Adaptive Parameter Setting Research Articles

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters. In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.

A fast nonlocally centralized sparse representation algorithm for image denoising

The sparsity from self-similarity properties of natural images, which has received significant attention in the image processing community of researchers, is widely applied for image denoising. The recently proposed nonlocally centralized sparse representation (NCSR) algorithm that takes advantage of the sparse representations (SRs) and the nonlocal estimate of sparse coefficients (NESCs) has shown promising results with respect to noise reduction. Despite successful combination of the above two techniques, the iterative dictionary learning and the nonlocal estimate of unknown sparse coefficients make this algorithm computationally demanding, which largely limits its applicability in many applications. To address this problem, a fast version of the NCSR algorithm called FNCSR algorithm, which is based on pre-learned dictionary and adaptive parameter setting approaches, was proposed in this paper. Specifically, we adopted the same dictionary learning approach, i.e, the K-means and principal component analysis (PCA), with the NCSR algorithm to obtain a dictionary for each image in a selected image dataset including high-quality natural and texture images. Then we applied PNSR index to objectively assess the image quality of the reconstructed images using these dictionaries throughout the image dataset. The dictionary providing the best average reconstructed quality was selected as fixed dictionary, i.e., the pre-learned dictionary, for sparse coding throughout the iterative denoising process, which implies that it no longer requires dictionary learning procedure within the framework of the proposed FNCSR algorithm, resulting in greatly decreased execution time. In order to further improve computational efficiency, we employed quality-aware features and support vector regression (SVR) technique to build a fast noise level estimator (NLE) to estimate the noise level from a single noisy image. The parameters related to the NESC, i.e., the search window and the search step, which influences the computational performance of the NCSR algorithm strongly, were chosen automatically according to the estimated noise level. Compared to the original NCSR algorithm, these modifications lead to substantial benefits in computational efficiency (a performance gain of about 90% can be achieved) without sacrificing image quality too much (the largest decline is less than 0.55dB and 0.014 in terms of PSNR and SSIM indices). Compared with other state-of-the-art denoising algorithms, experimental results show that the proposed FNCNR algorithm also achieves comparable performance in terms of both quantitative measures and visual quality.

Automated vision system for fabric defect inspection using Gabor filters and PCNN.

In this study, an embedded machine vision system using Gabor filters and Pulse Coupled Neural Network (PCNN) is developed to identify defects of warp-knitted fabrics automatically. The system consists of smart cameras and a Human Machine Interface (HMI) controller. A hybrid detection algorithm combing Gabor filters and PCNN is running on the SOC processor of the smart camera. First, Gabor filters are employed to enhance the contrast of images captured by a CMOS sensor. Second, defect areas are segmented by PCNN with adaptive parameter setting. Third, smart cameras will notice the controller to stop the warp-knitting machine once defects are found out. Experimental results demonstrate that the hybrid method is superior to Gabor and wavelet methods on detection accuracy. Actual operations in a textile factory verify the effectiveness of the inspection system.

Generalization of Pareto-Optimality for Many-Objective Evolutionary Optimization

The vast majority of multiobjective evolutionary algorithms presented to date are Pareto-based. Usually, these algorithms perform well for problems with few (two or three) objectives. However, due to the poor discriminability of Pareto-optimality in many-objective spaces (typically four or more objectives), their effectiveness deteriorates progressively as the problem dimension increases. This paper generalizes Pareto-optimality both symmetrically and asymmetrically by expanding the dominance area of solutions to enhance the scalability of existing Pareto-based algorithms. The generalized Pareto-optimality (GPO) criteria are comparatively studied in terms of the distribution of ranks, the ranking landscape, and the convergence of the evolutionary process over several benchmark problems. The results indicate that algorithms equipped with a generalized optimality criterion can acquire the flexibility of changing their selection pressure within certain ranges, and achieve a richer variety of ranks to attain faster and better convergence on some subsets of the Pareto optima. To compensate for the possible diversity loss induced by the generalization, a distributed evolution framework with adaptive parameter setting is also proposed and briefly discussed. Empirical results indicate that this strategy is quite promising in diversity preservation for algorithms associated with the GPO.

Bayesian network as an adaptive parameter setting approach for genetic algorithms

Parameter setting currently ranks among the most actively researched topics in the evolutionary algorithm (EA) community. This can be explained by the major impact EA parameters have on search performance. However, parameter setting has been shown to be both problem dependent and evolution dependent. Moreover, because parameters interact in complex ways, developing an efficient and beneficial parameter setting approach is not an easy feat, and no broadly recognized solution has emerged to date. In this paper, we borrow the notion of parameter adaptation with the objective of addressing the parameter setting dependencies mentioned above, using a strategy based on a Bayesian network. The adaptive framework is elaborated for a steady-state genetic algorithm (SSGA) to control nine parameters. To judge parameter state productivities, we consider the population’s fitness improvement, as well as exploration/exploitation balance management. The performance of this proposal, a Bayesian network for genetic algorithm parameter adaptation (BNGA), is assessed based on the CEC’05 benchmark. BNGA is compared to a static parameter setting, a naive approach, three common adaptive systems (PM, AP, and FAUC–RMAB), and two state-of-the-art EAs (CMA-ES and G-CMA-ES). Our results statistically demonstrate that the performance of BNGA is equivalent to that of FAUC–RMAB, CMA-ES, and G-CMA-ES, and overall is superior to that of all the other SSGA parameter setting approaches. However, these results also reveal that all the approaches considered have great difficulty finding global optima in a multimodal problem set. This suggests a lack of complementarity and/or synergy among parameter states.

Adaptive firefly algorithm with chaos for mechanical design optimization problems

Firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems. Popularity of FA has increased recently due to its effectiveness in handling various optimization problems. To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings. Moreover, chaotic maps are also embedded into AFA for performance improvement. It is shown through experimental tests that some of the best known results are improved by the proposed algorithm.

Method of parametric optimization of each component of adaptive compensatng inertial sensors operating in a self-oscilation mode

The paper deals with a crucial task of parametric optimization of an adaptive inertial sensor capable of varying the parameters in order to reduce errors which are caused by the influence of high-frequency transient disturbances. A method of parametric optimization of each component that makes it possible to vary the parameters of the nonlinear element of a compensating inertial sensor operating in the self-oscillation mode is developed. The method was tested with a pendulous accelerometer taken as an example. Numerical investigations confirmed the possibility of filtering external vibrations and internal noise of the inertial sensor while maintaining its sensitivity in the required measuring range due to the synthesis of the adaptive loop based on a non-linear element of the "hysteresis loop" type. The essence of the method is based on parametric synthesis of the adaptive loop with a nonlinear element. Numerical investigations show that the error of the pendulous accelerometer can be reduced, on the average, by an order of magnitude thanks to the adaptive parameter setting of the non-linear element, depending on the amplitude of the external vibrations. The above method of parametric optimization of each component can be applied to most compensating inertial sensors measuring motion parameters of spacecraft operating in conditions of non-stationary external and internal disturbances. The adaptation circuit in adaptive inertial sensors can be implemented through the use of modern microcontrollers.

Modified cuckoo search algorithm with self adaptive parameter method

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, the proposed method uses two new mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, the new rules are combined through a linear decreasing probability rule. Then, self adaptive parameter setting is introduced as a uniform random value to enhance the diversity of the population based on the relative success number of the proposed two new parameters in the previous period. To verify the performance of SACS, 16 benchmark functions chosen from literature are employed. Experimental results indicate that the proposed method performs better than, or at least comparable to state-of-the-art methods from literature when considering the quality of the solutions obtained. In the last part, experiments have been conducted on Lorenz system and Chen system to estimate the parameters of these two chaotic systems. Simulation results further demonstrate the proposed method is very effective.

ADMIRE: Anomaly detection method using entropy-based PCA with three-step sketches

Network anomaly detection using dimensionality reduction has recently been well studied in order to overcome the weakness of signature-based detection. Previous works have proposed a method for detecting particular anomalous IP-flows by using random projection (sketch) and a Principal Component Analysis (PCA). It yields promising high detection capability results without needing a pre-defined anomaly database. However, the detection method cannot be applied to the traffic flows at a single measurement point, and the appropriate parameter settings (e.g., the relationship between the sketch size and the number of IP addresses) have not yet been sufficiently studied. We propose in this paper a PCA-based anomaly detection algorithm called ADMIRE to supplement and expand the previous works. The key idea of ADMIRE is the use of three-step sketches and an adaptive parameter setting to improve the detection performance and ease its use in practice. We evaluate the effectiveness of ADMIRE using the longitudinal traffic traces captured from a transpacific link. The main findings of this paper are as follows: (1) We reveal the correlation between the number of IP addresses in the measured traffic and the appropriate sketch size. We take advantage of this relation to set the sketch size parameter. (2) ADMIRE outperforms traditional PCA-based detector and other detectors based on different theoretical backgrounds. (3) The types of anomalies reported by ADMIRE depend on the traffic features that are selected as input. Moreover, we found that a simple aggregation of several traffic features degrades the detection performance.

An extension to the ADL SCORM standard to support adaptivity: The t-learning case-study

Current e-learning standards have been designed to provide reusability and interoperability. Besides these features, content personalisation is also necessary, although current standards do not fully support it. In this paper, we study the adaptation possibilities of the SCORM standard and present an extension to permit adaptivity according to user's characteristics. It comprises a syntax for adaptivity rules based on a set of adaptation parameters. The actual values of these adaptation parameters are deduced from the user profile, using inference rules. As a result, adaptive courses are obtained, created with the aim of being personalised before shown to the student.

Adaptive fuzzy sliding mode active queue management algorithms

Active queue management (AQM) is aimed at achieving the tradeoff between link utilization and queuing delay to enhance TCP congestion control and is expected to perform well for a wider-range of network conditions. Static AQM schemes despite their simplicity, often suffer from long response time due to conservative parameter setting to ensure stability. Adaptive parameter settings, which might solve this problem, remain difficult from implementation point of view. In this paper, we propose an adaptive fuzzy sliding mode (AFSM) AQM algorithm to achieve fast response and yet good robustness. The AFSM algorithm uses the queue length and its differential as the input of AQM and adjusts fuzzy rules by the measurement of packet loss ratio dynamically. The stability analysis under heterogeneous round trip times provides guidelines for parameter settings in AFSM and guarantees that the stability of AFSM is independent of the active TCP flows. This merit as well as other performances is examined under various network environments. Compared to some typical AQMs, the AFSM algorithm trades off the throughput with queuing delay better and achieves a higher per-flow throughput. Finally, AFSM can be executed at a scale of seconds with the least fuzzy rules.

LRED: A Robust and Responsive AQM Algorithm Using Packet Loss Ratio Measurement

Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, random early detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. Despite their simplicity, these approaches often suffer from unstable behaviors in a dynamic network. Adaptive parameter settings, though might solve the problem, remain difficult in such a complex system. Recent proposals based on analytical TCP control and AQM models suggest the use of both queue length and traffic input rate as congestion indicators, which effectively enhances stability. Their response time generally increases however, leading to frequent buffer overflow and emptiness. In this paper, we propose a novel AQM algorithm that achieves fast response time and yet good robustness. The algorithm, called Loss Ratio-based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length for adaptively adjusting the packet drop probability. We develop an analytical model for LRED, which demonstrates that LRED is responsive even if the number of TCP flows and their persisting times vary significantly. It also provides a general guideline for the parameter settings in LRED. The performance of LRED is further examined under various simulated network environments, and compared to existing AQM algorithms. Our simulation results show that, with comparable complexities, LRED achieves shorter response time and higher robustness. More importantly, it trades off the goodput with queue length better than existing algorithms, enabling flexible system configurations

LRED: A Robust and Responsive AQM Algorithm Using Packet Loss Ratio Measurement

Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, random early detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. Despite their simplicity, these approaches often suffer from unstable behaviors in a dynamic network. Adaptive parameter settings, though might solve the problem, remain difficult in such a complex system. Recent proposals based on analytical TCP control and AQM models suggest the use of both queue length and traffic input rate as congestion indicators, which effectively enhances stability. Their response time generally increases however, leading to frequent buffer overflow and emptiness. In this paper, we propose a novel AQM algorithm that achieves fast response time and yet good robustness. The algorithm, called Loss Ratio-based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length for adaptively adjusting the packet drop probability. We develop an analytical model for LRED, which demonstrates that LRED is responsive even if the number of TCP flows and their persisting times vary significantly. It also provides a general guideline for the parameter settings in LRED. The performance of LRED is further examined under various simulated network environments, and compared to existing AQM algorithms. Our simulation results show that, with comparable complexities, LRED achieves shorter response time and higher robustness. More importantly, it trades off the goodput with queue length better than existing algorithms, enabling flexible system configurations

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature convergence problem and improve the final results. One of the adaptive approaches are the adaptive parameter setting techniques based on the use of fuzzy logic controllers, the fuzzy adaptive genetic algorithms (FAGAs). In this paper, we analyse the FAGAs in depth. First, we describe the steps for their design and present an instance, which is studied from an empirical point of view. Then, we propose a taxonomy for FAGAs, attending on the combination of two aspects: the level where the adaptation takes place and the way the Rule-Bases are obtained. Furthermore, FAGAs belonging to different groups of the taxonomy are reviewed. Finally, we identify some open issues, and summarise a few new promising research directions on the topic. From the results provided by the approaches presented in the literature and the experimental results achieved in this paper, an important conclusion is obtained: the use of fuzzy logic controllers to adapt genetic algorithm parameters may really improve the genetic algorithm performance.

Foveal Flicker Sensitivity Abnormalities in Early Glaucoma

This study tested the hypothesis that (a) evidence of foveal visual dysfunction could be elicited in glaucoma subjects by measuring flicker sensitivity as a function of time after onset of an adapting field for a suitably chosen set of test and adaptation parameters and that (b) such dysfunction would be related to high blood pressure. Three groups of subjects were tested: (a) subjects with primary open-angle glaucoma but only minimal field loss, (b) normal control subjects, and (c) control subjects found to be suspect for glaucoma. The protocol included measurement of pulse rate and blood pressure, administration of Humphrey 30-2 visual fields and optic nerve head photography, and administration of a battery of psychophysical tests in Maxwellian view. This battery included a test of flicker sensitivity measured at middle wavelengths as a dynamic function of time after onset of a long-wavelength adapting field. The dynamic light-adaptation functions of subjects with glaucoma were much more likely to be unstable than were the corresponding functions of normal subjects. In addition, the dynamic light-adaptation functions of subjects with high blood pressure for their pulse rate were significantly less stable than the correspond ing functions of subjects without high blood pressure for their pulse rate. Moreover, the ratio of mean arterial pressure to pulse rate was significantly less for normal subjects than for either glaucoma subjects or for glaucoma-suspect subjects. We infer that among people with primary open-angle glau coma but with only minimal field loss, there often is foveal dysfunction associated with cardiovascular disease. Evidence of such dysfunction appears to require the use of stimulus conditions that tax the ability of the visual system to respond appropriately.