In this paper, an Industrial machine vision system incorporating Optical Character Recognition (OCR) is employed to inspect the marking on the Integrated Circuit (IC) Chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instrument is used in the investigation. The inspection has to identify the print errors such as illegible character, missing characters and up side down printing. The vision inspection of the printed markings on the IC chip is carried out in three phases namely image preprocessing, feature extraction and classification. Projection profile and Moments are employed for feature extraction. A neural network is used as a classifier to detect the defectively marked IC chips. Both feature extraction methods are compared in terms of marking inspection time.
Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental 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 feature extraction 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 in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.
Bin picking robots require vision sensors capable of recognizing objects in the bin irrespective of the orientation and pose of the objects inside the bin. Bin picking systems are still a challenge to the robot vision research community due to the complexity of segmenting of occluded industrial objects as well as recognizing the segmented objects which have irregular shapes. In this paper a simple object recognition method is presented using singular value decomposition of the object image matrix and a functional link neural network for a bin picking vision system. The results of the functional link net are compared with that of a simple feed forward net. The network is trained using the error back propagation procedure. The proposed method is robust for recognition of objects.