On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Intelligent Self-Organized Robust Control Design based on Quantum/Soft Computing Technologies and Kansei Engineering

System of systems engineering technology describes the possibility of ill-defined (autonomous or hierarchically connected) dynamic control system design that includes human decision making in unpredicted (unforeseen) control situations. Kansei/Affective Engineering technology and its toolkit include qualitative description of human being emotion, instinct and intuition that are used effectively in design processes of smart/wise robotics and intelligent mechatronics. In presented report the way how these technologies can be married using new types of unconventional computational intelligence is described. System analysis of interrelations between these two important technologies is discussed. The solution of an important problem as robust intelligent control system design based on quantum knowledge base self-organization in unpredicted control situations and information risk is proposed. The background of applied unconventional computational intelligence is soft and quantum computing technologies. Applications of the developed approach in robust integrated fuzzy intelligent control systems are considered using concrete Benchmarks

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Proceedings of 3. International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4’2020)

Çevrimiçi ( XIV, 67 pages

Special oils for halal and safe cosmetics

Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products

DNAgents: Genetically Engineered Intelligent Mobile Agents

Mobile agents are a useful paradigm for network coding providing many advantages and disadvantages. Unfortunately, widespread adoption of mobile agents has been hampered by the disadvantages, which could be said to outweigh the advantages. There is a variety of ongoing work to address these issues, and this is discussed. Ultimately, genetic algorithms are selected as the most interesting potential avenue. Genetic algorithms have many potential benefits for mobile agents. The primary benefit is the potential for agents to become even more adaptive to situational changes in the environment and/or emergent security risks. There are secondary benefits such as the natural obfuscation of functions inherent to genetic algorithms. Pitfalls also exist, namely the difficulty of defining a satisfactory fitness function and the variable execution time of mobile agents arising from the fact that it exists on a network. DNAgents 1.0, an original application of genetic algorithms to mobile agents is implemented and discussed, and serves to highlight these difficulties. Modifications of traditional genetic algorithms are also discussed. Ultimately, a combination of genetic algorithms and artificial life is considered to be the most appropriate approach to mobile agents. This allows the consideration of agents to be organisms, and the network to be their environment. Towards this end, a novel framework called DNAgents 2.0 is designed and implemented. This framework allows the continual evolution of agents in a network without having a seperate training and deployment phase. Parameters for this new framework were defined and explored. Lastly, an experiment similar to DNAgents 1.0 is performed for comparative purposes against DNAgents 1.0 and to prove the viability of this new framework

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems