NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF CAREER proposal are presented

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Measurements, Models, Systems and Design

531 s.

Navigation Techniques for Control of Multiple Mobile Robots

The investigation reported in this thesis attempt to develop efficient techniques for the control of multiple mobile robots in an unknown environment. Mobile robots are key components in industrial automation, service provision, and unmanned space exploration. This thesis addresses eight different techniques for the navigation of multiple mobile robots. These are fuzzy logic, neural network, neuro-fuzzy, rule-base, rule-based-neuro-fuzzy, potential field, potential-field-neuro-fuzzy, and simulated-annealing- potential-field- neuro-fuzzy techniques. The main components of this thesis comprises of eight chapters. Following the literature survey in Chapter-2, Chapter-3 describes how to calculate the heading angle for the mobile robots in terms of left wheel velocity and right wheel velocity of the robot. In Chapter-4 a fuzzy logic technique has been analysed. The fuzzy logic technique uses different membership functions for navigation of the multiple mobile robots, which can perform obs..

Nonlinear and distributed sensory estimation

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Synesthetic Sensor Fusion via a Cross-Wired Artificial Neural Network.

The purpose of this interdisciplinary study was to examine the behavior of two artificial neural networks cross-wired based on the synesthesia cross-wiring hypothesis. Motivation for the study was derived from the study of psychology, robotics, and artificial neural networks, with perceivable application in the domain of mobile autonomous robotics where sensor fusion is a current research topic. This model of synesthetic sensor fusion does not exhibit synesthetic responses. However, it was observed that cross-wiring two independent networks does not change the functionality of the individual networks, but allows the inputs to one network to partially determine the outputs of the other network in some cases. Specifically, there are measurable influences of network A on network B, and yet network B retains its ability to respond independently

Integrated Framework for Data Quality and Security Evaluation on Mobile Devices

Data quality (DQ) is an important concept that is used in the design and employment of information, data management, decision making, and engineering systems with multiple applications already available for solving specific problems. Unfortunately, conventional approaches to DQ evaluation commonly do not pay enough attention or even ignore the security and privacy of the evaluated data. In this research, we develop a framework for the DQ evaluation of the sensor originated data acquired from smartphones, that incorporates security and privacy aspects into the DQ evaluation pipeline. The framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor data quality and security metrics. The framework employs a knowledge graph to facilitate its adaptation in new applications development and enables knowledge accumulation. Privacy aspects evaluation is demonstrated by the detection of novel and sophisticated attacks on data privacy on the example of colluded applications attack recognition. We develop multiple calculi for DQ and security evaluation, such as a hierarchical fuzzy rules expert system, neural networks, and an algebraic function. Case studies that demonstrate the framework\u27s performance in solving real-life tasks are presented, and the achieved results are analyzed. These case studies confirm the framework\u27s capability of performing comprehensive DQ evaluations. The framework development resulted in producing multiple products, and tools such as datasets and Android OS applications. The datasets include the knowledge base of sensors embedded in modern mobile devices and their quality analysis, technological signals recordings of smartphones during the normal usage, and attacks on users\u27 privacy. These datasets are made available for public use and can be used for future research in the field of data quality and security. We also released under an open-source license a set of Android OS tools that can be used for data quality and security evaluation

Recent Advances in Hybrid Biomimetic Polymer-Based Films: from Assembly to Applications

Biological membranes, in addition to being a cell boundary, can host a variety of proteins that are involved in different biological functions, including selective nutrient transport, signal transduction, inter- and intra-cellular communication, and cell-cell recognition. Due to their extreme complexity, there has been an increasing interest in developing model membrane systems of controlled properties based on combinations of polymers and different biomacromolecules, i.e., polymer-based hybrid films. In this review, we have highlighted recent advances in the development and applications of hybrid biomimetic planar systems based on different polymeric species. We have focused in particular on hybrid films based on (i) polyelectrolytes, (ii) polymer brushes, as well as (iii) tethers and cushions formed from synthetic polymers, and (iv) block copolymers and their combinations with biomacromolecules, such as lipids, proteins, enzymes, biopolymers, and chosen nanoparticles. In this respect, multiple approaches to the synthesis, characterization, and processing of such hybrid films have been presented. The review has further exemplified their bioengineering, biomedical, and environmental applications, in dependence on the composition and properties of the respective hybrids. We believed that this comprehensive review would be of interest to both the specialists in the field of biomimicry as well as persons entering the field