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This study proposes a fusion method for high dynamic range images based on gradient information transformation. In the proposed work, the authors first measure the three exposure weights of the source images, namely, local contrast, luminance and spatial structure. Then, the exposure weights are merged through a multi‐scale Laplacian pyramid scheme. For the weight maps measurement, the dense scale‐invariant feature transform method is used to calculate the local contrast around each pixel location, rather than a single pixel. The image luminance levels are computed in the gradient domain to get more visual information and the authors leverage the dictionary learning to effectively extract the luminance of images. Additionally, to better preserve the spatial structure of the source images, the just‐noticeable‐distortion technique is employed. By comparing the experimental results both subjectively and objectively, it is evident that the proposed method represents an improvement over some exciting methods. [ABSTRACT FROM AUTHOR]

We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data. Existing methods either use sampling density compensations which sacrifice reconstruction accuracy, or circulant preconditioners which increase per-iteration computation. Our approach overcomes both shortcomings. Concretely, we show that viewing the reconstruction problem in the dual formulation allows us to precondition in k-space using density-compensation-like operations. Using the primal-dual hybrid gradient method, the proposed preconditioning method does not have inner loops and are competitive in accelerating convergence compared to existing algorithms. We derive $\ell 2$ -optimized preconditioners, and demonstrate through experiments that the proposed method converges in about ten iterations in practice. [ABSTRACT FROM AUTHOR]

This study discusses the parameter estimation of the Hammerstein output‐error moving average system using the dual‐rate sampled data. The polynomial transformation technique is used to obtain the identification model of the discussed dual‐rate sampled systems. The stochastic gradient optimisation method is an effective optimisation method. Compared with the Newton optimisation, it only needs to calculate the first derivative during the optimisation and the amount of calculation is relatively small. It is a good choice to use the stochastic gradient algorithm for the identification of Hammerstein dual‐rate model after using the polynomial transformation technique. In order to improve the convergence speed, a maximum likelihood forgetting factor stochastic gradient identification algorithm is proposed by combining the maximum likelihood principle and the gradient search method. The convergence of the algorithm is analysed by using the stochastic process theory. Furthermore, in order to improve the estimation accuracy of the identification algorithm, a maximum likelihood multi‐innovation forgetting factor stochastic gradient algorithm is proposed by using the multi‐innovation identification theory. The effectiveness of the proposed algorithms is illustrated by a numerical simulation example and a water tank system. [ABSTRACT FROM AUTHOR]

We propose a hierarchical distributed algorithm to solve optimal power flow (OPF) problems that aim at dispatching controllable distributed energy resources (DERs) for voltage regulation at minimum cost. The proposed algorithm features unprecedented scalability to large multi-phase distribution networks by jointly exploring the tree/subtrees structure of a large radial distribution network and the structure of the linearized distribution power flow (LinDistFlow) model to derive a hierarchical, distributed implementation of the primal-dual gradient algorithm that solves OPF. The proposed implementation significantly reduces the computation loads compared to the centrally coordinated implementation of the same primal-dual algorithm without compromising optimality. Numerical results on a 4,521-node test feeder show that the designed algorithm achieves more than 10-fold acceleration in the speed of convergence compared to the centrally coordinated primal-dual algorithm through reducing and distributing computational loads. [ABSTRACT FROM AUTHOR]

Visual evaluation of many magnetic resonance images is a difficult task. Therefore,computer‐assisted brain tumor classification techniques have been proposed.These techniques have several drawbacks or limitations. Capsule based neuralnetworks are new approaches that can preserve spatial relationships of learnedfeatures using dynamic routing algorithm. By this way, not only performance oftumor recognition increases but also sampling efficiency and generalisationcapability improves. Therefore, in this work, a Capsule Network (CapsNet) isused to achieve fully automated classification of tumors from brain magneticresonance images. In this work, prevalent three types of tumors (pituitary,glioma and meningioma) have been handled. The main contributions in this paperare as follows: 1) A comprehensive review on CapsNet based methods is presented.2) A new CapsNet topology is designed by using a Sobolev gradient‐basedoptimisation, expectation‐maximisation based dynamic routing and tumor boundaryinformation. 3) The network topology is applied to categorise three types ofbrain tumors. 4) Comparative evaluations of the results obtained by othermethods are performed. According to the experimental results, the proposedCapsNet based technique can achieve extraction of desired features from imagedata sets and provides tumor classification automatically with 92.65%accuracy. [ABSTRACT FROM AUTHOR]

The basis of image processing is to evaluate and monitor image quality using algorithms rather than subjective methods. Conventional gradient operators have been popularly used in previous image quality assessment tasks to reflect the edge contour of an image, while there are some obvious defects in terms of the selection of mask scale and direction. Some improved versions are also less than ideal since they fail to consider the gradient information of the same pixel in different directions at the same time. The authors adopt a powerful gradient operator that can simultaneously capture edge information in all four directions at the same pixel point with more relevant values being considered instead of selecting the maximum in these four directions. Furthermore, four complementary types of features extracted from the spatial and transform domains are considered. A set of 12‐dimensional feature vectors is generated for each image by multi‐feature fusion. Ultimately, random forest regression technique is employed to train their model and then map the distortion effects to the prediction scores. The experimental results show that the proposed FVC‐G has better overall performance, more powerful cross‐database operation capability, and higher visual consistency than other advanced methods. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of parameter identification in an electrochemical model of a Lithium-ion battery. The starting point is the development of an extended model based on the absolute nodal coordinate formulation (ANCF-e), which provides high accuracy, yet low computational complexity. Using such a model, this paper proposes an adaptive observer to carry out real-time parameter estimation. The complex spatiotemporal relation between inputs, states, and outputs, and the combined presence of multiple nonlinearities related to the open circuit, electrolyte, solid, and overpotentials prevents the application of standard tools of parameter estimation and adaptive observers. These challenges are overcome by breaking the ANCF-e model into four subsystems which consist of key dynamic relationships between the molar flux and either measurable quantities or states of the solid and electrolyte dynamics, as well as a nonlinear subsystem that relates inputs, outputs, and states on one hand, and potentials on the other. Adaptive observers are proposed to identify parameters of each subsystem and are validated using simulation studies. Significant extensions of currently available adaptive observers are proposed for this purpose. [ABSTRACT FROM AUTHOR]

Matrix computations are indispensable tools in science and engineering, while the electronic matrix computations suffered from limited speed. Alternatively, the optical methods offered a high-speed solution. The optical matrix computation is impractical unless it is capable of extending to a large scale, reconfiguring for a general purpose and operating with high efficiency. Here, we report and experimentally demonstrate an optical matrix computing processor based on an integrated linear optical network. The proposed photonic processor is capable of performing fundamental matrix computations including XB = C, AB = X and AX = C, where A, B, C are known matrices, and X is the matrix to be solved. An optical PageRank algorithm is further demonstrated based on the matrix computing processor for the first time. Our demonstration offers an optical method to achieve matrix computations and PageRank algorithm. Meanwhile, it suggests great potential for chip-scale fully programmable matrix computations with self-configuring methods. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy classification approach based on colour features has been investigated to estimate the ripeness of apple fruits according to three maturity stages; unripe, turning‐ripe and ripe. The K nearest neighbour algorithm was applied in order to segment the fruit image into four regions namely background, green area, yellow area and red area. The last three regions represent the colour features and were subsequently given as inputs to the fuzzy classifier. Gradient method has been used for tuning the fuzzy classifier in order to obtain the best performance. Image database used for simulation has been collected and exploited for the training and testing phases using cross‐validation. Simulation results indicate that the best classifier parameters can be obtained. The efficiency of the proposed system compared with the non‐use of the gradient method has been proved by the confusion matrix and the most known classification evaluation metrics. Moreover, the trained fuzzy classifier demonstrates its outperformance in terms of accuracy and execution time compared with other existing methods. [ABSTRACT FROM AUTHOR]

In this study, a tomography reconstruction problem of binary images is considered on the isometric grid. On this grid, the triangle pixels have two types of orientations, accordingly, the authors call them delta or nabla shape pixels. The proposed reconstruction method uses data of projections of three natural directions. They are the lane directions of the triangular tessellation (these directions are somewhat analogous to row/column directions on the rectangular grids). The projection ray, penetrating through a grid lane, now not passing through the middle of pixels (i.e. through the middle line of triangle shape pixels), as usually taken, but little bit shifted from the middle parallel to the lane. This method provides the exact information about the number of nabla and delta shape triangle pixels in each lane of the image. This additional information is included in the reconstruction process to improve the quality of reconstruction. They formulate the suggested model into an energy‐minimisation problem and apply a gradient‐based approach for its minimisation. They show and analyse various experimental results on test images. The presented approach shows both better quality reconstructions and shorter running time than the earlier approaches. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging is a powerful, ubiquitous imaging technique that provides detailed high‐contrast images differentiating soft tissues. The low radio‐frequency bias field creates intensity inhomogeneity generating low contrast that often creates difficulty for quantitative and qualitative analyses. Segmentation aids in analysis of changes occurring in brain, where bias effect severely affects performance. The graph‐cut (GC) segmentation provides supervised computer‐assisted diagnosis and treatment. GC's interactive nature requires manual selection of kernels for initialisation. The shrinkage behaviour of GC creates inaccurate and fallacious extraction. On the basis of these problems, this study proposes gradient‐based kernel selection GC method that simultaneously removes shrinkage problem and locates tumour in image, eliminating human interaction with accurate segmentation for even bias field images. The proposed method addresses these problems by emphasising on directive inclination of intensity scales of symmetrical halves of images. The proposed method is evaluated for high‐grade glioma and low‐grade glioma images with and without bias field. The average performance metrics evaluated for these images depict remarkable improvement in comparison with existing techniques. The proposed technique is validated by applying on real‐time dataset of tumour images obtained from State Government Hospital, Shimla, India. [ABSTRACT FROM AUTHOR]

This study focuses on the recursive parameter estimation problems for the non‐linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extended stochastic gradient (ESG) algorithm is derived. Considering the difficulty of determining the step‐size in the ESG algorithm, a numerical approach is proposed to obtain the optimal step‐size. In order to improve the parameter estimation accuracy, the authors employ the multi‐innovation identification theory to develop a multi‐innovation ESG (MI‐ESG) algorithm for the ExpARMA model. Introducing a forgetting factor into the MI‐ESG algorithm, the parameter estimation accuracy can be further improved. With an appropriate innovation length and forgetting factor, the variant of the MI‐ESG algorithm is effective to identify all the unknown parameters of the ExpARMA model. A simulation example is provided to test the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a distributed discrete‐time algorithm for unconstrained optimisation with event‐triggered communication over weight‐balanced directed networks. They consider a multi‐agent system where each agent has a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function. Agents send the states and auxiliary variables to their neighbours when their trigger errors exceed thresholds. They derive a convergence rate of the proposed algorithm which shows faster convergence to the optimal solution compared to the subgradient‐based method. [ABSTRACT FROM AUTHOR]

Reinforcement learning is regarded as a potential method to be applied in automated vehicles, but the stability and efficiency of algorithms are concerns. To improve them, the deterministic promotion reinforcement learning method is put forward, which can promote the policy determinately. Correspondingly, the policy evaluation in critic and the exploration in actor are improved, which combines a normalization-based evaluation and a model-free search guide. The aim is finding the right action exploration direction by critic, then the direction is used to update and guide action exploration in actor. The modified method decreases the dependencies of exploring a good action for promotional updating and only makes deterministic promotion in policy. Consequently, the efficiency of the algorithm is improved without loss in stability. More notably, it can relieve the cold-start and circumvent the limitations in learning with constrained physical systems. To verify the proposed method, the longitudinal velocity control problem for automated vehicles is considered, which contains car-following and non-car-following conditions in a unitized form. The learning system is established in Carsim. Furthermore, some different reinforcement learning technologies are used to accelerate learning. Real-vehicle experiments for validation are also given. The results indicate that the proposed method can achieve permissible learning performance in the longitudinal velocity continuous control problem. [ABSTRACT FROM AUTHOR]

In the present paper, we present a multi-start method with dynamic cutting of "unpromising" starts of locally optimal algorithms for solving continuous unconditional optimization problems. We also describe our results of testing of the proposed method for solving problems of feed-forward neural network training. [ABSTRACT FROM AUTHOR]

Multitask feature selection (MTFS) methods have become more important for many real world applications, especially in a high-dimensional setting. The most widely used assumption is that all tasks share the same features, and the $l_{2,1}$ regularization method is usually applied. However, this assumption may not hold when the correlations among tasks are not obvious. Learning with unrelated tasks together may result in negative transfer and degrade the performance. In this paper, we present a flexible MTFS by graph-clustered feature sharing approach. To avoid the above limitation, we adopt a graph to represent the relevance among tasks instead of adopting a hard task set partition. Furthermore, we propose a graph-guided regularization approach such that the sparsity of the solution can be achieved on both the task level and the feature level, and a variant of the smooth proximal gradient method is developed to solve the corresponding optimization problem. An evaluation of the proposed method on multitask regression and multitask binary classification problem has been performed. Extensive experiments on synthetic datasets and real-world datasets demonstrate the effectiveness of the proposed approach to capture task structure. [ABSTRACT FROM AUTHOR]

In developed countries, air conditioning systems have become major contributors to energy consumption in buildings. Cooling installations made up of independent chiller units connected in parallel pose a challenge in finding the most energy-efficient management strategy. This article proposes a novel economic model predictive control strategy to optimize the operation of multiple-chiller HVAC systems according to an economic cost comprising energy consumption as well as a thermal comfort index. Provided that the gradient of the economic cost function can be calculated or estimated, the proposed controller only entails the solution of a single quadratic programming (QP) problem at each sampling period, reducing computational requirements and thus facilitating the deployment on commercial embedded HVAC management platforms. The proposed controller improves economic performance. As our numerical analysis shows, optimal sequencing is achieved for the case of dual-chiller plants. Moreover, the controller adapts to possible variations in the economic criteria, enabling the system to respond to changes in electricity price and user preferences. A realistic case study, using a high fidelity model of a building simulated in TRNSYS, demonstrates the effectiveness of the proposed methodology. In particular, the proposed controller demonstrates to be more efficient than state-of-the-art QP-based economic predictive controllers in achieving a reduction of the energy consumption up to 5.19%, which is in line with the targeted reduction of 7.9% by 2030 in the EU Green Deal's 'Fit for 55' package. • QP approach that enables economic MPC in commercial HVAC management platforms. • Allocates the cooling power of each chiller individually to maximize overall efficiency. • Adapts to changes in economic criteria, e.g., electricity cost and user preferences. • A practical case study shows an energy efficiency boost greater than 5%. [ABSTRACT FROM AUTHOR]

This paper describes a feedback assisted stochastic gradient algorithm for transmission tracking of the dominant channel subspaces for multiple-input-multiple-output (MIMO) communications systems. Subspace tracking is introduced as a means of tracking multiple transmission weights, being the MIMO generalization of beam steering in the familiar multiple-input-single-output case. The subspace solution approximates that of water filling (WF) in some cases, without the complete rate/power allocation required by WE The gain of subspace tracking in low rank systems is demonstrated, particularly, in the case where the number of transmit antennas exceeds the number of receive antennas. Simulations of ergodic capacity show the utility of both subspace tracking in general and of the specific adaptation algorithm, and simulations of frame-error rates show the utility in a specific coding example.

In this study, a 2D adaptive beamforming algorithm for sparse array is proposed. Firstly, a signal model based on matrix completion theory for adaptive beamforming in sparse array is established, which is proved to satisfy null space property. Secondly, in order to enhance the performance of reconstructing complete received signal matrix, genetic algorithm is used to optimise the sparse sampling array. Thirdly, the accelerated proximal gradient algorithm is adopted to reconstruct the complete received signal matrix. Finally, the adaptive beamforming weight is provided directly to form beam patterns, which can be obtained as a result of reconstructing complete received signal matrix. The proposed method could improve the utilisation rate of the sparse array elements and reduce the computational complexity in interference suppression. Simulation results show the effectiveness of the method.

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l(1) in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.

Small multi-rotor unmanned aerial vehicles (SMR-UAVs) are a promising platform for low-cost synthetic aperture radar (SAR) systems. However, SMR-UAVs usually suffer from serious position errors due to their unstable motion and low actuary position sensors, and autofocus is an indispensable step for their high-quality imaging. An efficient back-projection autofocus method is proposed for SMR-UAV SAR systems by the principle of minimum entropy. The position error estimation model via minimum entropy is derived. The conjugate-gradient method is used to efficiently estimate the position errors. Moreover, to improve the computing efficiency, the strong scatterer areas are estimated as the input of entropy estimation. The effectiveness of the algorithm is demonstrated using both simulation and experimental data.

In recent years, deep convolutional neural networks (CNN) have been widely used in computer vision and significantly improved the performance of image recognition tasks. Most works use softmax loss to supervise the training of CNN and then adopt the output of last layer as features. However, the discriminative capability of the softmax loss is limited. Here, the authors analyse and improve the softmax loss by manipulating the cosine value and input feature length. As the approach does not change the principle of the softmax loss, the network can easily be optimised by typical stochastic gradient descent. The MNIST handwritten digits dataset is employed to visualise the features learned by the improved softmax loss. The CASIA-WebFace and FER2013 training set are adopted to train deep CNN for face and expression recognition, respectively. Results on both the LFW dataset and FER2013 test set show that the proposed softmax loss can learn more discriminative features and achieve better performance.

Pixelated optical communication systems transmit data using a series of encoded pixelated images. The decoding of these pixelated images is important for reliable data communication. This study proposes a novel intensity gradient-based edge detection (IGED) method for the received pixelated images. In the first stage of the proposed edge detection algorithm, the intensity difference of neighbouring pixels is calculated in the horizontal or vertical direction depending on the presence of a horizontal or vertical edge. In the second stage, the false edge pixels whose intensity values are less than the threshold are removed. Next, slopes are calculated between the consecutive edge pixels and the appropriate lines are found considering the slopes that have almost the same value. The new IGED method performs edge detection without requiring any anchor pixel blocks. This study also provides a description of a misalignment correction algorithm suitable for pixelated images. Next, the effectiveness of the proposed IGED method is tested through an experimental demonstration. It is shown that the IGED method can properly detect the edges even for pixelated images affected by rotational misalignment. For a given transmitter resolution, the proposed method provides better data rates than the existing methods.

This study deals with path scheduling problem of cooperative target tracking by multiple sensors with bearings only measurements. First, the authors derive a closed-form expression of the determinant (D-optimality criterion) of the Fisher information matrix (FIM) as the cost function, which contains the knowledge of the target and the locations of sensors. Second, a penalty function is introduced to modify the cost function for threats avoidance and physics constraints are applied to limit directions of sensors. Then, an efficient strategy based on steepest descent is proposed to solve the optimisation problem. Finally, the effectiveness of the proposed algorithm is demonstrated both in localisation of a stationary target and tracking a moving target. Simulation results show the trajectories of sensors for cooperative target tracking are almost identical to a grid-based search method; however, the computational complexity is reduced by several orders of magnitude.

In order to solve synthetic aperture radar image-focus problem when space-variant motion errors could not be ignored, this study proposes a partitioned phase gradient autofocus (PGA) method. In this method, it is most important whether the dominant scatters is in the partitioned images or not. In order to find the dominant scatters, energy gradient factor is defined and energy inflection point is determined. Then, the judgment is made by energy gradient factor and its threshold. Based on measured data processing, it is indicated that the method could distinguish the partitioned images with significant dominant scatters, un-significant dominant scatters, and without dominant scatters. PGA is implemented on all blocks with significant dominant scatters and partial blocks with un-significant dominant scatters. There is no need to use PGA for the blocks without dominant scatters.

For a non-linear anisotropic permanent magnet synchronous machine (PMSM), a prediction model for model predictive control (MPC) considering effects like cross-coupling and saturation is developed in a straight forward procedure. The objective of the designed MPC is either tracking of reference currents or torque tracking. Both approaches use the projected fast gradient method (PFGM) as optimisation algorithm. The latter approach makes look-up-tables for current references obsolete and additionally minimises winding losses. This two approaches are compared in a simulation study with a state of the art PI controller.

The over‐relaxed monotone fast iterative shrinkage‐thresholding algorithm (OMFISTA) needs to satisfy a complex convergence condition with respect to its additional parameters. To simplify the convergence condition, this study proposes a new OMFISTA, termed OMFISTAv2, using a parameter setting strategy which will derive a simple sufficient condition with respect to the additional parameters to guarantee the convergence of OMFISTAv2. Moreover, the authors find experimentally that OMFISTAv2 can accelerate MFISTA in some cases where the system matrix is ill‐conditioned or rank‐deficient, while OMFISTA cannot. [ABSTRACT FROM AUTHOR]

This study proposes a vision‐based method for traffic sign attribute estimation, i.e. 3D position and pose, from image sequences by binocular or monocular cameras. The method starts with acquiring robust feature correspondences based on homography constraints from image pairs. Then the objective function is designed to integrate the feature correspondences to optimise the parameters of the traffic sign plane in the 3D coordinate. Finally, the sign plane is utilised for attribute estimation. In addition, the authors provide an extension for the raw KITTI dataset, which can be utilised for 3D tasks of traffic sign localisation and pose estimation. In the experiments, three popular methods are employed for comparisons based on the publicly available BelgiumTS and KITTI datasets. The results show that the authors' method based on SIFT and SURF features can locate the traffic signs with a mean error of ∼0.44 and 0.51 m in the BelgiumTS and KITTI datasets, respectively, and estimate the pose with a mean error of ∼14.45° in the KITTI dataset. [ABSTRACT FROM AUTHOR]

This study presents a novel online, accurate and simple model‐based maximum torque per ampere (MTPA) strategy for input–output feedback linearisation control of induction motor (IM) drives. Despite conventional MTPA control principles which are under the assumption of iron loss neglecting, the proposed strategy takes iron loss effect into account. This study highlights the iron loss influence on MTPA scheme. Firstly, the cross‐coupling effects in the IM model and torque non‐linearity due to the iron loss in torque–speed characteristics of the IM are discussed. The criterion for the MTPA scheme is then introduced and investigated by gradient approach so that when the gradient vectors of the torque and stator current magnitude are parallel, the MTPA strategy is satisfied. Finally, to confirm the validity of the proposed control scheme, experiments are carried out. [ABSTRACT FROM AUTHOR]

This study considers the parameter estimation of a multi‐variable output‐error‐like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares‐based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

In this paper is developed an accelerated version of the steepest descent method by a two-step iteration. The new algorithm uses information with delay to define the iterations. Specifically, in the first step, a prediction of the new test point is calculated by using the gradient method with the exact minimal gradient steplength and then, a correction is computed by a weighted sum between the prediction and the predecessor iterate of the current point. A convergence result is provided. In order to compare the efficiency and effectiveness of the proposal, with similar methods existing in the literature, numerical experiments are performed. The numerical comparison of the new algorithm with the classical conjugate gradient method shows that our method is a good alternative to solve large-scale problems. [ABSTRACT FROM AUTHOR]

Pixelated optical communication systems transmit data using a series of encoded pixelated images. The decoding of these pixelated images is important for reliable data communication. This study proposes a novel intensity gradient-based edge detection (IGED) method for the received pixelated images. In the first stage of the proposed edge detection algorithm, the intensity difference of neighbouring pixels is calculated in the horizontal or vertical direction depending on the presence of a horizontal or vertical edge. In the second stage, the false edge pixels whose intensity values are less than the threshold are removed. Next, slopes are calculated between the consecutive edge pixels and the appropriate lines are found considering the slopes that have almost the same value. The new IGED method performs edge detection without requiring any anchor pixel blocks. This study also provides a description of a misalignment correction algorithm suitable for pixelated images. Next, the effectiveness of the proposed IGED method is tested through an experimental demonstration. It is shown that the IGED method can properly detect the edges even for pixelated images affected by rotational misalignment. For a given transmitter resolution, the proposed method provides better data rates than the existing methods. [ABSTRACT FROM AUTHOR]

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness. [ABSTRACT FROM AUTHOR]

The path optimisation problem of mobile sensor networks for arrival‐of‐angle (AOA) target localisation, using the consensus‐based extended information filter is considered, in this study. A new idea of equipping sensors with information‐driven mobility to improve the estimation accuracy with respect to a stationary target is proposed by the authors. A gradient descent method is used for mobile sensors, which are subject to geometric constraints, to choose the next optimal waypoints. The corresponding optimisation problem is solved in a distributed manner, by selecting a proper cost function for each mobile sensor. It is shown that the boundedness of the estimation error is guaranteed. Moreover, they find that the mobility of sensors does decrease the estimation error bounds compared with the static sensor networks, which is beneficial for the localisation performance. Simulation is carried out to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]