Driving Scene Understanding using Spiking Neural Networks

One of the applications of AI lies in developing intelligent systems for safe on-road driving, other than building and perfecting self-driving vehicles, and many others. Driving Scene Understanding (DSU) is one such area where AI algorithms can be used to infer the current actions of driver, pedestrians, nearby vehicles, etc. to improve the on-road decision making capability of the in-vehicle driver. Another related front of technological advancement in transportation is the production and development of electric vehicles. A future with battery electric vehicle and safe driving necessitates the creation of AI algorithms which not only assist in increasing the on-road safety but are also energy efficient. This thesis is an attempt towards developing such an energy efficient AI model for DSU using Spiking Neural Networks (SNNs). Low power neuromorphic hardware (e.g. Intel’s Loihi) can be leveraged for the deployment of such SNNs which offer low inference latency and energy efficiency. Out of a number of ways to build SNNs, an established method is to first train an Artificial Neural Network (ANN) with traditional neurons (e.g. ReLU) and then replace those neurons with spiking neurons (e.g. Integrate & Fire neurons) along with some other network modifications. Therefore, Chapter 4 first presents a 3D-CNN based ANN model, and identifies the appropriate spatial resolution and temporal depth of the incoming video frames for DSU. Through extensive experiments, it was found that MaxPooling performs better than AveragePooling in such a 3D-CNNs based model; however there exists no method to convert a network with MaxPooling layers into an SNN which can be entirely deployed on a specialized neuromorphic hardware. Chapter 6 presents two novel approaches to implement MaxPooling on a neuromorphic hardware; thus facilitating the conversion of networks with MaxPooling layers to fully neuromorphic-hardware compatible SNNs. These approaches have been tested with 2D-CNNs based SNNs for image recognition, and can be extended to the 3D-CNNs based SNNs as well; thus, theoretically realizing an energy efficient SNN for DSU

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

Engineering handbook

1999 handbook for the faculty of Engineerin

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

On the power of the Multiple Associative Computing (MASC) Model related to that of reconfigurable bus-based models

Abstract: The MASC model is a multi-SIMD model that uses control parallelism to coordinate the interaction of data parallel threads. It supports a generalized associative style of parallel computation. The power of this model has been compared to that of priority CRCW PRAM and enhanced meshes. In this paper, we present the work on simulations between MASC and reconfigurable bus-based models, in particular, different versions of the Reconfigurable Multiple Bus Machine (RMBM). It is shown that MASC and the Basic RMBM (B-RMBM) can simulate each other in constant time if the number of buses on the B-RMBM is �(j) where j is the number of MASC instruction streams. Thus, when these two models satisfy the preceding condition, they have the same power. Simulations of other stronger versions of RMBM using MASC are also considered. Since the RMBM model has been shown to be as powerful as a general Reconfigurable Mesh (RM), our simulations can be used to establish a relationship between MASC and RM. As RM has been widely accepted as an extremely powerful model, our work gives a better understanding of the MASC model and provides useful information concerning its power. Key Words: parallel computational model, associative computing, simulation, reconfigurable buses, Multi-SIMD 1

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Removal of antagonistic spindle forces can rescue metaphase spindle length and reduce chromosome segregation defects

Regular Abstracts - Tuesday Poster Presentations: no. 1925Metaphase describes a phase of mitosis where chromosomes are attached and oriented on the bipolar spindle for subsequent segregation at anaphase. In diverse cell types, the metaphase spindle is maintained at a relatively constant length. Metaphase spindle length is proposed to be regulated by a balance of pushing and pulling forces generated by distinct sets of spindle microtubules and their interactions with motors and microtubule-associated proteins (MAPs). Spindle length appears important for chromosome segregation fidelity, as cells with shorter or longer than normal metaphase spindles, generated through deletion or inhibition of individual mitotic motors or MAPs, showed chromosome segregation defects. To test the force balance model of spindle length control and its effect on chromosome segregation, we applied fast microfluidic temperature-control with live-cell imaging to monitor the effect of switching off different combinations of antagonistic forces in the fission yeast metaphase spindle. We show that spindle midzone proteins kinesin-5 cut7p and microtubule bundler ase1p contribute to outward pushing forces, and spindle kinetochore proteins kinesin-8 klp5/6p and dam1p contribute to inward pulling forces. Removing these proteins individually led to aberrant metaphase spindle length and chromosome segregation defects. Removing these proteins in antagonistic combination rescued the defective spindle length and, in some combinations, also partially rescued chromosome segregation defects. Our results stress the importance of proper chromosome-to-microtubule attachment over spindle length regulation for proper chromosome segregation.postprin