In this letter, we consider a secondary network (SN) in which an ambient backscatter device (BD) utilizes the secondary transmitter (ST) signal to communicate its own information to the secondary destination (SD). Optimizing performance of such networks is complicated by signal reflections by the BD. It is shown in this work how the secondary transmit power and the reflection coefficient of the BD can both be jointly optimized using a simple restricted one dimensional search to satisfy the quality of service (QoS) constraints of SN as well as the primary network (PN) while maximizing performance of the backscatter link, which is termed the tertiary network (TN). Only statistical channel knowledge is used for this purpose. It is seen that careful optimization can improve spectral efficiency. Simulations validate the derived analytical expressions.

An unidentified continuous functions is an function that looks infinitely differentiable from far away view. Here any unidentified continuous function is assumed to have either digital continuum (non-archimedian continuum) or archimedian continuum. Author mainly found an method to indicate an distinction for both continuum models. This method can be used on to unidentified continuous functions in order to obtain a discrete unit value relation of coarse structure. An example could be energy-frequency equation for photons.

Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches. A wellknown optimization-based method, the Variational Mode Decomposition (VMD), relies on the formulation of an optimization problem, utilizing constant bandwidth Wiener filters. However, this poses limitations in constant bandwidth and the need for constituent count. In this paper, a new method, namely Dynamic Bandwidth VMD (DB-VMD), is proposed to generalize VMD by addressing the Wiener filter limitations through enhancement of the optimization problem with an additional constraint. Experiments in synthetic signals highlight DB-VMDâ\euro™s noise robustness and adaptability in comparison to VMD, paving the way for many applications, especially when the analyzed signals are contaminated with noise.

Feature fusion is an effective solution for improving image retrieval performance. Although the more feature types, the better accuracy, complexity also increases. Applications in practice typically afford a limited number of feature types. Due to the strong complementarity, global and local features form an ideal combination for many fusion applications. However, the two kinds of features are intrinsically different in nature, thus cannot be fused in a straightforward way. In this work, we propose an integrated image retrieval and feature fusion framework for global and local features. It is based on inverted index fusion, a technique for efficient image retrieval. The core idea is to rank candidates by weighted voting during candidate selection, which is named pre-ranking. This procedure takes place before re-ranking, and is potentially superior to conventional late fusion. Extensive experiments on three public datasets show that the light-weight pre-ranking stage significantly contributes to accuracy, and brings substantial improvement when used together with re-ranking. Our method is robust and versatile, and can be applied to any scenario where inverted indexing is used. It is a promising technique for multimedia retrieval in the big data era.

This paper introduces Continual Learning for Multilingual ASR (CL-MASR), a benchmark for continual learning applied to multilingual ASR. CL-MASR offers a curated selection of medium/low-resource languages, a modular and flexible platform for executing and evaluating various CL methods on top of existing large-scale pretrained multilingual ASR models such as Whisper and AWavLM, and a standardized set of evaluation metrics.

Intelligent behavior is an emergent phenomenon observed in biological organisms across all scales. It describes the cooperative behavior of low complexity entities to accomplish complex tasks, which exceed their individual capabilities. This property is particularly important for the Internet of Bio-Nano Things (IoBNT), which consists of Bio-Nano Things (BNTs) used in the human body, where they face many restrictions, such as bio-compatibility and size constraints. In this paper, we present a novel BNT-architecture, called Molecular Nano Neural Networks (M3N), which allows the implementation of intelligence on the micro-/nano-scale. The proposed structure consists of compartments (low complexity entities) that are connected to each other to form a network. Based on reaction and diffusion of molecules in and between connected compartments, this network mimics an artificial neural network, which is an important step towards  artificial intelligence in the IoBNT. We provide design guidelines for the proposed M3N and successfully validate it by applying a regression and classification task.

BREN-Tech deals with the advancement of human brain and human body.It is a more effective and faster way where a chip like micro/macro machine is inserted into the body and many procees takes place in a short time.It involves minute comonents and can be connected to external systems as well.It can help towards the growth of human evolution which can be equal or more than the advancement of AI in the modern world

Voice likability is a critical factor in machine-human interaction. However, studies on speech likability typically does not apply the harmony theory in music, which suggests general rules for pleasant sounds. In this paper, I propose a new method that estimates the likability of vocal signals using the harmonic relation of pitch and the first formant (F1). I extract the pitch and F1 from the vowel signal and compute the average cent value between notes in the musical scale from each pitch and F1. A small cent value indicates a consonant relation between pitch and F1. I compared the calculated cent values with the MOS test results from ten speech samples. The results showed a clear correlation between the subjective MOS scores and the consonance of pitch and F1 in vowels.

X-ray computed tomography (CT) is a crucial tool for non-invasive medical diagnosis that uses differences in materials’ attenuation coefficients to generate contrast and provide 3D information. Grating-based phase- and dark-field-contrast X-ray imaging is an innovative technique that utilizes refraction and small-angle scattering to generate additional co-registered images with improved contrast and microstructural information. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. However, the susceptibility of Talbot-Lau interferometers to mechanical vibrations coupled with a need to minimize data acquisition times has hindered its application in clinical routines and the combination of the two techniques in the past. In this work, we propose a processing pipeline to address this issue in a human-sized clinical dark-field CT system. We present the corrective measures that have to be applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. This is achieved by identifying and mitigating spatially and temporally variable vibrations in the interferometer. By exploiting correlations in the modular grating setup, we can identify relevant fluctuation modes and separate the fluctuation and sample information, enabling vibration-artifact free sample reconstruction.

A wireless channel suffers from two fundamental impairments; fading and noise. While fading is multiplicative, noise is additive. It is well-known that higher the noise, lower is the signal to noise ratio and lower the capacity. However, fading can be helpful in increasing the capacity when using multiple transmit and receive antennas. In this paper, we give an intuitive explanation for this. Anybody with a background in linear algebra and matrices can understand this.Â

The aim of this article is to introduce a novel approach to identifying flow regimes and void fractions in microchannel flow boiling, which is based on binary image segmentation using digital image processing and deep learning. The proposed image processing pipeline uses adaptive thresholding, blurring, gamma correction, contour detection and histogram comparison to separate vapour from liquid areas, while the deep learning method uses a customized version of a convolutional neural network (CNN) called Unet to extract meaningful features from video frames. Both approaches enabled automatic detection of flow boiling conditions, such as bubbly, slug, and annular flow, as well as automatic void fraction calculation. Especially the CNN has demonstrated its ability to deliver fast and dependable results, presenting an appealing substitute to manual feature extraction. The U-net-based CNN was able to segment flow boiling images with a Dice score of 99.1 % and classify the above flow regimes with an overall classification accuracy of 91 %. In addition, the neural network was able to predict resistance sensor readings from image data and assign them to a flow state with a mean squared error (MSE) < 10−6. This sensor signal prediction is a promising first step towards automated, imageless prediction of two-phase flow in microchannels using only the measurement data from resistance sensors. The approaches discussed in this paper were performed on an ordinary 6 GB NVIDIA laptop GPU using Python and are general enough to be applied to other similar applications. The deep learning model can be downloaded from: github.com/schepperlemark

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (NoC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, NoC, diameter, and radius demonstrated significant differences (p<0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

Low-density parity check codes (LDPC) are efficient in terms of coding performance and parallelism but need a higher code length to reduce the decoding complexity. In modern5G networks, hardware utilization issues have been addressed with a min-sum algorithm adopting quasi-cyclic LDPC. The present paper proposes a modified layered min-sum algorithm by presenting an intelligent strategy to introduce concurrency in processing by grouping the rows in the base matrix. The algorithm also considers the case where the column weight of a layer is greater than one and makes a suitable connection hierarchy to maximize hardware re-usability. The architecture employs the tree-structure (TS) approach to design an effective hardware block for the check-node unit (CNU). The proposed CNU architecture processes input belief parallelly and enhances hardware reusability by adapting data path reconfiguration. This scheme ensures that even though the processing of the grouped rows in the layer happens simultaneously, the rows are isolated from each other during this process. The routing and processing hardware architecture of the proposed system has been synthesized on Zinc-ultra scale+ zcu106 after functional verification on Xilinx-Vivado to claim an increase in throughput.

The World Health Organization (WHO) has instituted the Caregiver Skill Training (CST) program to assist families with children diagnosed with Autism Spectrum Disorder. The Joint Engagement Rating Inventory (JERI) protocol evaluates participants’ engagement levels within the CST initiative. Traditionally, JERI assessments rely on retrospective video analysis conducted by qualified professionals, thus incurring substantial labor costs. This study aims to augment the evaluation efficiency of the Expressive Language Level and Use (EXLA) criterion within JERI, striving for consistency with human-based scoring. To this end, we introduce a multimodal behavioral signal-processing framework designed to analyze both child and caregiver behaviors, thereby offering grading recommendations as an alternative to medical professional input. Initially, raw audio and video signals are segmented into concise intervals via voice activity detection, speaker diarization and speaker age classification, serving the dual purpose of eliminating non-speech content and tagging each segment with its respective speaker. Subsequently, we extract an array of audio-visual features, encompassing our proposed interpretable, hand-crafted textual features, end-to-end audio embeddings and end-to-end video embeddings. Finally, these features are fused at the feature level to train a linear regression model aimed at predicting the EXLA scores. Our framework has been evaluated on the largest in-the-wild database currently available under the CST program. Experimental results indicate that the proposed system achieves a Pearson Correlation Coefficient of 0.713 against the expert ratings, evidencing performance comparable to that of human experts. This approach not only provides immediate feedback for CST participants but also optimizes resource allocation in less developed regions.

Intelligent reflecting surface (IRS) is a promising concept for 6G wireless communications that allows tuning of the wireless environments to increase spectral and energy efficiency.   Many optimization techniques have been proposed in literature to deal with the joint passive and active beamforming design problem, but without any optimality guarantees for the multiple access points (APs), multiple IRSs, and multiple users scenario.  Moreover, the multiple access problem is also considered with the beamformer design which has not been addressed in literature, except in the context of joint transmission, which is not considered herein.  To further maximize ground based and support non-terrestrial communications, the joint aerial IRS (AIRS) positioning and beamformer design problem is also considered.    In the first part of the paper, an algorithm considering predefined AP-user pairing is proposed, which allows beamforming vectors to be designed distributively at each access point by using Generalized Bender Decomposition (GBD), consequently resulting in certain level of optimality.  The problem can be transformed via mathematical manipulation and semidefinite relaxation (SDR) into a convex problem and solve using semidefinite programming (SDP).  Another algorithm was developed to solve for optimal AP-user pairing at the same time by introducing additional binary variables, making the problem into a mixed-integer SDP (MISDP) problem, which is solved using GBD-MISDP solver, albeit with higher computational and time complexity than the GBD for the original problem.  A heuristic pairing algorithm, called GBD-iterative link removal (GBD-ILR), is proposed to combat this problem and it is shown to achieve solution close to that of the GBD-MISDP method.  A joint AIRS positioning and beamformer design problem is solved in the second part by  using the proposed successive convex approximation-alternating direction of method of multipliers-GBD (SAG) method.  Simulation results show the effectiveness of all proposed algorithms for joint beamformer design, joint beamformer design with AP-user pairing in a multiple access points system, and the joint AIRS positioning and beamformer design.  In addition to simulation results, an analysis of communication overhead incurred due to use of the IRS is also given.

Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system. However, machine learning, especially deep learning models have been shown to demonstrate a systematic bias towards certain subgroups of people. For instance, they yield a preferential predictive performance to males over females, which is unfair and potentially harmful especially in healthcare scenarios. In this literature survey, we give a comprehensive review of the current progress of fairness studies in medical image analysis (MedIA) and healthcare. Specifically, we first discuss the definitions of fairness, the source of unfairness and potential solutions. Then, we discuss current research on fairness for MedIA categorized by fairness evaluation and unfairness mitigation. Furthermore, we conduct extensive experiments to evaluate the fairness of different medical imaging tasks. Finally, we discuss the challenges and future directions in developing fair MedIA and healthcare applications.

In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the Terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures, which leads to frequency selectivity, a problem known as beam squint. Multi-carrier transmission schemes which are usually deployed to address this problem, suffer from high peak-to-average power ratio, which is specifically dominant in THz communications where low transmit power is realized. Diverging from the usual approach, we devise a novel channel estimation problem formulation in the time domain for single-carrier (SC) modulation, which favors transmissions in THz, and incorporate the beam-squint effect in a sparse vector recovery problem that is solved via sparse optimization tools. In particular, the beam squint and the sparse MIMO channel are jointly tracked by using an alternating minimization approach that decomposes the two estimation problems. The presented performance evaluation results validate that the proposed SC technique exhibits superior performance than the conventional one as well as than state-of-the-art multi-carrier approaches.

This paper deals with the implementation of a light fifth generation (5G) base station (gNB) intended  for specific use cases requiring an airborne deployment of the network. We focus on the radio unit (RU) and give details on the components, the implementations, and the technical choices that have been made, driven by the strong constraints inherent to the considered use cases.

Magnetic particle imaging (MPI) is a tracer imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIOs), enabling sensitive, radiation-free imaging of cells and disease pathologies. The arbitrary waveform relaxometer (AWR) is an indispensable platform for developing magnetic nanoparticle tracers and evaluating tracer performance for magnetic particle imaging applications. One of the biggest challenges in arbitrary waveform excitation is direct feedthrough interference, which is usually six orders of magnitude larger than the signal from magnetic nanoparticles. This work will showcase hardware that suppresses this interference by an order of magnitude, increasing the dynamic range of the instrument and enabling mass-limited detection at full scale range.

Learning graph topology online with dynamic dependencies is a challenging problem.  Most existing techniques usually assume the generative model to be a diffusion process instigated by a graph shift operator (GSO) and that a first-order method,  such as proximal gradient or least-mean-square (LMS), are used to track the graph topology.  However, they are often susceptible to noisy observations and does not perform well against second-order methods.  This work proposed two forward-backward splitting algorithms called the proximal Newton-iterated extended Kalman filter (PN-IEKF) and PN-IEKF-vector autoregressive (PN-IEKF-VAR) algorithms to track non-causal and causal graph topology with dynamic dependencies, respectively.  The proposed methods directly maximize the posterior probability distribution of the observable graph signal and graph matrix, which make our PN-IEKF framework to be more robust toward additive white Gaussian noise.  The two methods can directly handle streaming data which process them as they become available.  Effectiveness of the proposed methods can be further improved by including a $T$-squared detector in the tracking procedure, which helps to inject proper perturbation to the latent dynamic model such that the time-varying nonstationary graph can be reacquired faster amid abrupt changes in the underlying system. Results on relative error and normalized mean square error using synthetic data on Erd\fH{o}s-R\'enyi graph establish the efficacy of the proposed approach. Simulation results using data from the Dataset for Emotion Analysis Using EEG, Physiological and Video Signals (DEAP) and National Oceanic and Atmospheric Administration (NOAA) are encouraging. Computational and time complexity analysis of the proposed algorithm are given and compared with other algorithms.

The series includes two parts, articulating the two novel avenues of research on intelligent fault diagnosis (FD) for nonlinear feedback control systems. In Part I of the series, we design a novel FD paradigm by elaborating an invertible neural network (INN) for feedback control systems.

This manuscript presents a novel cloud detection algorithm utilizing a neural network technique, developed for identifying cloudy and clear pixels over sea ice in MISR images. Our methodology is based on an extensive multi-angular dataset covering various Arctic regions in different seasons, demonstrating strong performance metrics, including F score and Accuracy. We believe that this research contributes significantly to the remote sensing domain and offers a fresh approach to enhancing cloud detection accuracy over sea ice.

This paper deals with a new spatial domain-based self-interference cancellation (SIC) method called constrained minimum mean square error (C-MMSE) for massive multiple-input multiple-output (mMIMO) full-duplex (FD) communication systems. The main idea is to treat the self-interference (SI) signal emitted from an FD node as a particular spatial stream arriving at the receiver part of that same FD node which needs to be spatially postcoded along with other useful signals coming from other transmitters, so that it falls into the null space of the MIMO channel that includes the FD node transmitter part as an input. On this basis, we first adapt the expressions of the spatial combiners with respect to the conventional zero forcing (ZF) and minimum mean square error combining (MMSE) criteria and show that the latter is not capable to efficiently cancel the SI signal unless an additional constraint is added to properly perform SIC. We hence design the new so-called C-MMSE combiner and derive its expression. In addition to our proposal, the originality of our work lies in the consideration of spherical wave model (SWM) for modeling the SI channel, which is justified by the close proximity of the transmit and receive antenna panels in the FD node. We examine and compare the SIC performance of the adapted ZF combiner, the adapted MMSE combiner and the newly introduced C-MMSE combiner by evaluating the obtained spectral efficiency (SE). We also highlight the robustness of the SWM-based SI channel modelling compared to conventional planar wave modelling (PWM) and therefore the relevance of using it.

Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, Emotion recognition based on touch event’s temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this paper, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users’ emotions, and a PAD emotion scale is used to evaluate and label the degree of emotion induction. A Random Forest (RF) based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.