This paper investigates utilizing a hybrid configuration that merges a Reverberation Chamber (RC) with a Compact Antenna Test Range (CATR) to achieve a controllable K-factor for testing directive antennas. The focus has been on the lower frequency bands of FR2 (24.25-29.5 GHz). The study encompasses a thorough evaluation of 39 unique configurations. A stationary horn antenna is consistently employed throughout the investigation for reference K-factor characterization, with considerations such as integrating absorbers, attenuators, and varied CATR polarizations. An examination of the signal-to-noise ratio reveals that the samples consistently maintain a substantial margin above the noise level, demonstrating independence across all cases. The Bootstrap Anderson-Darling goodness-of-fit test is employed to meticulously assess conformity to Rician and/or Rayleigh distributions, yielding results that closely align with anticipated outcomes. The analysis encompasses crucial parameters, including total received power, stirred power, unstirred power, and K-factor, with an additional exploration into frequencydependent modeling. Notably, total received, stirred, and unstirred powers exhibit an inverse relationship with frequency, while the K-factor does not demonstrate a clear frequencydependent pattern. Fitted models effectively capture frequency variability for stirred power but fall short in elucidating K-factor trends. A noteworthy insight emerges when utilizing CATR signal co-polarized with the horn antenna, resulting in heightened K-factors, reduced system losses, and improved frequency stability.

While deep learning and backpropagation continue to dominate the field of machine learning in terms of benchmarks and versatility, recent neuroscientific advances shed light on more biologically plausible approaches. Spiking neural networks (SNNs), modelled after action potential dynamics, offer inherent time sensitivity and more efficiency in terms of performance to complexity. While investigating paradigms to support such alternatives, we attempt to answer whether reservoir computing can benefit from a spiking network based implementation with elements of biologically realistic models. This is done by varying both hyperparameters and reservoir generation approaches and comparing implementations to spot potential improvements. We demonstrate how customized training of SNNs can result in competitive performance levels at lower operational complexity and be readily applied to other paradigms, such as the development of reservoir dynamics.

Double entry bookkeeping is a widely used accounting method that provides a system for recording financial transactions. Recently, this method has been applied to the field of distributed consensus algorithms, particularly the raft consensus algorithm. Raft is an innovative algorithm that is used to achieve consensus in a distributed system. The application of double entry bookkeeping to this algorithm aims to provide a more efficient and reliable method for tracking transactions and ensuring consistency. The use of double entry bookkeeping allows for improved fault tolerance, as well as better detection and prevention of data inconsistencies. Additionally, it enables the detection of malicious activities, such as double-spending or tampering with the transaction history. This application of double entry bookkeeping to raft consensus algorithm shows promise for improving the overall security and reliability of distributed systems. This paper presents Raft’s application to distributed accounting ledgers with double entry accounting for a more transparent and fraud resistant transaction system. Double entry accounting is essential to modern commerce to rationalize transactions and prevent fraud. Using this concept of double entry with consensus can be used for preventing financial fraud. The killer ’feature’ in this paper is also the process of comparing research that is envisioned by the author and further improved or enhanced by AI or more commonly known language models. In this case this language model product is ChatGPT by the OpenAI Research and Development Organization. We will also compare the results to get interesting insights on generated results.

Touching a warm stimulator with the base of the finger and a cold stimulator with the middle part of the finger causes a sense of warmth at the fingertip outside the cold stimulator, revealing Extrapolation of Thermal Sensation (ETS). Although ETS shares similarities with the Thermal Grill Illusion (TGI) regarding spatial thermal integration, the spatial distributions of sensations are different. The TGI is limited inside boundaries that envelop physical stimuli, whereas ETS crosses the boundaries. This difference indicates ETS, although TGI is reproduced accompanied by overestimation of the cold stimulus, which is influenced by the spinal segmental distance between warm and cold stimuli, it remains to be seen whether ETS carries out the same. The present study investigated ETS and TGI by simultaneous warm, cold, and neutral stimulation on the fingers or the lower leg. To show the difference between ETS and TGI, we manipulated the segmental distance between warm and cold stimuli and observed the resulting perceived temperature of neutral and cold stimulators. The perceived temperatures of ETS as well as TGI varied in units of segmental distance, however, ETS was not reproduced where TGI was. Thus, we concluded ETS has a different mechanism from the TGI. The experimental results suggest a non-uniform intersegmental connection contributes to the lower reproducibility of ETS on the lower leg. INDEX TERMS Extrapolation of thermal sensation, thermal grill illusion, thermal referral

This paper describes the design and implementation processes of a radio telescope suitable for the detection of the hydrogen line, proposing a receiver system based on Software Defined Radio (SDR). The design and fabrication of the antenna are presented, as well as the results of its characterization (electrical, radiation), both with simulations and measurements in an anechoic chamber. The functionality of the instrument is demonstrated by the satisfactory observations of the hydrogen line emitted by our Galaxy. This is an affordable project that allows the development and application of the skills of telecommunications students since it includes many of the fundamental aspects of electronics applied in this field.

Electric Vehicles (EVs) based on Cascaded H Bridge (CHB) promise reduced consumption and improved modularity, repairability, resilience, and versatility. This study focuses on evaluating the efficiency of CHB inverters utilizing lowvoltage Si MOSFETs to improve EV performance and range. Through a comprehensive system-level approach and modeling, a simulation of the CHB-based powertrain is developed and experimentally validated. Electrical and mechanical simulations are conducted separately and finally combined to streamline computation times. Subsequently, CHB-based EV is compared with two-level inverters (2LI) across different driving cycles, considering multiple sources of losses from the battery to the road. Despite increased battery losses, CHB proves reduction of consumption during urban driving cycles, making it a compelling choice for sustainable commuter vehicles.

The detection of brain impulses and emotions has been a research topic of late. To define emotion as a psychological variable, various machine learning algorithms were applied but there are still some challenges with high dimensional data. To date, much research has been done on this topic to classify a maximum number of emotions and find clarity on the behaviors of the brain signal with certain stimuli. In this paper, we have thoroughly studied the data and experimented with various techniques to find out the best one and according to that we have designed the model to classify a maximum number of emotions in a short time. We have extracted various domains of features including time, frequency, wavelet, etc., and applied the Ensemble Feature Selection method to find out the best subset of features. This technique includes correlation technique, information gain measurement, and finally recursive feature elimination method to find the optimized feature set. For the classification part, various method has been reviewed and then a stacked ensemble generalization model has been adopted with respect to bagging and boosting results of state-of-the-art machine learning techniques. The results show that the ensemble approach of feature optimization (En-FS) combined with the stacked ensemble generalized model (SEn-G) classification performs better. The suggested technique has been tested using the DEAP Dataset, and the experimental findings support the efficacy of the strategy while also being compared to state-of-the-art approaches.

As spiking neural networks (SNNs) are event-driven, energy efficiency is higher than conventional artificial neural networks (ANNs). Since SNN delivers data through discrete spikes, it is difficult to use gradient methods for training, limiting its accuracy. To keep the accuracy of SNNs similar to ANN counterparts, pre-trained ANNs are converted to SNNs (ANNto-SNN conversion). During the conversion, encoding activations of ANNs to a set of spikes in SNNs is crucial for minimizing the conversion loss. In this work, we propose a single-spike phase coding as an encoding scheme that minimizes the number of spikes to transfer data between SNN layers. To minimize the encoding error due to single-spike approximation in phase coding, threshold shift and base manipulation are proposed. Without any additional retraining or architectural constraints on ANNs, the proposed conversion method does not lose inference accuracy (0.58% on average) verified on three convolutional neural networks (CNNs) with CIFAR and ImageNet datasets. In addition, graph convolutional networks (GCNs) are converted to SNNs successfully with an average accuracy loss of 0.90%. Most importantly, the energy efficiency of our SNN improves by 4.6∼17.3× compared to the ANN baseline.

Automating the sewing process presents significant challenges due to the inherent softness of fabrics and the limited control capabilities of sewing systems. To realize sewing automation, we propose a time-scaling modeling and control architecture of the robotic sewing system. By using the time-scaling modeling, the nonholonomic kinematics of the sewing process of the industrial sewing machine is linearized precisely. Based on this model, a two-layer real-time control architecture is proposed. The upper layer controls the sewn seam line trajectory using the model-based feedback control implemented in the time-scaling domain, while the lower layer controls the manipulator and the sewing machine using geometric-based trajectory generation and coordinated motion control of the robot and the sewing system in the time domain. The experimental results demonstrate that the sewing trajectories exponentially converge to the desired trajectories without overshooting under different initial conditions and sewing speeds. Besides, the same sewing trajectories under different sewing speeds are obtained for a given stitch size. The sewing results show the good performance and application potential of the proposed robotic sewing system.

To solve problems of neurodegenerative disorder patients, Brain-Computer Interface (BCI) based solutions require datasets relevant to the languages spoken by patients. BCI Research sometimes gets restricted due to the lack of datasets. For example, Marathi, a prominent language spoken by over 83 million people in India, lacks BCI datasets based on the language for research purposes. To tackle this gap, we created a dataset comprising of Electroencephalograph (EEG) signal samples of selected common Marathi words. EEG samples were captured using the OpenBCI Cyton device for constructing a dataset by volunteers who speak commonly used Marathi words. The dataset contains EEG recordings involving volunteers pronouncing commonly used Marathi words. It encompasses three main categories: (i) Utterances of Marathi words (Vocal), (ii) English translations of these Marathi words (Vocal), and (iii) Silent pronunciation (sub-vocalization) of the Marathi words. We compiled data for 100 distinct words, each with recordings for these three categories. Ten trials were conducted for each phrase. This dataset is valuable for developing BCI solutions to assist Marathi-speaking patients with neurodegenerative diseases. BCI solutions using Machine Learning (ML) classifiers and Deep Learning methods can be used to translate EEG signals into Marathi words, both vocal and sub-vocal.

A document by Hani Rabiee. Click on the document to view its contents.

In this paper, the novel concept of using heat flux sensors (HFS) to measure arterial pulse on the skin surface is validated. The heat flux (HF) signal is compared with simultaneously measured electrocardiogram (ECG) and photoplethysmography (PPG) signals during both rest and initial recovery from exercise. It is found that the waveform measured with the HF sensor above the radial artery has similar shape to PPG and demonstrates a clear temporal alignment between the HF pulse waves and both the PPG and ECG signals. Further, it is shown that HF measurement can be used to consistently track the arterial pulse at varying skin-to-ambient temperature gradients.

Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Methods: Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. Results: The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. Conclusion: The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification accuracy by incorporating synthetic data. Significance: This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability.

This material is dedicated to the rather old, but always actual, topic in theoretical analysis of applied problems: how to simplify an analytical investigation by applying multidimensional Probability Density Functions (n-PDFs). A brief overview of the applied interpretations for n-PDF approximation is presented, which allows to make choice of the practically useful analytical presentations for n-PDFs. For analytical solutions, two approximations for the n-PDF were chosen: Poli-Gaussian approximation (mix of Gaussian PDF's) and Functional Approximation or Gaussian-like. The first one is found to be suitable (and is recommended) for the class of scenarios better described by the term "scenarios of random structure". The second seems to be more adequate for analysis of the numerical characteristics of the communication system's behavior Recommendations for validation of the models with given n-PDF approximations, based on goodness of fit criteria's can be found as well.

In the realm of virtual reality (VR), immersive experiences are enhanced by sophis=cated tracking and inference technologies that me=culously monitor user movements, interac=ons, and behaviors. While these advancements offer unprecedented levels of engagement and personaliza=on, they also raise significant privacy concerns. This study explores the dual-edged nature of tracking and inference in VR, examining how these technologies influence user percep=ons of privacy, trust, and comfort within virtual environments. Through a mixed-methods approach that combines quan=ta=ve surveys with qualita=ve interviews, this research delves into the aJtudes of VR users towards the data collec=on prac=ces embedded within these digital realms. The study inves=gates the awareness levels among users regarding the extent of tracking and inference, their reac=ons to such prac=ces, and the poten=al impact on their con=nued use of VR technologies.

ECG-imaging has been receiving increasing clinical and commercialization interest as a non-invasive technology for computing cardiac electrical activity and facilitating clinical management of heart rhythm disorders. Despite its potential, the standard ECG-imaging pipeline requires thorax imaging for constructing patient-specific heart-thorax geometry – being outside standard-of-care clinical workflow, this component constitutes a major barrier to the clinical adoption of ECG-imaging. The advent of 3D cameras into ECG-imaging workflow to replace thorax imaging has shown promise, although existing works largely neglect the registration between camera-derived thorax models with an individual’s heart geometry. In this work, we address this gap with a novel system that generates patient-specific torso geometry using a 3D camera, registers the torso to heart geometry obtained from preexisting cardiac scans, and optimally deforms the torso to the skin surface of the patient. We evaluated the presented camera-based ECG-imaging system on five patients undergoing ablation of scar-related ventricular tachycardia, where we evaluate both the accuracy of the surface electrode localization and the ECG-imaging solutions in comparison to those obtained from computed tomography based thorax imaging. We further evaluate the use of the presented camera-based patient-specific heart-thorax model versus a generic heart-thorax model, highlighting the importance of the registration between the camera-based thorax models with cardiac scans. 

A document by Filipe Almeida. Click on the document to view its contents.

Parkinson's disease significantly impacts speech, particularly affecting phonemic groups like stop-plosives, fricatives, and affricates. However, its objective impact on the different phonemic groups has been briefly addressed in the past.This study introduces a new model, called MARTA, built upon a Gaussian Mixture Variational AutoEncoder with metric learning to measure the disease's impact on the phonemic grouping automatically and objectively. MARTA was trained on normophonic speech before adapting it to parkinsonian speech. The model effectively clusters phonemic groups unsupervised and demonstrates enhanced discriminative power when supervised using forced-aligned labels. Our findings reveal that beyond the traditionally affected phonemes, Parkinson's disease not only affects stop-plosives, voiced-plosives, and nasals, but also significantly impacts liquids, vowels, and fricatives, with the model achieving a benchmarking 91% ± 9 discrimination capability. An in-depth evaluation of the impact of the disease on the different phonemic groups represents an advance in the current knowledge of its effects on the speech, and has clear implications in the speech therapy of people with Parkinson's disease. Moreover, regardless of the specific application domain presented, the model introduced has potential downstream utility in assessing the manner of articulation, whether influenced by other medical conditions or certain dialectal variations.