As the deployment of Internet-of-Things (IoT) devices in intelligent systems from smart health to smart city expands, ensuring sensor data reliability is critical for accurate decision-making. Individual sensor data suffer data drift due to unwanted noises, faults and false data injections during deployment, which paves the way for exploiting inter-sensor data correlation to improve anomaly detection. Studies suggest that sensor readings are correlated in many applications. Existing methods employ data correlation among sensors in pre-processing steps but do not integrate it into the bedrock of the Kalman Filter (KF) state transitions. Such integration will provide a way to detect anomalies in dynamic and complex applications. This paper proposes a novel, two-stage anomaly detection system named BL-DKF to tackle these challenges. First, an innovative 2D dynamic KF (DKF) is introduced that incorporates inter-sensor data correlation for adaptive estimation through noise and fault/false data injections. DKF dynamically adjusts its process covariance matrix by analyzing correlation deviations, reducing its influence when the anomalies cause correlation drops to emphasize internal predictions. The estimates produced by DKF are fed to a compact Bidirectional LSTM (BL) model in the second stage, which analyzes anomalous patterns. We evaluate BL-DKF on two real-world sensor datasets, including the data collected from our IoT lab testbed. Results demonstrate that BL-DKF robustly detects nonlinear and physical anomaly injections, outperforming existing methods with an average of 9.36% detection accuracy improvement across four experiments. The proposed system presents an analytical solution to embed inter-sensor correlations into KF, advancing its effectiveness in many application domains.

Since diverse noises and irregularities impact on sensor data, self-declaration of sensor data reliability is crucial for advancing Internet of Things applications and industrial automation. Relevant works on reliability include sensor self-attribution of data confidence, and self-diagnosis of sensor faults using temporal data redundancy or neighboring sensor data. Models are built on edge devices and then transferred to sensors. Overall, the existing methods are computationally expensive, require real-time data from other sensors and incur considerable transmission overhead. Therefore, they are not suitable for independent sensor data reliability assessment. Addressing these issues, we introduce an independent reliability self-declaration method for sensors. Two Kalman filter-inspired, block-based lightweight algorithms are designed that handle isolated and burst noises and estimate block data reliability. Moreover, a conceptual model to dynamically adjust block size is proposed leveraging noise level and maximum TCP/IP packet size to reduce data transmissions. The reliability levels are conveyed using TCP header reserved bits to avoid communication overhead. The approach was tested using water quality monitoring (WQM) and healthcare application datasets. Results show, for burst noise, our lightweight and scalable approach attains superior accuracy in WQM (89.06%) and healthcare (82.63%) for five-level reliability estimation. A real-world deployment using an Arduino-based sensor node demonstrates the feasibility of the approach for in-sensor operation.

Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor’s historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.

Performance in an engineering system tends to degrade over time due to a variety of wearing or ageing processes. In supervisory controlled processes there are typically many signals being monitored that may help to characterize performance degradation. It is preferred to select the least amount of information to obtain high quality of predictive analysis from a large amount of collected data, in which labeling the data is not always feasible. To this end a novel unsupervised feature selection method, robust with respect to significant measurement disturbances, is proposed using the notion of "weak monotonicity" (WM). The robustness of this notion makes it very attractive to identify the common trend in the presence of measurement noises and population variation from the collected data. Based on WM, a novel suitability indicator is proposed to evaluate the performance of each feature. This new indicator is then used to select the key features that contribute to the WM of a family of processes when noises and variations among processes exist. In order to evaluate the performance of the proposed framework of the WM and suitability, a comparative study with other nine state-of-the-arts unsupervised feature evaluation and selection methods is carried out on well-known benchmark datasets. The results show a promising performance of the proposed framework on unsupervised feature evaluation in the presence of measurement noises and population variations.

Summary form only given, as follows. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. Universal digital access to technology can be as seemingly straightforward as providing electricity access to a remote location or as overwhelmingly complicated as developing a healthcare system that provides immediate and secure access to medical experts, insurance companies, and all of their accompanying infrastructure. This session, featuring members of the SSIT Technical Committee on Universal Access to Technology, discusses how scholars, researchers, practitioners, and educators can actively reduce this digital divide which separates communities and individuals on the basis of ethnicity, religious conviction, sexuality, gender identity, income, age and in many other ways. Drawing on their professional experiences, panelists will discuss strategies for placing humanitarian concerns at the centre of all we do as we strive towards universal digital access; they will demonstrate how to carefully and ethically balance social, cultural and technological dimensions of society to the benefit of all people, particularly those living in rural and underserved areas; and they will elaborate on the role of education, encouragement, and empowerment in the pursuit of these goals.

The expansion of frontiers in neural engineering is dependent on the ability to track, detect and predict dynamics in neural tissue. Recent innovations to elucidate information from electrical recordings of brain dynamics, such as epileptic seizure prediction, have involved switching to an active probing paradigm using electrically evoked recordings rather than traditional passive measurements. This paper positions the advantage of probing in terms of information extraction, by using a coupled oscillator Kuramoto model to represent brain dynamics. While active probing performs better at observing underlying system synchrony in Kuramoto networks, especially in non-Gaussian measurement environments, the benefits diminish with increasing relative size of electrode spatial resolution compared to synchrony area. This suggests probing will be useful for improved characterization of synchrony for suitably dense electrode recordings.

This paper aims to establish a post processing algorithm to estimate the upper limb motion, given a set of measurements from wearable sensors representing the orientation of the shoulder, upper arm and lower arm. The motivation of the development is the measurement of the upper limb motion for subjects with motor impairments, such as post-stroke patients preventing the use of specific motions for calibration purposes and allowing the sensors to be relatively insensitive to their mounting positions. The type of sensors has been left general, with the experimental validation in this paper carried out using inertial sensors and magnetic trackers. The method is validated both numerically and experimentally, and shows improvements compared to the common inverse kinematics approach, especially in the practical conditions where sensor mounting alignment is suboptimal.

In this paper, a model predictive control is developed for motion planning and control of nonholonomic autonomous forklifts. The proposed model predictive motion control (MPMC) determines the control inputs for tracking a desired path through minimising path-following error, subject to nonholonomic and dynamic balance constraints of the forklift. MPMC also automatically adjusts the tracking velocity and acceleration to traverse the path as fast as possible without violating constraints while minimising the tracking error. In order to facilitate the real-time implementation of MPMC for industrial applications, a successive online linearisation of the nonlinear discrete-time kinematic model of the forklift is employed along with linear time-varying approximation of the tracking errors. The effectiveness of the proposed method has been demonstrated through simulations.

In this work, a simple model is used to characterize the learning behaviour of humans. Based on this model, it is possible to define a similarity measure between two tasks in order to quantify skill generalisation during the learning of simple motor tasks by humans. By fully exploring this similarity measure, a sequence of tasks capable of improving the learning efficiency for both healthy subjects and patients with motor impairment may be generated. A validation protocol is introduced and preliminary experimental results with six subjects are presented to validate the learning model and the similarity measure. Results show that the human learning of trajectory tracking tasks can accurately be modelled by an exponential decay of the average tracking error. The model fits well when the task is new or far away from a previously learnt task. Model parameters are used to analyse the learning performances of the subjects and the influence of previous tasks learning. Finally, it is shown that the similarity index can be constructed based on the proposed model to reflect skill generalisation.

In waveform design for magnetic resonance applications, periodic continuous-wave excitation offers potential advantages that remain largely unexplored because of a lack of understanding of the Bloch equation with periodic continuous-wave excitations. Using harmonic balancing techniques the steady state solutions of the Bloch equation with periodic excitation can be effectively solved. Moreover, the convergence speed of the proposed series approximation is such that a few terms in the series expansion suffice to obtain a very accurate description of the steady state solution. The accuracy of the proposed analytic approximate series solution is verified using both a simulation study as well as experimental data derived from a spherical phantom with doped water under continuous-wave excitation. Typically a five term series suffices to achieve a relative error of less than one percent, allowing for a very effective and efficient analytical design process. The opportunities for Rabi frequency modulated continuous-wave form excitation are then explored, based on a comparison with steady state free precession pulse sequences.

Human motor control computational model is an important component in the study and the successful realisation of human-robot interaction. In this paper, the Operational Space Formulation is presented as a suitable framework of human motor control computational model based on the Equilibrium Point Hypothesis (EPH) approach. The iterative adaptive control strategy was incorporated to simulate human motor adaptation to different tasks. The strategy involves the use of an Equilibrium Model which represents the ideal human motor response to a given task. The combined strategy was simulated to match a set of data gathered experimentally from several human subjects. The results were observed to explain many of the features found in the recorded behaviours in the EPH-based approach of human motor modelling.

Irrigation networks of open-water channels are used throughout the world to support agricultural activity. By and large, these networks are managed in open loop. To achieve closed-loop water distribution management, it is necessary to augment these civil engineering systems with an appropriate information infrastructure-sensors, actuators, information processing, and communication resources. Recent pilot projects in Australia demonstrate the significant potential of closed-loop management, which can yield a significant improvement in the quality of service, while achieving improved water distribution efficiency. This paper focuses on the modelling and closed-loop control of open-water channels from the perspective of large-scale irrigation network management. Several feedback information structures are discussed and the key design tradeoffs identified

This paper presents adaptive repetitive learning control for trajectory tracking of uncertain robotic manipulators. Through the introduction of a novel Lyapunov-like function, the proposed method only requires the system to start from where it stopped at the last cycle, and avoids the strict requirement for initial repositioning for all the cycles. In addition, it is more applicable, as it only requires the variables to be learned in an iteration-independent manner, rather than satisfying the periodicity requirement in a number of the conventional methods. With the adoption of fully saturated learning, all the signals in the closed loop are guaranteed to be bounded, and the iterative trajectories are proven to follow the profiles of desired trajectories over the entire operation interval. The effectiveness of the proposed method is shown through extensive numerical simulation results.

In various estimation problems, the system being estimated may be represented by a sparse parameter vector, such that only a 'small' number of the vector elements are 'significant' or 'active'. In this paper we propose a normalised least mean square (NLMS) estimator which incorporates a least squares based active parameter criterion; such that NLMS adaptation is applied only to those system parameters detected as being active. This results in a significant improvement in convergence rates, as compared to the standard NLMS estimator. Importantly, for sparse systems, the computational cost of the newly proposed detection guided NLMS estimator is only slightly greater than that of the standard NLMS estimator.

This paper analyses the fast subspace tracking algorithm recently proposed by Attallah and Abed-Meraim. Specifically, the associated Ordinary Differential Equation is derived and its properties studied in detail. Partial comparisons with other subspace tracking algorithms are made.

It is known that precoding a signal prior to its transmission through an unknown finite impulse response channel facilitates the equalisation of the channel. Moreover, since linear precoders spread the spectrum, the equalisation process mitigates the effects of channel spectral nulls caused by frequency selective fading. This paper argues that certain affine precoders are more efficient than linear precoders. Here, the efficiency of a precoder is a measure of its ability to enable the receiver to recover the source signal relative to the amount of redundancy introduced by the precoder. An efficient affine precoder is heuristically derived and simulations used to demonstrate that it is superior to both the traditional training sequence method and the filter bank precoding scheme. This precoding and equalisation scheme naturally extends to time varying channels.

We give degree of approximation results for decision regions which are defined by polynomial and neural network parametrizations. The volume of the misclassified region is used to measure the approximation error, and results for the degree of L/sub 1/ approximation of functions are used. For polynomial parametrizations, we show that the degree of approximation is at least 1, whereas for neural network parametrizations we prove the slightly weaker result that the degree of approximation is at least r, where r can be any number in the open interval (0, 1).

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

A good radar warning receiver should observe a radar very soon after it begins transmitting, so in designing our radar warning receiver we would like to ensure that the intercept time is low or the probability of intercept after a specified time is high. We consider a number of problems concerning the overlaps or coincidences of two periodic pulse trains. We show that the first intercept time of two pulse trains started in phase is a homogeneous Diophantine approximation problem which can be solved using the convergents of the simple continued fraction (s.c.f.) expansion of the ratio of their pulse repetition intervals (PRIs). We find that the intercept time for arbitrary starting phases is an inhomogeneous Diophantine approximation problem which can be solved in a similar manner. We give a recurrence equation to determine the times at which subsequent coincidences occur. We then demonstrate how the convergents of the s.c.f. expansion can be used to determine the probability of intercept of the two pulse trains after a specified time when one or both of the initial phases are random. Finally, we discuss how the probability of intercept varies as a function of the PRIs and its dependence on the Farey points.

We propose and analyse a populational version of stepwise gradient descent suitable for a wide range of learning problems. The algorithm is motivated by genetic algorithms which update a population of solutions rather than just a single representative asistypical for gradient descent. This modi cation of traditional gradient descent (as used for example in the backpropagation algorithm) avoids getting trapped in local minima. We use an averaging analysis of the algorithm to relate its behaviour to an associated ordinary di erential equation. We derivea result concerning how long one has to wait in order that with a given high probability, the algorithm is within a certain neighbourhood of the global minimum. We also analyse the e ect of di erent population sizes. An example is presented which corroborates our theory very well. 1

By using a simple example we illustrate the effect of autocorrelation and cross correlation of subscriber signals on the achievable performance of adaptive echo cancelers in a four-wire telephone network.>