This paper uses X wave and Bessel pulses for multi-channel ultrasound data communications. The method can be extended to optical and electromagnetic data communications and other areas. Implications of X wave in other areas such as quantum mechanics (deBroglie waves) are discussed.

The final paper of this paper has been accepted at USENIX Annual Technical Conference (ATC) 2024, with the title: "TileClipper: Lightweight Selection of Regions of Interest from Videos for Traffic Surveillance". The final version of this paper can be found here.

Requirement engineering is mostly concerned with communicating with customers to gather the essential and relevant domain information that forms the base of requirements and which is the key area for research on how to gather and record information from stakeholders (customers, end-users, domain experts). The main obstacle here is the ambiguity of Natural Language (NL). In this research, we present a user-centered process by applying a process model for creating and deriving natural language requirements in several steps/workshops. The process model was supported by examples of preventing natural language defects, applying templates, and helping to use logical decomposition to derive technical requirements.

The rapidly changing software industry presents challenges in teaching software engineering concepts and skills, especially in the area of DevOps. This encompasses both technical aspects, such as pipeline automation, and non-technical ones, such as culture, principles, and team interaction modes. This presents a significant challenge and there is high demand for skilled DevOps practitioners. To address these challenges, we propose a teaching strategy for DevOps that is based on real-world experiences from companies adopting DevOps and real projects. The strategy incorporates two teaching techniques: Research-based Teaching and Open Source Software projects. Our findings can help educators effectively plan, implement, and evaluate DevOps courses. The results also highlight several opportunities for practitioners to benefit from academic environments and experiment with new ideas to train abilities expected in new hires.

Radiation detection systems for security applications are often equipped with neutron sensors and required to promptly respond with an alarm if the neutron flux exceeds the background level. As the ambient neutron flux is essentially caused by cosmic radiation, this background could be evaluated by assessing the rate of muons crossing the gamma-ray detector which is comprised in such systems anyway. A study performed with a commercial Backpack Radiation Detection System (BRD) demonstrates the feasibility of this approach, supposed the gamma spectrometer is capable of discriminating muon signals from energetic events caused by the gamma-ray cascades following neutron captures. This requires a dynamic range exceeding the usual 3-4 MeV. The instrument used provided standard spectroscopy up to 10 MeV and facilitated charge-loss compensated spectroscopy up to 1 GeV, which allowed a separate assessment of normal gamma events (< 3 MeV), neutron-capture gamma events (3-7 MeV), and muon events (> 10 MeV). In this case, the rate of neutron-capture gamma events can be used as a neutron indicator on its own, while the muon rate signals the ambient neutron background level.

The diagnostic approach of interstitial lung diseases (ILDs) may be a challenge and requires a multidisciplinary discussion (MDD). While the definition of histological patterns of surgical lung biopsy by pathologists is helpful to the final diagnosis of MDD, its recognition could be challenging due to various degrees of lung remodeling and distortion. The pleura is a reference structure to identify some histological findings for the recognition of a pathological ILD pattern. When a pattern is found, it is important to know whether it is close to the pleura to determine its specific type and severity. This manual process is a tedious and laborious one for the pathologist. Automating this task is important for a complete computed-system-assisted ILD diagnostic process. We introduce “RadPleura” a framework for pleura classification of histopathological images using a radiomics-based approach. Our framework performs image pre-processing, region-of-interest segmentation, and extraction of a radiomic-signature fitted to  ILD classification. To evaluate the radiomic-signature, we classified it into pleura and non-pleura, using two classifiers: Support Vector Machine and Gradient-boosted Decision Trees. Our experiments yield promising results, with F-scores of 92% for SVM, and 91% for GBD. We also created a dataset of lung histopathology images with respective ground truth for pleura classification. To the best of our knowledge, this study is the first disclosed attempt to explore and develop a radiomic-signature for pleura classification. The methods and techniques are integrated into the RadPleura framework developed.

In intelligent reflecting surface (IRS)-assisted communications, the ultimate gain is achieved when the phases of the reflected signals are optimally selected to maximize the signal-to-noise ratio (SNR). However, practical hurdles, particularly the imperfect phase estimation and quantization can reduce the potential gain. Therefore, this work aims at evaluating the impact of applying a quantized phase in the presence of phase estimation errors. Towards this goal, we derive the probability density function (PDF) of the estimated quantized phase, then using the sinusoidal addition theorem (SAT), the PDF of the received signal envelope is derived and used to derive closed-form expressions of the symbol error rate (SER) and outage probability (OP). The obtained analytical and simulation results show that the SER and OP jointly depend on the SNR, phase estimation accuracy, number of IRS elements, and number of quantization levels. The imperfect phase and quantization demonstrated several counterintuitive results. In particular, it is shown that increasing the number of IRS elements or the number of quantization levels may degrade the system performance. Moreover, the results reveal that the impact of phase quantization increases as the phase estimation accuracy decreases. The results also show that the performance is susceptible to phase errors with an even number of reflectors and binary quantization levels.

Intelligent reflecting surface (IRS) is considered as an enabling technology for millimeter wave (mmWave) communications since it can provide an extra reflection link to increase the macrodiversity gains and improve the transmission performance. These advantages principally rely on the assumption that the blocking of direct and reflection links is mutually independent. However, due to the nonnegligible sizes of blockages in some practical cases, both links may be simultaneously blocked by the same blockage, which is the so-called blockage correlation. To this end, in this work we investigate the impact of blockage correlation in the IRS-assisted mmWave communication systems. Firstly, we provide a new joint line-of-sight/non-line-of-sight (LOS/NLOS) probability model with blockage correlation. The correlation coefficient of direct and reflection link states is also derived. Secondly, we study the effects of blockage correlation on the system performance of IRS-assisted mmWave communication systems by deriving the expression of transmission success probability. Moreover, we provide a deployment optimization algorithm for IRS when blockage correlation is considered. Finally, simulations verify our theoretical results and validate the IRS deployment optimization algorithm. It is shown that the widelyused independent blocking assumption not only always incur an overestimation of the performance of IRS-assisted mmWave communication systems, but also misdirect the optimal deployment of IRS.

Recent studies have shown that subtle changes in human face color due to heartbeats can be captured by regular RGB digital video cameras. It is possible, though challenging, to track one’s pulse rate when a video contains significant subject’s body motions in a fitness setting. The robustness gain in the recently proposed systems is often achieved by adding or changing certain modules in the system’s pipeline. Most existing works, however, only evaluate the performance of the pulse rate estimation at the system level of particular pipeline configurations, whereas the contribution from each module remains unclear. To gain a better understanding of the performance at the module level and facilitate future research in explainable learning and artificial intelligence (AI) in physiological monitoring, this paper conducts an in-depth comparative study for video-based pulse rate tracking algorithms; a special focus is placed on challenging fitness scenarios involving significant movement. The representative efforts over the past decade in the field are reviewed, upon which a reconfigurable rPPG framework/pipeline is constructed comprising of major processing modules. For performance attribution, different candidates for each module are evaluated while having the rest of modules fixed. The performance evaluation is based on a signal quality metric and four pulse-rate estimation metrics and uses the simultaneously recorded ECG-based heart rate measurement as a reference. Experimental results using a challenging fitness dataset reveals the synergy between pulse color mapping and adaptive motion filtering in obtaining accurate pulse rate estimates. The results also suggest the importance of robust frequency tracking for accurate PR estimation in low signal-to-noise ratio fitness scenarios.

Our study aims to detect multilingual Out-of-Vocabulary (OOV) and matching among multilingual OOV. Based on the original OOV issue, many multilingual OOVs also emerged at the same time. In order to solve this problem, this paper proposes a graph embedding-based matching among multilingual OOV. The method is divided into two parts. The first part is to extract OOV from the network corpus and understand it. In the second part, the OOV in the first part is taken as the target node, and the understood part is taken as the feature node of the target node to construct the graph and embed the graph. Our study uses Chinese, Korean, and Japanese for the experiment. The result of the method that Our study proposed is that F1- score reached 93.94%. Our study also compares this method with other embedding algorithms, and the F1-score is higher than the average of other algorithms F1-score by 9.62%.

The main goal of eNeuron H2020 project (Nov 2020-Oct. 2024, ID: 957779) is to develop innovative tools for the optimal design and operation of local energy communities, integrating distributed energy resources and multiple energy carriers at different scales. This paper presents a review study of the enabling conditions for the deployment of integrated local energy communities (ILECs) in Europe, performed within the project. The enabling conditions are addressed by defining the key actors and their interests in the implementation of an energy community at local level and through a detailed mapping of the enabling and emerging energy and information and communication technologies at both household and community levels. Special focus is also on mapping of the demand-side flexibility technologies to understand the benefits of local flexibility and impacts on the larger systems. In such analysis, the key issues for implementation and adaptation of this new energy paradigm are investigated through covering technological, socio-economic, environmental, and regulatory aspects. Paper accepted at 2022 Workshop on Blockchain for Renewables Integration (BLORIN) - DOI:  10.1109/BLORIN54731.2022.10028420

In the realm of Deep Learning (DL), optimization of hyperparameters like learning rate has been one of the well-known challenges. To address this problem, previous works have introduced Learning Rate Scheduling (LRS) and Adaptive Learning Rate (ALR) algorithms that used different learning rates at distinct phases of the training process to enhance the performance of DL models. But, these algorithms exhibit their own set of drawbacks. Most of the LRS techniques do not consider the learning behavior of those models while determining the learning rate. On the other hand, standard ALR algorithms such as RMSprop, Adam, and their variants optimize the training by consid- ering the local gradient patterns. However, these patterns are insufficient to determine the training efficiency when multiple local optima exist concentrated around the global optima. In this context, the proposed work introduces a new ALR method known as the Learning Rate Tuner with Relative Adaptation (LRT-RA), which decides the learning rate per training iteration based on the global loss curve. Lastly, the proposed algorithm (1) prevents a premature convergence of the training process and (2) intends to remove the cross-validation overhead to determine the precise learning rates required for the model’s training.

Local energy communities and electricity markets have emerged as possibilities for interaction among prosumers. A substantial effort has been invested into creating efficient pricing mechanisms for various market arrangements, all of which take into consideration distinct characteristics of local electricity trading. However, since they are all evaluated in terms of various systems and market conditions, it is challenging to directly compare the mechanisms. In this research, three well-established pricing mechanisms from the literature are systematically compared and evaluated under identical settings on their influence on welfare distribution across various market participant groups, privacy protection, transparency and complexity level. According to the findings, the supply-demand ratio pricing system leads to the lowest costs for consumers and is also the most privacy compliant and transparent. Furthermore, prosumers obtain the highest cost-savings through the consensus alternating direction method of multipliers pricing mechanism, whereas the equilibrium pricing mechanism performs best regarding economic fairness. The aim of this article is to provide insight into the performance of different pricing mechanisms to energy regulators and local electricity market facilitators. The comparative analysis should aid in making informed decisions on the implementation of local electricity markets.

Many people have lost their lives in weapon incidents every year. According to CDC report 124 people died every day in 2020 due to gun violence. Research has been carried out in this field in recent years for object detection using machine learning methodologies. In the following research methodology, we have carried out research on deep learning based automated weapon detection system which allows system to detect weapons such as Knife, Handgun, and rifle automatically. We have implemented object detection model You Only Look Once YOLO V4 and trained it on our datasets. Training showed that You Only Look Once YOLO V4 outperformed convolutional neural network CNN. By using this model for closed circuit televisions CCTV, we can help people in saving their lives and reduce human mass slaughter. Moreover, we proposed a method to improve accuracy of Convolutional Neural Network CNN by adding N times N number of layers. It will reduce the complexity of the model and improve accuracy.

An Artificial Neural Network (ANN) based Optimal Feedforward Torque Control (OFTC) strategy for electrically excited synchronous machines (EESMs) is proposed. After design, data set creation, training and validation of the ANN, the analytical computation of the optimal stator and exciter currents is achieved which allows to minimize copper and iron losses and to produce the desired (or maximally feasible) machine torque. Voltage and currents constraints of stator and exciter are considered as well. In contrast to conventional OFTC, the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal current triple while machine nonlinearities, magnetic cross-coupling, saturation and speed-dependent iron losses are taken into account. In addition, the proposed ANN design procedure allows to consider measurable OFTC goals and computational resources that ensures a real-time capable implementation. Comprehensive simulation results for a real and nonlinear EESM clearly show these benefits by comparing the proposed ANN-based OFTC with results of a nonlinear optimization problem (NLP) solver (which cannot be used in real-time).

Synchronous machines (SMs) like reluctance synchronous machines (RSMs) or interior permanent magnet synchronous machines (IPMSMs) are characterized by nonlinear stator flux linkages which are crucial for optimal control and operation management. This paper presents a novel disturbance observer that allows to estimate the nonlinear flux linkages online for any SM (with constant or no excitation). Moreover, a simple identification sequence with ramp-like voltage signals is proposed to extract key information of the nonlinear stator flux linkage maps within a short period of time and without the need of any controller, torque sensor or prime mover. Finally, the proposed observer with identification sequence is implemented and validated for a real and highly nonlinear IPMSM by realistic and detailed simulation results.

Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson’s Disease (PD). Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with video-based clinical scores of symptom severity (Spearman rank test, p < 0.006). These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic.

The origin of electromagnetism has been a recurring question since the discovery of electricity. In the 19th century, Maxwell unified electricity and magnetism into a single electrodynamic theory described by Maxwell’s equations. This was followed by the discovery of the electrodynamic force on a moving charge by Lorentz. Later, Einstein demonstrated that magnetism and the Lorentz force can be considered a relativistic effect. However, despite these important theories, there is at present no physical theory that identifies the single fundamental cause of all known classical electromagnetism. In an attempt to identify this single fundamental cause, this paper shows that classical electromagnetism can be considered as effect of scalar waves emanating from charges. It is shown in this paper that Coulomb’s law, Maxwell’s equations, the magnetic and electric field, the Lorentz force, and the Lienard-Wiechert potentials, can all be derived only from this single hypothesis that scalar waves are emitted by the charges. In addition, it was shown that the Lorentz transformation can also be derived from this hypothesis of scalar waves. To achieve this, an important new quantity was introduced: the momentum of the scalar waves. It was shown that the force caused by these scalar waves is simply the time derivative of this momentum. It was also shown that both the Lorentz force and the Coulomb force can be represented as the time derivative of the scalar wave momentum.