification particularly suitable for industrial and commercial applications, which impose severe constraints of low latency and high reliability. In this work, we present a simple MAC scheme to address these requirements of emergency response sensing applications for wireless sensor networks. We evaluate the proposed scheme for varying channel and traffic load conditions using simulations. Our results show that the latency and packet loss rate performance of emergency response traffic, consisting of guaranteed time slot (GTS) frames, significantly improves in comparison with the performance of original IEEE 802.15.4 MAC. The proposed MAC was also evaluated for potential adverse impact on the non-critical traffic, which consists of frames that use contention access period (CAP) of the superframe. It is shown that under realistic load conditions, the impact on the CAP traffic is minimal.

A novel concept of portable charging station networks to serve Electric Vehicles is described. An optimum charging station deployment method is explained, and its performance has been simulated for single highway, two intersecting highways, and Manhattan-like grid traffic models. Outage probability and service waiting delay performances are evaluated. Impact of the number PCSs and the ratio of EVs to PCSs in the service area on outage probability and waiting delay are studied. The gained insights will be used in extending this pioneer step to a stochastic framework with more realistic traffic models.

Wireless sensor networks are emerging as an important area for communications. They enable a wealth of new applications including surveillance, building control, factory automation, and in-vehicle sensing. The sensor nodes have to operate under severe constraints on energy consumption and form factor, and provide the ability for precise self-location of the nodes. These requirements can be fulfilled very well by various forms of ultra-wide-band (UWB) transmission technology. We discuss various techniques and tradeoffs in UWB systems and indicate that time-hopping and frequency-hopping impulse radio physical layers combined with simple multiple-access techniques like ALOHA are suitable designs. We also describe the IEEE 802.15.4a standard, an important system that adopts UWB impulse radio to ensure robust data communications and precision ranging. In order to accommodate heterogeneous networks, it uses specific modulation, coding, and ranging waveforms that can be detected well by both coherent and noncoherent receivers.

Industrial sensing, monitoring and automation offer a lucrative application domain for networking and communications. Wired sensor networks have traditionally been used for these applications because such networks adequately meet two vital requirements, i.e., low latency and high reliability, needed for an industrial deployment. Wired sensor networks, however, are not very cost effective due to higher components' cost. These networks also lack the flexibility needed for subsequent topological changes. Wireless sensor networks (WSN), on the other hand, are less expensive and offer high degree of flexibility. Wireless networks, therefore, can offer an attractive and viable solution for industrial sensing and automation. IEEE 802.15.4 standard defines a specification for MAC and PHY layers for short- range, low bit-rate, and low-cost wireless networks. However, the specified system is inefficient in terms of latency and reliability and fails to meet the stringent operational requirements for industrial applications. In this paper, we propose a set of new MAC superframes with an aim to enhance both performance metrics. We then use simulation to compare the performance of our proposed systems with that of the one specified in IEEE 802.15.4 standard.

In an ultra-wide band (UWB) sensor network signal reflections from objects can be used to accurately determine the location. UWB signals are preferred in these types of sensor networks since they provide a very good resolution due to their fine time granularity. We propose an artificial neural network based localization algorithm to detect single object in a sensor network and compare its performance to Cramer-Rao bound and least squares estimator. Then we propose a two phase algorithm for multiple object detection and evaluate the algorithm for the case when there are two objects in a sensor network with three nodes.

A common technique for wireless positioning is to estimate time-of-arrivals (TOAs) of signals traveling between a target node and a number of reference nodes, and then to determine the position of the target node based on those TOA parameters. In determining the position of the target node from TOA parameters, linear or nonlinear least-squares (LS) estimation techniques can be employed. Although the linear LS techniques are suboptimal in general, they facilitate low- complexity position estimation. In this paper, performance of various linear LS techniques are compared, and suboptimality of the linear approach is quantified in terms of the Cramer-Rao lower bound (CRLB). Simulations are performed to compare the performance of the linear LS approaches versus the CRLBs for linear and nonlinear techniques.

In this paper, we design a novel energy-detection based receiver architecture to detect unknown UWB signals in a strong narrowband interference (NBI) environment. Designed receiver is capable of suppressing NBI at low cost without any need for searching its frequency location. This is made possible by preprocessing the received signal using a cascaded nonlinear energy operator followed by a high-pass filter before regular energy detection.

In this paper, Cramer-Rao lower bounds (CRLBs) for estimation of vital signal parameters, such as respiration and heart-beat rates, using ultra-wideband (UWB) pulses are derived. In addition, a simple closed-form CRLB expression is obtained for sinusoidal displacement functions under certain conditions. Moreover, a two-step suboptimal solution is proposed, which is based on time-delay estimation via matched filtering followed by least-squares (LS) estimation. It is shown that the proposed solution is asymptotically optimal in the limit of certain system parameters. Simulation studies are performed to evaluate the lower bounds and performance of the proposed solution for realistic system parameters.

In an ultra-wideband (UWB) impulse radio (IR) system, a number of pulses, each transmitted in an interval called a "frame," is employed to represent one information symbol. Conventionally, a single type of UWB pulse is used in all frames of all users. In this paper, IR systems with multiple types of UWB pulses are considered, where different types of pulses can be used in different frames by different users. Both stored-reference (SR) and transmitted-reference (TR) systems are considered. First, the spectral properties of a multipulse IR system with polarity randomization is investigated. It is shown that the average power spectral density is the average of the spectral contents of different pulse shapes. Then, approximate closed-form expressions for the bit-error probability of a multipulse SR-IR system are derived for Rake receivers in asynchronous multiuser environments. The effects of both interframe interference (IFI) and multiple-access interference (MAI) are analyzed. The theoretical and simulation results indicate that SR-IR systems that are more robust against IFI and MAI than a "conventional" SR-IR system can be designed with multiple types of ultra-wideband pulses. Finally, extensions to multipulse TR-IR systems are briefly described.

This paper studies efficient and simple data broadcast in IEEE 802.15.4-based ad hoc networks (e.g., ZigBee). Since finding the minimum number of rebroadcast nodes in general ad hoc networks is NP-hard, current broadcast protocols either employ heuristic algorithms or assume extra knowledge such as position or two-hop neighbor table. However, the ZigBee network is characterized as low data rate and low cost. It cannot provide position or two-hop neighbor information, but it still requires an efficient broadcast algorithm that can reduce the number of rebroadcast nodes with limited computation complexity and storage space. To this end, this paper proposes self-pruning and forward node selection algorithms that exploit the hierarchical address space in ZigBee networks. Only one-hop neighbor information is needed; a partial list of two-hop neighbors is derived without exchanging messages between neighboring nodes. The ZigBee forward node selection algorithm finds the minimum rebroadcast nodes set with polynomial computation time and memory space. Using the proposed localized algorithms, it is proven that the entire network is covered. Simulations are conducted to evaluate the performance improvement in terms of the number of rebroadcast nodes, number of duplicated receivings, coverage time, and communication overhead.

In this paper, we consider time of arrival estimation of ultra-wideband signals based on low-rate samples that are obtained after a square-law device. Signal conditioning techniques based on a bank of cascaded multi-scale energy collection filters and wavelets are introduced, where correlations across multiple scales are exploited for edge and peak enhancements towards a more accurate detection. The performances of the discussed algorithms are tested on IEEE 802.15.4a residential line-of-sight (LOS) channels.

Due to scarce resources, such as transmission power, storage space and communication bandwidth, current broadcast approaches for general ad hoc networks can not be applied to IEEE 802.15.4 based ad hoc networks (e.g., ZigBee networks). This paper proposes a forward node selection algorithm that significantly reduces broadcast redundancy. The algorithm exploits the hierarchical address space in ZigBee networks. Only one-hop neighbor information is needed: a partial list of two-hop neighbors is derived at a node without exchanging messages between neighboring nodes. The complexity of the proposed algorithm is polynomial in terms of both computation time and memory space. The localized algorithm provides an optimal and feasible solution of selecting the minimum number of rebroadcast nodes in ZigBee networks, which is an NP-hard problem for general ad hoc networks. The proposed algorithm is extended to deal with packet loss during data transmission. A ZigBee rebroadcast algorithm is also proposed to further reduce the number of rebroadcast nodes and cover the whole network faster by assigning a non-random rebroadcast timer determined by the number of neighbors to be covered, distance and link quality. Simulations are conducted to evaluate the broadcast redundancy, coverage time, and coverage ratio.

We propose an energy efficient JPEG 2000 image transmission system over point-to-point wireless sensor networks. The objective is to minimize the overall processing and transmission energy consumption with the expected end-to-end QoS guarantee, which is achieved by jointly adjusting the source coding schemes, channel coding rates, and transmitter power levels in an optimal way. The advantages of the proposed system lie in three aspects: adaptivity, optimality, and low complexity. Based on the characteristics of the image content, the estimated channel conditions, and the distortion constraint, the proposed low-complexity joint source channel coding and power control algorithm adjusts the coding and transmission strategies adaptively, which can approximate the optimal solution with a tight bound.

Heterogeneity is the key distinguishing characteristic of wireless sensor networks (WSN) as compared to wireless cellular networks (WCN). It may appear in the form of communication ranges of sensor devices, synchronization, and signal processing capabilities. In this article, we develop a hybrid location estimation scheme for heterogeneous WSN with unsynchronized short-range simple relays and mobile sensors nodes, and synchronized stations. Our scheme employs combination of time difference of arrival (TDOA) and received signal strength (RSS) measurements, whereby the TDOA is used to filter out the clock offset that appears due to the lack of synchronization. We quantify the estimation accuracy of the scheme by deriving the Cramer-Rao bound (CRB), and discuss the performance trade-off between the number of synchronized and non-synchronized devices involved.

It is known that relay assisted wireless transmission in a triplet, which consists of a source node, intermediate relay node and a destination node results in less power consumption than direct transmission between the source and destination pair. However, extra power compensation is needed for error propagation in the relay-assisted scenario to provision the same end-to-end bit error rate (BER) constraint as the direct transmission. This key point is neglected in the literature. In this paper, we first quantify the impact of power compensation on power savings due to relaying for M-ary QAM modulation schemes, and compare power savings performances of simple relaying and regenerative repeating at the intermediate node. Second, we present upper and lower bounds for the power savings under consideration of lognormal shadowing.

Investigations of high-resolution Ethernet local area network (LAN) traces and VBR video traffic has demonstrated that these traffic types exhibit self-similarity. In this paper, we study two commonly used methods in estimating traffic self-similarity level, and compare them in terms of their applicability in providing real-time feedback to resource management algorithms. These methods are variance-time plot and wavelet-analysis. The constraints for selection of a method, wavelet filter types and lengths, number of scales and length of traffic data to be stored and analyzed are given.

Traffic measurements are key in the development of robust resource management and scheduling algorithms. Bandwidth allocation methods are either static or they adaptively change the bandwidth assigned to applications. Adaptive algorithms predict the near future behavior of traffic based on average queue size and utilization information to update the allocated bandwidth. This work introduces a novel feedback mechanism that uses wavelets and measures the energy of a byte counting process at the output of each dyadic subband filter. The output is then used to predict short and long term fluctuations in the incoming traffic pattern and to readjust the bandwidth allocation accordingly.

This paper introduces a scheme for online dynamic bandwidth allocation for variable bit rate (VBR) traffic over ATM networks. The presented method determines the optimum bandwidth renegotiation time and bandwidth amount to allocate to a VBR traffic source by minimizing predefined cost functions. A traffic rate predictor designed by wavelets is provided as feedback to the system. The results show that the introduced scheme minimizes both under-utilization of the available capacity and queuing delays. The proposed method can also be deployed in weighted fair queuing disciplines to update dynamically a weight coefficient assigned to an application.

After a novel paper by W.E. Leland et al. published in 1994, self-similar properties of multimedia network traffic became a well known fact. More precisely, aggregate packet level network traffic exhibits fractal-like behavior over time scales on the order of a few hundreds of milliseconds and larger as long as the individual data streams that generate the aggregate traffic have a heavy tailed distribution. This finding has provided new insights into how self-similarity can have an impact on network performance and in what way the quality of service (QoS) parameters can be kept at an optimal level under self-similar traffic. The level of self-similarity is directly related to the degradation of the network performance and this level changes over time depending on the load on the network. In our previous study, we had investigated the methods to detect Hurst parameter (H), a measure of self-similarity, in terms of their reliability and complexity to be implemented on real time applications. Now, this paper is focused on the multiresolution decomposition technique. The optimum number of scales and the optimum number of the taps of a basis filter were addressed and the results have proved that different H is returned for different scale depth and the length of the filters deployed. Also, we, in this study, have demonstrated that H itself is not a good representative of the burstiness of traffic traces. It is shown that, in addition to H, also peak, mean, peak/mean ratio and the burst length should be considered to be able to characterize the subjected traffic.