Smart Dust Security – Key Infection Revisited

Sensor network is a notion denoting an interesting subset of self-organising wireless networks. These networks are rather dense as each node have typically more than dozen neighbours, and large – with tens to hundreds thousands of nodes. Applications of such networks assume distributed environmental sensing performed by each sensor in the network, where data from a particular sensor gain value only when combined with data from a relatively high number of other sensors. One of the open security questions in this specific environment is a possibility to lower requirements on key distribution and key management and thus decrease production costs. One of the possible ways is “key infection”. The paper recaps a protocol and already published results. It also elaborates the concept of key infection by introducing a new variant of security amplification protocol, and presents some interesting results obtained by simulations.     

Proximal object and hazard detection for autonomous underwater vehicle with optical fibre sensors

This paper describes short range and tactile optical fibre sensors for marine applications. The sensors are designed for obstacle avoidance on unmanned underwater vehicles (UUVs) operating in confined spaces, but have other possible applications. The fibre sensors augment the sensory abilities derived from ultrasonic and other sensors employed for marine proximity measurement. Of particular interest is proximity detection in the “near” (less than 1 m) and tactile areas. The paper describes the basic principle of operation and alternative sensor configurations. Results are given based on laboratory tests and deployment on a mini autonomous submersible in a test pool.     

Reducing thermal transient induced errors in thermopile sensors in ear thermometer applications

In ear thermometers and similar applications, thermopile sensors have to face a challenging thermal environment. The steady-state sensor temperature as well as the change of sensor temperature, heat flow, and thermal gradients have a significant impact on the sensor's reading. As opposed to the well-known steady-state sensor temperature (so-called “ambient temperature”), the effects of thermal transients have not jet been understood well in the past. They occur when the sensor warms up while the thermometer is inserted into the ear. They have to be treated in addition to the traditional (steady state) ambient temperature compensation.Isothermal packaging of the sensor significantly reduces the error due to thermal transients. This can be combined with mathematical prediction of the remaining error. Based on an analytical thermal model, we show that the error can be represented by a series of derivatives of the sensor housing temperature. Numerical calculation and compensation of the error in only first order combined with an isothermal package allows for an error reduction by the factor of 30.     

Analog VLSI Implementation of Wide-field Integration Methods

In this paper a novel integrated, single-chip solution for autonomous navigation inspired by the computations in the insect visuomotor system is proposed. A generalization of the theory of wide field integration (WFI) is presented which supports the use of sensors with a limited field of view, and the system concept is validated based on experiments using a prototype single-chip WFI sensor. The VLSI design implements (1) an array of Elementary Motion Detectors (EMDs) to derive local estimates of optic flow, (2) a novel mismatch compensation approach to handle dissimilarities in local motion detector units, and (3) on-chip programmable optic flow pattern weighting (Wide-Field Integration) to extract relative speed and proximity with respect to the surrounding environment. Computations are performed in the analog domain and in parallel, providing outputs at 1 kHz while consuming only 42.6 ??W of power. The resulting sensor is integrated with a ground vehicle and navigation of corridor-like environments is demonstrated.     

Designing robust optimal dynamic experiments

The quality of experiments designed using standard optimisation-based techniques can be adversely affected by poor starting values of the parameters. Thus, there is a necessity for design methods that are insensitive (“robust”) to these starting values. Here, two novel, robust criteria are presented for computing optimal dynamic inputs for experiments aiming at improving parameter precision, based on previously proposed methods for providing design robustness — the expected value criterion and the max–min criterion. In this paper, both criteria are extended by use of the information matrix derived for dynamic systems. The experiment design problem is cast as an optimal control problem, with experiment decision variables such as sampling times of response variables, time-varying and time-invariant external controls, experiment duration, and initial conditions. Comparisons are made of experiment designs obtained using the conventional approach and the newly proposed criteria. A typical semi-continuous bioreactor application is presented.     

The characteristics of a “stop–flow” mode of sensor array operation using data with the best classification performance

The paper presents an approach to characterizing a “stop–flow” mode of sensor array operation. The considered operation mode involves three successive phases of sensors exposure: flow (in a stream of measured gas), stop (in zero flow conditions) and recovery (in a stream of pure air). The mode was characterized by describing the distribution of information, which is relevant for classification of measured gases in the response of sensor array. The input data for classifier were the sets of sensors output values, acquired in discrete time moments of the measurement. Discriminant Function Analysis was used for data analysis. Organic vapours of ethanol, acetic acid and ethyl acetate in air were measured and classified. Our attention was focused on data sets which allowed for 100% efficient recognition of analytes. The number, size and composition of those data sets were examined versus time of sensor array response. This methodology allowed to observe the distribution of classification-relevant information in the response of sensor array obtained in “stop–flow” mode. Hence, it provided for the characterization of this mode.     

Ontology-based modeling of dynamic ubiquitous computing applications as evolving activity spheres

As the computer disappears in the environments surrounding our activities, the objects therein become augmented with sensors, actuators, processors, memories, wireless communication modules and they can receive, store, process and transmit information. In addition to objects, spaces also undergo a change towards becoming smart and eventually Ambient Intelligence (AmI) spaces. In order to model the way everyday activities are carried out within an AmI environment, we introduce the notion of “activity sphere”. In this paper, we are interested in the ontology-based representation of activity spheres from two different perspectives (as creators and as observers), as well as the modeling and control of the dynamic nature of activity spheres.     

Remote flood monitoring system based on plastic optical fibres and wireless motes

This paper summarises an effort in the development of a remote flood monitoring system based on plastic optical fibre (POF) sensors and a wireless mote network. The wireless mote, comprising of a network of MICA2DOT™ units, was used as a platform to monitor and record the signal from the POF sensors and transmit this information to a base station wirelessly. A prototype of the integrated wireless POF sensor unit has been constructed, rendering it possible to deploy the autonomous unit remotely at multiple monitoring points as required. A flood monitoring simulation was carried out in a 24 m × 10 m × 0.9 m wave basin where four of these wireless optical fibre mote sensors were used to detect the rising water level in the basin. The novelty of the work lies in the successful integration of the wireless platform to a POF-based liquid level sensor and the subsequent demonstration of the prototype of the system for the purposes of flood monitoring applications.The sensing principle of the POF sensor developed here is well-known and is based on the loss of total internal reflection of the optical signal as the sensor probe comes in contact with the liquid. Compared to optical fibre-based sensors reported previously in the literature, the probe profile used in this study differs in terms of its simplicity in design, while exhibiting an excellent signal intensity loss ratio without the need for additional attachments to the probe such as optical prisms. The tests carried out showed that the POF sensor is capable of detecting a variety of fluids. Exhibiting good signal stability, the sensor also detects the liquid level reliably when the liquid rises or falls to the predetermined level. The responsiveness of the optical fibre sensor was evaluated by simulating different rates at which the liquid rises by immersing the sensor tip into the liquid and vice-versa at various speeds ranging from 1 mm/min to 500 mm/min.     

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

Multiple-access insights from bounds on sensor localization

In ad hoc location-aware sensor networks, unlocalized sensors can estimate their locations based on the triangulation of range measurements from location-aware sensors or “anchors”, whose locations are known or estimated a priori. In this paper, we investigate the relationship between multiple-access design parameters and the accuracy of location estimation in ad hoc sensor networks. Bounds on the average localization accuracy in a packet-based sensor network with time-of-arrival (TOA) based distance estimation are used to investigate the connection between the average effective throughput of packets and the average localization accuracy. On this basis, we (i) develop an analytical framework that allows us to analyze the relationship between network parameters and the average localization accuracy obtained in ad hoc sensor networks with a spread-spectrum physical layer, and (ii) show that, for such networks, minimizing the average localization error is equivalent to maximizing the average effective throughput. We further demonstrate that the developed framework allows the extraction of the optimal network parameters that maximize localization accuracy. The trends in the localization accuracy with respect to the network parameters observed through analysis are then validated via simulation studies. Finally, some aspects of the modeling of location-aware sensor networks that warrant investigation and require more sophisticated modeling strategies are listed.     

Ultra Dense DNA Microarray Design using Magnetoresistive Detectors

Microelectronic chip-based systems are available for a wide variety of applications. Many of these systems rely on NON-INTEGRATED optical detection schemes to collect data from the chips. A magnetoresistive detection format, however, can be completely integrated. This paper presents some basic concepts for optimizing micron-sized magnetoresistive sensors for single nucleotide polymorphism (SNP) analysis and DNA diagnostics. Magnetoresistive sensors are nano-fabricated thin film resistors whose resistance changes as a function of magnetic field. The magnetic DNA assay replaces the EXTERNAL optical reader apparatus with an INTEGRATED magnetoresistive sensor at each “pixel” of the array. The EXTERNAL light source can be replaced by an INTEGRATED magnetic field generation strap, or by a simple external coil. Magnetoresistive pixel sizes could presently be ˜ 3 microns on a side, and decrease to ˜ 100 nm with technological improvements. It is shown that, taking reasonable values for critical parameters, a signal to noise ratio of 10,000 : 1 is achievable using 10 nm paramagnetic beads as the assay label. As early demonstrations of the feasibility of this system, data have been collected using NVE's magnetoresistive sensors (non-optimized) to easily detect single micron-sized magnetic beads. Presently NVE is working on 1 million bit arrays of magnetoresistive sensors which are being fabricated into magnetoresistive random access memory (MRAM) chips. These arrays have many similarities to what is required for the magnetoresistive DNA assay including sub-micron bit size and single bit addressability.     

Design of an auditory guidance system for the blind with signal transformation from stereo ultrasonic to binaural audio

We have designed an auditory guidance system for the blind using ultrasonic-to-audio signal transformation. We first investigated the system requirements, and designed a simple but useful portable guidance system for the blind. The system derives visual information using multiple ultrasonic sensors, and transforms it to binaural auditory information using a suitable technique. The user can recognize the position of obstacles and the surrounding environment. The system is composed of two parts. One is a glasses-type system, and the other is a cane-type system with guide wheels. The former functions as an environment sensor, and the latter functions as a clear-path indicator. Wide-beam-angle ultrasonic sensors are used to detect bojects over a broader range. The system is designed as a battery-supplied portable model. Our design is focused on low power consumption, small size, light weight, and easy manipulation.     

A criterion for optimal design of multi-axis force sensors

This paper deals with the design of multi-axis force (also known as force/torque) sensors, as considered within the framework of optimal design theory. Optimal design procedures consists of finding the combination of design variables that extremizes some optimality criterion: provided a suitable mathematical formulation of the problem, solutions can be efficiently obtained through currently available numerical techniques. The principal goal of this paper is to identify a mathematical objective function, whose minimization corresponds to the optimization of sensor accuracy. The methodology employed is derived from linear algebra and analysis of numerical stability. An objective function which can be applied to a large class of sensor configurations is proposed. The problem of optimizing the number of basic transducers employed in a multi-component sensor is also addressed. Finally, applications of the proposed method to the design of a simple sensor as well as to the optimization of a novel, 6-axis miniaturized sensor are discussed.     

Coordinated dispatching of proximity sensors for the surveillance of manoeuvring targets

The surveillance of a manoeuvring target with multiple sensors in a coordinated manner requires a method for selecting and positioning groups of sensors in real time. Herein, the principles of dispatching, as used for the effective operation of service vehicles, are considered. The object trajectory is first discretized into a number of demand instants (data acquisition times), to which groups of sensors are assigned, respectively. Heuristic rules are used to determine the composition of each sensor group by evaluating the potential contribution of each sensor. In the case of dynamic sensors, the position of each sensor with respect to the target is also specified. Our proposed approach aims to improve the quality of the surveillance data in three ways: (1) The assigned sensors are manoeuvred into “optimal” sensing positions, (2) the uncertainty of the measured data is mitigated through sensor fusion, and (3) the poses of the unassigned sensors are adjusted to ensure that the surveillance system can react to future object manoeuvres. If a priori target trajectory information is available, the system performance may be further improved by optimizing the initial pose of each sensor off-line. The advantages of dispatching dynamic sensors over similar static-sensor systems are demonstrated through comprehensive simulations.     

Sensor scheduling in continuous time

Let N be the number of available sensor sources. Noisy observations of an underlying state process are available for these N sources. We consider the continuous time sensor scheduling problem in which N₁ of these N sources are to be chosen to collect data at each time point. This sensor scheduling problem (with switching costs and switching constraints) is formulated as a constrained optimal control problem. In this framework, the controls represent the sensors that are chosen at a particular time. Thus, the control variables are constrained to take values in a discrete set, and switchings between sensors can occur in continuous time. By incorporating recent results on discrete valued optimal control, we show that this problem can be transformed into an equivalent continuous optimal control problem. In this way, we obtain the sensor scheduling policy as well as the associated switching times.     

Optimal operation of Petlyuk distillation: steady-state behavior

The “Petlyuk” or “dividing-wall” or “fully thermally coupled” distillation column is an interesting alternative to the conventional cascaded binary columns for separation of multi-component mixtures. However, the industrial use has been limited, and difficulties in operation have been reported as one reason. With three product compositions controlled, the system has two degrees of freedom left for on-line optimization. We show that the steady-state optimal solution surface is quite narrow, and depends strongly on disturbances and design parameters. Thus it seems difficult to achieve the potential energy savings compared to conventional approaches without a good control strategy. We discuss candidate variables which may be used as feedback variables in order to keep the column operation close to optimal in a “self-optimizing” control scheme.     

Optimal design of two interconnected enzymatic bioreactors

This paper deals with the optimal design of two interconnected continuous stirred bioreactors in which a single enzymatic reaction occurs. The term “optimal” should be understood here as the minimum of the total volume of the reactors required to perform a given conversion rate, given a quantity of matter to be converted per time unit. In determining the optimal volume, it is considered that the input flow may be distributed among the tanks and also that a recirculation loop may be used. The optimal design problem is solved for a wide class of kinetics functions including, in particular, the well-known Michaelis–Menten kinetics function. The analysis of the optimal configurations is investigated, and it is shown that the concept of “Steady State Equivalent Biological System” (SSEBS) first introduced by Harmand et al. [AIChE J., in press] for microbial reactions only applies to enzymatic systems which have non-monotonic kinetics. In addition, a stability as well as a sensitivity analysis of the optimal configurations are performed.     

Sensor fusion

Sensor fusion is a method of integrating signals from multiple sources. It allows extracting information from several different sources to integrate them into single signal or information. In many cases sources of information are sensors or other devices that allow for perception or measurement of changing environment. Information received from multiple-sensors is processed using “sensor fusion” or “data fusion” algorithms. These algorithms can be classified into three different groups. First, fusion based on probabilistic models, second, fusion based on least-squares techniques and third, intelligent fusion. The probabilistic model methods are Bayesian reasoning, evidence theory, robust statistics, recursive operators. The least-squares techniques are Kalman filtering, optimal theory, regularization and uncertainty ellipsoids. The intelligent fusion methods are fuzzy logic, neural networks and genetic algorithms. This paper will present three different methods of intelligent information fusion for different engineering applications. Chapter 2 is based on Sasiadek and Wang (2001) paper and presents an application of adaptive Kalman filtering to the problem of information fusion for guidance, navigation, and control. Chapter 3 is based on Sasiadek and Hartana (2000) and Chapter 4 on Sasiadek and Khe (2001) paper.     

Optimal control of continuous-time linear systems with a time-varying, random delay

We study a finite horizon optimal control problem for a class of linear systems with a randomly varying time-delay. The systems of this type may arise in embedded control applications and in certain applications in economics. The delay value is treated as an unknown variable but with known statistical properties, modelled by a Markov process with a finite number of states. The “probabilistic delay averaging” approach is employed to determine the optimal control in the form which is independent of the delay value.     

The development of a metal clad leaky waveguide sensor for the detection of particles

A novel leaky waveguide device for the detection of particles has been developed and shown to be more sensitive than the currently used waveguide sensors. These metal clad leaky waveguide (MCLW) devices have been used as preliminary study on the feasibility for the detection of soluble proteins but can also be used for the detection of large particles such as bacteria, bacterial spores, yeasts and latex beads. A MCLW device has been fabricated to increase the overlap of the evanescent field extension from the sensor surface with the particles in the bulk solution of a flowing system, to place most of the entire volume of the particles within the evanescent field. Increasing the overlap of the evanescent field with the particles and permitting mode propagation along the direction of a flow for a few millimetres provides an effective interrogation approach for multi-particle detection in a single flow channel.