A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

An Architecture for Multilevel Learning and Robotic Control based on Concept Generation

Robot and multi-robot systems are inherently complex systems, for which designing the programs to control their behaviours proves complicated. Moreover, control programs that have been successfully designed for a particular environment and task can become useless if either of these change. It is for this reason that this thesis investigates the use of machine learning within robot and multi-robot systems. It explores an architecture for machine learning, applied to autonomous mobile robots based on dividing the learning task into two individual but interleaved sub-tasks. The first sub-task consists of finding an appropriate representation on which to base behaviour learning. The thesis explores the viability of using multidimensional classification techniques to generalise the original sensor and motor representations into abstract hierarchies of 'concepts'. To construct concepts the research used standard classification techniques, and experimented with a novel method of multidimensional data classification based on 'Q-analysis'. Results suggest that this may be a powerful new approach to concept learning. The second sub-task consists of using the previously acquired concepts as the representation for behaviour learning. The thesis explores whether it is possible to learn robotic behaviours represented using concepts. Results show that is possible to learn low-level behaviours such as navigation and higher-level ones such as ball passing in robot football. The thesis concludes that the proposed architecture is viable for robotic behaviour learning and control, and that incorporating Q-analysis based classification results in a promising new approach to the control of robot and multi-robot systems

Autonomous Navigation in (the Animal and) the Machine

Understanding the principles underlying autonomous navigation might be the most enticing quest the computational neuroscientist can undertake. Autonomous operation, also known as voluntary behavior, is the result of higher cognitive mechanisms and what is known as executive function in psychology. A rudimentary knowledge of the brain can explain where and to a certain degree how parts of a computation are expressed. However, achieving a satisfactory understanding of the neural computation involved in voluntary behavior is beyond today’s neuroscience. In contrast with the study of the brain, with a comprehensive body of theory for trying to understand system with unmatched complexity, the field of AI is to a larger extent guided by examples of achievements. Although the two sciences differ in methods, theoretical foundation, scientific vigour, and direct applicability, the intersection between the two may be a viable approach toward understanding autonomy. This project is an example of how both fields may benefit from such a venture. The findings presented in this thesis may be interesting for behavioral neuroscience, exploring how operant functions can be combined to form voluntary behavior. The presented theory can also be considered as documentation of a successful implementation of autonomous navigation in Euclidean space. Findings are grouped into three parts, as expressed in this thesis. First, pertinent back- ground theory is presented in Part I – collecting key findings from psychology and from AI relating to autonomous navigation. Part II presents a theoretical contribution to RL theory developed during the design and implementation of the emulator for navigational autonomy, before experimental findings from a selection of published papers are attached as Part III. Note how this thesis emphasizes the understanding of volition and autonomous navigation rather than accomplishments by the agent, reflecting the aim of this project – to understand the basic principles of autonomous navigation to a sufficient degree to be able to recreate its effect by first principles

PV System Information Enhancement and Better Control of Power Systems.

abstract: Due to the rapid penetration of solar power systems in residential areas, there has been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional power flow creates a need to know where Photovoltaic (PV) systems are located, what their quantity is, and how much they generate. However, significant challenges exist for accurate solar panel detection, capacity quantification, and generation estimation by employing existing methods, because of the limited labeled ground truth and relatively poor performance for direct supervised learning. To mitigate these issue, this thesis revolutionizes key learning concepts to (1) largely increase the volume of training data set and expand the labelled data set by creating highly realistic solar panel images, (2) boost detection and quantification learning through physical knowledge and (3) greatly enhance the generation estimation capability by utilizing effective features and neighboring generation patterns. These techniques not only reshape the machine learning methods in the GIS domain but also provides a highly accurate solution to gain a better understanding of distribution networks with high PV penetration. The numerical validation and performance evaluation establishes the high accuracy and scalability of the proposed methodologies on the existing solar power systems in the Southwest region of the United States of America. The distribution and transmission networks both have primitive control methodologies, but now is the high time to work out intelligent control schemes based on reinforcement learning and show that they can not only perform well but also have the ability to adapt to the changing environments. This thesis proposes a sequence task-based learning method to create an agent that can learn to come up with the best action set that can overcome the issues of transient over-voltage.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page