Wireless communications at 300 GHz are expected to play an important role in the deployment of 6G and beyond networks. Multiple electronic and photonic technologies compete in this regard, each bringing its own particular benefits. While purely optoelectronic links are interesting for incorporating photonic advantages in signal transmission and reception, electronic receivers typically based on Schottky mixers are far superior in conversion efficiency. Thus, the link signal-to-noise ratio (SNR) is improved, and higher throughput can be achieved. Here, we demonstrate a fully optoelectronic 300 GHz band multi-channel link using a novel low-barrier Schottky mixer driven with a photonically generated local oscillator (LO) signal in the receiver using a modified uni-travelling-carrier photodiode (MUTC-PD). This combines the efficient down-conversion of Schottky-based mixers and the advantages of photonic LO signals such as tuneability, remote generation and distribution, and the reuse of coherent technology used in fibre networks. Up to three frequency channels are generated in the transmitter which is also based on a MUTC-PD photomixer, achieving a maximum aggregated line rate of 180 Gbps over 1.5 m under SD-FEC limit using 16-QAM format and optical intensity modulation. To the best of the authors' knowledge, this is an unprecedented data rate for fully optoelectronic terahertz communications.

In this paper, we demonstrate probabilistic operational planning on the power system model used by the Norwegian TSO. It is a detailed model with thousands of lines and buses. By using load and price data from publicly available sources, we demonstrate that probabilistic operational planning can be performed sufficiently fast on a realistic model. Moreover, we compare the probabilistic and the classical deterministic approach where the probabilistic approach gives lower operational costs by higher tie-line flows.

This research focuses on inducing the perception of acceleration in the occupant in accordance with a given motion for the vehicle. For a desired driving experience without using a highdegree-of-freedom and expensive mechanism, without the actual presence of the occupant in the moving vehicle, the presence of the occupant in a two-degree-of-freedom simulator creates the perception of being in that motion. Human perception of motion is created through the measurement of various physical parameters, primarily vision, acceleration, and sound, and by different receivers. This research targets only the vestibular acceleration sensor, as the most important acceleration sensor in the human body, to induce the desired sensation and, due to the lower degrees of freedom of the mechanism compared to the actual motion, the simulator motion in the presence of various limitations is optimally planned and discussed based on the closest motion sensation to the desired motion in two horizontal and vertical directions.

Conventional computer-aided diagnosis (CADx) systems play a crucial role in assisting medical professionals with the detection of skin diseases. However, these systems often involve manual, time-consuming, and error-prone processes. Recent studies show that machine learning models have potential to improve the accuracy of CADx systems. In this work, we present research findings aimed at improving the performance of CADx systems for detecting skin diseases by applying ensemble machine learning models. The investigation encompasses the exploration of three popular classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and convolutional neural network (CNN); and an ensemble model of CNN with SVM. The HAM10000 dataset from Kaggle is used to train and test all classification models. Resampling is employed to address class imbalance in the dataset. Through rigorous experiments, the results highlight the compelling efficacy of the ensemble CNNSVM model, unveiling heightened accuracy up to 92% (from CNN accuracy 85% and SVM accuracy 83%). The outcome of this work has profound implications for artificial intelligence (AI) accelerated medical domains in advancing the accuracy and efficiency of skin disease treatment.

We are in the midst of a race of who can scrape most of the internet and put it on their servers in the least timeframe. Yes, that’s how all the language models are conventionally built. However, this process is slow, this process is old, this process hallucinates, and doesn’t know what is happening in the real-world. And that’s why this convectional process needs a replacement. Real-Time Retrieval Argumentation is the alternate architecture for LLMs that the new age models need to adapt. With RTRA, a model trained on just 7 billion parameters can beat models trained on hundreds of billions of parameters. This research paper marks a paradigm shift by challenging our dependency on huge computational resources to build a precise and efficient model by introducing a novel approach to train and develop language models.

In 2022, OpenAI's unveiling of generative AI Large Language Models (LLMs)-ChatGPT, heralded a significant leap forward in human-machine interaction through cutting-edge AI technologies. With its surging popularity, scholars across various fields have begun to delve into the myriad applications of ChatGPT. While existing literature reviews on LLMs like ChatGPT are available, there is a notable absence of systematic literature reviews (SLRs) and bibliometric analyses assessing the research's multidisciplinary and geographical breadth. This study aims to bridge this gap by synthesizing and evaluating how ChatGPT has been integrated into diverse research areas, focusing on its scope and the geographical distribution of studies. Through a systematic review of scholarly articles, we chart the global utilization of ChatGPT across various scientific domains, exploring its contribution to advancing research paradigms and its adoption trends among different disciplines. Our findings reveal a widespread endorsement of ChatGPT across multiple fields, with significant implementations in healthcare (38.6%), computer science/IT (18.6%), and education/research (17.3%). Moreover, our demographic analysis underscores ChatGPT's global reach and accessibility, indicating participation from 80 unique countries in ChatGPT-related research, with the most frequent countries keyword occurrence, USA (719), China (181), and India (157) leading in contributions. Additionally, our study highlights the leading roles of institutions such as King Saud University, the All India Institute of Medical Sciences, and Taipei Medical University in pioneering ChatGPT research in our dataset. This research not only sheds light on the vast opportunities and challenges posed by ChatGPT in scholarly pursuits but also acts as a pivotal resource for future inquiries. It emphasizes that the generative AI (LLM) role is revolutionizing every field. The insights provided in this paper are particularly valuable for academics, researchers, and practitioners across various disciplines, as well as policymakers looking to grasp the extensive reach and impact of generative AI technologies like ChatGPT in the global research community.

This paper proposes a concise methodology of Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). Initially, the IPMSM model is revisited. The approach for modeling an IPMSM is initially presented. Then, a step-by-step procedure for designing a vector controlled strategy is presented. The formulation is based on a dq-rotating reference frame which is aligned with the rotor shaft position. One simple decision that makes the methodology presented to be different from traditional ways for such controller is that the current controller is designed on time-domain based on desired time constants, while the speed control is formulated based on a frequency response domain framework. The proposed hybrid approach allows accurately tuning the parameters of the Proportional-Integrator (PI) controllers of the current and speed control loops. It is also performed a parameter variation analysis to enhance the proposed methodology. The validity region for the design procedure is presented where for any wide speed variation of the machine there is a scheduling of loop gains. The most noticeable advantage of the methodology in this paper is to make a fast, reliable and accurate technique for implementation IPMSM drive systems. Hardware-in-Loop (C-HIL) with external DSP based digital control. Results from a Controller Hardware-in-Loop (C-HIL) with external microcontroller are presented. The comprehensive design approach is verified under two different IPMSM parameters and the results showed its effectiveness.

Hearing loss (HL) is a common public health concern which has implications beyond communication, with potential physiological effects on diverse aspects of health. Audiology and biomechanics have traditionally been distinct fields, but recent research has started to bridge the gap. Studies have hinted at potential correlations between hearing loss and changes in gait, opening up new avenues for exploration. In this study, we investigate the intersection of audiology and biomechanics by exploring the relationship between hearing loss and gait. During a walk test, gait data were collected using advanced sensor technologies. Audiometric measurements at key frequencies (500, 1000, 2000 and 4000 Hz) from both ears were also obtained. Two rigorous analysis pipelines were established: traditional machine learning (ML) pipeline, and deep learning (DL) pipeline. Our traditional ML pipeline utilized ANOVA F-test for feature selection and a battery of ML classification algorithms. In addition, we introduced a deep learning pipeline which directly learned from raw gait data, eliminating the need for manual feature engineering. Our analysis unveiled a compelling relationship between gait and the presence of hearing loss. Notably, random forest classifier emerged as the most effective among ML models with an average area under the receiver operating characteristic curve (AUROC) of 82.11%±8.30%. Concurrently, the deep learning model, leveraging raw gait data, consistently demonstrated an exceptional AUROC exceeding 90%. "Footprints of hearing loss" presents an innovative exploration of the relationship between gait data and hearing loss, showcasing the potential of both traditional ML and DL approaches. This study not only contributes to our understanding of the interplay between specific gait features and hearing loss but also highlights the transformative impact of DL in healthcare. It marks a substantial step toward a deeper insight into the physiological impact of hearing loss and its broader healthcare implications.

This study focuses on characterizing the channel of a reconfigurable intelligent surface (RIS)-assisted wireless system operating over Nakagami-m fading channels. Although numerous works have proposed approximate or asymptotic solutions for the RIS channel statistics (namely, the probability density function (PDF) and the cumulative distribution function (CDF)), only a limited number of studies have tackled this problem by resorting to exact approaches. Regrettably, as the number of RIS elements increases, these approaches lead to solutions that are computationally expensive or entail a high mathematical intricacy. This has prompted the analytical development undertaken in this work. Herein, our emphasis is on an exact approach. Specifically, we furnish handy and tractable formulas for the PDF and the CDF of the investigated RIS channel. The expressions introduced in this study stand out as new contributions to the literature and are arguably the most efficient exact solutions available to date. Numerical simulations revealed the heightened efficiency of our proposed PDF and CDF expressions against the state-of-the-art solutions. Furthermore, we conducted a performance assessment analysis for the considered RIS-assisted wireless communication system by deriving exact and asymptotic expressions for key performance indicators, namely the outage probability (OP) and the average bit-error rate (ABER). Comprehensive numerical simulations validated the accuracy of our analytical results.

Team composition is a critical factor influencing collaboration within agile software development. This study investigates the impact of gender distribution on teamwork quality in software engineering capstone courses. We examined the experiences of 240 students organized into 40 teams during an agile project course. We analyzed two surveys, one conducted before team composition and one at the end of the project work. As much as 91% of the students chose to use the practice of conducting stand-up meetings in their project work and the majority were satisfied with the practice. Further, our analysis reveals that while women tend to engage more in design and men in programming, an increase in the proportion of women within a team correlates with a higher involvement of women in programming tasks. Our findings highlight gender differences in perceptions and experiences related to project involvement in agile software engineering courses.

Classification of modern architectures of devices for input of analog sensor signals, as well as IP-modules of the first level of complexity, which can be made on the basis of high-temperature technologies (SiGe, SOI, GaN, SiC, GaAs), is given. It turns out that at present there are more than 250 construction variants of primary converters of analog sensor signals. They include 105 operational amplifier's modifications, 7 differential difference amplifier's modifications, 22 buffer amplifier's modifications, and also more than 100 construction variants of special active elements for signal selection tasks under the general name of "current conveyors". Priorities in realization of basic functional nodes of high-temperature interfaces based on wide-gap semiconductors (GaN, SiC, GaAs) and high-temperature platforms (SiGe, SOI) are determined.

A document by Swaroop Gopalam . Click on the document to view its contents.

We revisit the Fourier transform of a Hankel function, of considerable importance in the theory of knife edge diffraction. Our approach is based directly upon the underlying Bessel equation, which admits manipulation into an alternate second order differential equation, one of whose solutions is precisely the desired transform, apart from an a priori unknown constant and a second, undesired solution of logarithmic type. A modest amount of analysis is then required to exhibit that constant as having its proper value, and to purge the logarithmic accompaniment. The intervention of this analysis, which relies upon an interplay of asymptotic and close-in functional behaviors, prompts our somewhat ironic, mildly puckish caveat, our negation "not" in the title. In a concluding section we show that this same transform is still more readily exhibited as an easy byproduct of the inhomogeneous wave equation in two dimensions satisfied by the Green's function G, itself proportional to a Hankel function. This latter discussion lapses of course into the argot of physicists and, in its rôle of a mere afterthought, makes no claim whatsoever to any originality.

In today’s rapidly evolving corporate world, the role of data in shaping HR practices and decisions cannot be overstated. Human Resources (HR) departments in large corporations are increasingly relying on data-driven approaches to drive their strategies, improve existing processes, and motivate employees to succeed in their roles. Tracking HR data allows organizations to gain valuable insights into employee feedback, performance, and engagement, ultimately leading to a healthier and more productive workplace.

This paper delves into the possibility of creating a precise mathematical equation to calculate the exact number of prime numbers below a certain threshold. By utilizing tools from function analysis, series of functions, and innovative methods, the goal is to create a comprehensive formula that can accurately determine the quantity of primes within a specified numerical range. The research explores the theoretical underpinnings of prime numbers, using advanced techniques to improve and expand upon current methodologies. Through a combination of insights from function analysis and series theory, the proposed formula seeks to offer a strong and efficient solution for computing prime counts. This effort not only adds value to the field, but it also enhances our understanding of prime number distribution.

In this paper, we investigate the joint resource allocation and trajectory design for a multiuser , multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where the link capacity between a ground base station (BS) and the UAV is limited. The UAV conducts target sensing and information transmission in orthogonal time slots to prevent interference. As is common in practical systems, sensing is performed while the UAV hovers, allowing the UAV to acquire high-quality sensing data. Subsequently, the acquired sensing data is offloaded to the ground BS for further processing. We jointly optimize the UAV trajectory, UAV velocity, beamforming for the communication users, power allocated to the sensing beam, and time of hovering for sensing to minimize the power consumption of the UAV while ensuring the communication quality of service (QoS) and successful sensing. Due to the prohibitively high complexity of the resulting nonconvex mixed integer non-linear program (MINLP), we employ a series of transformations and optimization techniques, including semidefinite relaxation, big-M method, penalty approach, and successive convex approximation, to obtain a low-complexity suboptimal solution. Our simulation results reveal that 1) the proposed design achieves significant power savings compared to two baseline schemes; 2) stricter sensing requirements lead to longer sensing times, highlighting the challenge of efficiently managing both sensing accuracy and sensing time; 3) the optimized trajectory design ensures precise hovering directly above the targets during sensing, enhancing sensing quality and enabling the application of energy-focused beams; and 4) the proposed trajectory design balances the capacity of the backhaul link and the downlink rate of the communication users.

Sagittal views provide detailed and critical information about the anatomy and pathology in diagnosing and managing lumbar spine diseases. Radiologists comprehensively evaluate the spinal alignment, structural integrity, and health of bone and soft tissue elements in the sagittal view in diagnosing various spinal conditions and planning appropriate treatments. The first step in the automatic diagnosis is localizing the region of interest, typically the lumbar spine segments. To accomplish this, we present a modified U-Net (MU-Net) for the segmentation and localization of the lumbar spine from sagittal views of Magnetic Resonance Imaging (MRI) images and compare the performances with a series of state-of-the-art YOLOv implementations. We employ different techniques to augment data, address issues with limited training samples, and improve the generalization of deep models. We use the two YOLOv series for localization and MU-Net for segmentation. The MU-Net model achieves an accuracy of 98.97%, accompanied by a Mean Intersection Over Union (IoU) of 82.49%. For the localization, YOLOv8 yields a Precision, Recall, and Mean Average Precision (mAP) of 99.6%, 99.2%, and 99.4%, respectively.

In the area of affective computing, speech has been identified as a promising biomarker for assessing depression and attention deficit hyperactivity disorder (ADHD). These disorders manifest as abnormalities in speech across various frequency bands and exhibit temporal variations. Most existing work on speech features relies on the magnitude spectrogram, which discards phase spectrogram information and also does not consider the impact of different frequency bands on depression and ADHD detection. Inspired by these, we propose a novel multi-scale complex feature refinement and a dynamic attention-aware network for enhanced speech-based assessment of depression and ADHD. Our approach incorporates three key components: multi-scale complex feature refinement (MSFR), dynamic convolutional neural network (Dy-CNN), and dual-attention feature enhancement (DAFE) module. The MSFR module utilizes depth-wise convolutional networks to process both magnitude and phase information, selectively emphasizing frequency bands associated with depression and ADHD. Importantly, the Dy-CNN module employs an attention mechanism to autonomously generate multiple convolution kernels that adapt to input features and capture relevant temporal dynamics linked to depression and ADHD. Additionally, the DAFE module includes channel shuffle attention (CSA) and spatial axial attention (SAA) mechanisms to improve the feature representation ability and detection performance by leveraging inter- and intra-channel relationships to examine the time-frequency characteristics of the feature map. Extensive experiments conducted on four publicly available datasets, i.e. AVEC 2013, AVEC 2014, E-DAIC and a self-collected authentic ADHD dataset demonstrated the effectiveness of the proposed method over previous approaches and showed superior generalization capabilities across different languages for depression and ADHD assessment.