Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher. To address these challenges, in this work, we develop an adaptive cross-modal fusion strategy for unsupervised multimodal medical image fusion. Specifically, we propose a novel lightweight cross Transformer based on cross multi-axis attention mechanism. It includes cross-window attention and cross-grid attention to mine and integrate both local and global interactions of multimodal features. The cross Transformer is further guided by a spatial adaptation fusion module, which allows the model to focus on the most relevant information. Moreover, we design a special feature extraction module that combines multiple gradient residual dense convolutional and Transformer layers to obtain local features from coarse to fine and capture global features. The proposed strategy significantly boosts the fusion performance while minimizing computational costs. Extensive experiments, including clinical brain tumor image fusion, have shown that our model can achieve clearer texture details and better visual quality than other state-of-the-art fusion methods.

Dynamic optimization problems are pervasive in various fields, ranging from chemical process control to aerospace, autonomous driving, physics, robotics, and beyond. These problems involve optimizing a dynamic system considering inputs, parameters, constraints, and a cost function. For dynamic optimization, two broad classes of strategies emerge: deterministic and heuristic methods. Deterministic optimization methods leverage the analytical properties of the problem, generating a sequence of points that converge to the optimal solution. These techniques are suitable when explicit models and constraints are available and easy to evaluate. On the other hand, heuristic approaches treat the problem as a black box, relying on iterative improvements to a fitness function. They are employed for complex problems with challenging system models or significant uncertainty.

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

Ramp metering offers great potential to mitigate traffic congestion and improve freeway management efficiency under traffic congestion conditions. This paper proposes an optimization program for freeway dynamic ramp metering based on Cell Transmission Model (CTM). This problem has been formulated as a discrete time optimal control problem with smooth state equations and constraints to meter traffic inflow from on-ramps. In the proposed model, the ‘min’ operators in the primal CTM are non-differentiable and thus, the corresponding optimal control problem cannot be solved directly using conventional gradient based methods. In this paper, we introduce a smooth approximation to approximate the ‘min’ operators and then a unified computational approach is developed to solve the problem. Theoretical analysis is carried out, showing that the optimal solution obtained from the approximated problem converges to the optimal solution of the primal CTM. Compared to the classical inequality relaxation method, our method can resolve the flow holding-back problem and reduce under fundamental diagram phenomenon. Compared with the Big-M method, our method has better efficiency. To achieve the desired traffic response control in real application, a series of online optimal control problems are solved using Model Predictive Control (MPC). Simulation studies show that our method can significantly improve freeway traffic management efficiency.

Perimeter control is to manipulate traffic flows in different regions through adjusting traffic signals at the border of the regions. Traditionally, the complete trips in a region are assumed to be dependent on the current accumulated vehicles in this region. This assumption is invalid because the vehicle needs time to complete its trip in a region. In order to solve this shortcoming, some recent studies have introduced a time-delay dynamical system to describe the dynamical behaviour of the accumulated vehicles in a region. However, in these studies, the perimeter control problem is formulated as a tracking problem with a given optimal reference point. In reality, such an optimal reference point is unavailable in advance. This paper will fill this gap through formulating perimeter control as an optimal control problem governed by a state-dependent time delay system. The control parametrization technique and an exact penalty method are introduced to solve such a challenging optimal control problem. Model predictive control is applied to obtain close-loop solution through solving a series of online optimal control problems. Some experiments are performed to demonstrate the effectiveness of our method.

Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical‐based methods for performing the non‐invasive blood glucose estimation for the consumer applications becomes very important. The authors’ previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non‐invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L 1 norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state‐of‐the‐art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.

This study proposes an optimal design of the orders of the discrete fractional Fourier transforms (DFrFTs) and construct an overcomplete transform using the DFrFTs with these orders for performing the sparse representations. The design problem is formulated as an optimisation problem with an ‐norm non‐convex objective function. To avoid all the orders of the DFrFTs to be the same, the exclusive OR of two constraints are imposed. The constrained optimisation problem is further reformulated to an optimal frequency sampling problem. A method based on solving the roots of a set of harmonic functions is employed for finding the optimal sampling frequencies. As the designed overcomplete transform can exploit the physical meanings of the signals in terms of representing the signals as the sums of the components in the time–frequency plane, the designed overcomplete transform can be applied to many applications.

This letter is concerned with a robust minimum variance beamformer design. To hedge the mismatch between the true and the assumed steering vectors, a distributionally robust beamformer (DR-beamformer) is proposed. The tractable reformulation of this beamformer is developed. Compared with the existing robust beamformers (e.g., worst-case robust beamformer and Gaussian robust beamformer), the proposed robust beamformer does not assume full knowledge of the channel mismatch. Therefore, it is more flexible in practice and more general in formulation. In addition, the relationships of the proposed robust beamformer to the existing ones are investigated. The performance gain of the DR-beamformer over the other robust beamformers is highlighted through numerical simulations.

Manpower planning is a very important component of human resource management. However, there are many indeterminate factors that should be taken into consideration in manpower planning. For example, the decision of employees to quit the job is determined by their preference, which is beyond the control of human resource department. It can be realistically modeled as a random variable when the historical data of quitting rate are large enough. Otherwise, it can only be regarded as an uncertain variable when the historical data are inadequate. In this paper, we discuss a manpower planning optimization problem for a manufacturing company with hierarchical system, where the quitting rate of employees is modeled as an uncertain variable. First, we formulate a mathematical model for this uncertain manpower planning optimization problem, where the influence on the production outputs by employees is taken into consideration. Second, we present a deterministic conversion method to transform this uncertain manpower planning optimization problem into an equivalent deterministic discrete-time optimization problem. It is further converted into an equivalent linear programming model with an equality constraint and an inequality constraint. Finally, we use the real data from Singapore, Denmark, and China to carry out a numerical simulation and make a comparison with the results obtained based on stochastic model to show the advantages of our method.

A receiver design problem for multi-access space-time block coded multiple-input multiple-output systems is considered. To hedge the mismatch between the true and the estimated channel state information (CSI), several robust receivers have been developed in the past decades. Among these receivers, the Gaussian robust receiver has been shown to be superior in performance. This receiver is designed based on the assumption that the CSI mismatch has Gaussian distribution. However, in real-world applications, the assumption of Guassianity might not hold. Motivated by this fact, a more general distributionally robust receiver is proposed in this paper, where only the mean and the variance of the CSI mismatch distribution are required in the receiver design. A tractable semi-definite programming (SDP) reformulation of the robust receiver design is developed. To suppress the self-interferences, a more advanced distributionally robust receiver is proposed. A tight convex approximation is given and the corresponding tractable SDP reformulation is developed. Moreover, for the sake of easy implementation, we present a simplified distributionally robust receiver. Simulations results are provided to show the effectiveness of our design by comparing with some existing well-known receivers.

Zero-forcing beamforming (ZFBF) is a popular pre-coding scheme for MIMO systems. Most of the studies in the literature are under total power constraints. However, the per-antenna power constraints (PAPC) are more realistic. The state-of-the-art method is interior point method which is expensive to realize in practice due to the high computational complexity. Hence, a low complexity zero-forcing precoding scheme under the per-antenna power constraints is proposed in this paper. This is achieved by introducing a regularized dual method. Simulations are carried out to show the effectiveness of the proposed method, which has a low computational complexity. In addition, the algorithm can be implemented in parallel to further reduce the computational complexity.

This paper addresses the problem of robust fuzzy L2-L∞ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering error dynamic system is stochastically stable and that it has a prescribed L2-L∞ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.

This paper extends the definition of an exact consensus complex social network to that of a near consensus complex social network. A near consensus complex social network is a social network with nontrivial topological features and steady state values of the decision certitudes of the majority of the nodes being either higher or lower than a threshold value. By using eigen theories, the relationships among the vectors representing the steady state values of the decision certitudes of the nodes, the influence weight matrix and the set of vectors representing the initial state values of the decision certitudes of the nodes that satisfies a given near consensus specification are characterized.

This letter presents a new computational procedure for the second-order gradient-based blind signal separation (BSS) problem with convolutive mixtures that has improved convergence characteristics over the steepest descent algorithm. The BSS problem is formulated as a constrained optimization problem with complex unmixing weight matrices where the constraints are formulated to overcome the permutation effects. This problem is then transformed into an unconstrained optimization problem, so that the conjugate gradient algorithm can be applied. The convergence of the proposed procedure is compared with the steepest descent algorithms in real and simulated environments.

This correspondence is concerned with the robust stability for a class of impulsive switched systems under the LQ guaranteed cost control. Some results on robust stability for this class of impulsive switched systems are obtained. Sufficient conditions for the existence of a guaranteed cost control law are also given. Subject to these sufficient conditions, the closed-loop uncertain impulsive switched system under the guaranteed cost control law is robustly stable with a guaranteed cost value.

A method for optimizing FRM-based FIR filters with optimum coefficient sensitivity is presented. This technique can be used in conjunction with nonlinear optimization techniques to design very sharp filters that do not only have very sparse coefficient values but also very low coefficient sensitivity.

This paper considers the problems of global exponential stability and exponential convergence rate for impulsive high-order Hopfield-type neural networks with time-varying delays. By using the method of Lyapunov functions, some sufficient conditions for ensuring global exponential stability of these networks are derived, and the estimated exponential convergence rate is also obtained. As an illustration, an numerical example is worked out using the results obtained.

This paper presents a new method for the design of sharp linear phase FIR filters using the frequency-response masking (FRM) technique. The method is based on a weighted least square (WLS) approach, and the design problem is solved iteratively. The original least square (LS) problem is decomposed into two LS problems, each of which can be solved analytically. The effectiveness of the method is demonstrated through the design of a lowpass linear phase sharp FIR digital filter.

Subband adaptive filters have been proposed to avoid the drawbacks of slow convergence and high computational complexity associated with time domain adaptive filters. However, subband processing causes signal degradations due to aliasing effects and amplitude distortions. This problem is unavoidable due to further filtering operations in subbands. In this letter, the problems of aliasing effect and amplitude distortion are studied. Prototype filters which are optimized with respect to those properties are designed and their performances are compared. Moreover, the effect of the number of subbands, the oversampling factors and the length of the prototype filter are also studied. Using the multicriteria formulation, all Pareto optimums are sought via the nonlinear programming technique. We find that the prototype filter designed via the Kaiser window provides the best overall performance among the methods we studied. Also, there is a critical oversampling factor beyond which the improvement of performance is diminishing. Finally, if the length of the prototype filter increases with the number of subbands, an increase in the number of subbands will not deteriorate the performance.

Broadband microphone arrays has important applications such as hands-free mobile telephony, voice interface to personal computers and video conference equipment. This problem can be tackled in different ways. In this paper, a general broadband beamformer design problem is considered. The problem is posed as a Chebyshev minimax problem. Using the l/sub 1/-norm measure or the real rotation theorem, we show that it can be converted into a semi-infinite linear programming problem. A numerical scheme using a set of adaptive grids is applied. The scheme is proven to be convergent when a certain grid refinement is used. The method can be applied to the design of multidimensional digital finite-impulse response (FIR) filters with arbitrarily specified amplitude and phase.

The Davies transformation is a method to transform the steering vector of a uniform circular array (UCA) to a vector with Vandermonde form. As this form is similar to that of the steering vector of a uniform linear array (ULA), we can apply to UCAs the many tools that have been developed for ULAs. However, the Davies transformation can be highly sensitive to perturbations of the underlying ideal array model. In this paper, we present a method for deriving a more robust transformation using novel optimization techniques. In particular, we consider its application to direction of arrival estimation in the presence of correlated signals. The effectiveness of the method is illustrated through a numerical example.

A new design method for broadband microphone arrays is presented. Using sequences of calibration signals, the method is able to design finite-impulse response (FIR) filters with specific performance. The method can control and adjust the speech distortion, noise suppression, and echo cancellation directly. It turns out that a significantly shorter filter length can be applied to achieve better overall performance than the least-squares method or the signal-to-noise plus interference method.

In using the B-spline network for nonlinear system modeling, owing to a lack of suitable theoretical results, it is quite difficult to choose an appropriate set of knot points to achieve a good network structure for minimizing, say, a minimum error criterion. In this paper, a novel knot-optimizing B-spline network is proposed to approximate the general nonlinear system behavior. The knot points are considered to be independent variables in the B-spline network and are optimized together with the B-spline expansion coefficients. The simulated annealing algorithm with an appropriate search strategy is used as an optimization algorithm for the training process in order to avoid any possible local minima. Examples involving dynamic systems up to six dimensions in the input space to the network are solved by the proposed method to illustrate the effectiveness of this approach.

A new type of adaptive scheme was proposed recently for designing a deterministic envelope-constrained (EC) filter such that the generated sequence of filters converges to the optimum filter. Previous results at this level of generality linked convergence only to within a neighborhood of the optimum filter. Based on the adaptive scheme, two new theorems are established in a stochastic environment for which the adaptive EC filter converges in mean square sense and with probability one to the noiseless optimum filter for a fixed step-size and a decreasing sequence of step-sizes, respectively. Numerical examples involving pulse compression Barker-coded signal are studied for solving the EC filtering problem.

The envelope-constrained filtering problem is concerned with the design of a filter such that the noise enhancement is minimized while the noiseless filter response stays within an envelope. Naturally, the optimum filter response to the prescribed input signal tends to touch the output boundaries at some points. Consequently, any disturbance to the prescribed input signal could result in the output constraints being violated. In this paper, we formulate a semi-infinite constrained optimization problem in which the margin of the constraint robustness of the filter is maximized. Using a smoothing technique, it is shown that the solution of the optimization problem can be obtained by solving a sequence of strictly convex optimization problems with integral cost.

We consider a class of FIR filters defined by the first order difference routing digital filter (DRDF) structure and sums of two powers-of-two coefficients. A novel design method is developed for constructing high quality filters with reference to the min-max error criterion. This method is highly efficient in terms of computational time. Simulation studies show a large improvement over existing methods such as quantization. In some cases, the peak ripple magnitude over the stop and pass bands is reduced by up to 13 dB over the quantization method. These results are achieved even for cases involving small number of delays.

Consider an unknown, possibly unstable and linear time-invariant LTI system with output disturbance. Based on the concept of learning identification introduced in Teo et al. 1995 and the iterative scheme proposed in Zang et al. 1992, a new scheme for mutually enhanced data-based least squares identification and model-based LQG robust control design is presented. One of the novel features of this scheme is that both the identification and the robust control design tasks can be performed by minimizing a single cost function. To illustrate the effectiveness of the proposed scheme, a numerical example is presented.

Envelope-constrained filtering is concerned with the design of a time-invariant filter to process a given input signal such that the noiseless output of the filter is guaranteed to lie within a prespecified output mask. In this paper, using Laguerre filters and H/sub /spl infin// optimization techniques, the continuous-time envelope-constrained filter design problem has been reformulated and solved as a constrained H/sub /spl infin// model-matching problem. To illustrate the effectiveness of the design method, a numerical example is presented which deals with the design of an equalization filter for a digital transmission channel.

The envelope constrained (EC) filtering problem is concerned with the design of a filter with minimum gain to white input noise and with a response to a given signal that fits into a prescribed envelope. Using the discrete-time orthonormal Laguerre network, the EC filtering problem is posed as a quadratic programming (QP) problem with affine inequality constraints. An iterative algorithm for solving this QP problem is proposed which serves as a basis for the derivation of adaptive algorithms for applications where the parameters of the underlying signal model are either not known or varying with time.

The discrete-time envelope constrained (EC) filtering problem can be formulated as a quadratic programming (QP) problem with linear inequality constraints. In this paper, the QP problem is approximated by an unconstrained minimization problem with two parameters. These parameters can be selected so that given an acceptable deviation from the norm of the optimal EC filter, the solution to the unconstrained problem satisfies both the deviation and envelope constraints. Newton's method with line search is applied to solve the unconstrained problem iteratively.

This paper presents new recursive procedures for designing optimum envelope-constrained (EC) filters. Using a constraint transcription technique, the inequality constrained quadratic programming problem associated with EC filtering can be approximated as an unconstrained minimization problem. Two types of optimization methods are developed to solve this unconstrained problem in a recursive adjusting manner. Simulations of the proposed recursive procedures are presented which support the theoretical predictions.

The problem of envelope-constrained filters with uncertain input (ECUI) was first formulated as a nonsmooth optimization problem. Previously, it was shown that this nonsmooth problem is equivalent to a standard quadratic programming problem which can be solved efficiently. The objective of the present paper is to investigate several issues relating to the sensitivity of ECUI filters. They are: (i) the output mask tolerance; (ii) the effect of the location of the pulse peak in the output response on the output mask tolerance; and (iii) the effect of the order of ECUI filters on the output mask tolerance. Theoretical analysis is supported by numerical simulation studies.>

This paper is concerned with the maximum robustness design of envelope-constrained filters with uncertain input (ECUI). It is shown that the maximum robustness ECUI filter design can be formulated as a minimax optimization problem in which the set of feasible filters is characterized by nonsmooth matrix inequalities. On the basis of the use of a newly developed transformation technique and the use of standard linear programming and quadratic programming, an efficient design algorithm is developed to solve this problem. The attractive feature of the proposed algorithm is that the ECUI filter thus designed can allow for the greatest uncertainty in the input signal while still achieving the acceptable filter performance. A numerical example is given to illustrate the effectiveness of the algorithm.>