GRANT: Ground Roaming Autonomous Neuromorphic Targeter

Neuromorphic computing, or computing inspired by the cognitive processes of the brain, has garnered attention as the need for a more scalable, while also energy and space efficient, computational construct than the traditional Von Neumann based architectures has grown. Particularly, computing structures that perform complex tasks such as classification, anomaly detection, pattern recognition, and control automation are desired. Using the novel neuromorphic computing architecture developed by TENNLab (Laboratory of Tennesseans Exploring Neural Networks), DANNA2 (Dynamic Adaptive Neural Network Array 2), along with TENNLab\u27s hardware/software co-design framework and evolutionary optimization for neuromorphic systems (EONS) as the training method, we present GRANT (Ground Roaming Autonomous Neuromorphic Targeter): a roaming, obstacle avoiding robot controlled by a spiking neural network. With an array of DANNA2 neuromorphic elements loaded onto a Pynq Z1 FPGA, GRANT uses LiDAR to read sensory input from its surroundings and uses this data as input to the neural network. The outputs from the neural network are processed and used to control the motors allowing GRANT to navigate its surroundings and avoid obstacles along the way. Future work will be the addition of more complex operations in the form of object identification and targeting

Integrating Evolutionary Computation with Neural Networks

There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique

A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research