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

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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 emergence of 5G systems brought to fore the importance of network slicing (NS) as it allows infrastructure providers (InPs) to create logical networks (slices) and virtually share network resources to their tenants. However, due to the limited resources of the InP, the resource management algorithms like resource allocation and admission control are required to ensure efficient management of these scarce resources. Indeed, admission control algorithms play a critical role of regulating access to the network, by determining whether a slice request should be accepted or not with respect to some standards such as maximizing the InP’s revenue and maintaining service level agreements (SLAs). In this paper, we propose an admission control algorithm that employs the concept of overbooking to admit slice requests beyond the InP’s nominal available resources. Moreover, we employ a dynamic queue adaption priority, step-wise pooling and dynamic buyback price mechanism to ensure efficient and profitable admission decision for the InP. We assess the performance of the proposed algorithm against state of the art (SOTA) solution considering different priority schemes. The results show that the proposed solution outperforms the SOTA solution as it yields i) higher revenue, ii) lower buyback cost and iii) higher net revenue for the InP while still maintaining a marginally higher slice acceptance rate.

Physical layer security (PLS) is seen as the means to enhance physical layer trustworthiness in 6G. This work provides a proof-of-concept for one of the most mature PLS technologies, i.e., secret key generation (SKG) from wireless fading coefficients during the channel’s coherence time. As opposed to other works, where only specific parts of the protocol are typically investigated, here, we implement the full SKG chain in four indoor experimental campaigns. In detail, we consider two legitimate nodes, who use the wireless channel to extract secret keys and a malicious node placed in the immediate vicinity of one of them, who acts as a passive eavesdropper. To estimate the final SKG rate we evaluate the conditional min-entropy by taking into account all information available at the eavesdropper. Finally, we use this paper to announce the first ever physical layer security challenge, mirroring practices in cryptography. We call the community to scrutinize the presented results and try to “break” our SKG implementation. To this end, we provide, i) the full dataset observed by the  eavesdroppers, ii) 20 blocks of 16−byte long ciphertexts, encrypted using one-time pad with 20 distilled secret keys, and, iii) all codes and software used in our SKG implementation. An attack will be considered successful if any part(s) of the plaintext are successfully retrieved.

The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel’s coherence time. This work acknowledges the fact that the CSI consists of  deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, espectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.

In this paper, we study the use of the angle of arrival (AoA) as a feature for performing robust, machine learning (ML)-based physical layer authentication (PLA). In fact, whereas most previous research on PLA relies on physical properties such as channel frequency/impulse response or received signal strength, the use of the AoA in this context has not yet been studied in depth as a means of providing resistance to impersonation (spoofing) attacks. In this study, we first prove that an effective impersonation attack on AoA-based PLA can only succeed under very stringent conditions on the attacker in terms of location and hardware capabilities, and thus, the AoA can in many scenarios be used as a robust feature for PLA. In addition, we exploit machine learning in our study to perform lightweight, model-free, intelligent PLA. We show the effectiveness of the  proposed AoA-based PLA solutions by testing them on experimental outdoor massive multiple input multiple output data.

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.

Adverse weather conditions such as snow, rain, and fog are natural phenomena that can impair the performance of the perception algorithms in autonomous vehicles. Although LiDARs provide accurate and reliable scans of the surroundings, its output can be substantially degraded by precipitation (e.g., snow particles) leading to an undesired effect on the downstream perception tasks. Several studies have been performed to battle this undesired effect by filtering out precipitation outliers, however, these works have large memory consumption and long execution times which are not desired for onboard applications. To that end, we introduce a novel outlier detector for 3D LiDAR point clouds captured under adverse weather conditions. Our proposed detector 3D-OutDet is based on a novel convolution operation that processes nearest neighbors only, allowing the model to capture the most relevant points. This reduces the number of layers, resulting in a model with a low memory footprint and fast execution time, while producing a competitive performance compared to state-of-the-art models. We conduct extensive experiments on three different datasets (WADS, SnowyKITTI, and SemanticSpray) and show that with a sacrifice of 0.16% mIOU performance, our model reduces the memory consumption by 99.92%, number of operations by 96.87%, and execution time by 82.84% per point cloud on the real-scanned WADS dataset. Our experimental evaluations also showed that the mIOU performance of the downstream semantic segmentation task on WADS can be improved up to 5.08% after applying our proposed outlier detector. We release our source code, supplementary material and videos in https://sporsho.github.io/3DOutDet . Upon clicking the link you will have to option to go to source code, see supplementary information and view videos generated with our 3D-OutDet.

Clustering plays a pivotal role in exploratory data analysis, especially in scenarios where prior knowledge about a subset of samples is available, prompting a rapid development of Semi-supervised clustering approaches. In this work, we introduce a novel method for seamlessly integrating prior knowledge into the widely recognized Normalized Cut Clustering (NCC) algorithm, thereby offering a natural extension within the realm of semi-supervised clustering. Similarly to NCC, the solution of the proposed method is spectral-based in the form of an inhomogeneous eigenvalue problem. Also, we show that the proposed method can be seen as a generalization of NCC, enhancing its applicability in scenarios where prior information is available. In addition, we design an adequate and comprehensive experimental setup. This setup not only tests the competitiveness of our approach but also showcases its superiority in terms of performance metrics.

This paper highlights the contribution of utilizing ensemble deep learning with auto-encoders (AEs) for out-of-distribution data detection. The key innovation is treating ensemble UQ as a regression problem, mapping uncertainty distribution to a single model, reducing computational demands. This approach aligns well with the ensemble of AEs’ uncertainty distribution, making it valuable for resource-constrained systems and rapid decision-making in computational intelligence.

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.

Ensuring safety and achieving human-level driving performance remain challenges for autonomous vehicles, especially in safety-critical situations. As a key component of artificial intelligence, reinforcement learning is promising and has shown great potential in many complex tasks; however, its lack of safety guarantees limits its real-world applicability. Hence, further advancing reinforcement learning, especially from the safety perspective, is of great importance for autonomous driving. As revealed by cognitive neuroscientists, the amygdala of the brain can elicit defensive responses against threats or hazards, which is crucial for survival in and adaptation to risky environments. Drawing inspiration from this scientific discovery, we present a fear-neuro-inspired reinforcement learning framework to realize safe autonomous driving through modeling the amygdala functionality. This new technique facilitates an agent to learn defensive behaviors and achieve safe decision making with fewer safety violations. Through experimental tests, we show that the proposed approach enables the autonomous driving agent to attain state-of-the-art performance compared to the baseline agents and perform comparably to 30 certified human drivers, across various safety-critical scenarios. The results demonstrate the feasibility and effectiveness of our framework while also shedding light on the crucial role of simulating the amygdala function in the application of reinforcement learning to safety-critical autonomous driving domains.