Multi-unit calibration rejects inherent device variability of chemical sensor arrays

Inherent sensor variability limits mass-production applications for metal oxide (MOX) gas sensor arrays because calibration for replicas of a sensor array needs to be performed individually. Recently, calibration transfer strategies have been proposed to alleviate calibration costs of new replicas, but they still require the acquisition of transfer samples. In this work, we present calibration models that can be extended to uncalibrated replicas of sensor arrays without acquiring new samples, i.e., general or global calibration models. The developed methodology consists in including multiple replicas of a sensor array in the calibration process such that sensor variability is rejected by the general model. Our approach was tested using replicas of a MOX sensor array in the classification task of six gases and synthetic air, presented at different background humidity and concentration levels. Results showed that direct transfer of individual calibration models provides poor classification accuracy. However, we also found that general calibration models kept predictive performance when were applied directly to new copies of the sensor array. Moreover, we explored, through feature selection, whether particular combinations of sensors and operating temperatures can provide predictive performances equivalent to the calibration model with the complete array, favoring thereby the existence of more robust calibration models

Artificial Olfaction in the 21st Century

The human olfactory system remains one of the most challenging biological systems to replicate. Humans use it without thinking, where it can measure offer protection from harm and bring enjoyment in equal measure. It is the system's real-time ability to detect and analyze complex odors that makes it difficult to replicate. The field of artificial olfaction has recruited and stimulated interdisciplinary research and commercial development for several applications that include malodor measurement, medical diagnostics, food and beverage quality, environment and security. Over the last century, innovative engineers and scientists have been focused on solving a range of problems associated with measurement and control of odor. The IEEE Sensors Journal has published Special Issues on olfaction in 2002 and 2012. Here we continue that coverage. In this article, we summarize early work in the 20th Century that served as the foundation upon which we have been building our odor-monitoring instrumental and measurement systems. We then examine the current state of the art that has been achieved over the last two decades as we have transitioned into the 21st Century. Much has been accomplished, but great progress is needed in sensor technology, system design, product manufacture and performance standards. In the final section, we predict levels of performance and ubiquitous applications that will be realized during in the mid to late 21st Century

Analytical applications of sensor arrays and virtual instrumentation

An ammonium detection system using Ion-Selective Electrodes (ISEs) in Flow-Injection Analysis (FIA) is described. Because of the low selectivity of the nonactin ammonium selective electrode towards some common ions, different selectivity enhancement techniques have been examined. A Sensor Array Detector (SAD) which comprises ISEs selective for ammonium, sodium, potassium and calcium was used. A modified form of the Nikolskii-Eisenman Equation is proposed in which the charge power function of the interfering ion activity is linearised. Selectivity is quantified for the PVC membrane electrodes (NH4+, Na , K \ Ca ') in terms of constants rather than conventional coefficients. These constants and other electrode parameters such as cell constant and slope are estimated by means of the FIA-SAD approach. The SAD response was modeled via the Nikolskii-Eisenman equation with SIMPLEX regression model The applicability of the resulting values for these parameters is demonstrated through the determination of unknowns by direct solution of the system of modified Nikolskii-Eisenman equations describing the array response. The results show that the use of an array of ISEs under FIA regimes for the detection of ammonium in the concentration range 10 '4 to 10 '2 mol dm'3 gives a much higher improvement in the determination of ammonium in aqueous samples than the use of a single ammonium electrode in steady-state or kinetic measurements. This approach is suitable for use in real-time monitoring applications where batch calibration techniques cannot easily be implemented. Computer controlled laboratory instrumentation is of growing importance both in research and in industry. Different hardware and software approaches may be chosen which allow the development of high quality products, Last trends in hardware and software strategies are analyzed and some general guidelines are given for instrumentation development. The graphical compiler Lab VIEW 3.0 for instrumentation from National Instruments is presented and evaluated in terms of flexibility and low cost for the production of virtual instrumentation for research, biomedical applications and industrial environmental monitoring

A Neuromorphic Machine Learning Framework based on the Growth Transform Dynamical System

As computation increasingly moves from the cloud to the source of data collection, there is a growing demand for specialized machine learning algorithms that can perform learning and inference at the edge in energy and resource-constrained environments. In this regard, we can take inspiration from small biological systems like insect brains that exhibit high energy-efficiency within a small form-factor, and show superior cognitive performance using fewer, coarser neural operations (action potentials or spikes) than the high-precision floating-point operations used in deep learning platforms. Attempts at bridging this gap using neuromorphic hardware has produced silicon brains that are orders of magnitude inefficient in energy dissipation as well as performance. This is because neuromorphic machine learning (ML) algorithms are traditionally built bottom-up, starting with neuron models that mimic the response of biological neurons and connecting them together to form a network. Neural responses and weight parameters are therefore not optimized w.r.t. any system objective, and it is not evident how individual spikes and the associated population dynamics are related to a network objective. On the other hand, conventional ML algorithms follow a top-down synthesis approach, starting from a system objective (that usually only models task efficiency), and reducing the problem to the model of a non-spiking neuron with non-local updates and little or no control over the population dynamics. I propose that a reconciliation of the two approaches may be key to designing scalable spiking neural networks that optimize for both energy and task efficiency under realistic physical constraints, while enabling spike-based encoding and learning based on local updates in an energy-based framework like traditional ML models. To this end, I first present a neuron model implementing a mapping based on polynomial growth transforms, which allows for independent control over spike forms and transient firing statistics. I show how spike responses are generated as a result of constraint violation while minimizing a physically plausible energy functional involving a continuous-valued neural variable, that represents the local power dissipation in a neuron. I then show how the framework could be extended to coupled neurons in a network by remapping synaptic interactions in a standard spiking network. I show how the network could be designed to perform a limited amount of learning in an energy-efficient manner even without synaptic adaptation by appropriate choices of network structure and parameters - through spiking SVMs that learn to allocate switching energy to neurons that are more important for classification and through spiking associative memory networks that learn to modulate their responses based on global activity. Lastly, I describe a backpropagation-less learning framework for synaptic adaptation where weight parameters are optimized w.r.t. a network-level loss function that represents spiking activity across the network, but which produces updates that are local. I show how the approach can be used for unsupervised and supervised learning such that minimizing a training error is equivalent to minimizing the network-level spiking activity. I build upon this framework to introduce end-to-end spiking neural network (SNN) architectures and demonstrate their applicability for energy and resource-efficient learning using a benchmark dataset

The 1991 research and technology report, Goddard Space Flight Center

The 1991 Research and Technology Report for Goddard Space Flight Center is presented. Research covered areas such as (1) earth sciences including upper atmosphere, lower atmosphere, oceans, hydrology, and global studies; (2) space sciences including solar studies, planetary studies, Astro-1, gamma ray investigations, and astrophysics; (3) flight projects; (4) engineering including robotics, mechanical engineering, electronics, imaging and optics, thermal and cryogenic studies, and balloons; and (5) ground systems, networks, and communications including data and networks, TDRSS, mission planning and scheduling, and software development and test

Proceedings of the 11th international Conference on Cognitive Modeling : ICCM 2012

The International Conference on Cognitive Modeling (ICCM) is the premier conference for research on computational models and computation-based theories of human behavior. ICCM is a forum for presenting, discussing, and evaluating the complete spectrum of cognitive modeling approaches, including connectionism, symbolic modeling, dynamical systems, Bayesian modeling, and cognitive architectures. ICCM includes basic and applied research, across a wide variety of domains, ranging from low-level perception and attention to higher-level problem-solving and learning. Online-Version published by Universitätsverlag der TU Berlin (www.univerlag.tu-berlin.de

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Sustainability Issue of the Total Quality Management (TQM) System in the Manufacturing Industry

The purpose of this research is to understand the sustainability issue of total quality management (TQM) and its effects in the manufacturing industry. Having exploratory and descriptive objectives, this research used a flexible design single case study on a water treatment company in the southeastern United States to facilitate the examination of the phenomenon using real‐life, present‐day context, and multiple perspectives from participants. The single bounded case study collected and integrated many forms of qualitative data ranging from interviews, observations, and quality‐related archived documents to answer the research questions. The results revealed that failure in sustaining the quality system in place resulted in high product defects, leading to excessive reject costs and loss in productivity. The analysis of the data showed that the firmness of TQM methodology, orientation of the organization culture, type of leadership style, and highly competitive strategies and operational targets affected the sustainment of TQM in the site. The single case study is limited to the understanding of TQM sustainability challenges in the manufacturing industry and the findings will not be generalizable to other business groups or sectors in the same context. The future study could focus on a broader field of prevailing conflicts between opposing objectives, logics, interests, and missions within one multinational organization or within its line companies. This research aims to contribute to the understanding of establishing alignment and coherence of TQM practices to the organizational strategic goals and objectives to improve overall performance with significant value for customer focus and continuous improvement

Management: A continuing bibliography with indexes

This bibliography lists 551 reports, articles, and other documents introduced into NASA scientific and technical information system in 1980

Psychological Engagement in Choice and Judgment Under Risk and Uncertainty

Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and decision reward (Kusev et al, 2013, Shafir et al., 2002)