Human activities are progressively defined by interactions with products that embed increasingly higher levels of immaterial component (software, patents, design principles and patterns). After presenting the continuity between seminal concepts in Cybernetics, Information Theory, Network Analysis, AI practices on one side and cognitive behaviour by leveraging a minimalist approach to cybernetic systems based on the concept of capacity of network; the text tries to establish the concept of interface to define logical rule-based automata that can be considered shareable instances of a self-model approach to consciousness: in first instance, a rule-based descriptive construct for an ‘economy of information exchange’ between systems (or an ‘economy of time’), and in second instance a rule-based framework for approaching the hard problem of consciousness. These are critical waypoints in navigating fast-growing knowledge-intensive landscapes. This review into patterns of growing complexity is concluded with an hypothesis that aims to extend Ashby’s definition of machine to include interfaces to enable self-modeling for possibly intelligent digital machines. Starting from this background, conclusion tries to define a clearer pathway to tackle the difficult problems about the positioning of biological and digital machines in what the paper points out to be the sentience-intelligence-consciousness spectrum.

Includes synthetic data processing and real data (IRIS community Wavefield experiment) processing examples.

Delayed signal cancellation (DSC) is a popular method to separate the fundamental frequency positive- and negative-sequence components from unbalanced three-phase signals. Conventional DSC can separate the sequence components using a quarter-cycle delay. Fast DSC tools can achieve the same with less than a quarter-cycle delay. However, neither conventional nor fast DSC can handle DC offset without requiring additional delayed signals. This letter addresses this issue by proposing a modified DSC to estimate the sequence components with DC offset rejection and having arbitrarily fast convergence speed, i.e., low memory requirement. Two equidistant delayed samples of the measured grid voltages/currents are required to implement the proposed technique and can easily be applied in a phase-locked loop (PLL). Comparative experimental results demonstrate the suitability of the proposed approach over other DSC methods.

Given a content image and an artistic style one, style transfer usually refers to applying the patterns learned from the style image to the content image to generate a new stylized image. Despite the thrilling success achieved by existing style transfer methods, most of them are in the mire of two limitations: 1) cannot preserve the structure of the content image well; 2) cannot generate delicate enough style effects or may produce significant artifacts. It is a challenge to maintain a balance between content structure preservation and style pattern transformation. In this work, we observe that multi-level content-style cross attention can extract the content features matching to the style characteristics at different feature levels. In addition, we also find that through multi-level dynamic normalization and alignment, hierarchical content-style cross attention can effectively transform the content image through the style characteristics of different levels while preserving its local structure and semantics as much as possible. The perceptual loss and the contextual loss are introduced individually to ensure the generated stylized image is close both to the content image and the style image in the feature space. At the same time, the identity loss of the content image and the style one is deployed to encourage the proposed model to retain the global appearance and the feature semantics of the input images without overall statistical deviation. A large number of qualitative and quantitative experiments and evaluations on the benchmark MSCOCO and WikiArt datasets demonstrate that, compared with other state-of-the-art (SOTA) methods, the proposed approach can obtain high-quality stylized images with structure-style balance. The project code is available at https://github.com/hengliusky/MNCAA.

In modern psychotherapy, digital health technology offers advanced and personalized therapy options, increasing availability as well as ecological validity. These aspects have proven to be highly relevant for children and adolescents with obsessive-compulsive disorder (OCD). Exposure and Response Prevention therapy, which is the state-of-the-art treatment for OCD, builds on the reconstruction of everyday life exposure to anxious situations. However, while compulsive behavior predominantly occurs in home environments, exposure situations during therapy are limited to clinical settings. Telemedical treatment allows to shift from this limited exposure reconstruction to exposure situations in real life. In the SSTeP KiZ study (smart sensor technology in telepsychotherapy for children and adolescents with OCD), we combine video therapy with wearable sensors delivering physiological and behavioral measures to objectively determine the stress level of patients. The setup allows to gain information from exposure to stress in a realistic environment both during and outside of therapy sessions. In a first pilot study, we explored the sensitivity of individual sensor modalities to different levels of stress and anxiety. For this, we captured the obsessive-compulsive behavior of five adolescents with an ECG chest belt, inertial sensors capturing hand movements, and an eye tracker. Despite their prototypical nature, our results deliver strong evidence that the examined sensor modalities yield biomarkers allowing for personalized detection and quantification of stress and anxiety. This opens up future possibilities to evaluate the severity of individual compulsive behavior based on multivariate state classification in real-life situations.

This article focuses on insider-threat detection within healthcare infrastructures. There are inherent dangers to digitising patient records and considering the sensitive nature of the data, EHR is equally at risk of both external threats and insider attacks, but security applications are predominantly facing the outer boundary of the network. In this work, the focus is on insider data misuse detection. The approach involves the use of supervised classification (decision tree, random forest and support vector machine) based off pre-labelled real world data collated from a UK-based hospital for the detection of EHR data misuse. The results demonstrate that by employing a machine learning approach to analyse EHR data access, anomaly detection can be achieved with a 0.9896 accuracy from a test set and 0.9908 from the validation set using a support vector machine classifier. The emphasis of this research is on the detection of EHR data misuse, through the detection of anomalous behavioural patterns. Based on the results, the recommendation is to adopt an SVM for data misuse/insider threat detection

This article has been submitted to the IEEE Transactions on Microwave Theory and Techniques and is currently under review. © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field three-dimensional imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. Their accuracy, however, quickly degrades when applied to close-range imaging, where amplitude variations are significant and where the range distance to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses a measured point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann-Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography.

The output of the nearest neighbor (1-NN) classification rule, gS,q(x), depends on a given learning set SN and on a distance function ρq(x,X). We show that transforming S_{N} into a set A_{N} whose patterns have a Hanan grid-like structure, results in the equivalence gA,q(x) = gA,p(x) that holds for any NN classifier with distance functions ‖x-X‖q and with any q ∈ (0,∞). Thanks to the equivalence, AN can be used to learn gA,q(x) to mimic a behavior of the classifier gS,p(x) based on the original set SN even when q is unknown (and varying). Possible application of the proposed framework (inspired also by a time-varying stimuli perception phenomenon) in autism spectrum disorder (ASD) therapeutic tools design is discussed.

Grid-forming inverters are sensitive to large grid disturbances that may engender overcurrent due to their voltage source behavior. To overcome this critical issue and ensure the safety of the system, current limitation techniques have to be implemented. In this context, the variable virtual impedance (VI) appears as a suitable solution for this problem. The design of the variable virtual impedance basically rests on static considerations, while, its impact on the system stability and dynamics considering both small-signal and large-signal aspects can be significant. This paper proposes small-signal and nonlinear power models to assess the impact of the virtual impedance parameters on the grid current dynamics and on the angle stability. Thanks to the developed theoretical approaches in this paper, the virtual impedance ratio X/R, which is the unique degree of freedom has shown a contradictory effect on the small and large signal stability. To overcome this constraint, a Variable Transient Virtual Resistance (VRTVR) has been proposed as an additional degree of freedom to the variable virtual impedance. It acts as a transient damper to ensure the maximum angle stability margin allowed by the variable virtual impedance. The effectiveness of the proposed control has been proven through time-domain simulations.

This paper presents a novel compact rectifier array with both broadband and wide input power range characteristics. The rectifier array consists of two rectifier units operating at lowpower and high-power levels, respectively, and an adaptive input power distribution network (AIPDN) enabling automatic distribution of radio frequency (RF) energy at different power levels. In addition, a matching inductance is introduced to counteract the capacitance of the diode. As a result, the high RFDC power conversion efficiency (PCE) is achieved in both broadband and wide input power range. For demonstration, a prototype of the presented rectifier array is fabricated and measured. The measured results are in good accordance with the simulated results. With an input power range of 8 – 25 dBm and a bandwidth of 1.82 – 2.75 GHz (fractional bandwidth of 40.7%), the PCE is greater than 50%. At 2.4 GHz, the input power ranges from -5.5 to 27.5 dBm with PCE greater than 50%. Besides, a peak PCE of 72.1% is obtained at an input power of 25 dBm. This work can lay the foundation for the development of a new RF energy harvest systems with wide frequency band and dynamic input power.

Following image classification, the focus is now shifting to text categorization. Text classification has numerous real-world uses. Using categories to tag information or items to improve browsing or identify related stuff on your website. The practise of categorising text into ordered groupings is known as text classification, sometimes known as text tagging or text categorization. Text classifiers can automatically assess text and assign a set of pre-defined tags or categories depending on its content using Natural Language Processing (NLP). In this study, we will try to create a model that can detect abnormalities in a log data collection by combining NLP features with other methodologies. This is handled as a text categorization challenge. That is why we evaluate two of the most well-known techniques while also using extra features extracted from the data set. We shall contrast the bag of words technique with the embedding technique. As opposed to Bag of words, embedding tries to preserve the meaning of the sentence, which can aid with text classification.

In this paper, the effect of 3-level inverter’s MOSFET switching at 33 different PWM frequencies, has been compared from 15 kHz to 2 MHZ, in relation to control loop stability, transfer function’s coefficients variation, system response and speed characteristics. The position of rotor in 8 pole, wye-winding 7.5 Hp i.e., 5.5 kW with service factor of 1.15 (≈8.6 Hp), 72VDC, 85.6A, 14.27 Nm torque, with per phase insulation temperature of 311 ◦ F, at an efficiency of 89.2% is estimated without sensor using sliding mode observer implemented with field oriented control algorithm. The decoupled flux and torque components are controlled by discrete PI with anti-windup/reset and IIR filters. The coefficients of IIR applied in the speed and SMO control block, and other parameters like gain, ADC are mentioned along with the resulting without SMO or with SMO closed loop transfer functions at each of these 33 frequencies along with the change in the coefficients of PI (errors, Ki, Kp), of Iq, Id, field weakening, speed controllers are mentioned. The 132 values, 33 values of e, Ki, Kp for each of these four controllers at these 33 PWM frequencies are tabulated, which can be used to design prototypes with higher power densities. The relative control loop stability with SMO is analyzed and tabulated with gain, phase margins along with eigen values of the characteristic equation.

Fault responses of inverter-based resources (IBR) are primarily dictated by the control algorithms and are fundamentally different from conventional synchronous generators. Hardware-in-the-loop simulation, commonly viewed as a robust tool for protection setting prototyping and testing, is used in this paper to investigate the impact of a wind farm, with type-4 full converter wind turbines, on the protection of a 345 kV transmission line in Texas, United States. This paper discusses two critical topics in detail: (1) wind farm IBR model development and validation against vendor PSCAD model, and (2) IBR impacts on distance and current differential protection.

Multi-source domain adaptation has been intensively studied. The distribution shift in features inherent to specific domains causes the negative transfer problem, degrading a model’s generality to unseen tasks. In Federated Learning (FL), learned model parameters are shared to train a global model that leverages the underlying knowledge across client models trained on separate data domains. Nonetheless, the data confidentiality of FL hinders the effectiveness of traditional domain adaptation methods that require prior knowledge of different domain data. We propose a new federated domain generalization method called Federated Knowledge Alignment (FedKA). FedKA leverages feature distribution matching in a global workspace such that the global model can learn domain-invariant client features under the constraint of unknown client data. FedKA employs a federated voting mechanism that generates target domain pseudo-labels based on the consensus from clients to facilitate global model fine-tuning. We performed extensive experiments, including an ablation study, to evaluate the effectiveness of the proposed method in both image and text classification tasks using different model architectures. The empirical results show that FedKA can achieve performance gains of 8.8% and 3.5% in Digit-Five and Office-Caltech10, respectively, and a gain of 0.7% in Amazon Review with extremely limited training data. Moreover, we studied the effectiveness of FedKA in alleviating the negative transfer of FL based on a new criterion called Group Effect. The results show that FedKA can reduce negative transfer, improving the performance gain via model aggregation by 4 times.

We propose an Ekblom promoting adaptive algorithm (EPAA) which uses Ekblom norm to construct a data reusing scheme to achieve better performance under impulsive noise (IN) environments. By exerting Ekblom-norm constraint on the a posterior error vector, a sliding window cost function is created to realize the EPAA that is derived to reduce IN effects. The derivation and theoretical convergence analysis of the EPAA are presented in detail. The simulation examples are setup to illustrate the superior performance of the EPAA compared to popular data reusing algorithms for system identification under IN environments.

Swift evolution of novel computing and communication technologies in beyond 5G networks (B5G) opens up the possibilities for advanced techniques to tackle various issues which can not be solved by the existing frameworks. As the number of devices continuously increase, blockchain can provide a secure platform for communication among all the users in the network. Moreover, along with the security, blockchain requires low computation and also provides fast network response. Furthermore, artificial intelligence enhances the ability of devices to learn and construct knowledge about dynamic wireless environments. Recently, a lot of researchers have shown interest in integration of both the platforms to solve the complex problems of B5G networks. This work reviews the application of both the technologies in various networks related problems recently completed by the authors. The work also discusses various possible research issues that can be handled by the integration of both platforms.

Dynamic system modeling methods have become a hot topic for stationary and nonstationary signal processing. Nonnegativity is a desired constraint that usually exerts on to be estimated parameters, and its generation usually based on the inherent physical characteristics of unknown system. Moreover, non-Gaussian noise is present in many practical system identification situations. In this paper, an adaptive nonnegative maximum correntropy criterion (NNMCC) algorithm is proposed for system identification under non-negativity constraints. We derive the NNMCC algorithm based on the Karush-Kuhn-Tucker conditions and a fixed-point iteration scheme. The first-order and second-order moments of the NNMCC algorithm adaptive weights are theoretically analyzed. Experimental results validate the theoretical analysis and illustrate the superior performance of NNMCC in non-Gaussian noise environments.

Lncosh function has been used for constructing a cost function for devising an adaptive filter algorithm to provide a desired performance under non-Gaussian noise, naming as least Lncosh algorithm (LLA). However, its transient behavior analysis has not been analyzed and investigated. As an important characteristic for the LLA, the transient behavior affects its performance in real-world applications. Herein, we present the theoretical model for evaluating the mean weight behavior and the transient excess mean square error (EMSE) behavior of the LLA. In addition, the proposed models can also be used to predict the transient behavior of the adaptive filtering algorithms with error nonlinearities {under non-Gaussian noises}. The experiment results have verified the validity of the presented models.