This article presents a comparison of 4 different types of rectifier circuits for piezoelectric energy harvesting. Energy is harvested from multiple piezoelectric elements embedded in an in?sole while simultaneously collecting gait information of the subject wearing it. The study is based on an asymmetric signal generated while walking and the harvested energy is utilized to operate a system under a fixed electrical load. The influence of back-up battery and insole design optimization on the accuracy of collected information is taken into consideration. A comprehensive approach for shoe insole application based on simultaneous piezoelectric sensing and energy harvesting is presented in this work.

A big issue that isn’t stressed enough for adolescents and emerging adults is depression. Depression can affect you in various ways, whether it be physically, mentally, or socially. Professional and academic performance, personal relationships, and other factors that are important to long-term success are negatively impacted. In trying to help those that need help, we come across a major issue; identifying depression. Social media has become increasingly popular over the years, with many younger generations spending vast amounts of time on it. Detecting depression from a platform that adolescents and emerging adults frequently visit would contribute significantly to helping those suffering from it. In this study, the datasets of tweets were utilized to create multiple machine-learning models that would accurately detect signs of depression. Out of 4 different machine learning models, this research used the Random Forest Classifier model to achieve an accuracy of 96.52%.

A  methodology to build multi-compartment lumped elements equivalent circuits for the neuron/electrode systems is proposed. The equivalent circuit topology is derived by careful scrutiny of accurate multiphysics finite-elements method (FEM) simulations that couple ion transport in the intra- and extracellular fluids, activation of ion channels in the cellular membrane, and signal collection by the electronic readout, thus improving upon most common area contact models. We show that the equivalent circuits derived with our method match with good accuracy the reference FEM simulations over a wide range of geometrical/physical parameters such as the neuron and electrode size, the thickness of the electrolytic cleft, the input impedance of the readout amplifier, even in presence of nonuniform ion channel distributions. The impact of the number of compartments on the model accuracy is also analyzed in detail. We finally illustrate by FEM simulations the effect of extracellular ion transport on the reversal potentials of the Hodgkin-Huxley neuron model and how it can affect the recorded signal for very thin electrolyte clefts between the neuron and the electrode, in a way not yet captured by equivalent circuits of the neuron/electrode system.

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as decentralized topology, information-centric routing and semantic communications entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

Models of seven discrete facial expressions are built on macro-level facial muscle variations for separating distinct affective states. We propose a step-wise Hierarchical Separation and Classification Network (HSCN) that discovers dynamic and continuous macro- and micro-level variations in facial expressions. The HSCN first invokes an unsupervised cosine similarity-based separation method on continuous facial expression data and extracts twenty-one dynamic expression classes from the seven common discrete affective states. Separation between the clusters is then optimised for discovering the macro-level changes in facial muscle activations followed by splitting the upper and lower facial regions for realising and modelling changes pertaining to upper and lower facial muscle activations. A linear discriminant space is developed for clustering the upper and lower facial images on the basis of similar muscular activation patterns. Actual dynamic data and linear discriminant features are mapped for developing a rule-based expert system that would facilitate classification of twenty-one upper and twenty-one lower facial micro-expressions. Using the random forest algorithm, classification accuracies of 76.11\% were observed for dynamic macro-level facial expression classification. A support vector machine provided 73.63\% and 87.68\% accuracies respectively while classifying upper and lower facial micro-expressions. This work provides a novel framework for the dynamic assessment of affective states. Reported methods and results also provide new insight into the dynamic analysis of facial expressions of affective states.

This work aims to develop an adaptive remeshing procedure for finite element method on electromagnetic computations. A thorough comparison of metric computation strategies is carried out as it constitutes a cornerstone of this developments. This procedure will focus on the mesh size adaptation to distribute the error uniformly over a computational domain, in order to obtain a user-prescribed accuracy of the solution. Also, it shall enable dealing with complex geometries for electromagnetic-coupled material processing applications. For this purpose, a quasi-steady state approximation of the Maxwell’s equations in a time-domain formalism is considered. The automatic remeshing procedure is based on the following key steps: An a posteriori error estimator to pinpoint the critical areas needing refinement or allowing coarsening. An anisotropic metric approximation. Both steps use a global field recovery algorithm in order to enable robust gradient computation. Finally, several 3D test cases are presented.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Online monitoring of mechatronic systems and production environments helps operators to steer their processes into regions of optimal performance. Filtering methods, such as the Kalman filter, emerge in this setting thanks to their ability to process sensor data in real-time. Extensive engineering efforts are required to attain a behavioral model on the system dynamics for this filter. What is more, the model needs updating when facing varying conditions that are prevalent in mechatronic and production systems during operation. At the same time, datadriven methods excel in adapting models to match measurements without relying heavily on expert knowledge, but suffer from main drawbacks such as the lack of interpretability and the batchwise approach. This paper proposes a novel methodology relying on two key ideas. First a dual extended Kalman filter allows to jointly estimate and adapt model parameters in an online setting. Secondly, within this filter a hybrid model is embedded that depends on physics-based and data-driven parameters that are being updated, leading up to increased prediction capabilities and hence improved estimations on the state of the system. We evaluate the presented hybrid dual Extended Kalman filter on a cam follower system where it shows the ability to learn the cam shape during operation whilst tracking the state with an increased accuracy of more than 22% compared to other filtering techniques.

Air pollution is an ever-growing problem in our world today, with its effects being felt across the globe. As such, it's no surprise that scientists and researchers have been hard at work to develop solutions for this pressing issue. One of the most promising solutions is the use of IoT devices to monitor air pollution. In this article, we will explore how the use of these devices can better understand and address air pollution.

Machine learning (ML)-based network intrusion detection systems (NIDS) have become a contemporary approach to efficiently protect network communications from cyber attacks. However, ML models can become exploited by adversarial poisonings, like random label manipulation (RLM), which can compromise multi-controller software-defined network (MSDN) operations. In this paper, we develop the Trans-controller Adversarial Perturbation Detection (TAPD) framework for NIDS in multi-controller SDN setups. The detection framework takes advantage of the SDN architecture and focuses on the periodic transference of network intrusion detection models across the SDN controllers in the topology, and validates the models using the local datasets to calculate errors in their predictions with the ground truth. We demonstrate the efficacy of this framework in detecting RLM attacks in an MSDN setup. Results indicate efficient detection performance achieved by the TAPD framework in determining the presence of Random Label Manipulation attacks and the localization of the compromised controllers. We find that the framework works well even when there is a significant number of compromised agents in a large multi-controller setting. However, the performance begins to deteriorate when more than 40% of the SDN controllers in the MSDN have become compromised.

Camera pose estimation has long relied on geometry-based approaches and sparse 2D-3D keypoint correspondences. With the advent of deep learning methods, the estimation of camera pose parameters (i.e., the six parameters that describe position and rotation) has decreased from tens of meters to a few centimeters in median error for indoor applications. For outdoor applications, errors can be quite large and highly dependent on the levels of variations in occlusion, contrast, brightness, repetitive structures, or blur introduced by camera motion. To address these limitations, we introduce, BPose, a Bayesian Convolutional deep network capable of not only automatically estimating the camera’s pose parameters from a single RGB image but also providing a measure of uncertainty in the parameter estimation. Reported experiments on outdoor and indoor datasets demonstrate that B-Pose outperforms SOTA techniques and generalizes better to unseen RGB images. A strong correlation is shown between the prediction error and the model’s uncertainty, indicating that the prediction is almost always incorrect whenever the model’s uncertainty is high.

Precision positioning plays critical roles in 5G-Advanced and 6G systems for various indoor and outdoor applications. Conventionally, positioning accuracy can be dramatically degraded under non-line-of-sight channels or with moving terminal nodes (TNs). To overcome this issue, we propose an orthogonal time-frequency space (OTFS) based positioning with applying distributed cooperative positioning (DCoP) at the ends of TNs, which can yield an ultra-most precise positioning. This is so, since the positioning with OTFS is robust against channel delays and Doppler spreads, and superior to conventional orthogonal frequency-division multiplexing (OFDM) based positioning. Further, DCoP uses cooperations such that TNs can serve as additional anchor nodes (ANs) to others, and improves the positioning accuracy for all. With this proposal, we further elaborate the positioning-reference-signal with OTFS (PRS-OTFS) in delay-Doppler (DD) domain, and design an OTFS-transceiver that is backward compatible to OFDM. Furthermore, we also propose a DD-domain time-of-arrival estimation algorithm that exploits the OTFS waveform and outperforms conventional time-frequency based estimators. Moreover,  we propose a loopy belief-propagation algorithm associated with belief-discretizing for an effective implementation and message exchange in DCoP. In addition, we show that the average Cramer-Rao low-bound per TN can decrease faster than linearly in the number of cooperating TNs. We illustrate with simulations that the proposed OTFS-DCoP scheme is very effective in improving positioning accuracy within a few iterations, and robust against double-selective channels.

A document by Tengchao Yu . Click on the document to view its contents.

This paper presents a method for islanding detection of power-electronic interfaced grid-tied distributed energy resources. A dynamic phasor-based modeling approach is used to synthesize a state-space model of the islanded network. This model leads to the formation of a  generalized framework wherein the application of state feedback is designed to destabilize the islanded network. The proposed method can detect islands within one fundamental cycle of operation with passive loads of unity, lagging, and leading power factors. The detection scheme has a zero Non-detection Zone (NDZ), and detection times are within the  criteria suggested in IEEE Std. 1547-2018.

The development of modern cryptography has not specifically prevented Side-Channel attacks at the outset. Today, the rapid growth in Side-Channel Attacks is a significant threat. This article proposes a Secret-Hashing method that improves the main drawback of the explicitly reversible AES key expansion function. Using an extra Secret-Hashing function based on round-keys of AES to generate new round-keys will break the reversibility between the original round-keys. Let the initial attack requires only the easiest one of the round-keys to be necessary for all round-keys to complete the cracking of AES's encryption and decryption, increases the resistance of the AES algorithm to Side-Channel Attacks by 10 to 100 times. The method is compatible with the original AES algorithm and signal leakage countermeasures, and the AES encryption and decryption process do not increase any extra payload.

This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC). Due to the evaluation of navigation learning, which is centered on the practice innovation ability devel?opment, it is difficult to assess the learning effectiveness in cultivating students’ ability objectively by questionnaire or other assessment methods. Research in the field of brain science has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Therefore, a navigation learning assessment method was applied to detect the cognitive training traces by EEG-based MTSC. To verify the validity of the method, three navigation missions with increasing cog?nitive difficulty were designed, and 41 participants attended the experiment. The features of participants’ EEG signals were extracted and classified by MTSC. Comparing the results between experimental group and control group, it can be seen that MTSC can effectively distinguish the cog?nitive training traces of students in the navigation problem?solving process. In addition, MTSC achieved 89.2% accu?racy and 0.883 F1-Score on features classification, which had an average improvement of 15.7% over other three tra?ditional EEG classification models in accuracy. The results indicate that the navigation learning assessment method provides a useful evaluation approach for learning effec?tiveness using EEG-based MTSC.

The concept of intelligent software is flawed. The behaviour of software depends upon the hardware that interprets it. This undermines claims regarding the behaviour of theorised, software superintelligence. Here we characterise this problem as "computational dualism", where instead of mental and physical substance, we have software and hardware. We argue that to make objective claims regarding performance we must avoid computational dualism. We propose using an alternative based upon pancomputationalism, which defines all aspects of the environment as nothing more than relations between otherwise irreducible states. We formalise systems as behaviour (inputs and outputs, with policy being a causal intermediary), and cognition as embodied, embedded, extended and enactive. The result is cognition formalised as a part of the environment, rather than as a disembodied policy interacting with the environment though an interpreter. This allows us to make objective claims regarding intelligence, which we argue is the ability to "generalise", identify causes and adapt. We then propose objective upper bounds for intelligent behaviour.

Data availability (DA) attack is a well-known problem in certain blockchains where users accept an invalid block with unavailable portions. Previous works have used LDPC and 2-D Reed Solomon (2DRS) codes with Merkle trees to mitigate DA attacks. These codes perform well across various metrics such as DA detection probability and communication cost. However, these codes are difficult to apply to blockchains with large blocks due to large decoding complexity and coding fraud proof size (2D-RS codes), and intractable code guarantees for large code lengths (LDPC codes). In this paper, we focus on large block size applications and address the above challenges by proposing the novel Polar Coded Merkle Tree (PCMT): a Merkle tree encoded using the encoding graph of polar codes. We provide a specialized polar code design algorithm called Sampling Efficient Freezing and an algorithm to prune the polar encoding graph. We demonstrate that the PCMT built using the above techniques results in a better DA detection probability and communication cost compared to LDPC codes, has a lower coding fraud proof size compared to LDPC and 2D-RS codes, provides tractable code guarantees at large code lengths (similar to 2D-RS codes), and has comparable decoding complexity to 2D-RS and LDPC codes.

The code and data that support the findings of this study are available from the corresponding author upon reasonable request.