Ultrafast single-channel machine vision based on neuro-inspired photonic computing

High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena.Comment: 30 pages, 12 figure

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

Performance Analysis of An Experimental Micro Flexible Manufacturing System (FMS)

Due to advanced technology, it is very important the performance of FMS for sensivity, production quality, repeatability and energy consumptions. Flexible manufacturing systems (FMSs) are the most automated and technologically sophisticated of the machine cell types used to implement cellular manufacturing. An FMS usually has multiple automated stations and is capable of variable routings among stations, while its flexibility allows it to operate as a mixed model system. The FMS concept integrates many of the advanced technologies that we met in previous units, including flexible automation, CNC machines, distributed computer control, and automated material handling and storage. In this experimental investigation, vibration and accelerations analysis of an experimental FMS with 5 degrees of freedom robot manipulator are presented. Firstly, experimental measurement of accelerations and vibrations are trained with a vibration measurement system and sensors. However, the process of production of part is a cycle of exact production time

Image compression approach for improving deep learning applications

In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best scenario for deep learning models that provided good and acceptable classification accuracy was one that had the following parameters: 80×80 image size, 10 epochs, 64 batch size, 40 images dataset quality (images compressed 60%), and gray image mode. For this scenario a Dog vs Cat dataset is used, and the training time was 48 minutes, classification accuracy was 86%, and images dataset size was 317 MB on storage device. This size makes up 58% of the size of the original image’s dataset, saves 42% of the storage space and reduces the processing resources consumption