Some Words About Nature-Inspired Computing

The use of artificial intelligence (AI) in various domains has drastically increased during the last decade. Nature-inspired computing is a strong computing approach that belongs to AI and covers a wide range of techniques. It has successfully tackled many complex problems and outperformed several classical techniques. This chapter provides the original ideas behind some nature-inspired computing techniques and their applications, such as the genetic algorithms, particle swarm optimization, grey wolf optimizer, ant colony optimization, plant propagation algorithm, cuckoo optimization algorithm, and artificial neural networks.

Reconfigurable Computing for Reactive Robotics Using Open-Source FPGAs

Reconfigurable computing provides a paradigm to create intelligent systems different from the classic software computing approach. Instead of using a processor with an instruction set, a full stack of middleware, and an application program running on top, the field-programmable gate arrays (FPGAs) integrate a cell set that can be configured in different ways. A few vendors have dominated this market with their proprietary tools, hardware devices, and boards, resulting in fragmented ecosystems with few standards and little interoperation. However, a new and complete toolchain for FPGAs with its associated open tools has recently emerged from the open-source community. Robotics is an expanding application field that may definitely benefit from this revolution, as fast speed and low power consumption are usual requirements. This paper hypothesizes that basic reactive robot behaviors may be easily designed following the reconfigurable computing approach and the state-of-the-art open FPGA toolchain. They provide new abstractions such as circuit blocks and wires for building intelligent robots. Visual programming and block libraries make such development painless and reliable. As experimental validation, two reactive behaviors have been created in a real robot involving common sensors, actuators, and in-between logic. They have been also implemented using classic software programming for comparison purposes. Results are discussed and show that the development of reactive robot behaviors using reconfigurable computing and open tools is feasible, also achieving a high degree of simplicity and reusability, and benefiting from FPGAs’ low power consumption and time-critical responsiveness.

Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an ‘agile’ optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.

Multi-target ranging using optical reservoir computing approach in the laterally coupled semiconductor lasers with self-feedback

In this work, we utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays. Three radar probe signals are generated by driving lasers constructed by a three-element lase array with self-feedback. The response lasers are implemented also by a three-element lase array with both delay-time feedback and optical injection, which are utilized as nonlinear nodes to realize the reservoirs. We show that each delayed radar probe signal can well be predicted and to synchronize with its corresponding trained reservoir, even when there exist parameter mismatches between the response laser array and the driving laser array. Based on this, the three synchronous probe signals are utilized for ranging to three targets, respectively, using Hilbert transform. It is demonstrated that the relative errors for ranging can be very small and less than 0.6%. Our findings show that optical reservoir computing provides an effective way for applications of target ranging.

A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.