The dung beetle optimizer (DBO) is a metaheuristic algorithm with fast convergence and powerful search capabilities, which has shown excellent performance in solving various optimization problems. However, it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems. Therefore, we propose an adaptive DBO (ADBO) based on an elastic annealing mechanism to address these issues. First, the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation, thus improving the convergence speed and search quality. Second, a greedy difference optimization strategy is introduced to increase population diversity, improve the global search capability, and avoid premature convergence. Finally, the elastic annealing mechanism is used to perturb the randomly selected individuals, helping the algorithm escape local optima and thereby improve solution quality and algorithm stability. The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness, superiority, and universality of ADBO.

The power optimization of mixed polarity Reed–Muller (MPRM) logic circuits is a classic combinatorial optimization problem. Existing optimization approaches often suffer from slow convergence and a propensity to converge to local optima, limiting their effectiveness in achieving optimal power efficiency. First, we propose a novel multi-strategy fusion memetic algorithm (MFMA). MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor (COA-OLA), complemented by population management through truncation selection. Second, leveraging MFMA, we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit power. Experimental results based on Microelectronics Center of North Carolina (MCNC) benchmark circuits demonstrate significant improvements over existing power optimization approaches. MFMA achieves a maximum power saving rate of 72.30% and an average optimization rate of 43.37%; it searches for solutions faster and with higher quality, validating its effectiveness and superiority in power optimization.

Traditional logic synthesis methods are based on Boolean logic, which tends to produce redundant logic structures in dense circuit applications, such as complex number operations and error detection/correction coding. Traditional Boolean and Reed-Muller (TB-RM) logic synthesis method combining traditional Boolean (TB) logic and Reed-Muller (RM) logic can improve comprehensive optimization indexes and reduce cost. The existing TB design method is not effective when dealing with constrained systems with high-resource utilization requirements. In addition, traditional synthesis methods do not consider multiobjective optimization of area, power consumption and reliability. To solve these problems, we propose an effective dual logic synthesis method (EDSM), which includes dual logic detection method (DDM) and differential evolution algorithm based on multidimensional mutation strategy (DE-MMS). DDM can complete the logic detection function, and DE-MMS can further optimize the polarity. In addition, to evaluate the soft errors occurring more efficiently at the logic level, we propose a soft error rate (SER) estimation model. Experimental results show that compared with state-of-the-art evolutionary algorithms, EDSM can search for optimal solutions in all optimization problems; compared with commonly used TB-based minimization methods, EDSM has obvious advantages in multiobjective optimization of area, power consumption and SER. After 6-LUT FPGA technology and standard cells mapping, by selecting area as the optimal cost implementation, we obtain average improvements in the area of 14% and 2%, respectively.

Area and reliability optimization have become the primary constraints in circuits logic synthesis. To address the increasing area and transient fault susceptibility in combinational circuits, we propose a high-dimensional genetic algorithm (HGA). HGA adopts an evolutionary scheme based on ternary tree, and uses adaptive crossover operator and flight operator to jump out of local optimum. Moreover, based on the HGA, we propose an area and reliability optimization method (AROM) for mixed polarity Reed-Muller logic circuits, which searches the best polarity with minimum area and soft error rate. The experimental results confirm that AROM can search for more desirable nondominated solutions in less time compared to existing optimization methods, and can be used as an effective electronic design automation tool for multi-objective optimization.

Logic synthesis is a crucial step in integrated circuit design, and power optimization is an indispensable part of this process. However, power optimization for large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits is an NP-hard problem. In this article, we divide Boolean circuits into small-scale circuits based on the idea of divide and conquer using the proposed Dynamic Adaptive Grouping Strategy (DAGS) and the proposed circuit decomposition model (CDM). Each small-scale Boolean circuit is transformed into an MPRM logic circuit by a polarity transformation algorithm. Based on the gate-level integration, we integrate small-scale circuits into an MPRM and Boolean Dual Logic (RBDL) circuit. Furthermore, the power optimization problem of RBDL circuits is a multi-task, multi-extremal, high-dimensional combinatorial optimization problem, for which we propose an Adaptive Multi-task Intelligent Algorithm (AMIA), which includes global task optimization, population reproduction, valuable knowledge transfer (VKT), and local exploration to search for the lowest power for RBDL circuits. Moreover, based on the proposed Fast Power Decomposition Algorithm (FPDA), we proposed a Power Optimization Approach (POA) for an RBDL circuit with the lowest power using the AMIA. Experimental results based on Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the POA compared to state-of-the-art POAes.

Area and power optimization of Fixed Polarity Reed–Muller (FPRM) circuits has received a lot of attention. Polarity optimization for FPRM circuits is essentially a binary multi-objective optimization problem. However, the existing area and power optimization approaches for FPRM logic circuits rarely produce a frontier and a greater number of Pareto optimal solutions . In this paper, a Multi-strategy Multi-objective Artificial Bee Colony (MMABC) algorithm is proposed to solve the binary multi-objective optimization problem. The main innovation of MMABC can be summarized as follows: a flexible foraging behavior strategy for employed bees is proposed to improve the searching ability of the algorithm; a genetic retention evolution for onlooker bees is proposed to improve the quality of the population; an efficient transform strategy is proposed to help the algorithm to jump out the local optimal and increase convergence speed. Moreover, we propose an area and power optimization approach for FPRM logic circuits, which uses the MMABC to search for the polarities (i.e., Pareto optimal solutions) with smaller area and lower power. Experimental results demonstrated the effectiveness and superiority of our approach in optimizing area and power of FPRM logic circuits.

Power optimization can reduce heat dissipation costs and has become an important step of circuit logic synthesis. Because the power optimization for mixed polarity Reed–Muller (MPRM) circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive memetic algorithm (MAMA), which includes global exploration optimizer, local heuristic optimizer, and initial population optimizer. We use the proposed differential evolution optimization, simulated annealing optimization, and data matching algorithm to make the population evolve. Moreover, based on the proposed matrix decomposition strategy and parallel polarity conversion algorithm, we propose a power optimization approach (POA) for MPRM circuits, which searches for an MPRM circuit with a minimum power using the MAMA. Experimental results demonstrated the effectiveness and superiority of the POA in optimizing the power of MPRM circuits.

Area optimization is one of the most important contents of circuits logic synthesis. The smaller area has stronger testability and lower cost. However, searching for a circuit with the smallest area in a large-scale space of polarity is a combinatorial optimization problem. The existing optimization approaches are inefficient and do not consider the time cost. In this paper, we propose a multi-strategy wolf pack algorithm (MWPA) to solve high-dimension combinatorial optimization problems. MWPA performs global search based on the proposed global exploration strategy, extends the search area based on the Levy flight strategy, and performs local search based on the proposed deep exploitation strategy. In addition, we propose a fast area optimization approach (FAOA) for fixed polarity Reed-Muller (FPRM) logic circuits based on MWPA, which searches the best polarity corresponding to a FPRM circuit. The experimental results confirm that FAOA is highly effective and can be used as a promising EDA tool.

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA.

Delay optimization has now emerged as an important optimization goal in logic synthesis. The delay optimization for ternary fixed polarity Reed–Muller (FPRM) circuits aims to find a ternary FPRM circuit with a minimum delay. Because the delay optimization for ternary FPRM circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive quantum genetic algorithm (MAQGA), which divides individuals into three-level populations: high-level population, intermediate-level population, and low-level population and uses the proposed ternary quantum rotation gate, proposed ternary quantum correction gate, and proposed multi-operator adaptive mutation mechanism to make the three-level populations evolve. Moreover, based on the proposed delay decomposition strategy, we propose a delay optimization approach (DOA) for ternary FPRM circuits under the unit delay model, which searches for a ternary FPRM circuit with a minimum delay using the MAQGA. Experimental results demonstrated the effectiveness and superiority of the DOA in optimizing the delay of ternary FPRM circuits.

Running is one of the most important workouts to keep our body fit. This paper presents RunnerPal - a runner monitoring and advisory system by harmonizing the rhythms of breathing, heart beating and striding based on smart devices. RunnerPal is a convenient, biofeedback-based, automated music recommendation system, which utilizes Bluetooth headset, Apple Watch and smartphone to obtain body sensed data. To improve the accuracy of the detection, we propose a novel approach to calibrate the result by integrating ambient sensed data with a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the striding and breathing frequencies. RunnerPal uses the sensed data and runner's contextual information to provide dynamic music suggestions to help the user achieve a target heart rate. We perform an empirical study to show the effect of music on heart rate and devise a Proportional Integral Differentiation Controller (PID - Controller) that recommends appropriate music to the user. RunnerPal has been validated by extensive experiments, and experimental results demonstrate that it can help runners achieve a target heart rate and maintain a stable running rhythm for indoor/outdoor running 91.6 percent of the time. In addition, RunnerPal can provide some advice to improve exercise effectiveness for runners.

With the pace of modern life quickening and increasing work stress, people don't have enough time to focus on their health and communicate with family members. The loneliness and chronic diseases (e.g., obesity, depression, diabetes, and dementia) have become more prevalent. In the paper, we propose SmartBuddy, a novel integrated mobile sensing and detecting system for monitoring people's family activities, which can motivate the user to do proper physical exercise and establish good relationships with family members for maintaining their physical and mental health. Specifically, SmartBuddy firstly uses smartphones and Apple Watches built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, etc. Secondly, SmartBuddy can accurately detect family activities including occurrence/duration of meal, cooking, TV viewing, conversations, in an unobtrusive manner based on sensed data. Thirdly, SmartBuddy will propose a personal plan to suggest the user doing some exercise and making continuous progress in the process of communicating with family members. We have fully implemented SmartBuddy on the Android platform and perform testbed experiments. The experimental results demonstrate that SmartBuddy is easy to use, accurate, and appropriate for family activities with the accuracy of 80% and the user satisfaction degree of 84.5%.

Taking part in family activities plays an important role in establishing good relationships with family members. It can solve the loneliness of elders, which related not only to their physical health, but also to the well-being of the whole family. In the paper, we propose FamilyPal, an effective system for detecting family activities, which can help users establish good relationship with family members. Specifically, FamilyPal firstly uses smartphones built-in sensors, such as GPS, accelerometer, microphone, gyroscope, and Wi-Fi to obtain the motion and location of users, the surrounding voice, etc. Secondly, with the sensed data, we propose an effective method based on Gaussian Mixtures Models (GMM) to detect family activities, including occurrence of meal, cooking, TV viewing, conversations, in an unobtrusive manner. Thirdly, we select appropriate sensors for classification to improve smartphones battery life. FamilyPal has been implemented on the Android platform and evaluation of the system with 10 subjects over one week shows that FamilyPal can accurately classify family activities with the average precision of 71%, the average recall of 73% and the F-measure of 71.99%.

Fixed polarity Reed-Muller expressions (FPRMs) are well-suited for many practical applications due to they have many excellent properties. In order to obtain an optimal FPRM with fewest product terms, we propose an efficient minimization algorithm (EMA-FPRMs) for FPRMs. The main idea behind the EMA-FPRMs is that, firstly, the incompletely specified Boolean function is transformed into the zero polarity incompletely specified fixed polarity RM expression (ISFPRM) by using the proposed ISFPRM acquisition algorithm; secondly, the polarity and allocation of don't care terms of ISFPRM is encoded as chromosome; lastly, the optimal FPRM with fewest product terms is obtained by using genetic algorithm (GA), in which the FPRM that corresponds to the given chromosome is obtained by using the proposed chromosome conversion algorithm. The experimental results on MCNC benchmark circuits show that compared with the traditional polarity optimization approach which neglects the don't care terms, the EMA-FPRMs is highly effective in minimizing the number of product terms of FPRMs. Moreover, the EMA-FPRMs is faster than the GA based minimization algorithm which also considers the don't care terms.

Breathing plays an important role in the process of running. A stable and harmonic breathing rhythm can postpone runners' fatigue and help to improve their running performances. This paper presents a method that can detect runner's breathing frequency continuously. We utilize Bluetooth headset and smart phone to obtain sensed data, such as striding frequency and breathing frequency. Due to the interference of ambient noise, the detection will be inaccurate. In order to cope with this problem, we calibrate the detection result by leveraging a physiological model, called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. Our method has been validated by extensive experiments and the experimental results indicate that it can accurately detect the breathing frequency for runners.

Digital Living Network Alliance (DLNA) puts forward an interoperable architecture of home network equipment to implement the simple and seamless interoperability between household appliances, mobile devices and computers, so as to enhance and enrich users' experience. Because DLNA is designed to implement the sharing of multimedia resources between devices in a family environment, it does not support cross-LAN accessing. Currently, the cross-network cooperative work between smart devices has become a hot issue. To overcome this problem, this paper presents CLMRS - a cross-LAN accessing solution of C/S architecture based on DLNA technology. CLMRS can be used to implement cross-network media resources sharing by designing DLNA gateway/router of applicaion-level. We use DLNA gateway as a agent of LAN which is designed to transpond communication messages and to redirect address. To achieve cross-LAN media resources sharing, CLMRS adapts DLNA router as a transfer server to transpond communication data and to manage users access right. We also optimize the transmission path of media Steam to reduce the pressure of DLNA router. In order to protect user privacy, CLMRS sets a family account to limit the access right and proposes an efficient and secure authentication mechanism with anonymity. Our design and theoretical model are validated via implementing an instance of DLNA cross-network communication. Experimental results show that the approach is practical and can protect user privacy effectively.