In traditional approaches to time series data augmentation, the focus has largely been on refining the architecture of Generative Adversarial Networks (GANs) to better align with the original data distribution while attempting to preserve the dynamic behavior inherent in the data. However, GANs inherently struggle to accurately retain the temporal dynamics of the original time series, primarily due to the absence of first-order difference information. To address this gap, we propose a novel framework, titled the GAN-MCMC approach, for generating multivariate time series data that integrates two key modules: (a) a GAN-based module for generating multivariate time series, and (b) an MCMC-based module designed to preserve the first-order difference distribution. This integrated approach ensures that the synthetic data not only replicates the original data distribution but also retains its dynamic properties.In this study, the multivariate time series data used were collected from Area X.O, which was employed to predict N2O emissions from farming. This dataset is ideal for our analysis because it originates from a complex dynamic system, and the equipment used to gather the data is prohibitively expensive to deploy on a wide scale. Therefore, data augmentation techniques to generate synthetic agricultural data are both necessary and valuable for improving the predictive models. A central aspect of the GAN-MCMC approach is adjusting the β factor in the modified Metropolis-Hastings algorithm, which is the core algorithm in the MCMC module. The β factor controls the extent to which information from the original time series is preserved. Our experiments demonstrate that small values of β effectively retain periodic information, and the joint distribution of the firstorder differences in the synthetic data remains consistent when the same β is used in the algorithm. Additionally, the memoryless property of the Markov Chain is preserved in the generated data, and we employ an exponential moving average (EMA) technique to simulate the long-term relationships present in the original time series.Finally, we use the synthetic time series data to train Long Short-Term Memory networks (LSTMs). Our results show that LSTMs trained on synthetic data generated by the GAN-MCMC framework outperform those trained on synthetic data produced by other GANs. The link to source code:  https://github.com/Developer2046/GAN-MCMC  

The transition to sustainable transportation has led to the emergence of electric vehicles (EVs) and the exploration of alternative fuels like Hydrotreated Vegetable Oil (HVO) for diesel cars. This study presents a comprehensive comparative analysis of electric cars and diesel cars powered by HVO, focusing on environmental impact, performance, economic aspects, infrastructure, and longevity. Electric cars, with their zero tailpipe emissions, offer a promising solution to reduce urban air pollution and greenhouse gas emissions. However, the environmental footprint of their battery production and electricity sources cannot be overlooked. On the other hand, diesel cars using HVO, a renewable biofuel, have the potential to significantly reduce carbon emissions compared to conventional diesel, while leveraging existing refueling infrastructure. Performance metrics, including acceleration, range, and efficiency, were evaluated for both vehicle types. From an economic perspective, the total cost of ownership, encompassing purchase price, maintenance, and fuel or electricity costs, was assessed. Infrastructure analysis revealed insights into the availability and convenience of charging stations for EVs versus HVO refueling stations. Additionally, the longevity and maintenance requirements of both vehicle types were examined to provide a holistic understanding of their long-term viability. The findings of this study offer valuable insights for policymakers, manufacturers, and consumers in making informed decisions about sustainable transportation options. The results also highlight areas for potential improvement and innovation in both EV and HVO technologies, paving the way for a cleaner and more efficient transportation future.Â

Basing on the distinction between the physical network and the virtual network, this paper defines an Internet reference model which has six layers: physical link, data link, physical network, virtual network, transport service and application layer. Comparing to the reference model used by most textbooks, one extra network layer is added to accommodate both the virtual and physical network layers. This paper gives clear definitions for every layers too. The goal of this paper is to present a correct and easy-to-understand reference model for Internet.Â

We investigate a simulation to real-world transfer of data-driven models for state estimation using measurements received over a wireless network. Real-world networks, such as vehicular communication systems, face issues like channel impairments and network congestion, and hence estimates are computed using a history of measurements that are received intermittently and are aged on reception. Training an estimator model directly with real-world data is expensive and impractical due to diverse network conditions. To address this, we present an approach that doesnâ\euro™t rely on real-world network data. We train a model using only data generated by a low-cost low-fidelity simulation of networks. To bridge the gap between simulation and reality, we employ domain randomization, appropriately randomizing simulation parameters during training. An in depth investigation of network simulation is provided that has a FCFS queue with two parameters, the rate of arrival of packets into the queue and packet service rates. The model is validated by two real network deployments. In one, we process vehicular trajectory from up to 70 nodes over WiFi to estimate vehicle positions and speeds. In the other, GPS coordinates are communicated by public transit buses over city-wide cellular networks to a transport authority database. This information is obtained by a server running in the AWS cloud, by polling at a certain fixed rate. The model uses the polled data to generate estimates of bus positions. Our proposed estimator model is a novel deep neural network (DNN) architecture, which outperforms existing approaches to data-driven estimation.

The medical care industry is on the cusp of an extraordinary period, with large language models (LLMs) arising as incredible assets for reclassifying medical care elements. This paper investigates the potential and effect of LLMs in different parts of medication, including diagnostics, patient consideration, drug revelation, and medical services organization. It dives into the open doors and difficulties introduced by LLMs, accentuating the moral contemplations and the requirement for capable reception. By looking at late turns of events and contextual investigations, this paper offers a brief look into the developing scene of medical services, where LLMs are ready to assume a focal part in reshaping the eventual fate of medication.

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.

This study introduces two innovative methodologies aimed at augmenting energy efficiency in satellite-to-ground communication systems through the integration of multiple Reflective Intelligent Surfaces (RISs). The primary objective of these methodologies is to optimize overall energy efficiency under two distinct scenarios. In the first scenario, denoted as Ideal Environment (IE), we enhance energy efficiency by decomposing the problem into two sub-optimal tasks. The initial task concentrates on maximizing power reception by precisely adjusting the phase shift of each RIS element, followed by the implementation of Selective Diversity to identify the RIS element delivering maximal power. The second task entails minimizing power consumption, formulated as a binary linear programming problem, and addressed using the Binary Particle Swarm Optimization (BPSO) technique. The IE scenario presupposes an environment where signals propagate without any path loss, serving as a foundational benchmark for theoretical evaluations that elucidate the systems optimal capabilities. Conversely, the second scenario, termed Non-Ideal Environment (NIE), is designed for situations where signal transmission is subject to path loss. Within this framework, the Adam algorithm is utilized to optimize energy efficiency. This non ideal setting provides a pragmatic assessment of the systems capabilities under conventional operational conditions. Both scenarios emphasize the potential energy savings achievable by the satellite RIS system. Empirical simulations further corroborate the robustness and effectiveness of our approach, highlighting its potential to enhance energy efficiency in satellite-to-ground communication systems.

In the pursuit of understanding the enigmatic world of prime numbers, a unique formula has been identified, which can be used to generate prime numbers, with the exception of 2 and 3. This formula is expressed as (n = positive integer, p = prime number), where, intriguingly, the output for other positive integers results in irrational numbers. This enigmatic formula not only reveals prime numbers but also unveils a peculiar pattern related to composite numbers derived from prime factors. These composite numbers, which arise as exceptions in the context of prime generation, exhibit regularity and may offer new insights into the interconnected of prime and composite numbers. This discovery promises to broaden our understanding of the intricate world of number theory and provides a fresh perspective on the nature of prime numbers. Further exploration of this formula may uncover deeper mathematical principles and unlock novel avenues in number theory research. p =√1 + 24n

Product pricing is a critical task that has a profound impact on demand and the target audience. Setting the price of an existing product is even more challenging, as it can significantly affect business growth, customer purchasing patterns, and brand perception. In this paper, we propose an approach that leverages a deep learning Multi-layer Perceptron (MLP) Neural Network to predict changes in drug prices. Our model differs from existing approaches as it focuses on utilizing the stated reasons for price changes rather than product or market attributes for price change prediction. The MLP-based model is designed to learn complex patterns and dependencies within the drug price data, providing a data-driven solution for accurate forecasting. The text field containing the reason for the price change is pre-processed and then fed into the MLP neural network. We employ a Continuous Bag of Words approach to convert the text into a numerical format for model development. The model is trained on a diverse dataset, and its performance is evaluated using various metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), which indicate the model’s predictive accuracy. The results demonstrate that the MLP-based model excels in predicting fluctuations in drug prices, showcasing its ability to adapt to the dynamic nature of the pharmaceutical market.

Most spectrum surveys conducted worldwide demonstrate that the radio-electric spectrum in use at any given location and instant of time is below 25%. Current spectrum management policies and spectrum utilization inefficiency are becoming unsustainable for the future development of radio technologies and services. In this context, dynamic spectrum access is a promising technique for improving spectrum utilization efficiency. A key scientific gap is identifying inaccurate spectrum data from hidden nodes that are not homogeneously distributed in the spatial domain and dynamically vary in time and frequency. For bridging this gap, our paper presents the research results of a spectrum feature extraction algorithm based on multi-correlation and Random Forest. Our algorithm is capable of estimating the spectrum utilization pattern in the spatial and frequency domain with a reliability up to 92% for a real heterogeneous networking scenario.

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

The profound association between music and human emotion has transcended epochs, underscoring the capacity of musical compositions to elicit a spectrum of feelings, from exuberance to introspection. In the contemporary landscape, the intersection of music and technological advancements has engendered a paradigmatic shift in the creation and interpretation of musical compositions. Central to this transformation is the integration of artificial intelligence (AI) into the realm of music composition, a domain historically governed by human creativity. This research endeavors to navigate this juncture, unraveling the prospect of imbuing AI-generated music with heightened emotional resonance, thereby amplifying the scope of artistic expression. At the crux of this exploration lies the innovative utilization of Generative Adversarial Networks (GANs) to infuse the synthesized musical compositions with an intricate tapestry of human-like emotions. This paper sets out to elucidate the multifaceted dimensions of this venture by charting a trajectory that traverses the historical lineage of emotional undertones in music, culminating in a contemporary synergy between AI capabilities and human sentiment. Our approach is encapsulated within the nexus of technology and creativity, where GANs are envisaged as a conduit to facilitate the infusion of emotions into AI-generated musical compositions. In subsequent sections, we delve into an immersive analysis of the seminal role that music has played in articulating emotions throughout history. Moreover, we embark on a comprehensive exploration of the confluence of AI advancements and the nuanced realm of emotional resonance, delineating the profound possibilities that emerge from this amalgamation. Crucially, the research postulates a novel framework that leverages GANs to imbue AI-generated harmonies with a poignant emotional depth, elucidating the pivotal role of technology in elevating the emotive tenor of musical compositions. The subsequent chapters unravel the intricate methodology underpinning this research, encapsulating data collection processes, GAN architecture elucidation, techniques for embedding emotional facets, and the meticulous training process. Furthermore, a meticulous analysis of the emotional impact of AI-generated music on human perception is presented, both quantitatively and qualitatively, shedding light on the efficacy of the GAN-powered approach. Conclusively, the research extends its purview to expound upon the ethical considerations embedded within this paradigmatic juncture, while also envisioning potential trajectories for the practical application and validation of the proposed GAN-powered methodology. As the curtains are drawn on this introductory exposition, the subsequent sections promise a symphony of insights, culminating in a harmonious synthesis of AI ingenuity and human emotional resonance within the tapestry of musical composition.

In the past decade resistance-based memory devices, or the resistive random access memory Devices (RRAM) have emerged as a potential candidate for multi-state memory storage and non-conventional computing applications. Reports on conduction quantization (QC) have added an interesting layer to the utility of these RRAM devices for ultra-dense memory and neuromorphic computing applications due to the occurrence of integral and half-integral conduction (resistive) states. Since the first reports of QC phenomena in RRAM devices, there have been detailed studies on the nature of the conducting filaments, switching mechanisms, and tunability of the QC states, but there exists a scarcity of studies exploring controllability of QC phenomena in scalable device geometries. In this work, we report compliance current controlled tunable QC phenomena in crossbar RRAM cells based on electrochemical metallization switching mechanism. The devices exhibited robust bipolar resistive switching, with well separated high and low resistance states.  The magnitude and number of the QC states were found to increase from ~2.5 to 3.5 and from 4 to 6, respectively as the IC increased from 50 to 200μA. The Cu/Ta2O5/Pt device structure was chosen to strategically govern the metallic nature of the conduction filament (CF) formation, which helped postulating factors contributing to the tunability of the states via compliance current. We report the lateral dimension variability as the main factor governing the magnitude and number of quantized steps observed in RRAM devices, where we also discuss a numerical method to approximate the diameter of the CFs. The increase in number and magnitude of QC steps with IC was explained considering the fact that thicker CF obtained at higher ICC, when undergoes a gradual rupture during reset process, results in larger number of QC steps compared to a thinner CF.

Although there has been a large amount of research focusing on the noise performance of GaN HEMTs in L-bands and Ku-bands, few studies have focused specifically on the noise performance at mm-Wave frequencies. In this letter, the fabricated 120 nm MISHEMTs exhibited a low minimum noise figure (NFmin) of 1.3 dB with an associated gain (Ga) of 7.7 dB at 30 GHz, and NFmin of 1.7 dB and Ga of 6 dB at 40 GHz, showing the lowest values of NFmin compared with the reported GaN HEMTs with the same gate length.Â

This paper presents the intricate architectural design and operational capabilities of the Autonomous multi-factor Energy Flow Controller (AmEFC) in managing energy flow within microgrids. Central to its function, the AmEFC Controller harmonizes software computations with hardware interfacing to guarantee optimal energy flow and system reliability. Notably, it factors in weather forecasting for predictive energy management and allows city-scale intercommunication between local AmEFC controllers for efficient energy surplus exchange. The framework is structured hierarchically, incorporating diverse energy sources and storage mechanisms, dynamic load management, supplemental energy, and advanced decision-making systems. A unique innovation lies in its adaptive response to surplus energy, directing it into gravitational potential energy via hydro pumps. The Simulink model of the on-grid AmEFC microgrid prototype is introduced, representing the integration of renewable energy sources, storage systems, and critical load distribution. Through scenario analysis in the Simulink model, the paper assesses the system’s reliability in consistently powering critical loads and its strategy in managing surplus energy. Conclusively, the AmEFC system emerges as a promising solution for future resilient, adaptable, and efficient microgrids, further empowered by its predictive and collaborative capabilities.

Introduction of a new model to explain the orientation of piezoelectrical materials during sputter deposition processes

A document by Navid Mohammadian . Click on the document to view its contents.

The synchronous reference frame phase-locked loop (SRF-PLL), also called dqPLL, is an electric circuit commonly used in power electronics to estimate the phase angle of a three-phase AC grid. If the voltage is unbalanced, the PLL is modeled as a periodically forced nonlinear oscillator and is known in practice to converge to a steady oscillation. In the existing literature, the oscillation has been studied via linearization assuming a low level of unbalance. Aiming for stronger nonlocal statements, we present nonlinear analysis. We apply the method of autonomous comparison systems and incremental stability to show that the steady oscillation is unique and attractive in a wider neighborhood. Its lock-in domain is estimated using numerical phase portrait analysis. The oscillation is further approximated up to the terms of the second order in the unbalance factor – it yields an estimation of the time average of the PLL’s phase error which is not visible by linearization only. The results provide stability guarantees and can guide the tuning of SRF-PLL.