Optimum Median Filter Based on Crow Optimization Algorithm

يُقترح مرشح متوسط ​​جديد يعتمد على خوارزميات تحسين الغراب (OMF) لتقليل ضوضاء الملح والفلفل العشوائية وتحسين جودة الصور ذات اللون الرمادي والملونة . الفكرة الرئيسية لهذا النهج هي أن أولاً ، تقوم خوارزمية تحسين الأداء بالكشف عن وحدات البكسل الخاصة بالضوضاء ، واستبدالها بقيمة وسيطة مثالية تبعًا لدالة الأداء. أخيرًا ، تم استخدام نسبة القياس القصوى لنسبة الإشارة إلى الضوضاء (PSNR) ، والتشابه الهيكلي والخطأ المربع المطلق والخطأ التربيعي المتوسط ​​لاختبار أداء المرشحات المقترحة (المرشح الوسيط الأصلي والمحسّن) المستخدمة في الكشف عن الضوضاء وإزالتها من الصور. يحقق المحاكاة استنادًا إلى MATLAB R2019b والنتائج الحالية التي تفيد بأن المرشح المتوسط ​​المحسّن مع خوارزمية تحسين الغراب أكثر فعالية من خوارزمية المرشح المتوسط ​​الأصلية ومرشحات لطرق حديثة ؛ أنها تبين أن العملية المقترحة قوية للحد من مشكلة الخطأ وإزالة الضوضاء بسبب مرشح عامل التصفية المتوسط ​​؛ ستظهر النتائج عن طريق تقليل الخطأ التربيعي المتوسط ​​إلى أدنى أو أقل من (1.5) ، والخطأ المطلق للتساوي (0.22) ,والتشابه الهيكلي اكثر من ( 95%) والحصول على PSNR أكثر من 45dB).) وبنسبة تحسين ( 25%) .          A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%)

Fuzzy averaging filter for impulse noise reduction in colour images with a correction step

[EN] In this paper we propose a fuzzy detection and reduction method for impulse noise in colour images. Detection is based on the fuzzyfication of a well-known statistic called ROD. The noise degrees obtained are used to reduce impulses by employing a fuzzy averaging between the input colour vector and a robust estimate of noise-free colour vector within the input neighbourhood. Fuzzy averaging has some advantages in terms of both noise reduction and detail preservation in front of detect and replace approaches because of threshold based decisions of the latter. However, robustness of the former is lower. We solve this problem by including a correction mechanism that checks the fuzzy noise degree of the output and replaces it with a robust colour vector either when noise has not been properly reduced or when a colour artefact has been introduced. We carry out a thorough study of the method parameter setting and give a convenient and robust setting. Experimental results show that our approach is very robust in front of four different types of impulse noise.The authors are very grateful to the reviewers for their valuable suggestions. Valentin Gregori and Samuel Morillas acknowledges the support of Ministry of Economy and Competitiveness of Spain under grant MTM 2015-64373-P (MINECO/FEDER, UE). Bernardino Roig and Almanzor Sapena acknowledges the support of Generalitat Valencians under grant AICO/2017/059.Gregori Gregori, V.; Morillas, S.; Roig, B.; Sapena Piera, A. (2018). Fuzzy averaging filter for impulse noise reduction in colour images with a correction step. Journal of Visual Communication and Image Representation. 55:518-528. https://doi.org/10.1016/j.jvcir.2018.06.025S5185285

On data-selective learning

Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancellation, signal prediction, and stock market. Therefore, reducing the energy consumption of the adaptive filtering algorithms has great importance, particularly in green technologies and in devices using battery. In this thesis, data-selective adaptive filters, in particular the set-membership (SM) adaptive filters, are the tools to reach the goal. There are well known SM adaptive filters in literature. This work introduces new algorithms based on the classical ones in order to improve their performances and reduce the number of required arithmetic operations at the same time. Therefore, firstly, we analyze the robustness of the classical SM adaptive filtering algorithms. Secondly, we extend the SM technique to trinion and quaternion systems. Thirdly, by combining SM filtering and partialupdating, we introduce a new improved set-membership affine projection algorithm with constrained step size to improve its stability behavior. Fourthly, we propose some new least-mean-square (LMS) based and recursive least-squares based adaptive filtering algorithms with low computational complexity for sparse systems. Finally, we derive some feature LMS algorithms to exploit the hidden sparsity in the parameters.Filtros adaptativos são aplicados em diversos aparelhos eletrônicos e de comunicação, como smartphones, fone de ouvido avançados, DSP chips, antenas inteligentes e sistemas de teleconferência. Eles também têm aplicação em várias áreas como identificação de sistemas, equalização de canal, cancelamento de eco, cancelamento de interferência, previsão de sinal e mercado de ações. Desse modo, reduzir o consumo de energia de algoritmos adaptativos tem importância significativa, especialmente em tecnologias verdes e aparelhos que usam bateria. Nesta tese, filtros adaptativos com seleção de dados, em particular filtros adaptativos da família set-membership (SM), são apresentados para cumprir essa missão. No presente trabalho objetivamos apresentar novos algoritmos, baseados nos clássicos, a fim de aperfeiçoar seus desempenhos e, ao mesmo tempo, reduzir o número de operações aritméticas exigidas. Dessa forma, primeiro analisamos a robustez dos filtros adaptativos SM clássicos. Segundo, estendemos o SM aos números trinions e quaternions. Terceiro, foram utilizadas também duas famílias de algoritmos, SM filtering e partial-updating, de uma maneira elegante, visando reduzir energia ao máximo possível e obter um desempenho competitivo em termos de estabilidade. Quarto, a tese propõe novos filtros adaptativos baseado em algoritmos least-mean-square (LMS) e mínimos quadrados recursivos com complexidade computacional baixa para espaços esparsos. Finalmente, derivamos alguns algoritmos feature LMS para explorar a esparsidade escondida nos parâmetros

Advances in Spacecraft Systems and Orbit Determination

"Advances in Spacecraft Systems and Orbit Determinations", discusses the development of new technologies and the limitations of the present technology, used for interplanetary missions. Various experts have contributed to develop the bridge between present limitations and technology growth to overcome the limitations. Key features of this book inform us about the orbit determination techniques based on a smooth research based on astrophysics. The book also provides a detailed overview on Spacecraft Systems including reliability of low-cost AOCS, sliding mode controlling and a new view on attitude controller design based on sliding mode, with thrusters. It also provides a technological roadmap for HVAC optimization. The book also gives an excellent overview of resolving the difficulties for interplanetary missions with the comparison of present technologies and new advancements. Overall, this will be very much interesting book to explore the roadmap of technological growth in spacecraft systems

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Flight Mechanics/Estimation Theory Symposium, 1991

Twenty-six papers and abstracts are presented. A wide range of issues related to orbit attitude prediction, orbit determination, and orbit control are examined including attitude sensor calibration, attitude dynamics, and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers

Adaptive filtering algorithms for quaternion-valued signals

Advances in sensor technology have made possible the recoding of three and four-dimensional signals which afford a better representation of our actual three-dimensional world than the ``flat view'' one and two-dimensional approaches. Although it is straightforward to model such signals as real-valued vectors, many applications require unambiguous modeling of orientation and rotation, where the division algebra of quaternions provides crucial advantages over real-valued vector approaches. The focus of this thesis is on the use of recent advances in quaternion-valued signal processing, such as the quaternion augmented statistics, widely-linear modeling, and the HR-calculus, in order to develop practical adaptive signal processing algorithms in the quaternion domain which deal with the notion of phase and frequency in a compact and physically meaningful way. To this end, first a real-time tracker of quaternion impropriety is developed, which allows for choosing between strictly linear and widely-linear quaternion-valued signal processing algorithms in real-time, in order to reduce computational complexity where appropriate. This is followed by the strictly linear and widely-linear quaternion least mean phase algorithms that are developed for phase-only estimation in the quaternion domain, which is accompanied by both quantitative performance assessment and physical interpretation of operations. Next, the practical application of state space modeling of three-phase power signals in smart grid management and control systems is considered, and a robust complex-valued state space model for frequency estimation in three-phase systems is presented. Its advantages over other available estimators are demonstrated both in an analytical sense and through simulations. The concept is then expanded to the quaternion setting in order to make possible the simultaneous estimation of the system frequency and its voltage phasors. Furthermore, a distributed quaternion Kalman filtering algorithm is developed for frequency estimation over power distribution networks and collaborative target tracking. Finally, statistics of stable quaternion-valued random variables, that include quaternion-valued Gaussian random variables as a special case, is investigated in order to develop a framework for the modeling and processing of heavy-tailed quaternion-valued signals.Open Acces