In this paper a new approach in human identification is investigated, For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.
During the last decades the research in the sensor fusion area has mainly been focused on fusion methods and feature selection methods. A possible further development in this area is to incorporate a process referred to as active perception. This means that the system is able to manipulate the sensing mechanisms to create a focus on selected information in the surrounding environment. This process may also be able to handle the feature selection process with respect to which features to be used and/or the number of features to use. This paper presents a model that contains a decision system based on active perception integrated with previous sensor fusion algorithms. The human body has perhaps one of the most advanced perceptual processing systems. The human perception process can be divided into sensation (measurement collection) and perception (interpret the surroundings). During the sensation process a huge amount of data is collected from different sensors that reflect the environment. The information has to be interpreted in an effective way, i.e. in the fusion process. The interpretation together with a decision system to control the sensors to focus on important information will correspond to the (active) perception process. The model presented in this paper capitalizes on the properties presented by the biological counterpart to achieve more human-like processes for a sensor fusion. Finally, the paper presents the testing of the model in two examples. The applications used have a safety approach of fire indication, identification and decision-making. The goal is to enlarge a conventional fire alarm system to not only detect fire, but also to propose different actions for a human in a dangerous area for example.
The problem of food- and water quality assessment is important for many practical applications, such as food industry and environmental monitoring. In this article we present a method for fast online quality assessment based on electronic tongue measurements. The idea is implemented in two steps. First we apply a fuzzy clustering technique to obtain prototypes corresponding to good and bad quality from a set of training data. During the second, online step we evaluate the membership of the current measurement to each cluster and make a decision about its quality. The result is presented to the user in a simple and understandable way, similar to the concept of traffic light signals. Namely, good quality is indicated with by a green light, bad quality with a red one, and a yellow light is a warning signal. The approach is demonstrated in two case studies: quality assessment of drinking water and baby food.
The paper presents a relatively new human-computer interaction paradigm, where a human operator's perceptual actions are mimicked by the computer. In this sensor controlled system concept, we estimate a specific feature characterizing, for example, product quality, and apply intelligent analysis and an optimization to assess product quality as acquired by a human expert. The human operator contributes his/her intelligence to this man-machine interaction through learning the measurement system. An illustrative example shows how the human operator's knowledge and experience are learned by a sensor based system within a complex dough mixing optimization process in an industrial bread baking plant. The resulting sensor system acts as an intelligent feature estimator in a complex industrial process for monitoring the dynamical behavior there. The system allows easy sensor observation and makes decisions based on learning interaction with a human.
A sensor system suitable for measuring qualitative changes In the chemical and the bacterial content in drinking water is presented. The sensor, an electronic tongue, is based on a voltammetric technique and is therefore robust, simple and sensitive to small changes of water quality in the measured sample. The sensor system is constructed so the liquid sample will flow through the sensing unit while measuring continuously. The sensor has a solid construction, does not contain any fragile parts and is independent of how it is positioned. This creates new approaches and the sensor can easily be mounted on a underwater vehicle for continuous inspection of drinking water reservoirs and continuously monitor the quality of the water as well as be mounted directly on a drinking water tap.
The concept of the “electronic tongue” has been used in some experiments to establish the needs of fast and virtual monitoring of aqueous samples, e.g., in the monitoring of drinking water quality. More specifically, the performance of a proposed multi-electrode sensor system, used for voltammetric analysis of aqueous samples, is described. It is, for example, shown how such an “electronic tongue” can be used to monitor the quality of water in a production plant for drinking water. It is pointed out that conventional techniques often determine single concentration of the measured test while in many areas of measurement technology the methodology to extract adequate information from the environment, e.g., the electronic tongue, makes a total water quality estimate based on predetermined constraints extracted from complicated pattern structures. In this approach, experiments are conducted using an electronic tongue to virtually monitor the drinking water quality, measured from the raw water in the river to the tap water of the consumer. It can be shown that a system based on the proposed multi-electrode virtual sensor system is able to detect water quality changes. In these experiments, with the use of signal analysis and statistical multivariate methods we are able to estimate the water quality
This paper addresses the problem of enabling autonomous agents (e.g., robots) to carry out human oriented tasks using an electronic nose. The nose consists of a combination of passive gas sensors with different selectivity, the outputs of which are fused together with an artificial neural network in order to recognize various human-determined odors. The basic idea is to ground human-provided linguistic descriptions of these odors in the actual sensory perceptions of the nose through a process of supervised learning. Analogous to the human nose, the paper explains a method by which an electronic nose can be used for substance identification. First, the receptors of the nose are exposed to a substance by means of inhalation with an electric pump. Then a chemical reaction takes place in the gas sensors over a period of time and an artificial neural network processes the resulting sensor patterns. This network was trained to recognize a basic set of pure substances such as vanilla, lavender and yogurt under controlled laboratory conditions. The complete system was then validated through a series of experiments on various combinations of the basic substances. First, we showed that the nose was able to consistently recognize unseen samples of the same substances on which it had been trained. In addition, we presented some first results where the nose was tested on novel combinations of substances on which it had not been trained by combining the learned descriptions - for example, it could distinguish lavender yogurt as a combination of lavender and yogurt.
This paper presents a system where different scenarios can be played in a synthetic natural environment in form of a modified commercial game used for scenario simulation. This environment is connected to a command and control system that can visualize, process, store, and distribute sensor data and their interpretations within several command levels. It is specifically intended for mobile sensors used in remote sensing tasks. The system has been used in a disaster management exercise and there distributed information from a virtual accident to different command levels in the crisis management. The information consisted of live and recorded video, reports and map objects.
In this article we present an approach to the design of human-like artificial systems. It uses a perception model to describe how sensory information is processed for a particular task and to correlate human and artificial perception. Since human-like sensors share their principle of operation with natural systems, their response can be interpreted in an intuitive way. Therefore, such sensors allow for easier and more natural human–machine interaction.
The approach is demonstrated in two applications. The first is an “electronic tongue”, which performs quality assessment of food and water. In the second application we describe the development of an artificial hand for dexterous manipulation. We show that human-like functionality can be achieved even if the structure of the system is not completely biologically inspired.
This paper focuses on the assumption that sensor system for personal use has optimal performance if coherent with the human perception system. Therefore, we provide arguments for this idea by demonstrating two examples. The first example is a personal taste sensor for use in finding abnormal ingredients in food. The second application is a mobile sniffing system, coherent with the behavior of a biological system when detecting unwanted material in hidden structures, e.g. explosives in a traveling bag
The paper focuses on the assumption that a sensor system for personal use has the optimal employment if coherent with the human perception system and provides redundant information by complementing sensors. Therefore, we provide argument for this thesis by demonstrating two examples within the quality indication of drinking water; two different electronic tongue sensors with similar operational principles have been used for making security assessments of drinking water in different environments, from tap, raw water and bottle. ©2008 IEEE.
The use of remotely operated robotic systems in security related applications is becoming increasingly popular However, the direct teleoperation interfaces commonly used today put a large amount of cognitive burden on the operators, thus seriously reducing the efficiency and reliability of these systems. We present an approach to alleviate this problem by exploiting both software and hardware autonomy. At the software level, we propose a variable autonomy control architecture that dynamically adapts the degree of autonomy of the robot in terms of control, perception, and interaction. At the hardware level, we rely on the intrinsic autonomy and robustness provided by the spherical morphology of our Ground-Bot robot. We also present a prototype system for facilitating the interaction between human operators and robots using our control architecture. This work is specifically aimed at increasing the effectiveness of the GroundBot robot for remote inspection tasks
In the steel industry, there are many processes that include measuring and control of temperatures. With higher demand on quality, increased production, and effective energy consumption, the use of noncontact temperature measuring techniques has increased. After the cooling section in a continuous annealing-pickling line, the strip temperature is estimated by using the grey box technique. Temperatures are measured in the cavity between the strip and the roller using radiation thermometers. A model is made for estimating strip temperature using the measured temperatures and knowledge of the physics of the process.
In the steel industry, there are many processes that include measuring and control of temperatures. With higher demand on quality, increased production and effective energy consumption the use of non-contact temperature measuring techniques has increased. After the cooling section in a continuous annealing-pickling line, the strip temperature is estimated by using the Grey box technique. Temperatures are measured in the cavity between the strip and the roller using radiation thermometers. A model is made for estimating strip temperature using the measured temperatures and knowledge of physics of the process.
In the present paper a multi-sensor system is considered wlicre the sensors comprising it utilize the principles of huinan olfactory sensing and the processing of the sensor iiicasurcnients is done by a fuzzy sensor fusion technique. 'l'hc enipliasis of the paper is on the fuzzy fusion technique used for the classification of the numerical measurements of a quality characteristic in different fuzzy quality profiles.
The purpose of this article is to demonstrate new paradigms in the analysis and design of virtual instrumentation in autonomous sensor systems. By autonomous sensor systems we mean mobile as well as immobile systems that employ a vast array of sensors to analyze or influence dynamic and uncertain external changes. These systems must perform operations in real time, in both expected and unexpected situations, using only limited human intervention. An autonomous sensor system can be used to collect data about a complex and dynamic environment, to perform interpretation and fusion of this data, and to present the resulting information to a human operator in a synthetic form that highlights features of interest of the environment. The system can then be regarded as a virtual instrument. A useful form to organize and present this information is a virtual spatial map-a representation of the environment in which colored geometric figures are placed to indicate that a given feature (or event) has been detected at that location. We illustrate our approach of building virtual instruments by presenting a case study of semi-autonomous remote environmental exploration. A mobile platform gathers information about a remote environment using multi-modal sensor data collection, information processing, and data fusion at different levels of abstraction and resolution. The result of the exploration is a fused virtual map that contains the important features of the environment
A practical implementation of a genetic algorithm for routing a real autonomous robot through a changing environment is described. Moving around in a production plant the robot collects information about its environment and stores it in a temporal map, which is virtually a square grid, taking account of changing obstacles. The evolutional optimizer continuously searches for short paths in this map using string representations of paths as chromosomes. The main features of the implementation include physical realization, random walk exploration, temporal mapping, and dedicated genetic operators.