Periodic interference frequently affects the measurement of small signals and causes problems in clinical diagnostics. Adaptive filters can be used as potential tools for cancelling such interference. However, when the interference has a frequency fluctuation, the ideal adaptive-filter coefficients for cancelling the interference also fluctuate. When the adaptation property of the algorithm is slow compared with the frequency fluctuation, the interference-cancelling performance is degraded. However, if the adaptation is too quick, the performance is degraded owing to the target signal. To overcome this problem, we propose an adaptive filter that suppresses the fluctuation of the ideal coefficients by utilizing a $rac{pi}{2}$ phase-delay device. This method assumes a frequency response that characterizes the transmission path from the interference source to the main input signal to be sufficiently smooth. In the numerical examples, the proposed method exhibits good performance in the presence of a frequency fluctuation when the forgetting factor is large. Moreover, we show that the proposed method reduces the calculation cost.

With the rapid development of information and Web technologies, people are facing ‘information overload’ in their daily lives. The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. In this paper, a PRS model based on the Support Vector Machine (SVM) is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.

Recently, the Static Heterogeneous Particle Swarm Optimization (SHPSO) has been studied by more and more researchers. In SHPSO, the different search behaviours assigned to particles during initialization do not change during the search process. As a consequence of this, the inappropriate population size of exploratory particles could leave the SHPSO with great difficulties of escaping local optima. This motivated our attempt to improve the performance of SHPSO by introducing the dynamic heterogeneity. The self-adaptive heterogeneity is able to alter its heterogeneous structure according to some events caused by the behaviour of the swarm. The proposed triggering events are confirmed by keeping track of the frequency of the unchanged global best position (pg) for a number of iterations. This information is then used to select a new heterogeneous structure when pg is considered stagnant. According to the different types of heterogeneity, DHPSO-d and DHPSO-p are proposed in this paper. In, particles dynamically use different rules for updating their position when the triggering events are confirmed. In DHPSO-p, a global gbest model and a pairwise connection model are automatically selected by the triggering configuration. In order to investigate the scalability of and DHPSO-p, a series of experiments with four state-of-the-art algorithms are performed on ten well-known optimization problems. The scalability analysis of and DHPSO-p reveals that the dynamic self-adaptive heterogeneous structure is able to address the exploration-exploitation trade-off problem in PSO, and provide the excellent optimal solution of a problem simultaneously.

In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is designed as follows. In the first stage, several artificial particles are added into the initialized swarm to guide the search direction. In the second stage, we redefine the operators of the discrete PSO to implement addition, subtraction and multiplication. Besides, a fuzzy-cost-based elite selection is used to improve the computational efficiency. Evaluation shows that the proposed algorithm achieves Pareto improvement in comparison to the state-of-the-art algorithms.

In this paper, we study a novel method to avoid a local minimum stagnation in the design problem of IIR (Infinite Impulse Response) filters using PSO (Particle Swarm Optimization). Although PSO is appropriate to solve nonlinear optimization problems, it is reported that a local minimum stagnation occurs due to a strong intensification of particles during the search. Then, multi-swarm PSO based on the particle reallocation strategy is proposed to avoid the local minimum stagnation. In this method, a reallocation space is determined by using some global bests. In this paper, the relationship between the number of swarms and the best value of design error is shown and the effectiveness of the proposed method is shown through several design examples.

This paper describes a novel harmonic-based robust voice activity detection (H-RVAD) method with harmonic spectral local peak (HSLP) feature. HSLP is extracted by spectral amplitude analysis between the adjacent formants, and such characteristic can be used to identify and verify audio stream containing meaningful human speech accurately in low SNR environment. And, an enhanced low SNR noisy speech recognition system framework with wakeup module, speech recognition module and confirmation module is proposed. Users can determine or reject the system feedback while a recognition result was given in the framework, to prevent any chance that the voiced noise misleads the recognition result. The H-RVAD method is evaluated by the AURORA2 corpus in eight types of noise and three SNR levels and increased overall average performance from 4% to 20%. In home noise, the performance of H-RVAD method can be performed from 4% to 14% sentence recognition rate in average.

A novel experimental design method based on a low-frequency active load-pull technique that includes harmonic tuning has been proposed for high-efficiency microwave power amplifiers. The intrinsic core component of a transistor with a maximum oscillation frequency of more than several tens of gigahertz can be approximately assumed as the nonlinear current source with no frequency dependence at an operation frequency of several gigahertz. In addition, the reactive parasitic elements in a transistor can be omitted at a frequency of much less than 1GHz. Therefore, the optimum impedance condition including harmonics for obtaining high efficiency in a nonlinear current source can be directly investigated based on a low-frequency active harmonic load-pull technique in the low-frequency region. The optimum load condition at the operation frequency for an external load circuit can be estimated by considering the properties of the reactive parasitic elements and the nonlinear current source. For an InGaAs/GaAs pHEMT, active harmonic load-pull considering up to the fifth-order harmonic frequency was experimentally carried out at the fundamental frequency of 20MHz. By using the estimated optimum impedance condition for an equivalent nonlinear current source, high-frequency amplifiers were designed and fabricated at the 1.9-GHz, 2.45-GHz, and 5.8-GHz bands. The fabricated amplifiers exhibited maximum drain efficiency values of 79%, 80%, and 74% at 1.9GHz, 2.47GHz, and 5.78GHz, respectively.

Considering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multi-objective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

In this letter, standard particle swarm optimization (PSO) with the center-symmetric trimmed correlation matrix and the orthogonal projection technique is firstly presented for blind carrier frequency offset estimation under interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. It doesn't require eigenvalue decomposition and only needs a single OFDMA data block. Second, this letter also presents adaptive multiple inertia weights with Newton method to speed up the convergence of standard PSO iteration process. Meanwhile, the advantage of inherent interleaved OFDMA signal structure also is exploited to conquer the problems of local optimization and the effect of ambiguous peaks for the proposed approaches. Finally, several simulation results are provided for illustration and comparison.

In most existing centralized lighting control systems, the lighting control problem (LCP) is reformulated as a constrained minimization problem and solved by linear programming (LP). However, in real-world applications, LCP is actually discrete and non-linear, which means that more accurate algorithm may be applied to achieve improvements in energy saving. In this paper, particle swarm optimization (PSO) is successfully applied for office lighting control and a linear programming guided particle swarm optimization (LPPSO) algorithm is developed to achieve considerable energy saving while satisfying users' lighting preference. Simulations in DIALux office models (one with small number of lamps and one with large number of lamps) are made and analyzed using the proposed control algorithms. Comparison with other widely used methods including LP shows that LPPSO can always achieve higher energy saving than other lighting control methods.

Operating swarm robots has the virtues of improved performance, fault tolerance, distributed sensing, and so on. The problem is, high overall system costs are the main barrier in managing a system of foraging swarm robots. Moreover, its control algorithm should be scalable and reliable as the foraging (search) spaces become wider. This paper analyzes a nature-inspired cooperative method to reduce the operating costs of the foraging swarm robots through simulation experiments. The aim of this research is to improve efficiency of mechanisms for reducing the cost by developing a new algorithm for the synergistic cooperation of the group. In this paper, we set the evaluation index of energy efficiency considering that the mission success rate as well as energy saving is important. The value is calculated as the number of successful operations against the total consumption of energy in order to also guarantee optimized for the work processing power than the one simple goal of energy savings. The method employs a behavioral model of a honey bee swarm to improve the energy efficiency in collecting crops or minerals. Experiments demonstrate the effectiveness of the approach. The experiment is set a number of strategies to combine the techniques to the proposed and conventional methods. Considering variables such as the area of search space and the size of a swarm, the efficiency comparison test is performed. As the result, the proposed method showed the enhanced energy efficiency of the average 76.9% as compared to the conventional simple model that means reduction of the recharging cost more than 40%.

The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.

Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.

The key factors in overcoming for weak global navigation satellite systems (GNSS) signal acquisition are sensitivity and dwell time. In the conventional MAX/TC criteria, a preset threshold value is used to determine whether the signal exists. Thus the threshold is calculated carefully to balance the sensitivity and the dwell time. Affected by various environment noise and interference, the acquisition circuit will enter verifying mode frequently to eliminate false alarms, which will extend the mean acquisition time (MAT). Based on the periodicity of spread spectrum code in GNSS, this paper presents an improved double-dwell scheme that uses no threshold in detecting weak GNSS signals. By adopting this method, the acquisition performance of weak signal is significantly improved. Theoretical analysis and numerical simulation are presented detailed. Compared with the conventional MAX/TC criteria, the proposed method achieves improved performance in terms of detection probability and false alarm rate. Furthermore, the MAT decreases 15s when C/N0 is above 20dB-Hz. This can enhance the receiver sensitivity and shorten the time to first fix (TTFF).

To design a wireless sensor network for farms, it is necessary to understand and predict the effect of vegetation. In this study, the change in the propagation loss characteristics in 920-MHz band is examined during the growth of mulberry bushes. The received signal strength indicator (RSSI) is measured as a function of the distance between the transmitting antenna (Tx) and the receiving antenna (Rx) in a 50×50m mulberry field. The Tx and Rx are placed at a height of 1.5m. Moreover, the horizontal and vertical polarizations are measured and the differences are shown. Three empirical vegetation attenuation models are introduced, and the measured data have been fitted to each model. The results show that the non-zero gradient model is the best model at predicting the vegetation attenuation in a mulberry farm regardless of the polarization or mulberry growth. It is found that the attenuation dependence on the plant height is linear. Furthermore, the results have revealed that the horizontal polarization had about 1.5 times as large an effect on the vegetation attenuation as the vertical polarization.

In this paper, we propose a method for designing finite impulse response (FIR) filters with canonic signed digit (CSD) coefficients using particle swarm optimization (PSO). In such a design problem, a large number of local minimums appear in an evaluation function for the optimization. An updating procedure of PSO tends to stagnate around such local minimums and thus indicates a premature convergence property. Therefore, a new framework for avoiding such a situation is proposed, in which the evaluation function is modified around the stagnation point. Several design examples are shown to present the effectiveness of the proposed method.

The harmonious coloring of an undirected simple graph is a vertex coloring such that adjacent vertices are assigned different colors and each pair of colors appears together on at most one edge. The harmonious chromatic number of a graph is the least number of colors used in such a coloring. The harmonious chromatic number of a path is known, whereas the problem to find the harmonious chromatic number is NP-hard even for trees with pathwidth at most 2. Hence, we consider the harmonious coloring of trees with pathwidth 1, which are also known as caterpillars. This paper shows the harmonious chromatic number of a caterpillar with at most one vertex of degree more than 2. We also show the upper bound of the harmonious chromatic number of a 3-regular caterpillar.

This paper presents a particle swarm optimization network (PSON) to improve the search capability of PSO. In PSON, multi-PSOs are connected for the purpose of communication. A variety of network topology can be realized by varying the number of connected PSOs of each PSO. The solving performance and convergence speed can be controlled by changing the network topology. Furthermore, high parallelism is can be realized by assigning PSO to single processor. The stability condition analysis and performance of PSON are shown.

A class-E power amplifier (PA) with novel dynamic biasing scheme is proposed to enhance power added efficiency (PAE) over a wide power range. A look-up table (LUT) adjusts input power and drain supply voltage simultaneously to keep switch mode condition of a power transistor and to optimize the PAE. Experimental results show that the class-E PA using the proposed scheme with harmonic suppression filter gives the PAE higher than 80% over 8.5,dB range with less than 40,dBc harmonic suppression.

The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.