Bin picking robots require vision sensors capable of recognizing objects in the binirrespective of the orientation and pose of the objects inside the bin. Bin picking systems arestill a challenge to the robot vision research community due to the complexity of segmenting ofoccluded industrial objects as well as recognizing the segmented objects which have irregularshapes. The problem becomes more complex when these objects look like entirely differentobjects in various orientations. In this paper a simple object recognition method is presentedusing singular value decomposition of the object image matrix and a functional link neuralnetwork for a bin picking vision system. The results of the functional link net are comparedwith that of a simple feed forward net. The network is trained using the error back propagationprocedure. The proposed method is robust for recognition of objects.

تتطلب منضو مات الالتقاط من الصنادیق استعمال منضو مات رؤیة قادرة على تمييز الأشياء داخل الصندوق. تصعبعملية التمييز هذه وذلك لأن الأشياء المراد تمييزها تأخذ أشكالا غير منتضمه. آما إن تغير وضعية اللاجسام داخلالصندوق یؤدي الى رؤیتها آأجسام مختلفة. یقدم هذا البحث إلى طریقه مبسطه لتمييز الأجسام داخل الصنادیق بطریقةتحليل مصفوفة الصورة وشبكه عصبيه ذات داله مرتبطة. تمت مقارنة النتائج مع تلك المستحصله من شبكه عصبيه بسيطةذات تغذیه أمامية. تم تعليم ألشبكه باستخدام خوارزمية انسياب الخطأ خلفا. وقد أضهرت النتائج أن المنضومه المفترحهذات متانة عالية

Object Recognition --- Neural Networks --- Bin Picking --- Singular Values

Brain machine interface provides a communication channel between the human brain and anexternal device. Brain interfaces are studied to provide rehabilitation to patients withneurodegenerative diseases; such patients loose all communication pathways except for theirsensory and cognitive functions. One of the possible rehabilitation methods for these patients isto provide a brain machine interface (BMI) for communication; the BMI uses the electricalactivity of the brain detected by scalp EEG electrodes. Classification of EEG signals extractedduring mental tasks is a technique for designing a BMI. In this paper a BMI design using fivemental tasks from two subjects were studied, a combination of two tasks is studied per subject.An Elman recurrent neural network is proposed for classification of EEG signals. Two featureextraction algorithms using overlapped and non overlapped signal segments are analyzed.Principal component analysis is used for extracting features from the EEG signal segments.Classification performance of overlapping EEG signal segments is observed to be better interms of average classification with a range of 78.5% to 100%, while the non overlapping EEGsignal segments show better classification in terms of maximum classifications

Brain Machine Interface --- EEG Signal Processing --- Recurrent Neural Networks