A Modified version of Joint Approximation Diagonalization Estimation of Real Signals algorithm (JADER) is proposed to enhance efficiency and speed of Blind Signal Separation (BSS). MJADER based on the mixture's dimensions minimization step, where the cumulant matrices have been estimated using a reduced-dimension observed mixture. The approach (M-JADER) is based on a threshold step, it is easy to implement, computationally efficient and faster than standard JADER about 50% where it has less running time. The comparison done under tow types of niose(semi-white Gaussian noise and Uniform noise).

Joint Approximation Diagonalization --- JadeR --- Blind Source Separation --- BSS.

The voice encryption plays a great role in many important communication systems, such as military communication systems, a bank communication system. This paper proposes the possibility of application of analog voice encryption using adaptive signal processing technique called the Independent Component Analysis (ICA) technique. Practically the devoice encryption process is implemented using the JADE algorithm that can be considered as an Off-Line mode of ICA technique, and taking into consideration the testability of many input speech signals in Arabic and English. The objective test using LPC and SNR is applied to evaluate the proposed System.

يلعب التشفير الصوتي دورا كبيرا في العديد من أنظمة الاتصالات الهامة، مثل أنظمة الاتصالات العسكرية، ونظام الاتصالات البنوك. يقدم هذا البحث نظاما مقترحا لتطبيق تشفير الصوت بتقنيات معالجة الإشارات المتكيفة وهو ما يسمى تحليل العناصر المستقلة (ICA). وقد تم عمليا تنفيذ عملية التشفير باستخدام خوارزمية JADE التي يمكن أن تعتبر خوارزمية دفعات ، ومع الأخذ في الاعتبار قابلية الاختبار من العديد من إشارات الكلام المدخلات باللغتين العربية والإنجليزية.وقيم النظام بأستخدام معايير LPC وSNR

Keywords— Adaptive Signal processing --- analog voice encryption --- Blind source Separation --- ICA --- JADE.

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, fromspeaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. Inparticular, it has been applied to analyze EEG data in order to estimate the sources form the measurements.However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICAalgorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadaysmany researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis ofsignals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully toEEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvaluedICA algorithm.In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-worldresting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “groundtruth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms.The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brainactivity and not to artifact activity. The performance of locating a dipole for each separated independent component isconsidered in the comparison as well.Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEGactivity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysisbeyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

Blind source separation --- cocktail party problem --- dipole localization --- electroencephalography --- independent component analysis --- inverse problem --- real signal processing

The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy.

لقد تطورت مشكلة تقدير القناة اللاسلكية بسبب بعض التأثيرات غير المرغوب فيها للخواص الفيزيائية للقناة على الإشارات المرسلة. في نهاية المستقبل، التشوه، والتأخير، والتوهين، والتداخلات، ونوبات الطور هي أكثر المشكلات التي تواجهها مع الإشارات المستقبلة. من أجل التغلب على تأثيرات القناة وتوفير جودة كاملة تقريبًا لنقل البيانات، يلزم تقدير معلومات القناة. في أنظمة المخرجات متعددة المدخلات والمخرجات (MIMO)، يعتبر تقدير القناة خطوة أكثر تعقيدًا مقارنة بأنظمة المخرجات ذات المدخلات المفردة، SISO، نظرًا لأن عدد القنوات الفرعية التي تحتاج إلى تقدير أكبر بكثير من انظمةSISO. الهدف الأساسي من هذه الورقة البحثية هو مراجعة شاملة لاغلب الخوارزميات الشهيرة والفعالة التي تم ابتكارها لحل مشكلة تقدير قناة MIMO في أنظمة الاتصالات اللاسلكية. في هذه الورقة، تم تصنيف هذه التقنيات إلى ثلاث مجموعات: غير المكفوفين، شبه الأعمى وتقدير أعمى. لكل مجموعة، يتم تقديم توضيح مختصر لخوارزميات التقدير المألوفة. وأخيرًا، نقارن بين هذه التقنيات استنادًا إلى التعقيد الحسابي والكمون ودقة التقدير.

Adaptive channel estimation --- Blind source separation --- Artificial neural networks and Hybrid optimization algorithms --- تخمين القناة المحسن، الفصل الاعمى للإشارات، شبكات الذكاء الاصطناعي، خوارزميات التحسين الهجين.