As an emerging technology, memristive devices have flourished to develop energy-efficient computing systems in the form of emerging NonVolatile Memories (eNVMs), synaptic connections, and logic design, which could play a critical role in microelectronic design. In neuromorphic applications, the power-off state of a nonvolatile memristor is crucial, as it determines the learning and forgetting of embedded synaptic efficacy. Due to the complexity of the transport mechanisms contributing to the memristor current, memristor modeling remains a critical challenge. The memductance of memristors includes both volatile and nonvolatile components, which can be modeled using a Memductance Components Adaptive Model (MCAM). The MCAM enables researchers to model the memductance of memristors, which can be used in neuromorphic applications and logic design. Mathematical explanations and two physical models of memristors extracted from experimental results demonstrate the model’s accuracy. The application of MCAM for synaptic updates in spiking neuromorphic systems is also shown.

An important area of research in humanoid robots is energy consumption, as it limits autonomy, and can harm task performance. This work focuses on power aware motion planning. Its principal aim is to find joint trajectories to allow for a humanoid robot to go from crouch to stand position while minimizing power consumption. Q-Learning (QL) is used to search for optimal joint paths subject to angular position and torque restrictions. A planar model of the humanoid is used, which interacts with QL during a simulated offline learning phase. The best joint trajectories found during learning are then executed by a physical humanoid robot, the Aldebaran NAO. Position, velocity, acceleration, and current of the humanoid system are measured to evaluate energy, mechanical power, and Center of Mass (CoM) in order to estimate the performance of the new trajectory which yield a considerable reduction in power consumption.

This paper proposes a light-weight online classification method to detect smarthpone user's postural actions, such as sitting, standing, walking, and running. These actions are named as “user states” since they are inferred after the analysis of data acquired from the smartphones equipped accelerometer sensors. To differentiate one user state from another, many studies can be found in the literature. However, this study differs from all others by offering a computational lightweight and online classification method without knowing any priori information. Moreover, the proposed method not only provides a standalone solution in differentiation of user states, but also it assists other widely used offline supervised classification methods by automatically generating training data classes and/or input system matrices. Furthermore, we improve these existing methods for the purpose of online processing by reducing the required computational burden. Extensive experimental results show that the proposed method makes a solid differentiation in user states even when the sensor is being operated under slower sampling frequencies.

This paper presents a novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model (HMM) and learning from data concepts. The framework either recognizes or estimates user contextual inferences called `user states' within the concept of Human Activity Recognition (HAR) for future context-aware applications. Context-aware applications require continuous data acquisition and interpretation from one or more sensor reading(s). Therefore, device battery lifetimes need to be extended due to the fact that constantly running built-in sensors deplete device batteries rapidly. In this sense, a framework is constructed to fulfill requirements needed by applications and to prolong device battery lifetimes. The ultimate goal of this paper is to present an accurate user state representation model, and to maximize power efficiency while the model operates. Most importantly, this research intends to create and clarify a generic framework to guide the development of future context-aware applications. Moreover, topics such as user profile adaptability and variant sensory sampling operations are examined. The proposed framework is validated by simulations and implemented in a HAR-based application by the smartphone accelerometer. According to the results, the proposed framework shows an increase in power efficiency of 60% for an accuracy range from 75% up to 96%, depending on user profiles.

Mobile device based sensing provides a vast variety of user contextual information. To continuously acquire, classify and reason any context through built-in sensors cause mobile device batteries to deplete rapidly. Therefore, an urgent need has arisen to apply power saving approaches at the low level sensor operations while inferring user relevant context. In this paper, an adaptive sensing strategy is proposed to fill the mentioned deficiency within the realm of Human Activity Recognition (HAR). The strategy aims at inferring any user relevant postural activities through smartphone accelerometer by providing two intuitive power saving methods, which assign adaptive pairs of sampling frequencies and duty cycles during sensor operations. The proposed methods show from 20% up to 50% efficiency enhancement in power consumption caused by the accelerometer with respect to overall 15% accuracy decrease in context inference depending on the degree of user activity dynamics.

This paper presents a practical and simple approach to the design of autonomous flight control of miniature helicopters. It considers a standard easily tunable PID control as the starting step of the design. Then, robust loop-shaping techniques are applied to derive a controller with optimal properties with respect to robustness, noise sensitivity, and bandwidth. Comprehensive analysis and simulation experiments show the validity and practicality of the proposed control design and analysis procedures.

Localization is a key function in Wireless Sensor Networks (WSNs). Many applications and internal mechanisms require nodes to know their location. This work proposes a new sequential estimation algorithm for distributed cooperative localization, whose simplicity makes it amenable to self-localization in Wireless Sensor Networks (WSNs), characterized by their restricted resources in energy and computation. The algorithm is inspired in sequential Monte-Carlo estimation techniques, viz. particle filters, that excel in robustness and simplicity for estimation applications. However, particle filters require significant amounts of memory and computational power for managing large numbers of particles. The presented technique reduces the number of particles, while retaining the convergence, accuracy and simplicity properties, as demonstrated in simulation experiments.

A simplified analysis is presented for small scale unmanned helicopter controller design suitable for non-aggressive flight scenarios. A Matlab based detailed approach has been followed to analyze system mathematical characteristics, and provide the design process tool for PID/PD and fuzzy logic controllers for the yaw, pitch, roll (Euler angles) and the height variables for hover and slow flight. Both controllers have performed well; have demonstrated promising results being at the same time simple and mathematically sound.

This paper describes the development of intelligent mapping from a haptic user interface to a remote manipulator to assist individuals with disabilities performing occupational therapy tasks. This mapping, referred to an assistance function, is determined on the basis of environmental model or sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. Three manual dexterity assessment tests, commonly used in the occupational therapy field, were chosen to implement the several forms of assistance functions designed to augment the human performance. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK software. This work is the continuation of the authors' previous work at the ICRA'02 [Pernalete, N, et al, 2002], in which only results of fully able individuals were presented. Two motion-impaired users with severe forms of muscular dystrophy and spinal cord injure, volunteered to participate in the experiments and their results are presented in this article. The results demonstrated that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks for the disabled individuals as well as they did for the fully-able persons. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job-related tasks.

This paper describes the development of intelligent mapping from a haptic user interface to a remote manipulator to assist individuals with disabilities performing vocational tasks. This mapping, referred to an assistance function, is determined on the basis of environmental model or sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. Three manual dexterity assessment tests, commonly used in the occupational therapy field, were chosen to implement the several forms of assistance functions designed to augment the human performance. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK software. The results demonstrate that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job-related tasks.

This paper describes several assistance approaches to use complex and variable telerobotic mapping, such that limited and irregular inputs from an individual with disabilities are mapped into complex motions. The approach presented implies several forms of assistance functions in which the human input is enhanced rather than superseded by the computer. Some complex tasks in the slave manipulator can be implemented through simple and restricted movements on the master's side coming from a person with restricted dexterity. Three different tasks were chosen to implement the assistance functions designed to augment the teleoperation performance of motion-impaired users. A comparison is made between the implementation of the constraints on the master and the slave sides of the system. The system used consisted of the Phantom haptic device and the RRC (Robotics Research Corporation) seven-degree-of-freedom manipulator. The results demonstrated that the forms of assistance provided not only allowed users to perform complex tasks, but also, reduced the execution times and increased the operation accuracy of the chosen tasks.

Fault tolerant design validation tests through laser fault injection (LFI) have been carried out at the Center for Microelectronics Research (CMR) of the University of South Florida (USF) by a team of scientists and engineers led by Dr. Wilfrido Moreno with cooperation from the Space and Strategic Systems Operation (SASSO) of Honeywell, Inc. The technique, demonstrated by previous work at the CMR involves the precise application of a laser pulse tailored as to power, pulse width and frequency into a very large scale integrated circuit (VLSIC) which is a component of an operating computer capable of detecting, logging and recovering from a transient fault and then proceeding with its operation. The test vehicle is the radiation hardened 32-bit processor (RH32) developed by Honeywell for the Rome Laboratory of the United States Air Force and the Laser facility is the Laser Restructuring Laboratory (LRL) of the CMR built under a grant from the Defense Advanced Research Project Agency (DARPA). Two system level series of tests have been completed. The first one involved the verification of initial demo tests performed by others on an early version of the computer which was limited to verifying that the computer detected and logged a hardware error in the register file of the central processing unit (CPU). These tests were expanded to observe the incrementing of the error count register of the same chip as laser pulses were applied. During the second series of rests, and for the first time, the result was obtained of observing the processor detect a hardware error, log and correct it and then proceed with the present instruction. The previous being evident by the data entered by the processor in the statusing registers.

The development of ASICs is a costly proposition in terms of the technology, equipment, and the time required. The advent of laser restructurable VLSI (LRVLSI), promises the design engineer the capability to interconnect cells that implement generic logic functions (e.g., sum of products) to achieve a higher level of complexity design. An operating environment and infrastructure for rapid prototyping and quick turnaround using laser restructuring of VLSI circuits were developed. The purpose of this facility is threefold: to pursue research on novel interconnect technologies using LRVLSI, to develop the capability of operating in a quick turnaround mode using optimized process parameters, and to maintain standardization and compatibility with commercially available equipment for feasible technology transfer.>