Hypoglycaemia detection using fuzzy inference system with multi-objective double wavelet mutation Differential Evolution

In this paper, a fuzzy inference system (FIS) is developed to recognize hypoglycaemic episodes. Hypoglycaemia (low blood glucose level) is a common and serious side effect of insulin therapy for patients with diabetes. We measure some physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The FIS captures the relationship between the inputs of heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QTc), change of HR, change of QT c and the output of hypoglycaemic episodes to perform the classification. An algorithm called Differential Evolution with Double Wavelet Mutation (DWM-DE) is introduced to optimize the FIS parameters that govern the membership functions and fuzzy rules. DWM-DE is an improved Differential Evolution algorithm that incorporates two wavelet-based operations to enhance the optimization performance. To prevent the phenomenon of overtraining (over-fitting), a validation approach is proposed. Moreover, in this problem, two targets of sensitivity and specificity should be met in order to achieve good performance. As a result, a multi-objective optimization using DWM-DE is introduced to perform the training of the FIS. Experiments using the data of 15 children with TIDM (569 data points) are studied. The data are randomly organized into a training set with 5 patients (199 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). The result shows that the proposed FIS tuned by the multi-objective DWM-DE can offer good performance of doing classification. © 2012 Elsevier B.V. All rights reserved

A New Differential Evolution with self-terminating ability using fuzzy control and k-nearest neighbors

A new Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm to determine the terminating condition is proposed. A technique called Iteration Windows is introduced to govern the number of iteration in each searching stage. The size of the iteration windows is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the iteration windows until the end of the searching process. The wavelet based mutation process is embedded in the DE searching process to enhance the searching performance of DE. The F weight of DE is also controlled by the fuzzy controller to further speed up the searching process. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration. © 2010 IEEE

General rational approximation of Gaussian wavelet series and continuous-time gm-C filter implementation

© 2020 John Wiley & Sons, Ltd. This is the accepted version of the following article: Li, M, Sun, Y. General rational approximation of Gaussian wavelet series and continuous‐time g m ‐C filter implementation. Int J Circ Theor Appl. 2020; 1– 17., which has been published in final form at https://doi.org/10.1002/cta.2834.A general method of rational approximation for Gaussian wavelet series and Gaussian wavelet filter circuit design with simple gm-C integrators is presented in this work. Firstly, the multi-order derivatives of Gaussian function are analysed and proved as wavelet base functions. Then a high accuracy general approximation model of Gaussian wavelet series is constructed and the transfer function of first order derivative of Gaussian wavelet filter is obtained using quantum differential evolution (QDE) algorithm. Thirdly, as an example, a 5th order continuous-time analogue first order derivative of Gaussian wavelet filter circuit is designed based on multiple loop feedback structure with simple gm-C integrator as the basic blocks. Finally, simulation results demonstrate the proposed method is an excellent way for the wavelet transform implementation. The designed first order derivative of Gaussian wavelet filter circuit operates from a 0.53V supply voltage and a bias current 2.5nA. The power dissipation of the wavelet filter circuit at the basic scale is 41.1nW. Moreover, the high accuracy QRS detection based on the designed wavelet filter has been validated in application analysis.Peer reviewe

Realization of Analog Wavelet Filter using Hybrid Genetic Algorithm for On-line Epileptic Event Detection

© 2020 The Author(s). This open access work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.Peer reviewe

Compressor valve failure detection and prognostics

Reciprocating compressors are commonly used machinery for industrial applications. Unscheduled downtime and maintenance activity on the compressors causes considerable loss in throughput and efficiency of a plant. Of all the failures that cause unscheduled downtime in reciprocating compressors, valve related causes are predominant. Most of the failures associated with the valves are tracked to the failure of moving elements within the valve. Achieving higher reliability of critical reciprocating systems requires continuously monitoring the system and performing dynamic analysis of the sensory data for valve fault diagnosis. Continuous monitoring will improve the time and cost to repair through keeping a constant vigil for failure events. Though there has been a good amount of work done for condition monitoring of compressors, there has been very little work on detecting and predicting valve failures. The objective of this thesis is to research detection and prediction of valve failures by wavelet analysis, logistic regression and neural network analysis of pressure and temperature signals, which are the most common measurements on a reciprocating compressor system. Valve failures are seeded on a reciprocating compressor testbed that is instrumented with only temperature and pressure sensor order emulate the reciprocating compressor systems used in the industry. The parameters are measured on a continuous basis and baselines are established for normal (or acceptable) behavior and failure (or fault) condition. Deviation of the system from the normal condition and the time for the system to reach the fault mode is quantified with the help of the above mentioned tools. --Abstract, page iii

Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling

Adaptive embedded systems are required in various applications. This work addresses these needs in the area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an optimized set of wavelet filters in less than 2 min whenever the input type of data changes

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Bio-inspired FPGA Architecture for Self-Calibration of an Image Compression Core based on Wavelet Transforms in Embedded Systems

A generic bio-inspired adaptive architecture for image compression suitable to be implemented in embedded systems is presented. The architecture allows the system to be tuned during its calibration phase. An evolutionary algorithm is responsible of making the system evolve towards the required performance. A prototype has been implemented in a Xilinx Virtex-5 FPGA featuring an adaptive wavelet transform core directed at improving image compression for specific types of images. An Evolution Strategy has been chosen as the search algorithm and its typical genetic operators adapted to allow for a hardware friendly implementation. HW/SW partitioning issues are also considered after a high level description of the algorithm is profiled which validates the proposed resource allocation in the device fabric. To check the robustness of the system and its adaptation capabilities, different types of images have been selected as validation patterns. A direct application of such a system is its deployment in an unknown environment during design time, letting the calibration phase adjust the system parameters so that it performs efcient image compression. Also, this prototype implementation may serve as an accelerator for the automatic design of evolved transform coefficients which are later on synthesized and implemented in a non-adaptive system in the final implementation device, whether it is a HW or SW based computing device. The architecture has been built in a modular way so that it can be easily extended to adapt other types of image processing cores. Details on this pluggable component point of view are also given in the paper