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This paper presents a mobility model for the variations in position and orientation of wearable antennas on dynamic users, considering walking and running motions. Motion is represented as a composition of a linear forward movement plus a periodic component, modeled by a Fourier series with up to two harmonics. The model is simple, yet realistic, as Motion Capture (MoCap) data are used to calculate its parameters. It is suitable for use with a variety of propagation channel models, including deterministic ray-tracing and stochastic geometry-based ones, but can also allow for analytical inference in simplified scenarios. Considering an off-body communication scenario, simulations show that the proposed mobility model provides similar received power as the skeleton-based model with MoCap data, the maximum difference in the considered scenario being below 1 dB. A significant influence of user's motion on the channel is observed for both free-space and multipath propagation, yielding received power variations up to 28 dB in the considered scenarios.

Basis functions of the Discrete Hermite transform (DHT) can be formed as the set of eigenvectors of a symmetric tridiagonal matrix which commutes with the centered discrete Fourier transform matrix. In this paper, we consider the optimization of the associated time-axis scaling factor, which is of crucial importance in the DHT-based applications. The parameter optimization is performed to enhance the signal representation by matching the smallest possible number of basis functions with the time-domain signal waveform, therefore minimizing the number of Hermite coefficients with significant or non-zero values. Such highly concentrated signal representation is particularly amenable for signal compression, filtering, and denoising, while the coefficients of the optimized transform can be exploited as features in signal classification and other machine learning applications. The proposed parameter optimization approach is verified on numerical examples, including the experiments with QRS complexes, specific segments of electrocardiogram (ECG) signals being particularly important in biomedical applications.

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This letter addresses the problem of target localization in harsh indoor environments based on range measurements. To mitigate the non-line-of-sight (NLOS) bias, we propose a novel robust estimator by transforming the localization problem into a generalized trust region sub-problem framework. Although still non-convex in general, this class of problems can be readily solved exactly by means of bisection procedure. The new approach does not require to make any assumptions about the statistics of NLOS bias, nor to try to distinguish which links are NLOS and which are not. Unlike the existing algorithms, the computational complexity of the proposed algorithm is linear in the number of reference nodes. Our simulation results corroborate the effectiveness of the new algorithm in terms of NLOS bias mitigation and show that the performance of our estimator is highly competitive with the performance of the state-of-the-art algorithms. In fact, they show that the novel estimator outperforms slightly the existing ones in general, and that it always provides a feasible solution.

This letter addresses the problem of target localization in a 3-D space, utilizing combined measurements of received signal strength and angle of arrival (AoA). By using the spherical coordinate conversion and available AoA observations to establish new relationships between the measurements and the unknown target location, we derive a simple closed-form solution method. We then show that the proposed method has straightforward adaptation to the case where the target’s transmit power is also not known. Simulation results validate the outstanding performance of the proposed method.

In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.

This paper presents firefly algorithm framework for designing convolutional neural network architecture. Convolutional neural networks can be classified as a special category of deep neural networks that in most cases consist of several convolution, fully connected (dense) and pooling layers. Wide set of image classification tasks and problems from the computer vision domain were successfully tackled by convolutional neural networks. One of the most challenging tasks from this domain is to find the convolutional neural network architecture that obtains the best performance for the specific application. The values of network's hyper-parameters have significant influence on the overall network performance. Research shown in this paper deals with convolutional neural network hyper-parameters optimization that define the network's architecture and structure. The hyper-parameters that were taken into account for this research include the number of convolutional and dense layers, the number of kernels per layer and the kernel size. We performed hyper-parameters optimization by the well-known firefly algorithm that belongs to the group of swarm intelligence metaheuristis. Solution's quality, robustness and performance of our proposed framework was tested against the MNIST dataset. Obtained empirical results showed that the proposed framework achieves promising performance in this domain.

This paper presents two improved versions of the monarch butterfly optimization algorithm adopted for solving multi-objective optimization problems. Monarch butterfly optimization is a relatively new swarm intelligence metaheuristic that proved to be robust and efficient method when dealing with NP hard problems. However, in the original monarch butterfly approach some deficiencies were noticed and we addressed these deficiencies by developing one modified, and one hybridized version of the original monarch butterfly algorithm. In the experimental section of this paper we show comparative analysis between the original, and improved versions of monarch butterfly algorithm. According to experimental results, hybridized monarch butterfly approach outperformed all other metaheuristics included in comparative analysis.

Locating the position of a remote node on a wireless network is becoming more relevant, as we move forward in the Internet of things and in autonomous vehicles. This paper proposes a new system to implement the location of remote nodes. A new prototype Android application has been developed to collect real measurements and to study the performance of several smartphone's sensors and location algorithms, including an innovative one, based on the second order cone programming (SOCP) relaxation. The application collects the WiFi access points information and the terminal location. An internal odometry module developed for the prototype is used when Android's service is unavailable. This paper compares the performance of existing location estimators given in closed form, an existing SOCP one, and the new SOCP location estimator proposed, which has reduced complexity. An algorithm to merge measurements from non-identical terminals is also proposed. Cooperative and terminal stand-alone operations are compared, showing a higher performance for SOCP-based ones, that are capable of estimating the path loss exponent and the transmission power. The heterogeneous terminals were also used in the tests. Our results show that the accurate positioning of static remote entities can be achieved using a single smartphone. On the other hand, the accurate real-time positioning of the mobile terminal is provided when three or more scattered terminal nodes cooperate sharing the samples taken synchronously.

This letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.

In a wiretap channel system model, the jammer node adopts the energy-harvesting signal as artificial noise (jamming signal) against the cooperative eavesdroppers. There are two eavesdroppers in the wiretap channel: eavesdropper E1 is located near the transmitter and eavesdropper E2 is located near the jammer. The eavesdroppers are equipped with multiple antennas and employ the iterative block decision feedback equalization decoder to estimate the received signal, i.e., information signal at E1 and jamming signal at E2. It is assumed that E1 has the channel state information (CSI) of the channel between transmitter and E1, and similarly, E2 has the CSI of channel between jammer and E2. The eavesdroppers establish communication link between them and cooperate with each other to reduce the information signal interference at E2 and jamming signal interference at E1. The performance of decoders depends on the signal to interference plus noise ratio (SINR) of the received signal. The power of information signal is fixed and the power of the jamming signal is adjusted to improve the SINR of the received signal. This research work is solely focused on optimizing the jamming signal power to degrade the performance of cooperative eavesdroppers. The jamming signal power is optimized for the given operating SINR with the support of simulated results. The jamming signal power optimization leads to better energy conservation and degrades the performance of eavesdroppers.

This letter addresses the hybrid range/angle-based target localization problem in a cooperative 3-D wireless sensor network where no central processor is available. Due to battery exhaust over time, sensors’ transmit powers are assumed different and unknown. Range and angle measurements are drawn from the received signal strength and angle-of-arrival models, respectively. By exploiting the measurement models, we derive a novel local-estimator by which each target updates its own estimate, based on the least squares criterion. Second-order cone relaxation technique is then applied to approximately solve the attained problem due to its non-convex nature. Our simulation results show that the proposed algorithm efficiently solves the localization problem.

In this study, the authors consider a peak-to-average power ratio (PAPR) reduction for orthogonal frequency-division multiplexing systems based on the decomposition of the set of subcarriers in subsets of subcarriers, denoted resource blocks, each one weighted by a different complex factor. They present a new iterative sphere-geodesic descent method for obtaining these weighting factors so as to minimise the PAPR of the transmitted signals. This method, which they term geodesic descent method, efficiently makes use of the Riemannian structure of the power constraint. The authors’ performance results show that the proposed technique provides good trade-off between the PAPR reduction and the bit error rate performance, for both uncoded and coded scenarios.

The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals&rsquo, recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

In this paper we propose a novel stochastic consensus seeking algorithm based on the introduction of a nonlinear transformation aimed at robustification with respect to noise influence. The introduced nonlinear transformation is selected according to the methodology of stochastic approximation and robust statistics. The proposed algorithm represents a general nonlinear stochastic consensus seeking scheme, not yet treated in the literature. It provides a significant improvement over the linear algorithms from the point of view of robustness to noise, ensuring better convergence rate and lower sensitivity of the limit state value at consensus. One of the main contributions of the paper is the proof that the algorithm converges almost surely to consensus under general conditions. A detailed analysis of the limit state value at consensus is provided together with an insight into achievable convergence rate. Illustrative simulation results are also provided, demonstrating great advantages of the proposed algorithm compared to the existing consensus schemes.

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which combined RSS-TOA measurements are inferior to RSS-only and TOA-only measurements. Here, we employ a state-of-the-art estimator for the considered target localization problem and study its performance against its counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Also, we propose a heuristic approach to choose the best measurement for each link in order to enhance the performance of the estimator. The new approach implicitly relies on the concept of critical distance, but does not assume certain link parameters as given. Our simulations show that the proposed approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

This work presents a field implementation of acoustic energy-based positioning, using Elephant Herding Optimization algorithm to solve the NP-hard optimization problem. Measurements are used to validate and adjust the decay acoustic model, and simulations are performed with an outdoor environment setup, where the implementation is fulfilled and compared with real acoustic measurements. The performance in both scenarios is studied, in terms of accuracy, with the variation of the number of sensors nodes used in the model. The field implementation shows that results are aligned with simulation ones, validating the acoustic model and the use of metaheuristic methods such as Elephant Herding Optimization for solving the localization problem.

Technology development enables progress in numerous areas and one of the relatively recent examples is wireless capsule endoscopy. It is used for detailed examination of a digestive track. Capsule size camera is swallowed by a patient and it takes thousands of images during the travel through digestive tract. The obtained images are used to detect different anomalies such as bleedings. In this paper we propose a method for automatic bleeding detection in capsule endoscopy images based on color and texture features. The proposed method is region based and it uses HSI and CIE Lab color spaces along with uniform local binary pattern for describing each region. Based on these features, regions are classified by support vector machine into three groups: background, bleeding or non-bleeding region. The proposed method was tested on benchmark dataset and the results were compared with other state-of-the-art method. Our proposed method shows competitive results based on the Dice similarity coefficient and misclassification error.

This review paper deals with recently proposed algorithms for distributed blind macro-calibration of sensor networks based on consensus (synchronization), not requiring any fusion center. The basic algorithm, performing the estimation of the local calibration parameters, is derived commencing from appropriate local criteria, and developing the corresponding gradient descent scheme. It is shown that the estimated parameters of the calibration functions asymptotically converge, in the mean-square sense and with probability one (w.p.1), to such values that ensure consensus on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are introduced: one using a simple compensation term, and a more robust one based on an instrumental variable. By utilizing stochastic approximation arguments it is shown that the modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. Convergence rate is analyzed in terms of an upper bound of the mean-square error. It is also shown that the communications between nodes can be completely asynchronous, which is of substantial importance for real-world applications. Suggestions for design of \textit{a priori} adjustable weights are given. Finally, it is shown that, if there is a subset of (precalibrated) reference sensors with fixed calibration parameters, the calibrated sensor gains and offsets of the rest of the sensors do not achieve consensus - they converge to different points dictated by the reference sensors and the network characteristics. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

In this paper we present bare bones fireworks algorithm implemented and adjusted for solving radio frequency identification (RFID) network planning problem. Bare bones fireworks algorithm is new and simplified version of the fireworks metaheuristic. This approach for the RFID network planning problem was not implemented before according to the literature survey. RFID network planning problem is a well known hard optimization problem and it poses one of the most fundamental challenges in the process of deployment of the RFID network. We tested bare bones fireworks algorithm on one problem model found in the literature and performed comparative analysis with approaches tested on the same problem formulation. We also performed additional set of experiments where the number of readers is considered as the algorithm's parameter. Results obtained from empirical tests prove the robustness and efficiency of the bare bones fireworks metaheuristic for tackling the RFID network planning problem and categorize this new version of the fireworks algorithm as state-of-the-art method for dealing with NP-hard tasks.

Portfolio selection problem that deals with the optimal allocation of capital is a well-known hard optimization problem in the domains of economics and finance. Basic version of the problem is multi-objective since it deals with maximization of return with simultaneous minimization of risk. Additional real world constraints, including cardinality, make the problem even harder. Many techniques and heuristics have been applied to this intractable optimization problem, however swarm intelligence algorithms have been implemented only few times for this task, even though they are known to be very successful for that class of problems. In this paper, we hybridized artificial bee colony algorithm with elements inspired by genetic algorithms to obtain better balance between intensification and diversification, especially during late stages, and applied the proposed improved algorithm to the cardinality constrained mean-variance version of the portfolio selection problem. Experimental results on standard benchmark datasets from five stock indexes and comparative analysis with other cutting edge algorithms have shown that our proposed algorithm achieved better results considering all relevant metrics i.e. mean Euclidean distance between standard efficiency frontier and heuristic efficiency frontier from sets of Pareto optimal portfolios obtained by tested algorithms, mean return error and variance of return error.

In this work, target localization problem in adverse indoor environments is addressed, where most (if not all) links are non-line-of-sight (NLOS). Localization accuracy in such environments is highly affected by multipath, which makes the problem very challenging. Hence, in order to enhance the localization accuracy, received signal strength (RSS) and time of arrival (TOA) integrated measurements, are considered here. Nevertheless, the derived joint maximum likelihood (ML) problem is highly non-convex and has no closed-form solution; thus, some approximations are required to solve it. We show that, for small noise power, the ML estimator can be tightly approximated by another (non-convex in general) one, given in a form of a generalized trust region sub-problem (GTRS). Hence, exact solution of the derived estimator can be readily obtained by merely a bisection procedure. The proposed algorithm is compared with the state-of-the-art (SOA) RSS/TOA algorithms, as well as its RSS-only and TOA-only complements. Our simulations validate the effectiveness of the proposed approach, outperforming the SOA algorithms in all considered scenarios, and show the benefit of the measurement fusion.

Vehicles need to locate other vehicles and network infrastructure elements on unmanned autonomous vehicle (UAV) systems. Human passengers also need to locate and be located by the vehicles, preferentially using a portable device, such as a smartphone. This paper analyses the accuracy of several localization algorithms in the remote location of entities running WiFi access points, using measurements collected in moving vehicles using a new application developed by us. The algorithms analysed include closed form estimators and one based on second order cone programming (SOCP) relaxation, which exhibits the best accuracy and is capable of estimating the path loss exponent and the transmission power. Although, due its lower complexity, the Levenberg-Marquardt algorithm was better suited for the stand-alone Android prototype application. The results show that real-time accurate positioning of static/slow moving remote entities is possible, even though the accuracy degrades when the measuring vehicle's speed increases. publishersversion published

Wireless sensor networks are widely used and consequently represent an important research field. The objective of the node localization problem, that belongs to the group of NP-hard tasks, is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Such hard optimization problems are successfully solved by the swarm intelligence algorithms. This paper presents hybridized recent swarm intelligence moth search algorithm adapted for solving localization problem in wireless sensor networks. The application of the moth search algorithm for node localization problem was not found in the literature survey. In the experimental section of this paper we show comparative analysis between the original and hybridized moth search algorithm, as well as with other state-of-the-art algorithms that were tested on the same problem instances of node localization problem. According to experimental results, both, basic moth search and hybridized moth search algorithms are promising approaches for dealing with this kind of problem.

This paper presents an implementation of the modified monarch butterfly optimization algorithm for solving the RFID network planning problem. This problem is one of the most studied challenges in the RFID domain, and was not tackled with the monarch butterfly optimization metaheuristic before. RFID system consists of tags and readers, where readers access from certain distance the information that is stored in tags. By testing basic monarch butterfly algorithm, we noticed that the algorithm can be improved by enhancing exploitation in the late iterations. We tested our approach on the standard RFID model and data set, and conducted comparative analysis with other approaches. According to the performed experiments, we concluded that the modified monarch butterfly optimization can be successfully applied to the RFID network planning problem.

In this paper, hybridized moth search algorithm adjusted for tackling constrained optimization problems, is presented. The moth search algorithm is new optimization method. The application of this approach for classic constrained functions was not published in any scientific paper before. By analyzing basic moth search, we noticed some deficiencies. To address these deficiencies, we hybridized the moth search algorithm with artificial bee colony metaheuristics. In order to evaluate robustness, convergence speed and solutions' quality of the hybridized moth search algorithm, we conducted tests on set of 13 classic constrained benchmarks. We performed comparative analysis with the original moth search, and with other algorithms, that proved to be robust and efficient optimization methods. According to the results obtained during experiments, and comparative analysis with other approaches, we came to the conclusion that the hybridized moth search algorithm has potential in dealing with constrained functions optimization.

In this paper we adjusted recent monarch butterfly optimization algorithm for solving NP-hard wireless sensor networks optimization problem, as one of the most important challenges in this area. The main goal of node localization problem is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Monarch butterfly optimization is novel swarm intelligence approach and it was not tested on this or similar problems before. We adjusted basic implementation of the monarch butterfly optimization method and tested it on several problem instances that are found in the literature. We also performed a comparative analysis with other swarm intelligence metaheuristics that were tested on the same problem instances. By examining results of empirical experiments and by comparative analysis of the results from other approaches, we established that butterfly optimization algorithm can be successfully applied to wireless sensor networks optimization problem.

This paper presents elephant herding optimization algorithm (EHO) adopted for solving localization problems in wireless sensor networks. EHO is a relatively new swarm intelligence metaheuristic that obtains promising results when dealing with NP hard problems. Node localization problem in wireless sensor networks, that belongs to the group of NP hard optimization, represents one of the most significant challenges in this domain. The goal of node localization is to set geographical co-ordinates for each sensor node with unknown position that is randomly deployed in the monitoring area. Node localization is required to report the origin of events, assist group querying of sensors, routing and network coverage. The implementation of the EHO algorithm for node localization problem was not found in the literature. In the experimental section of this paper, we show comparative analysis with other state-of-the-art algorithms tested on the same problem instance.

Digital images are of a great importance in medicine. Efficient and compact storing of the medical digital images represents a major issue that needs to be solved. JPEG lossy compression algorithm is most widely used where better compression to quality ratio can be obtained by selecting appropriate quantization tables. Finding the optimal quantization tables is a hard combinatorial optimization problem and stochastic metaheuristics have been proven to be very efficient for solving such problems. In this paper we propose adjusted bare bones fireworks algorithm for quantization table selection. The proposed method was tested on different medical digital images. The results were compared to the standard JPEG algorithm. Various image similarity metrics were used and it has been shown that the proposed method was more successful.

Locations of the sensors in wireless sensor networks usually have to be known. Wireless sensor networks contain large number of sensors so installing global position system device in each of them is not acceptable. Few anchor nodes with known positions are usually used. Estimating sensor nodes positions from radio strength signal index is a hard optimization problem. In this paper we proposed a two stage algorithm with semi-mobile anchors that uses swarm intelligence firefly algorithm for optimization. The proposed algorithm was tested by simulation of standard benchmark wireless networks where it obtained better results compared to other approaches from literature.

In this paper, we propose new approaches based on convex optimization to address the received signal strength (RSS)-based noncooperative and cooperative localization problems in wireless sensor networks (WSNs). By using an array of passive anchor nodes, we collect the noisy RSS measurements from radiating source nodes in WSNs, which we use to estimate the source positions. We derive the maximum likelihood (ML) estimator, since the ML-based solutions have particular importance due to their asymptotically optimal performance. However, the ML estimator requires the minimization of a nonconvex objective function that may have multiple local optima, thus making the search for the globally optimal solution hard. To overcome this difficulty, we derive a new nonconvex estimator, which tightly approximates the ML estimator for small noise. Then, the new estimator is relaxed by applying efficient convex relaxations that are based on second-order cone programming and semidefinite programming in the case of noncooperative and cooperative localization, respectively, for both cases of known and unknown source transmit power. We also show that our approaches work well in the case when the source transmit power and the path loss exponent are simultaneously unknown at the anchor nodes. Moreover, we show that the generalization of the new approaches for the localization problem in indoor environments is straightforward. Simulation results show that the proposed approaches significantly improve the localization accuracy, reducing the estimation error between 15% and 20% on average, compared with the existing approaches.

Collaborative beamforming is a technique for improving energy efficiency of uplink communications in wireless sensor networks. It is done by synchronizing carrier phases of individual sensor nodes precisely enough, so that they jointly form a beampattern with a stable mainlobe. In this paper, we analyze the impact of additive noise in the received signal on the behavior of an iterative phase synchronization scheme. Besides, we analyze the bit error rate performance of such collaborative transmission. In particular, we consider a decentralized antenna array where sensors are allowed to perform distributed carrier synchronization and collaborative data transmission simultaneously. We evaluate the system performance by using numerical simulations and provide a semi-analytical solution for the algorithm convergence characteristics and the overall bit error rate as a function of the algorithm’s iteration index.

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions which improved the quality of the paper. This work was partially supported by Fundacao para a Ciencia e a Tecnologia under Project PEst-OE/EEI/UI0066/2014, Project UID/EEA/50008/2013, Project UID/EEA/50009/2013, and Program Investigador FCT under Grant IF/00325/2015. This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine. publishersversion published

This work addresses the problem of target localization in harsh indoor environments. Based on a robust approach, a novel estimator whose solution can be obtained exactly by just a bisection procedure is developed. In sharp contrast to existing solutions, computational complexity of the proposed one is linear in the number of reference points. Moreover, our simulations show that the performance of our approach not only matches the performance of the state-of-the-art, but surpasses it in general.

Orthogonal frequency division multiplexing (OFDM) schemes have high envelope fluctuations and peak- to-average power ratio (PAPR), making them very prone to nonlinear distortion effects, which can affect significantly the performance when conventional receivers are employed. However, it was recently shown that strong nonlinear distortion effects on OFDM signals do not necessarily lead to performance degradation. In fact, nonlinear OFDM schemes can outperform linear ones when optimum maximum likelihood (ML) receivers are employed. In this paper, we considered OFDM schemes with strong nonlinear distortion effects and we proposed a low- complexity detection scheme able to approach the optimum ML performance. Our technique is based on the fireworks algorithm (FWA) and allows excellent trade-offs between performance and complexity.

This work addresses the target tracking problem that makes use of combined measurements, namely received signal strength (RSS) and angle of arrival (AoA). By linearizing the measurement models and incorporating the prior knowledge obtained from target state transition model, we show that the application of the Kalman filter (KF) to the considered tracking problem is straightforward. Then, an extension of the linearization approach to the case where the target transmit power is not known is introduced and applied to the measurement model to obtain an estimate of the transmit power. By taking advantage of this estimated value, we show that the proposed KF algorithm can easily be generalized to the case of unknown transmit power. Our simulation results confirm the efficacy of the proposed algorithms in comparison with the existing one, as well as the robustness of the proposed approach to not knowing the transmit power. Finally, the supremacy of using the Bayesian approach in comparison with the classical one which disregards the prior knowledge information is also validated through computer simulations.

Part 9: Distributed Infrastructure; International audience; This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.

This paper presents a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) in a more realistic scenario. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of arrival (AoA) information, respectively. The measurements are then used to derive a convex estimator, based on second order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations. Here, we test their performance considering different probabilities of communication failure between neighbour nodes at the broadcast phase. Our simulations show that their performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations.

Wireless sensor networks have many applications and accordingly represent an active research area. Coverage maximization of the area of interest by sensors that have limited sensing radius is an important hard optimization problem. Since sensors are initially often deployed randomly one way of solving maximal coverage problem is by using mobile sensors that move to optimal positions. Since power in sensor nodes is limited, minimization of the sensor nodes movement is secondary optimization goal. In this paper we propose use of recent swarm intelligence algorithm, firefly algorithm, for optimization of that hard multiobjective problem. We tested our approach on standard benchmark data and compared results with other techniques from literature. Our proposed approach was better considering all quality measures: coverage, energy consumption, robustness and convergence speed.

This work addresses the target tracking problem based on received signal strength (RSS) and angle of arrival (AoA) measurements. The Bayesian methodology, which integrates the information given by observations with prior knowledge extracted from target motion model in order to enhance the estimation accuracy was employed. First, by converting the considered highly non-linear measurement model into a linear one, i.e., a novel linearization technique of the measurement model is proposed. The derived model is then merged with the prior knowledge, and a novel maximum a posteriori (MAP) estimator whose solution is given in closed-form is proposed. It is also shown that the Kalman filter (KF) can be directly applied on top of the linearized observation model, which results in a proposal of a novel KF algorithm. Furthermore, to the best of authors’ knowledge, this paper premierly presents the application of the extended KF (EKF) and the unscented KF (UKF) to the considered tracking problem, by applying first-order linearization technique to the original non-linear model, and by applying the unscented transformation to carefully selected sample points, respectively. Finally, importance weights are computed for a large number of randomly selected sample points to render a well-known particle filter (PF) solution. Simulation results show that the proposed algorithms perform better than a naive one which uses only information from observations. They also confirm the effectiveness of the proposed linearization technique in comparison with the existing one, reducing the estimation error for about 25%.

This work was partially supported by Portuguese Foundation for Science and Technology under grants SFRH/ BPD/108232/2015 and Science 2008 Post-Doctoral Research grant, as well as projects UID/EEA/50008/2013 (Portuguese Telecommunications Institute) and PEst-OE/EEI/UI0066/2014 (UNINOVA), project PTDC/EEI-TEL/6308/2014-HAMLeT, GALNC EXPL/EEI-TEL/1582/2013 and Program Investigador FCT (IF/00325/2015). Energy efficient power amplification of multilevel constellations can be achieved by an amplification structure based on the constellation's decomposition as a sum of polar components, such as M BPSK (Bi-Phase Shift Keying), that are separately amplified. By doing this one can define highly efficient transmitters based on multiple amplifiers. However, amplifiers' imbalances might lead to substantial constellation distortion since phase and gain imbalances cause rotations and translations of the symbols associated to each amplification branch that are combined to generate the resulting constellation. Therefore, it becomes crucial the knowledge of the amplifiers' imbalances to overcome this problem at the receiver side. For that we propose and evaluate efficient two new methods for estimating amplifier imbalances. Simulation results demonstrate that the good performance attainable by the proposed estimate algorithms can be assured without significant increase in system and computational complexity. publishersversion published

This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., $P_{T}$ . We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known $P_{T}$ is straightforward to the case where $P_{T}$ is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing $P_{T}$ . The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.

One of the important problems and active research topics in digital image precessing is image segmentation where thresholding is a simple and effective technique for this task. Multilevel thresholding is computationally complex task so different metaheuristics have been used to solve it. In this paper we propose harmony search algorithm for finding optimal threshold values in color images by Otsu’s method. We tested our proposed algorithm on six standard benchmark images and compared the results with other approach from literature. Our proposed method outperformed other approach considering all performance metrics.

Source localization in wireless sensor networks (WSNs) aims to determine the position of a source in a network, given inaccurate position-bearing measurements. This paper addresses the problem of locating a single source from noisy acoustic energy measurements in WSNs. Under the assumption of Gaussian energy measurement errors, the maximum likelihood (ML) estimator requires the minimization of a nonlinear and nonconvex cost function which may have multiple local optima, thus making the search for the globally optimal solution hard. In this work, an approximate solution to the ML location estimation problem is presented by relaxing the minimization problem to a convex optimization problem, namely second-order cone programming. Simulation results demonstrate the superior performance of the convex relaxation approach. More precisely, the new approach shows an improvement of 20 % in terms of localization accuracy when compared to the existing approaches at moderate to high noise levels. Simulation results further show comparable performance of the new approach and the state-of-art approaches at low noise levels.

This work considers the problem of target localization in a 3-D space, using coupled measurements of angle of arrival (AoA) and received signal strength (RSS). By applying the transformation from Cartesian to spherical coordinates and by taking advantage of the available AoA observations, we establish novel relationships between the measurements and the unknown target location. From these relationships, we derive a simple closed-form solution estimator. Simulation results validate the remarkable performance of the proposed estimator, both in terms of estimation accuracy and execution time.

Digital images are widely used nowadays and there are powerful and convenient software tools for various types of image processing and enhancement. Unfortunately, these tools also facilitate for easy digital image forgery. In this paper we present an algorithm for digital image forgery detection that deals with the situation when some object, together with its shadow, is copied and pasted to some other location in the same or different image. Algorithm is based on the property that shadows do not change the texture of the underlaying surface. Our algorithms uses local binary patterns from shadow and adjacent non-shadow regions and features extracted from their histograms where energy and entropy proved to be the most discriminative. The proposed method was tested on some benchmark forged images and compared with other approaches from literature where it proved to be successful in detection of this type of forgery.

Support vector machines are widely used as superior classifiers for many different applications. Accuracy of the constructed support vector machine classifier depends on the proper parameter tuning. One of the most common used techniques for parameter determination is grid search. This optimization can be done more precisely and computationally more efficiently by using stochastic search metaheuristics. In this paper we propose using enhanced fireworks algorithm for support vector machine parameter optimization. We tested our approach on standard benchmark datasets from the UCI Machine Learning Repository and compared the results with grid search and with results obtained by other swarm intelligence approaches from the literature. Enhanced fireworks algorithm proved to be very successful, but most importantly it significantly outperformed other algorithms for more realistic cases for which there were separate test sets, rather than doing only cross validation.

This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ non-traditional methodology which merges distance and angle measurements, respectively withdrawn from the received signal strength (RSS) and angle-of-arrival (AoA) information. Based on RSS measurement model and effortless geometry, a novel non-convex estimator according to the weighted least squares (WLS) criterion is obtained, which closely approximates the maximum likelihood (ML) estimator for small noise. It is shown that the devised estimator is appropriate for distributed implementation. Following the squared range (SR) approach, we propose a suboptimal SR-WLS estimator according to the generalized trust region sub-problem (GTRS) framework, to estimate the locations of all targets in the WSN. According to our simulations, the new estimator has excellent performance in a great variety of considered settings, in which the effectiveness of fusing two radio measurements is confirmed.