Hasil untuk "Electric apparatus and materials. Electric circuits. Electric networks"

Subash Khanal, Adrian Tang, Sven Van Berkel et al.

The search for extraterrestrial bio-signatures and the origin of Earth’s water remain two of the most compelling questions in planetary science. While no direct evidence of life beyond Earth has been found, water is a key prerequisite for life, and tracing its presence throughout the solar system may provide vital clues. A leading theory suggests that Earth’s water may have originated from comets, supported by limited water isotopic measurements that match Earth’s ocean water. However, more data from a larger sample of comets is needed to validate this theory. Traditional sub-millimeter wave spectrometers, capable of such measurements, are often too large and power-intensive for small spacecraft platforms. To address this, we present WHATSUP—a next-generation, ultra-compact, low-power, room-temperature submillimeter-wave (500-600 GHz) spectrometer—designed primarily for CubeSat and SmallSat platforms, though equally well-suited for a range of other missions. WHATSUP utilizes advances in CMOS system-on-chip electronics, innovative low profile and low mass silicon lens antenna, Micro-electro mechanical system (MEMS)-based THz switching, and a novel programmable calibration load. Together, these innovations deliver a highly integrated system with a total mass of only 2 kg and power consumption under 7 W, which is a substantial improvement over previous submillimeter-wave instruments. This enables affordable, high-frequency spectral observations from multiple low-cost missions, potentially revolutionizing how isotopic studies of cometary water are conducted and opening new pathways for outer solar system exploration. WHATSUP instrument was flown on the NASA Hand Launch Payload (HLP) ballooncraft and performed atmospheric soundings across Texas, USA in July 2023.

Jiachen Xu, John Kan, Yuyi Shen et al.

This work presents a fully unrolled on-chip deep reinforcement learning (DRL) module with a deep Q-network (DQN) and its system integration for integrated circuits control and functionality augmentation tasks, including voltage regulation of a cryogenic single-input triple-output dc&#x2013;dc converter and recovery of RF fingerprints (RFFs) using a reconfigurable power amplifier (PA) under temperature variations. The complete DRL module features 6-bit fixed-point model parameters, 116 kB of memory, and 128 processing elements. It is equipped with on-chip training capabilities, fully unrolled on a 0.45-<inline-formula> <tex-math notation="LaTeX">${\mathrm { mm}}^{2}$ </tex-math></inline-formula> core area using 28-nm technology. The design achieves an efficiency of 0.12 nJ per action and a control latency of <inline-formula> <tex-math notation="LaTeX">$4.925~\mu $ </tex-math></inline-formula>s, with a maximum operational efficiency of 3.49 TOPS/W. Temperature effects on the chip are thoroughly demonstrated across a wide temperature range from 358 K (<inline-formula> <tex-math notation="LaTeX">$85~^{\circ }$ </tex-math></inline-formula>C) to 4.2 K (&#x2013;<inline-formula> <tex-math notation="LaTeX">$269~^{\circ }$ </tex-math></inline-formula>C).

Ane Muguruza-Sánchez, Susan Sananes-Israel, Enrique Moliner et al.

Current lithium-ion battery recycling processes are based on high-temperature calcination (pyrometallurgy) or leaching treatments (hydrometallurgy), requiring huge amounts of energy and producing considerable waste. Direct recycling protocols are based on the reconstruction and regeneration of materials, eliminating the need for further material processing. In this paper, graphite electrodes have been recycled via a direct recycling protocol based on mild leaching with H2SO4 and H2O2 and calcination to eliminate the impurities and regenerate the structure. A Design of Experiments (DOE) has been proposed to determine the leaching conditions that reduce the generated waste and environmental impact, for which a Life Cycle Assessment (LCA) has been carried out. This combination of experimental and analytical methods has been useful to determine the parameters that have the greatest impact on the environment and select the most sustainable leaching condition, which, in this case, has shown a reduction of 36 % in acidification and 14 % in water use. The established recycling route has been validated with graphite anodes from production scraps and cycled cells (End-of-Life condition, EoL, SOH%<80 %), and in both cases, the polymeric compounds used in the electrode slurry preparation have been eliminated and the graphitization degree has been restored. These results show that graphite can be recycled from LIBs to develop a direct recycling route that promotes a sustainable circular economy and diminishes material waste.

Yiyang Li, Maria R. Lukatskaya

Niu Yurong

With the integration and collision of the Internet of Things, machine learning, Big data computing and other technologies, related applications of the Internet of Things also need to process a lot of real-time data streams. This article focuses on the research of remote monitoring of falls using the Internet of Things and six axis acceleration sensors, and explores the efficient application of Internet of Things technology in the system. The hardware design and related software development of remote monitoring of falls for the elderly are the key parts, and the overall framework, main modules, and specific implementation of the system are elaborated in detail. A complete remote monitoring system is designed by selecting a suitable six-axis acceleration sensor, collecting and analyzing the data. The continuous development of the Internet of Things and six axis acceleration sensor technology can provide real-time intelligent remote monitoring. Compared to cloud computing platforms, edge clusters have limited computing and storage resources and diverse types of computing node architectures. Therefore, it is necessary to use lightweight application service deployment methods to build an efficient and autonomous data processing platform. Through research and innovation on the remote monitoring system for elderly falls, with optimized and comprehensive technology and detailed research support, the overall system design was experimentally debugged and the experimental plan was ultimately determined. Through data communication module, fall detection and diagnosis module, and database management module, rapid analysis of remote acceleration data and information exchange are achieved, thereby minimizing the possibility of accidents caused by falls in the elderly.

Lu Wang, Tianyu Yang, Yuehang Ju et al.

Abstract The realization of artificial synapses based on biomaterials is of great significance for the development of environmentally friendly neuromorphic hardware systems and artificial intelligence. In this sense, a bioartificial synapse composited with egg albumen (EA) and multiwalled carbon nanotubes (MWCNTs) is fabricated. Based on the adjustable weight of the artificial synapse, the plasticity of electrical synapses is explored. Due to the photogenerated carriers and thermoelectric effects of carbon nanotubes, the device has optoelectronic properties, so the optoelectronic synaptic plasticity of the device is explored under light pulses. The device is well suited for biological synapses and shows great potential for applications in future high‐density storage and neuromorphic computing systems. In addition, to further study the physical mechanism of the conductive process of the device, the electrical characteristics of the contact interface between carbon nanotubes doped with Fe substitution and the upper electrode Al are mainly analyzed by first principles, and the adsorption, charge distribution, and band structure between them are theoretically studied.

Pingda Guan, Haikun Jia, Wei Deng et al.

The rapidly advancing field of millimeter-wave (mm-wave) radio-frequency integrated circuit (RFIC) design has ushered in an era of remarkable innovation, particularly in the realm of on-chip passive devices. Among them, 8-shaped inductors have emerged as a novel and promising variant, attracting significant research interest thanks to their unique geometry and electromagnetic (EM) properties. The distinctive feature of 8-shaped inductors lies in their antiparallel magnetic fields due to the opposing current flows within the two turns, enabling manifold applications. In this article, we comprehensively explore the 8-shaped inductors with a focus on their diverse utilizations, including EM interference (EMI) reduction, compactness of RF layout, provision for a magnetic feedforward/feedback arrangement, and oscillation mode manipulation, thereby demonstrating that the 8-shaped inductor can be an essential addition to RFIC designers&#x2019; toolbox.

Yao Zheng, Zhang Le, Xiao Huanquan et al.

With the continuous increase in low altitude flight density, the issue of low altitude navigation safety has attracted widespread attention. Due to the complex low altitude environment, low altitude flight is more susceptible to ground obstacles and weather effects than commercial aviation. In order to ensure the flight safety of helicopters in low altitude airspace, this paper proposes an improved support vector machine based flight conflict detection model. By modeling the conflict network in low altitude flight areas and utilizing Support Vector Machine (SVM) classification features, the safety discrimination of low altitude flight was achieved, ultimately achieving the safety of aircraft in low altitude flight. This article adopts a protected area model that considers the shape of the aircraft as a conflict zone. In order to reduce the complexity of the conflict detection model, an improved ID3 decision tree algorithm and random forest are used to reduce the complexity of the classifier. The study solved the saturation problem of S-type functions in conflict detection models by using more sensitive functions for probability mapping. And use intelligent optimization algorithms to pre train key parameters, achieving efficient conflict detection suitable for low altitude flight.

Jungang Yang, Cao Meng, Li Ling

In the research of sports injury prevention, the recognition of sports action plays an important role in the action recognition model and the prediction evaluation model. In view of the above problems, this paper constructs a new mathematical model through the idea of BP neural network. The model combines wearable technology and can improve the recognition accuracy of sports actions. The model uses the BP network classifier, which can be used in data. Processes such as feature extraction improve efficiency and reliability. In this paper, the algorithm simulation is carried out, and the experiments are carried out for three movements: running, running and static. The results show that for the recognition of running and running action, when the hidden layer node is 11, the BP neural network classifier shows the best recognition effect. For static motion, the recognition effect of each classifier is basically the same. This paper analyzes the wearable sports action recognition system, including perception layer, application layer and service layer, to realize the recognition and classification of sports actions, predict actions in advance and prevent sports injuries. Finally, this paper analyzes the causes of sports injury, puts forward specific measures to prevent sports injury, and further reduces sports injury events through wearable devices.

Cong Zhang, Mingxin Zhang, Fengyuan Lin et al.

Abstract Light‐triggered synaptic plasticity (LTSP) induced by electron trapping in organic photoelectric synaptic transistors (OPSTs) offers potential prospects in neuromorphic wearable artificial intelligence. However, a consistent and universal comprehend for LTSP behaviors especially on oxygen effect in OPSTs is still a critical challenge hindering their practical applications. A mechanism on strong dependency between the oxygen‐induced and polar‐group electron trapping in OPSTs, is successfully unveiled in this study for the first time. And the interaction between the oxygen and polar dielectric interface can be enhanced by properly modulating the specific surface area of thin‐film semiconductors. This effectively binds the oxygen near conducting channel and further improves the trapping efficiency for photogenerated electrons, undergoing the time‐dependent photocurrent generation and subsequent prolonged decay, forming the typical LTSP behaviors. These experimental demonstrations differ from previous reports and therefore may contribute an innovative perspective in the design of functional layers for high‐performance OPST devices.

Akane Inda, Rikuto Oiwa, Satoru Hayami et al.

Chirality ubiquitously appears in nature, however, its quantification remains obscure owing to the lack of microscopic description at the quantum-mechanical level. We propose a way of evaluating chirality in terms of electric toroidal monopole, a practical entity of time-reversal even pseudoscalar (parity-odd) object reflecting relevant electronic wave functions. For this purpose, we analyze a twisted methane at the quantum-mechanical level, showing that the electric toroidal monopoles become a quantitative indicator for chirality. In the twisted methane, we clarify that the handedness of chirality corresponds to the sign of the expectation value of the electric toroidal monopole, and that the most important ingredient is the modulation of the spin-dependent imaginary hopping between the hydrogen atoms, while the relativistic spin-orbit coupling within the carbon atom is irrelevant for chirality.

Mohammad Al‐Mamun, Amrita Chakraborty, Marius Orlowski

Abstract A switching of resistive memory cells leads to a local accumulation of Joules heat in the device. In resistive RAM (ReRAM) arrays, the heat generated in one cell spreads via common electrode metal lines to the neighboring cells and may cause their performance degradation. The performance degradation results in reduced number of switching cycles and, in extreme cases, even in a loss of a bit, caused by the rupture of the nanofilament. The authors propose a thermal analysis of the thermal cross‐talk, describe its impact on cells’ electric performance, and identify three major mechanisms for the ReRAM reliability: (i) thermal conductivity, (ii) the specific heat capacity, and (iii) geometry of the electrodes. Several ReRAM arrays are manufactured to vary thermal conductivity, specific heat and geometry of the electrodes by depositing eight different inert electrodes: Pt(50 nm)/Ti(30 nm), Ru(50 nm)/Ti(30 nm), Co(50 nm)/Ti(30 nm), Pt(50 nm/Cu(100 nm)/Ti(30 nm), Pt(50 nm)/ Cu(200 nm)/Ti(30 nm), Ru(50 nm/Cr(30 nm), Ru(50 nm)/Ti(50 nm), and Rh(50 nm)/Cr(30 nm). The experimentally found differences of the degradation of electric performance of the array cells performed under identical circumstances can be correctly predicted by the proposed thermal analysis using the material properties and geometry parameters of the electrodes.

Vedant Agarwal, Kukatlapalli Pradeep Kumar, Kavalayil Philip CyrusManoj et al.

Studies linking two broader spectra of topics have fascinated scholars in many aspects. Here we tried associating two such far-reaching aspects which have finite connectivity between them. Multiverse has been the talk of the hour which explains the theory of multiple universes which exist in parallel. This is a topic in physics concerned with many relative matters. On the other side, Big data is the subject in computing and information science describing the volume, velocity, and variability of the data hitting computer-connected systems. Big data can only be handled with newer architectures, algorithms, and methodologies as its features are contradicting regular computer systems and networks. It is well known that multiple processors are required to handle big data existing in parallel performing a single job given by the data analyst. So as we know, multiverse consist of hypothetical concepts of several parallel universe having everything like information, energy, and time. However, we see this situation to draw an association connecting parallel universes of the multiverse with parallel processors of big data by incorporating the concepts of working of parallel universe in the processing of Big Data. We provide a comprehensive observation on both the topics and take positive lenience on bringing a newer terminology in data science. History of multiverse along with big data structures are brought in with related parameters. This aspect is novel in its nature and we complement the literature carried out by the researchers and scholars appropriate analysis. We also showcase a model of the school of thought mentioned above in drawing conclusions.

Xiaopan Song, Lei Wu, Yifei Liang et al.

Abstract Catalytic synthesized ultrathin silicon nanowires (SiNWs) are ideal 1D channel materials to fabricate high‐performance transparent and low‐cost thin film transistors (TFTs) that are widely needed for flexible electronics and displays. In this work, a scalable integration of orderly array of SiNW array, with a uniform diameter of only 52 ± 4 nm, grown directly upon glass/wafer substrates, via a guided in‐plane solid–liquid–solid (IPSLS) process, and passivated by a new solution oxidizing/etching cycling technique is demonstrated. This has enabled an all‐low‐temperature (<350 °C) fabrication of high‐performance SiNW‐TFTs, achieving Ion/Ioff current ratio and subthreshold swing (SS) of >106 and 120 mV dec−1 respectively, with excellent negative and positive bias stabilities. Importantly, the SiNW‐TFTs fabricated on glasses with ITO/or metal electrodes demonstrate a high transparency of 90% or 73% respectively, making them ideal candidates for building the next generation of high aperture displays, transparent electronics, and augmented reality applications.

Anil J, Padma Suresh L

The ability to identify a person's face from a digitized photo or video frame against a database of faces is known as facial recognition. In the past few years, algorithms that use deep learning to recognize faces have become more popular. The majority of them are predicated on extremely accurate but complicated Convolutional Neural Networks (CNNs), which require a lot of computational power, storage space, and a number of training epochs before they provide satisfying results, and are notably difficult to implement. In an effort to reduce the training time by reducing the number of epochs and increase accuracy, this paper introduces a novel fast hybrid face recognition approach HOG-CKELM, based on CNN that makes use of Kernel based Extreme Learning Machines (KELM) and Histogram of Oriented Gradients (HOG) as facial feature extractor. The effectiveness of the proposed hybrid face recognition technique is evaluated using AT &amp; T, Yale, and JAFFE datasets. When compared to traditional HOG-CNN based techniques, the experimental evaluation indicates that the proposed method for face recognition is capable of achieving excellent performance in terms of accuracy and training time.

Konka Kishan, B. Vijay Kumar

The widespread availability of the internet, in conjunction with very low-cost digital recording and storage devices, has ushered in an age in which the reproduction, illegal use, and malicious dissemination of digital information have become much more straightforward. Authentication of multimedia materials has garnered a lot of interest in recent days as a means of preventing illegal usage, theft, and misrepresentation of the content. Invisible watermarking tries to hide information in a medium in order to demonstrate ownership, integrity, or to hide a secret message. The goal of invisible watermarking is to hide the watermark and extract it without making it evident that the cover image is watermarked. This paper presents an invisible watermarking technique that can hide large color water marks in the cover media. The proposed model uses a Conditional Variational Autoencoder (CVAE) to encode the watermark into the cover image. The watermarked image is then decoded at the receiver to extract the watermark. Unlike the conventional watermarking techniques which hide a simple, small black and white image as watermark, the proposed model can embed large full color images as watermarks into the cover images. This makes the proposed model superior to the existing models by hiding large watermarks into the color images. The stego images produced a high PSNR greater than 40 for different watermarks and the images are visually indistinct from the original cover images.

Mustafa Taha Hussein Al-Musawi, Mustafa N. Mnati, Aeizaal Azman A. Wahab

Elastic optical networks (EONs) are a promising technology for the development of flexible and wideband communication systems. The (FSU) frequency slot unit concept is used in this technology to define the bandwidth unit in the frequency domain. The use of a specified number of consecutive FSUs allows bandwidth to be assigned to a data stream in a flexible manner. Nonblocking optical switching networks are required for a successful elastic optical network operation. This article concentrates on the Wavelength Space Wavelength,Space, Wavelength switching network topologies that were previously presented. In comparison to the other architectures, the SSW switching fabric requires fewer Spectrum converters (SCs). n relation to Wavelength Space Wavelength, a prior study clarified that using the meta-slot approach reduces hardware complexity, which is measured by the number of frequency slot unit. A frequency range with one or more (FSUs) is called a meta-slot. The previous study did not discuss how to optimize the meta-slot class sizes, despite the fact that its effectiveness depends on the sizes of the meta-slot classes. Additionally, the use of meta-slots for Space Space Wavelength was not taken into account in the earlier analysis. The optimization of meta-slot class sizes is looked into in this study, and it is shown that this optimization may be modeled as the shortest path issue. For Wavelength Space Wavelength, the previously reported nonblocking conditions are contrasted with the meta-slot scheme optimized using the shortest path model. The outcome attests to the optimized meta-slot scheme's superiority. The distribution of meta-slots among S-switches is also crucial for Space Space Wavelength. The assignment of meta-slots is modeled as a bin-packing problem in the study. Thus, a well-known bin-packing heuristic can be used to find the assignment that is close to ideal. The number of S-switches is calculated for using the optimized meta-slot scheme and previously known nonblocking conditions. The outcome confirms that the meta-slot scheme benefits both Space, Wavelength and Wavelength Space Wavelength.

Malte Wabnitz, Tobias Gemmeke

The capabilities of artificial neural networks are rapidly evolving, so are the expectations for them to solve ever more challenging tasks in numerous everyday situations. Larger, more complex networks and the need to execute them efficiently on edge devices are the two counteracting requirements of this trend. Novel devices and computation techniques show promising characteristics to address this challenge. A huge design space covering different combinations of neural networks and hardware architectures using these technologies needs to be explored. An efficient design flow is, therefore, crucial for a good quality of service. This work reviews a wide range of simulation tools for novel memristive devices and analyzes their applicability for the design space exploration. A modular toolflow is proposed that shrinks down the large design space step-by-step using state-of-the-art optimization techniques and builds upon existing tools to find the best trade-offs between network accuracy and hardware requirements.

Panagiotis Mougkogiannis, Andrew Adamatzky

Proteinoids (thermal proteins) are produced by heating amino acids to their melting point and initiation of polymerisation to produce polymeric chains. Amino acid-like molecules, or proteinoids, can condense at high temperatures to create aggregation structures called proteinoid microspheres, which have been reported to exhibit strong electrical oscillations. When the amino acids L-Glutamic acid (L-Glu) and L-Aspartic acid (L-Asp) were combined with electric fields of varying frequencies and intensities, electrical activity resulted. We recorded electrical activity of the proteinoid microspheres' ensembles via a pair of differential electrodes. This is analogous to extracellular recording in physiology or EEG in neuroscience but at micro-level. We discovered that the ensembles produce spikes of electrical potential, an average duration of each spike is 26 min and average amplitude is 1 mV. The spikes are typically grouped in trains of two spikes. The electrical activity of the ensembles can be tuned by external stimulation because ensembles of proteinoid microspheres can generate and propagate electrical activity when exposed to electric fields.

Daria Bondarenko, Igor Voronin, Pavel Voronin et al.