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A disturbance observer-based control (DOBC) approach is proposed in this paper for control design and qualitative robustness analysis of PWM-based DC-DC buck power converters. A disturbance compensation gain is constructed for the composite control algorithm to cancel the unmatched uncertainties in the output voltage channel. Rigorous criterion is provided to show the quantitative robustness between controller parameters and circuit uncertainties. The DOBC approach delivers not only a promising disturbance rejection performance but also a superior property of tracking performance recovery. The feasibility and efficiency of the proposed approach is validated by experimental results based on LabVIEW real-time control test setup. [ABSTRACT FROM AUTHOR]

This paper addresses the robust gradient learning (RGL) problem. Gradient learning models aim at learning the gradient vector of some target functions in supervised learning problems, which can be further used to applications, such as variable selection, coordinate covariance estimation, and supervised dimension reduction. However, existing GL models are not robust to outliers or heavy-tailed noise. This paper provides an RGL framework to address this problem in both regression and classification. This is achieved by introducing a robust regression loss function and proposing a robust classification loss. Moreover, our RGL algorithm works in an instance-based kernelized dictionary instead of some fixed reproducing kernel Hilbert space, which may provide more flexibility. To solve the proposed nonconvex model, a simple computational algorithm based on gradient descent is provided and the convergence of the proposed method is also analyzed. We then apply the proposed RGL model to applications, such as nonlinear variable selection and coordinate covariance estimation. The efficiency of our proposed model is verified on both synthetic and real data sets. [ABSTRACT FROM AUTHOR]

Conflicts in scheduling can be treated as defining an undirected linear graph independently of the relation of the activities in conflict to additional constraints of time and space. Each connected component of such a graph, which can be found by on algorithm described by Gotlieb and Corneil, corresponds to a set of events that must be scheduled at different times. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel model for the discrimination of complex three-dimensional connected regions. The modified model is grounded on the Allen–Cahn equation. The modified equation not only maintains the original interface dynamics, but also avoids the unbounded diffusion behavior of the original Allen–Cahn equation. This advantage enables us to accurately populate and extract the complex connectivity region of the target part. The model is discretized employing a semi-implicit Crank–Nicolson scheme, ensuring second-order accuracy in both time and space. This paper provides a rigorous proof of the unconditional energy stability of our method, thereby affirming the numerical stability and the physical rationality of the solution. We validate the discriminative ability of the proposed model for 3D complex connected regions. • A phase-field model has been proposed to identify connected regions. • Our method can identify connected regions within complex structures. • Our scheme has the second-order accuracy in both time and space. • Various experiments have substantiated the robustness and efficacy of our model. [ABSTRACT FROM AUTHOR]

The stability problem for a class of positive two-dimensional (2-D) continuous-time systems in Roesser model with non-differentiable time-varying delays is investigated in this paper. So far, the case where the time-delay is non-differentiable has not been considered in any of the literature and the paper is to deal with this unconsidered case. By developing a novel comparison method, a sufficient condition for the α -exponential stability of the considered system is firstly derived. For computing an α -exponential estimate, a linear programming-based algorithm is next developed. Lastly, a numerical example is considered to illustrate the obtained result. • A new method is developed to investigate the stability problem for positive 2-D continuous-time Roesser systems with time-varying delays. • A sufficient condition for the α -exponential stability is derived. • An α -exponential estimate with a minimized factor vector is provided. [ABSTRACT FROM AUTHOR]

The level of privacy may vary across different parts of an image. This paper proposes a semi-visual obfuscation algorithm for images that takes into account the varying levels of privacy in different areas of the image. Firstly, we present a novel One-dimensional Uniform Chaotic Amplifier (1_DUCA) aimed at expanding the parameter range and enhancing the uniformity of the standard one-dimensional chaotic map. Next, we employ a detection algorithm or autonomous frame selection to identify the precise location of the area with strong privacy. Finally, we apply noise to blur the selected area and conceal vital bit information within the image. At this point, the image has certain visual effects, and only people with prior knowledge can recognize the image. Furthermore, in the last stage of image encryption, we employ a weight scrambling and high-low bit coupled diffusion technology to completely obscure the visual effects of the image. It is noteworthy that the experimental results and performance analysis have verified the practicality and security of the encryption algorithm. Moreover, they have also demonstrated the robust amplification effect of the employed amplifier. • A semi-visual fuzzy encryption scheme for images with varying privacy levels is proposed. • This paper presents a self-hiding fuzzy algorithm using noise blur and information hiding techniques. • Proposes a scrambling method that perturbs pixel positions based on their own weight, enhancing security and robustness. • This encryption scheme introduces a novel high-low coupling diffusion method to enhance pixel coupling. [ABSTRACT FROM AUTHOR]

Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider stochastic formulations of optimization problems. In this paper, we present an analysis of a method that gives optimal convergence estimates for monotone stochastic finite-sum variational inequalities. In contrast to the previous works, our method supports batching, does not lose the oracle complexity optimality and uses an arbitrary Bregman distance to take into account geometry of the problem. Paper provides experimental confirmation to algorithm's effectiveness. • We consider stochastic variational inequalities and present a new method for them. • An analysis of the method gives optimal convergence estimates. • Our method supports batching, does not lose the oracle complexity optimality. • Our method uses an arbitrary Bregman distance to take into account geometry of the problem. [ABSTRACT FROM AUTHOR]

Concept cognition and knowledge discovery under network data combine formal concept analysis with complex network analysis. However, in real life, network data is uncertain due to some limitations. Fuzzy sets are a powerful tool to deal with uncertainty and imprecision. Therefore, this paper focuses on concept-cognitive learning in fuzzy network formal contexts. Fuzzy object-induced network three-way concept (network OEF-concept) lattices and their properties are mainly investigated. In addition, three fuzzy network weaken-concepts are proposed. As the real data is too large, attribute reduction can simplify concept-cognitive learning by removing redundant attributes. Thus, the paper proposes attribute reduction methods that can keep the concept lattice structure isomorphic and the set of extents of granular concepts unchanged. Finally, an example is given to show the attribute reduction process of a fuzzy network three-way concept lattice. Attribute reduction experiments are conducted on nine datasets, and the results prove the feasibility of attribute reduction. • Investigate the concept-cognitive learning in a fuzzy network formal context. • Propose two attribute reduction algorithms for network OEF concept lattices. • Experiments on nine datasets show the effectiveness of the attribute reduction algorithms. [ABSTRACT FROM AUTHOR]

Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm, which speeds up deep learning training and plays an important role in distributed deep learning. However, ASGD suffers from the stale gradient problem, i.e., the gradient of worker may mismatch the weight of parameter server. This problem seriously affects the model performance and even causes the divergence. To address this issue, this paper designs a dynamic adjustment scheme via the momentum algorithm, which uses both stale penalty and stale compensation , i.e., stale penalty is to reduce the trust in stale gradient, stale compensation is to compensate the hurt of stale gradient. Based on this dynamic adjustment scheme, this paper proposes a dynamic asynchronous stochastic gradient descent algorithm (DASGD), which dynamically adjusts the compensation factor and the penalty factor via stale size. Moreover, we prove that DASGD is convergent under some mild assumptions. Finally, we build a real distributed training cluster to evaluate our DASGD on Cifar10 and ImageNet datasets. Compared with four SOTA baselines, experiment results confirm the superior performance of DASGD. More specifically, our DASGD has nearly the same test accuracy as SGD on Cifar10 and ImageNet , and only uses around 27.6% and 40.8% training time that of SGD, respectively. [ABSTRACT FROM AUTHOR]

A recent study published in the journal Fuzzy Information and Engineering explores the use of genetic algorithms and fuzzy set theory in epidemic modeling. The research, conducted by the Faculty of Science and Technology at Oxus University in Tashkent, Uzbekistan, emphasizes the need for sophisticated modeling techniques to better understand and predict dynamic scenarios in epidemiology. The study proposes a novel approach that integrates continued fraction theory and a genetic algorithm to fit real-world epidemic data, providing insights crucial for public health planning and control measures. By addressing unexplored facets and bridging gaps in previous research, the study contributes to a more comprehensive understanding of epidemic dynamics and offers a foundation for effective public health strategies in the face of infectious diseases. [Extracted from the article]

A new study conducted by researchers at Tiangong University in Tianjin, China, focuses on the development of a deep learning-based noise reduction method for ambulatory electrocardiogram (ECG) testing. The study aims to improve the clarity of ECG signals obtained during exercise, which are often affected by various noise interferences. The proposed method, which incorporates a multi-kernel convolutional transformer network structure, was found to outperform eight other state-of-the-art methods in terms of noise reduction performance. The researchers believe that this method has the potential to be applied in future noise reduction analysis of clinical dynamic ECG signals. [Extracted from the article]

A report from the North University of China discusses the detection of abnormal blood flow regions using near-infrared correlation spectroscopy. The researchers used the NL-Bregman-TV imaging algorithm to visualize blood flow, but found that the resolution was too low for practical use. To improve the resolution, they employed the bicubic interpolation method and proposed a parameter index to evaluate its effectiveness. They also developed a threshold segmentation algorithm and a morphological processing algorithm to accurately locate and extract the contour of abnormal blood flow. This research has important implications for the diagnosis and treatment of various diseases. [Extracted from the article]

The existing hyperspectral image data contain significant local and non-local spatial redundancy, as well as a large amount of spectral redundancy. However, current algorithms inadequately explore these redundant information, limiting the compression performance. To address this issue, this paper introduces a lossy compression algorithm for hyperspectral images, named THSIC(Transformer-based HyperSpectral Image Compression). This algorithm first utilizes a channel-spatial attention module to fully exploit spatial and spectral redundancies in hyperspectral images, resulting in a better latent representation. Subsequently, the Transformer and CNN-based hyperprior branches are employed to extract non-local and local redundant information from the latent representation, respectively. These two hyperprior information, along with the locally contextual prior extracted from the local context, are fused to construct multiple hyperprior information. Then, a more accurate entropy model is built using these priors, thereby enhancing the rate–distortion performance of lossy compression for hyperspectral images. [Display omitted] • The paper introduces a hyperspectral image compression algorithm based on multiple residual modules and channel-spatial attention to enhance the compression performance of the backbone network. • To explore local and non-local redundancy in the latent representation of hyperspectral images, the paper proposes a hybrid hyperprior network with dual branches, utilizing both Transformer and CNN-based hyperpriors. • Experimental results on three hyperspectral remote sensing image datasets show that the proposed hyperspectral remote sensing image compression algorithm based on Transformer super-prior has achieved good performance. [ABSTRACT FROM AUTHOR]

This paper proposes a fast bit-level chaotic image encryption algorithm. The goal is to make the difference between 0 and 1 in the bit-level ciphertext as small as possible. By introducing the concept of the difference between 0 and 1, constantly adjust the number of binary 0 and 1 in the ciphertext, and the difference is fed back to the encryption system until the difference reaches the preset accuracy, so as to quickly achieve a secure encryption effect. To verify the security of the algorithm, this paper selects multiple chaotic systems for testing and applies different chaotic systems to the encryption algorithm. Moreover, the proposed algorithm is compared with the bit-level and non-bit-level image encryption algorithms proposed by other scholars in recent years, and the results show that the proposed algorithm has higher security than other algorithms. [ABSTRACT FROM AUTHOR]

The boundary element method (BEM) employing the partial pivot adaptive cross approximation (PACA) algorithm has been observed to experience convergence failures and reduced solution accuracy when solving elasticity problems, especially at large scales. To address this issue, this paper proposes an improved algorithm for both 2D and 3D elasticity problems. Our investigations revealed that when the normal of an element is parallel to the coordinate axes and the element is in the same plane as the source point, zero entries with regular distributions will appear in the coefficient matrix. This results in the premature satisfaction of the convergence criterion of the PACA algorithm, leading to the omission of some rows and columns of the coefficient matrix. To address this issue, this paper proposes improvements to the PACA algorithm through the Coordinate Rotation Method (CRM) and the Component Traversal Method (CTM), and validates the effectiveness of these two improved methods. Furthermore, we investigate reasons behind the decrease in accuracy when employing the CTM to solve large-scale problems. We attribute this to the oscillatory nature of the estimated relative error and propose the modified PACA (M-PACA) algorithm. M-PACA not only maintains the advantages of the PACA algorithm in reducing storage space and accelerating coefficient matrix generation but also addresses its limitations, ensuring more accurate and reliable solutions for elasticity problems. [ABSTRACT FROM AUTHOR]

The fast multipole boundary element method (FMBEM) is a powerful technique for solving large-scale problems. Its effectiveness heavily relies on the iterative solver, which in turn depends crucially on the performance of the preconditioner. Although a leaf-based preconditioner has proven effective in the single domain FMBEM (SFB), it encounters challenges in the multi-domain FMBEM (MFB). To overcome this challenge, this paper proposes a cell renumbering algorithm to construct a leaf-based preconditioner for MFB. This algorithm renumbers the cells in the tree structure, taking into account the influence of the tree structure's traversal order on the coefficient matrix distribution. This renumbering enables the generation of a leaf-based preconditioner matrix for MFB, with specialized processing for sub-matrix blocks associated with the interface, accommodating both aligned and misaligned cells on both sides of the interface. In cases where the tree structure is not aligned, each interface-related submatrix may correspond to two or more leaves, resulting in a larger matrix size and increased computational cost for preconditioner calculations. To mitigate this computational burden, this paper proposes an improved cell renumbering scheme, which directly manipulates the index set to align the cell indices within the tree structure, thereby reducing computational costs. The utilization of this renumbered preconditioner in MFB not only achieves fast convergence of iterative solvers but also retains the advantages as in SFB. Through these techniques, this paper proposes a new approach to MFB. Numerical results presented in this paper demonstrate the effectiveness and universality of the proposed cell renumbering algorithm and preconditioner for MFB. [ABSTRACT FROM AUTHOR]

This paper explores the conditions for determining fixed nodes in structured networks, specifically focusing on directed acyclic graphs (DAGs). We introduce several necessary and sufficient conditions for determining fixed nodes in p -layered DAGs. This is accomplished by defining the problem of maximum disjoint stems, based on the observation that all DAGs can be represented as hierarchical structures with a unique label for each layer. For structured networks, we discuss the importance of fixed nodes by considering their controllability against the variations of network parameters. Moreover, we present an efficient algorithm that simultaneously performs labeling and fixed node search for p -layered DAGs with an analysis of its time complexity. The results presented in this paper have implications for the analysis of controllability at the individual node level in structured networks. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to improve transient response and dynamic performance of automatic voltage regulator (AVR). Design/methodology/approach: This paper proposes a novel fractional order proportional–integral–derivative plus derivative (PIλDµDµ2) controller called FOPIDD for AVR system. The FOPIDD controller has seven optimization parameters and the equilibrium optimizer algorithm is used for tuning of controller parameters. The utilized objective function is widely preferred in AVR systems and consists of transient response characteristics. Findings: In this study, results of AVR system controlled by FOPIDD is compared with results of proportional–integral–derivative (PID), proportional–integral–derivative acceleration, PID plus second order derivative and fractional order PID controllers. FOPIDD outperforms compared controllers in terms of transient response criteria such as settling time, rise time and overshoot. Then, the frequency domain analysis is performed for the AVR system with FOPIDD controller, and the results are found satisfactory. In addition, robustness test is realized for evaluating performance of FOPIDD controller in perturbed system parameters. In robustness test, FOPIDD controller shows superior control performance. Originality/value: The FOPIDD controller is introduced for the first time to improve the control performance of the AVR system. The proposed FOPIDD controller has shown superior performance on AVR systems because of having seven optimization parameters and being fractional order based. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach: Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings: The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value: The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a high-performance bidirectional architecture for the quasi-comparison-free sorting algorithm. Our architecture improves the performance of the conventional unidirectional architecture by reducing the total number of sorting cycles via bidirectional sorting along with two auxiliary methods. Bidirectional sorting allows the sorting tasks to be conducted concurrently in the high- and low-index parts of our architecture. The first auxiliary method is boundary finding, which shortens the range for index searching by finding the boundaries of the range. The second auxiliary method is queue storing, which stores each useful index in a queue in advance to reduce the number of miss cycles during index searching. The performance of our architecture highly depends on the distribution of input data. For each set of input data to be sorted, five Gaussian distributions of the input data and four standard derivations for each distribution were adopted in our experiments. The results show that at the expense of some additional area cost, the number of sorting cycles and the energy consumption are significantly reduced by our method. [ABSTRACT FROM AUTHOR]

In a recently published paper, an estimation algorithm of carrier frequency offset (CFO) with I/Q mismatch was proposed. Errors in the derivation of the algorithm show that its precision can only be asserted in the case of relatively small I/Q mismatch. Averaging the intermediate variables among one short preamble period improves the estimation accuracy under conditions where I/Q mismatch is large. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to overcome the shortcomings of the marked method in identifying the number of regions in binary images, and to present a new algorithm to identify the number of bars. Design/methodology/approach – Identifying the number of bars automatically is widely used in the lumbering, iron, and steel industry. The marked methods need complex signing and have to scan the binary image several times. An algorithm that uses two chain lists to identify the number of bars is presented. It uses the characteristic of the chain list. When scanning the binary image, it creates two chain lists and then the number and the central position of the bar based on the relations of created chain lists can be determined. Findings – Test results have indicated this algorithm is feasible and effective on recognizing the number of bars. Research limitations/implications – When the connected area has too many pixels in the binary images, recognizing its number needs much more time. Originality/value – The paper presents a very useful approach for identifying the number of the bar, in the lumbering and steel industry. [ABSTRACT FROM AUTHOR]

Purpose – Shortest path problem has always been a hot topic in the study of graph theory, because of its wide application field, extending from operational research to the disciplines of geography, automatic control, computer science and traffic. According to its concrete application, scholars in the relevant field have presented many algorithms, but most of them are solely improvements based on Dijkstra algorithm. The purpose of this paper is to enrich the kinds of (and improve the efficiency of) the shortest path algorithms. Design/methodology/approach – This paper puts forward an improved calculation method of shortest path using cellular automata model, which is designed to search the shortest path from one node to another node. Cellular state set is adjusted with combination of breeding and mature states. Evolution rule is improved to enhance its parallelism. At the same time, recording manner of cellular state turnover is modified to record all information sources. Findings – The result indicates that the improved algorithm is correct and more efficient, in that it could reduce the times of cellular state turnover; meanwhile, it can solve multi-paths problem. Originality/value – In this paper, cellular state set in exiting shortest path algorithm based on cellular automata theory is adjusted; evolution rule is improved; and recording manner of cellular state turnover is modified to record all information sources. All of which make the parallelism of this algorithm enhanced and the multi-paths problem solved. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of the paper is to find a speed control structure with two degrees of freedom robust against drive parameters variations. Application of structure model following control (MFC) and fuzzy technique in the controller of PI type creates proper non-linear characteristics, which ensures controller robustness. Design/methodology/approach - The use of proper structure with two degrees of freedom and non-linear characteristic introduced by fuzzy technique ensures the robustness of the speed control system. The paper proposes a novel approach to MFC synthesis to be performed in two stages. The first stage consists in the set value of P type controller of model and the process controller simultaneously should be designing by fuzzy technique. At the second stage of the synthesis consist in tuning parameters of process fuzzy controller by the swarm of particles method (particle swarm optimization) on the basis of a defined quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system. Findings - Use of proper structure with two degrees of freedom of the non-linear fuzzy controller guarantees expected robustness and improves the dynamics of speed control significantly. Research limitations/implications - The proposed structure of MFC was tested on a single machine under well-defined conditions. Further investigations are required before any industrial applications. Practical implications - The proposed controller synthesis and its results may be very helpful in robotic system where changing of system parameters is characteristic for many industrial robots and manipulators. Originality/value - The paper proposes an original method of synthesis of robust system with two degrees of freedom system validated by simulation and experimental investigations. [ABSTRACT FROM AUTHOR]

This article presents an argument in favor of revising the escalator/de-escalator formula (EDF) and algorithm that were originally intended to protect buyers and sellers of corrugated paperboard. It is recommended that buyers or sellers identify the aggregated paper percentage of the customer's spend, base the rise or decline on that percentage, and re-set the percentage with every movement.

Existing algorithms for computing the nth-order frequency response functions of the Duffing oscillator have helped promote frequency domain analysis of nonlinear systems but still have a number of practical difficulties. A very efficient algorithm to enable the nth-order symmetric generalized frequency response functions (GFRFs) to be written down directly in terms of the coefficients of the generalized higher order Duffing oscillator model is developed in this paper. The analytical expression derived in this paper shows that the key procedure in the determination of the higher order GFRFs is the computation of the Stirling set of the second kind. This enables the structure of the higher order GFRFs to be seen more clearly. [ABSTRACT FROM AUTHOR]

This paper presents novel design procedures for class E switching circuits allowing implicit circuit equations. Because of the allowance, circuit simulators can be used in the proposed design procedures. Moreover, the proposed design procedures also allow any conditions considered until now. The proposed design algorithms are implemented by using PSpice and OPTIMUS. This paper shows the design examples of two kinds of class E switching circuits. In particular, the design example of the class E oscillator shows the benefit of the proposed design procedure eminently, i.e., it is unnecessary to make an equivalent model of the semiconductor devices for the design. These design examples show the validity and effectiveness of the proposed design procedures. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to present briefly a unified fractional step method for fluid dynamics, incompressible solid mechanics and heat transfer calculations. The proposed method is demonstrated by solving compressible and incompressible flows, solid mechanics and conjugate heat transfer problems. Design/methodology/approach - The finite element method is used for the spatial discretization of the equations. The fluid dynamics algorithm used is often referred to as the characteristic-based split scheme. Findings - The proposed method can be employed as a unified approach to fluid dynamics, heat transfer and solid mechanics problems. Originality/value - The idea of using a unified approach to fluid dynamics and incompressible solid mechanics problems is proposed. The proposed approach will be valuable in complicated engineering problems such as fluid-structure interaction and problems involving conjugate heat transfer and thermal stresses. [ABSTRACT FROM AUTHOR]

This paper addresses the design of gradient-based search algorithms for multivariable system estimation. In particular, the paper here considers so-called "full parametrization" approaches, and establishes that the recently developed "data-driven local coordinate" methods can be seen as a special case within a broader class of techniques that are designed to deal with rank-deficient Jacobians. This informs the design of a new algorithm that, via a strategy of dynamic Jacobian rank determination, is illustrated to offer enhanced performance. [ABSTRACT FROM AUTHOR]

Reducing address arithmetic operations by optimization of address offset assignment greatly improves the performance of digital signal processor (DSP) applications. However, minimizing address operations alone may not directly reduce code size and schedule length for DSPs with multiple functional units. Little research work has been conducted on loop optimization with address offset assignment problem for architectures with multiple functional units. In this paper, we combine loop scheduling, array interleaving, and address assignment to minimize the schedule length and the number of address operations for loops on DSP architectures with multiple functional units. Array interleaving is applied to optimize address assignment for arrays in loop scheduling process. An algorithm, Address Operation Reduction Rotation Scheduling (AORRS), is proposed. The algorithm minimizes both schedule length and the number of address operations. with to list scheduling, AORRS shows an average reduction of 38.4% in schedule length and an average reduction of 31.7% in the number of address operations. Compared with rotation scheduling, AORRS shows an average reduction of 15.9% in schedule length and 33.6% in the number of address operations. [ABSTRACT FROM AUTHOR]

This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem mb controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification. [ABSTRACT FROM AUTHOR]

This paper aims at solving the bipartite containment problem for linear singular multi-agent systems (MASs) with multiple dynamic leaders over general signed digraphs, where each leader can be autonomous or dynamically evolving via interactions with its neighboring leaders. To this end, we first establish some properties for the Laplacian matrices of signed digraphs. Then, three distributed observer-based bipartite containment protocols are proposed using different local output information. Multi-step algorithms for constructing the corresponding proposed protocols are also given. It is shown that under the proposed control protocols, bipartite containment can be achieved for any admissible initial states provided that the underlying signed digraph is weakly connected. Compared to the existing related works, the major contribution of present work is that the developed results are applicable for arbitrary weakly connected signed digraphs, no matter whether they are sign-symmetric or sign-asymmetric and whether they are structurally balanced or unbalanced. Two numerical examples are finally given to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]

Active learning method (ALM) is a powerful fuzzy–based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real–time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from $O(N^{2})$ to $O(2N)$ for a multi–input single–output (MISO) system. Also, the computational costs in both training and inference modes are decreased to only a few additions and multiplications. Furthermore, we develop a memory–efficient digital architecture for the proposed compressed ALM algorithm that can be leveraged for various computing systems through configuring a few registers. Finally, we assess the performance of the proposed approach using various function modeling and classification applications and provide a comparison with conventional ALM and some other well-know approaches. Simulation and hardware implementation results demonstrate that the proposed approach achieves reduced noise sensitivity with $128\times $ reduction in the average memory usage while realizing comparable accuracy compared to the other approaches studied herein. [ABSTRACT FROM AUTHOR]

This paper presents an area-efficient architecture for stochastic low-density parity-check (LDPC) decoder with high throughput and excellent bit-error-rate (BER) performance. The correlation effects of a stochastic Sum-Product Algorithm (SPA) are analyzed. Based on this analysis, a variable node (VN) structure is proposed and its similarity with a correlation divider (CORDIV) is pointed out. Based on the properties of CORDIV, the area of probability tracer in the VN is reduced significantly. In order to achieve more accurate results when the check-to-variable (C2V) messages are not strong enough, the 3-3 input grouping sub-node is replaced by an adder-based 5-1 input grouping sub-node of the degree-6 VN for (2048,1723) code. An unbiased stochastic sequence generator is adopted to get more accurate results from the smaller probability tracer. Furthermore, the soft bit-flipping prior-processing and the C2V-based hard decision updating method are combined in VN to reduce the decoding latency. A (2048,1723) stochastic LDPC decoder is designed in the TSMC 65 nm process to demonstrate the proposed decoder architecture. With the aid of early termination, the decoder occupies 2.34 mm2 core area and can achieve 116.17 Gb/s at 4.4 dB and 461.99 Gb/s at 5.5 dB under 970 MHz with better decoding performance. Compared with the state-of-the-art stochastic IEEE 802.3an LDPC decoders, the proposed architecture can achieve the best throughput, throughput-to-area ratio, and BER performance. [ABSTRACT FROM AUTHOR]

Integrating type analog-to-digital converters (ADC) used in column-parallel CMOS image sensors trade conversion speed with size, power, and complexity to achieve optimal performance. A new integrating ADC architecture called single-slope look-ahead ramp (SSLAR) ADC is introduced in this paper. It utilizes a statistical approach and code-prediction methods to improve the conversion speed of standard single-slope ramp (SSR) ADC. It is shown that SSLAR ADC reduces power consumption while achieving an increased frame rate. This is achieved by the SSLAR algorithm that was optimized for column-parallel CMOS active pixel sensor (APS) imager architecture. A 10-bit SSLAR ADC was designed in a $0.5~\mu \text{m}$ CMOS (2P3M) process and integrated with a column-parallel CMOS image sensor that has $200\times 150$ array with $15~\mu \text{m}$ pixels. Measurements showed that a 6x frame rate increase can be achieved while reducing power consumption 13% with minimal impact on image quality. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced optimal control technique based on adaptive Maximize-M Kalman filter (AM-MKF) is used. To maximize power extraction from solar PV (Photovoltaic) panel, a learning-based hill climbing (L-HC) algorithm is implemented for a grid integrated solar PV system. For the testing, a three-phase system configuration based on 2-stage topology, and the deployed load on a common connection point (CCP) are considered. The L-HC MPPT algorithm is the modified version of HC (Hill Climbing) algorithm, where issues like, oscillation in steady-state condition and, slow response during dynamic change condition are mitigated. The AM-MKF is an advanced version of KF (Kalman Filter), where for optimal estimation in KF, an AM-M (Adaptive Maximize-M) concept is integrated. The key objective of the novel control strategy is to extract maximum power from the solar panel and to meet the demand of the load. After satisfying the load demand, the rest power is transferred to the grid. However, in the nighttime, the system is used for reactive power support, which mode of operation is known as a DSTATCOM (Distribution Static Compensator). The capability of developed control strategies, is proven through testing on a prototype. During experimentation, different adverse grid conditions, unbalanced load situation and variable solar insolation are considered. In these situations, the satisfactory performances of control techniques prove the effectiveness of the developed control strategy. [ABSTRACT FROM AUTHOR]

The article offers highlights from the research paper "Data-Driven Algorithm Design" written by Gupta and Roughgarden.

Finite mixtures of skew distributions provide a flexible tool for modeling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expectation–Maximization (EM) algorithm can become very time consuming due to the complicated expressions involved in the E-step that are numerically expensive to evaluate. While parallelizing the EM algorithm can offer considerable speedup in time performance, current implementations focus almost exclusively on distributed platforms. In this paper, we consider instead the most typical operating environment for users of mixture models—a standalone multicore machine and the R programming environment. We develop a block implementation of the EM algorithm that facilitates the calculations on the E- and M-steps to be spread across a number of threads. We focus on the fitting of finite mixtures of multivariate skew normal and skew $t$ distributions, and show that both the E- and M-steps in the EM algorithm can be modified to allow the data to be split into blocks. Our approach is easy to implement and provides immediate benefits to users of multicore machines. Experiments were conducted on two real data sets to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Memory is one of the most important mechanisms in recurrent neural networks (RNNs) learning. It plays a crucial role in practical applications, such as sequence learning. With a good memory mechanism, long term history can be fused with current information, and can thus improve RNNs learning. Developing a suitable memory mechanism is always desirable in the field of RNNs. This paper proposes a novel memory mechanism for RNNs. The main contributions of this paper are: 1) an auxiliary memory unit (AMU) is proposed, which results in a new special RNN model (AMU-RNN), separating the memory and output explicitly and 2) an efficient learning algorithm is developed by employing the technique of error flow truncation. The proposed AMU-RNN model, together with the developed learning algorithm, can learn and maintain stable memory over a long time range. This method overcomes both the learning conflict problem and gradient vanishing problem. Unlike the traditional method, which mixes the memory and output with a single neuron in a recurrent unit, the AMU provides an auxiliary memory neuron to maintain memory in particular. By separating the memory and output in a recurrent unit, the problem of learning conflicts can be eliminated easily. Moreover, by using the technique of error flow truncation, each auxiliary memory neuron ensures constant error flow during the learning process. The experiments demonstrate good performance of the proposed AMU-RNNs and the developed learning algorithm. The method exhibits quite efficient learning performance with stable convergence in the AMU-RNN learning and outperforms the state-of-the-art RNN models in sequence generation and sequence classification tasks. [ABSTRACT FROM AUTHOR]

In model predictive control (MPC), the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set (AS) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control (UFTOC) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this paper, the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS-type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speedup can be important for AS-type solvers used for linear, nonlinear, and hybrid MPC. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the author discusses an article published in the journal about load balancing, matrix-vector multiplication, and their significance to machine algorithms.

• A many objective evolutionary algorithm based on decomposition and moth search is proposed. • Random and adaptive weights is used to break the limitation of uniform distribution weights. • Mutual evaluation value is used to evaluate the optimal individual in the neighborhood. • Improving scale factor α in MSA is to improve the performance of the proposed algorithm. Decomposition-based multi-objective evolutionary algorithm (MOEA/D) has good performance in solving multi-objective problems (MOPs) but poor performance in solving many-objective optimization problems (MaOPs). The weight vectors in MOEA/D are relatively fixed, which results in poor performance when dealing with complex MaOPs. In this paper, random and adaptive weights are introduced into MOEA/D to break the limitation of fixed weight vectors. And the moth search algorithm (MSA) is used as an operator to improve global search ability. The updating strategies in MSA are more consistent with the neighborhood learning strategy adopted in MOEA/D. In addition, to enable MSA to find the optimal solution in the neighborhood on the MaOPs to update other individuals. This paper introduces mutual evaluation value for evaluating the optimal individual in the neighborhood, and the proposed algorithm is abbreviated as MOEA/DMS. In comparative experiments on the MaF test suite, hypervolume (HV) and inverted generational distance (IGD) are used to measure MOEA/DMS and other many-objective evolutionary algorithms (MaOEAs). The results show that MOEA/DMS has an excellent performance in dealing with MaOPs. Besides, MOEA/DMS is compared with other state-of-the-art MaOEAs on two combinatorial MaOPs. The results show that MOEA/DMS also has significant advantages in dealing with combinatorial MaOPs. [ABSTRACT FROM AUTHOR]

A recent study conducted by Coltan Scrivner and his colleagues explores the relationship between behavioral attraction and morbid curiosity in women's mating interest. The researchers suggest that despite the potential costs associated with men who exhibit dangerous personalities, morbidly curious women may benefit from upregulating their preference for such men to satisfy short-term mating goals. The study does not delve into the business potential of this research, but it presents an opportunity for specialized morbid tool-making and tool use. Additionally, the article mentions a free online Morbid Curiosity Test created by Scrivner to measure one's position on the Morbid Curiosity Scale. [Extracted from the article]

This paper presents a geometric transversals approach for representing the probability of track detection as an analytic function of time and target motion parameters. By this approach, the optimization of the detection probability subject to sensor kinodynamic constraints can be formulated as an optimal control problem. Using the proposed detection probability function, the necessary conditions for optimality can be derived using calculus of variations, and solved numerically using a variational iteration method (VIM). The simulation results show that sensor state and control trajectories obtained by this approach bring about a significant increase in detection probability compared to existing strategies, and require a computation that is significantly reduced compared to direct methods. [ABSTRACT FROM PUBLISHER]

Finding the optimal solution to the constrained l0 -norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal solution, unless using other objective functions (e.g., the l1 norm or lp norm) for approximate solutions or using greedy search methods for locally optimal solutions (e.g., the orthogonal matching pursuit type algorithms). In this paper, a neurodynamic optimization method is proposed to solve the l0 -norm minimization problems for obtaining the global optimum using a recurrent neural network (RNN) model. For the RNN model, a group of modified Gaussian functions are constructed and their sum is taken as the objective function for approximating the l0 norm and for optimization. The constructed objective function sets up a convexity condition under which the neurodynamic system is guaranteed to obtain the globally convergent optimal solution. An adaptive adjustment scheme is developed for improving the performance of the optimization algorithm further. Extensive experiments are conducted to test the proposed approach in this paper and the output results validate the effectiveness of the new method. [ABSTRACT FROM PUBLISHER]

This article details Charles Babbage’s theoretical computer program, the first attempt to specific how to mechanize complex algorithms with a computer, written in 1837. The article also discusses his theoretical computer, the Analytical Engine, that his program would have run on. Topics include Babbage’s first and second code tables and the design of the Analytical Engine.

The article focuses on technological innovations that are resulting from research into origami. It states that origami has been described mathematically and modeled with computers, allowing engineers to create technologies that can be folded into a compact shape for transport. It mentions that each folded flap in origami uses a circular portion which allows engineers plan creases that will provide a desired shape. It comments that the folding algorithms are used in technological applications.

The article discusses the construction and maintenance of Big-Data analytics systems as of March 2013, focusing on machine-learning methods and statistical methods while describing the Hazy project for algorithm management. Topics include trained systems, interfaces between algorithms and systems, the GeoDeepDive application for the statistical analysis of geological research papers, and the Bismarck project for infrastructure abstractions. Data sets, probabilistic logic programming, debugging, convex programming, and scalability are mentioned.

Aiming at reducing the complexity of minimum mean square error (MMSE) detection in massive multiple-input multiple-output (MIMO) systems, this paper proposes a detection algorithm with high convergence rate and an efficient hardware architecture based on second-order Richardson iteration (SORI). In the proposed algorithm, a pre-iteration-based initialization method is presented to accelerate the convergence without extra complexity. In addition, the approximation of relaxation factor and the log-likelihood ratio (LLR) is introduced to further reduce computing load. Theoretical analysis demonstrates the advantages of the proposed algorithm in fast convergence and low complexity, and simulation results show that the proposed algorithm can efficiently approach MMSE performance. Based on this algorithm, a flexible hardware architecture is designed, which is deeply pipelined to support $128\times U$ ($8\leq U\leq 32$) massive MIMO detection with the configurable number of iterations, and a folded dual-mode systolic array (DMSA) is fully utilized to achieve the flexibility with low hardware consumption. Implemented on Xilinx Virtex-7 FPGA and SMIC 40nm CMOS technology, the proposed detector is competitive in terms of energy and area efficiency compared to state-of-the-art iterative detectors, and it can adapt to the varied channel condition and the number of users in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) has been widely studied due to its high performance and usability for various applications such as image classification, detection, segmentation, translation, and action recognition. Thanks to the universal applications and high performance of DNN algorithm, DNN is adopted for various AI platforms, including edge/mobile devices as well as cloud servers. However, high-performance DNN requires a large amount of computation and memory access, making it challenging to implement DNN operation on edge/mobile. There have been several ways to solve these problems, including algorithms as well as hardware for DNN. Algorithms that help accelerate DNN in hardware enable much more efficient operation of high-performance AI. This article aims to provide an overview of the recent hardware and algorithm co-design schemes enabling efficient processing of DNNs. Specifically, it will provide algorithm optimization methods for DNN structure, neurons, synapses, and data types. This paper also introduces optimization methods for hardware architectures, PE array, data-path control, and microarchitecture of PE. And we will also show examples of DNN algorithm and hardware co-designed ASICs. [ABSTRACT FROM AUTHOR]