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New efficient two channel forward set-membership partial-update NLMS algorithms for blind speech enhancement and acoustic noise reduction

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APPLIED ACOUSTICS 2018年 141卷 322-332页

作者： Cheffi, Abdelhak Djendi, Mohamed Guessoum, Abderrezak Univ Blida 1 Lab Signal Proc & Imaging LATSI Route SoumaaBP 270 Blida 09000 Algeria

This paper addresses the problem of speech enhancement by partial adaptive algorithms combined with the forward blind source separation (FBSS) structure. We propose three new two-channel forward partial update algorithms: (i) The first algorithm is based on the use of the correlation properties to adapt and control the variable step-size parameter of adaptive cross-filters of the FBSS structure, this algorithm is called the two channel correlated set membership partial update NLMS (TC-SM-PU-NLMScor). (ii) The second algorithm is based on the use of a new coefficients update relations to control the normalization energy of the adaptive NLMS cross filters of the FBSS structure. This algorithm is called the two channel set member ship partial update regularized NLMS (TC-SM-PU-RNLMS). (iii) The third algorithm is a combination of proposed 1 (TC-SM-PU-NLMScor) and proposed 2 (TC-SM-PU-RNLMS), this algorithm is called the two channel correlated set membership partial update regularized NLMS (TC-SM-PU-RNLMScor). In this paper, we show the obtained results of the comparison between the three proposed algorithms and the original two channel correlated set membership partial NLMS (TC-SM-PU-NLMS) algorithm. This comparison is expressed in terms of objective criteria as the System Mismatch (SM), the Segmental Mean Square Error (SegMSE) and the Segmental SNR (SegSNR).

关键词： Speech enhancement adaptive algorithm Convergence speed Variable-step-size Correlation MSE

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Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and adaptive Thresholding

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REMOTE SENSING 2019年第7期11卷 756-756页

作者： Liu, Peng Li, Ying Liu, Bingxin Chen, Peng Xu, Jin Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Dalian Maritime Univ Environm Informat Inst Dalian 116026 Peoples R China

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

关键词： X-band marine radar oil spill detection texture analysis machine learning adaptive algorithm

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algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST

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ATTENTION PERCEPTION & PSYCHOPHYSICS 2018年第6期80卷 1629-1645页

作者： Rinderknecht, Mike D. Ranzani, Raffaele Popp, Werner L. Lambercy, Olivier Gassert, Roger Swiss Fed Inst Technol Dept Hlth Sci & Technol Rehabil Engn Lab Lengghalde 5 CH-8092 Zurich Switzerland Balgrist Univ Hosp Spinal Cord Injury Ctr Forchstr 340 CH-8008 Zurich Switzerland

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.

关键词： Psychophysics Parameter Estimation by Sequential Testing (PEST) adaptive algorithm Bias correction Proprioception assessment

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Design and Implementation of Xinjiang Astronomical Observatory Astronomical Data Transmission Visualization System

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ADVANCES IN ASTRONOMY 2019年第1期2019卷 1-12页

作者： Ye, Xinchen Zhang, Hailong Zhu, Yan Wang, Jie Ergesh, Tohtonur Li, Huijuan Chinese Acad Sci Xinjiang Astron Observ Urumqi 830011 Peoples R China Chinese Acad Sci Key Lab Radio Astron Nanjing 210008 Jiangsu Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xinjiang Univ Finance & Econ Urumqi 830012 Peoples R China

With the development of astronomical observation technology, astronomical devices produce more data than ever. Astronomical telescopes are usually far away from city, so the long-distance data transmission between telescope and data center faces great challenges. Visualization system of astronomical data transmission with four-layer structure was built to manage data transmission. This visualization system has a four-layer structure: hardware layer, system layer, middle layer, and visualization layer. System function includes automatic data transmission, log recording of transmission process, and display of the transmission status in dynamic web pages. Besides, the middle layer contains an alarm subsystem that can automatically send system exceptions to administrator. We also design corresponding mechanisms to ensure the high stability of the system and to control the data transmission when the network is unstable through adaptive algorithms. In test, this visualization system can run stably for a long time in unmanned manner. This system also provides a solution for the astronomical observation bases to automatically transmit data to the data center.

关键词： Stability adaptive algorithm Observatory

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Nonlinear control for ground-air trajectory tracking by a hybrid vehicle: theory and experiments ⁎

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IFAC-PapersOnLine 2019年第8期52卷 19-24页

作者： J. Colmenares-Vázquez P. Castillo N. Marchand D. Huerta-García Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab 38000 Grenoble France Sorbonne Universités Université de Technologie de Compiègne Heudiasyc Lab. UMR 7253 France Instituto Tecnológico de Tlalnepantla Electrical Department State of Mexico

A nonlinear scheme for controlling a hybrid vehicle with capacities for terrestrial and aerial displacements is proposed in this paper. The nonlinear model of the vehicle is obtained from the Newton-Euler formalism taking into account two operation modes (ground and air). The attitude controller has been developed step by step for the full complete system and stabilizes both operation modes with the goal to produce a seamless transition between them. It has adaptive properties that helps to counteract the effects produced by the nonlinear uncertainties in the model. The nonlinear position controllers are conceived considering the gravity force for tracking an aerial or ground trajectory. Numerical simulations and real-time experiments are carried out for validating and verifying the good performance of the proposed algorithms.

关键词： non-linear control hybrid vehicle tracking stabilization adaptive algorithm

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Distributed Learning Fractal algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System

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SENSORS 2019年第6期19卷 1442.-1442.页

作者： Moreno, Jaime Morales, Oswaldo Tejeida, Ricardo Posadas, Juan Quintana, Hugo Sidorov, Grigori Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Mexico City 07738 DF Mexico Inst Politecn Nacl Escuela Super Turismo Mexico City 07630 DF Mexico Inst Politecn Nacl Ctr Invest Computac Mexico City 07738 DF Mexico

Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of CO2, humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

关键词： Hilbert curve WSNs quality of service centralized control topology adaptive algorithm L-system swarm intelligence optimization distributed learning

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Leader Based adaptive Fault Diagnosis algorithm for Distributed Systems

Leader Based Adaptive Fault Diagnosis Algorithm for Distribu...

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IEEE International Conference on Information, Communication, Instrumentation and Control (ICICIC)

作者： Manghwani, Juhi Taware, Rutuja Kelkar, Supriya Chinde, Priyanka Alwani, Saloni MKSSSs Cummins Coll Engn Women Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781509063147;9781509063130

This paper proposes a new algorithm named Leader Based adaptive Fault Diagnosis (L-AFD) algorithm for distributed systems. This algorithm detects all faulty nodes in the network where, the network is not fully connected. This algorithm works for the arbitrary network. The t-diagnosibility of a system under consideration is (n-1) where n is total number of nodes or computer systems in the network. This algorithm supports new node entry in the network. It allows re-entry of the repaired faulty nodes during the next diagnostic cycle. This algorithm can also work with more than one leader. This algorithm executes periodically on each node.

关键词： Fault diagnosis adaptive algorithm Arbitrary network Distributed network Distributed system

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Processing of Fetal Heart Rate through Non-invasive adaptive System Based on Recursive Least Squares algorithm 14

Processing of Fetal Heart Rate through Non-invasive Adaptive...

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Conference on Optical Materials and Biomaterials in Security and Defence Systems Technology XIV

作者： Fajkus, Marcel Nedoma, Jan Martinek, Radek Vasinek, Vladimir Tech Univ Ostrava VSB Fac Elect Engn & Comp Sci Dept Telecommun Ostrava Czech Republic Tech Univ Ostrava VSB Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn Ostrava Czech Republic

ISBN: (纸本)9781510613454;9781510613447

In this article, we describe an innovative non-invasive method of Fetal Phonocardiography (fPCG) using fiber-optic sensors and adaptive algorithm for the measurement of fetal heart rate (fHR). Conventional PCG is based on a noninvasive scanning of acoustic signals by means of a microphone placed on the thorax. As for fPCG, the microphone is placed on the maternal abdomen. Our solution is based on patent pending non-invasive scanning of acoustic signals by means of a fiber-optic interferometer. Fiber-optic sensors are resistant to technical artifacts such as electromagnetic interferences (EMI), thus they can be used in situations where it is impossible to use conventional EFM methods, e.g. during Magnetic Resonance Imaging (MRI) examination or in case of delivery in water. The adaptive evaluation system is based on Recursive least squares (RLS) algorithm. Based on real measurements provided on five volunteers with their written consent, we created a simplified dynamic signal model of a distribution of heartbeat sounds (HS) through the human body. Our created model allows us to verification of the proposed adaptive system RLS algorithm. The functionality of the proposed non-invasive adaptive system was verified by objective parameters such as Sensitivity (S+) and Signal to Noise Ratio (SNR).

关键词： fetal heart rate adaptive algorithm recursive least squares algorithm

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An adaptive Prioritized ε-Preferred Evolutionary algorithm for Approximate BDD Optimization 17

An Adaptive Prioritized ε-Preferred Evolutionary Algorithm ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Shirinzadeh, Saeideh Soeken, Mathias Grosse, Daniel Drechsler, Rolf Univ Bremen Dept Math & Comp Sci Bremen Germany Ecole Polytech Fed Lausanne Integrated Syst Lab Lausanne Switzerland DFKI GmbH Cyber Phys Syst Bremen Germany

ISBN: (纸本)9781450349208

Approximate computing is an emerging methodology that allows to increase efficiency in a range of resilient applications for an affordable loss of precision or quality. In this paper, we exploit approximation in a multi-criteria optimization approach for the widely used data structure Binary Decision Diagram (BDD) to achieve higher efficiency besides lowering the inaccuracy. For this purpose, we utilize an epsilon-preferred evolutionary algorithm giving a higher priority to minimize BDD sizes as well as maintaining certain error constraints. In particular, we propose an adaptive epsilon-setting method which adds an automated factor to the algorithm based on the behavior of the function under approximation. This improves the performances of the algorithm by correcting the effect of the user set error constraints which can restrict the dimensions of the search and can lead to immature convergence. In comparison with the non-optimized BDDs, the proposed algorithm achieves a high gain of 68.02% at a low cost of 2.12% inaccuracy for the whole benchmark set. The experimental results also reveal a considerable improvement of 25.19% in the average value of error rate besides reduction in BDD sizes compared to the manual s-setting approach.

关键词： Approximate BDD optimization prioritized evolutionary algorithm adaptive algorithm

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A novel adaptive butterfly optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING 2018年第4期7卷

作者： Singh, Bhupinder Anand, Priyanka Lovely Profess Univ Dept Comp Sci & Engn Jalandhar Punjab India

Butterfly optimization algorithm (BOA) is an interesting bio-inspired algorithm that uses a nature inspired simulation model, based on the food foraging behavior of butterflies. A common problem with BOA is that in early stages of simulation process, it may converge to sub-optimal solutions due to the loss of diversity in its population. The sensory modality is the critical parameter which is responsible for searching new solutions in the nearby regions. In this work, an adaptive butterfly optimization algorithm is proposed in which a novel phenomenon of changing the sensory modality of BOA is employed during the optimization process in order to achieve better results in comparison to traditional BOA. The proposed adaptive butterfly optimization algorithm (ABOA) is tested against seventeen standard bench mark functions. Its performance is then compared against existing standard optimization algorithms, namely artificial bee colony, firefly algorithm and standard butterfly optimization algorithm. The results indicate that the proposed adaptive BOA with improved parameter calculation mechanism produces superior results in terms of convergence and achievement of the global optimal solution efficiently.

关键词： Nature-inspired algorithm butterfly optimization algorithm adaptive algorithm sensory modality BOA convergence

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