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Cloud computing and digital media : fundamentals, techniques, and applications
\"While some related books cover separate aspects of digital media and cloud computing, none integrate both of these areas together. Bridging the gap between digital media and cloud computing, this book brings together technologies for media/data communication, elastic media/data storage, security, authentication, cross-network media/data fusion, inter-device media interaction/reaction, data centers, platform as a service (PaaS), and software as a service (SaaS). The book also covers interesting applications involving digital media in the cloud. \"-- Provided by publisher.
Book
Microwave Differential CSRR Sensor for Liquid Permittivity Measurement
In this work, we propose a differential complementary split-ring resonator (CSRR) sensor as the resonating element for permittivity analysis of liquid samples. In comparison to conventional non-differential sensors, differential sensors are found to be immune to environmental changes. The proposed sensor operates at 2.35 GHz of the ISM band and is built on a low-cost FR4 substrate. The sensor is thoroughly optimized and validated using Ansys High Frequency Structure Simulator software. Multiple liquid samples covering a wide dielectric range of 1–111 are used to determine the sensing performance of the sensor. The sensitivity of the sensor is found to be high and on par with recent works related to differential CSRR sensors. Based on the frequency of the transmission notch observed, a fit equation is developed to determine the dielectric constant of unknown samples. The proposed differential sensor promises to be a good alternative to traditional CSRR sensors for requirements involving noise and atmospheric fluctuations.
Journal Article
Computational Studies on the Nitrogen-Doped Graphene Quantum Dots as Potential Sensor for Hazardous Gases
Graphene quantum dot-based sensors have demonstrated significant promise in the detection of dangerous air pollutants that harm the environment and endanger human health. The efficient trapping and separation of numerous hazardous gases from the environment have recently garnered substantial experimental and computational attention. Nitrogen doping significantly tunes the properties of graphene quantum dots and expands their potential applications. In this study, we have investigated the sensing mechanism of pristine graphene quantum dots (GQD) along with pyridinic nitrogen-doped defective graphene systems, i.e., GQD-DV/N4, for various hazardous gases, such as arsine (AsH3), hydrogen sulfide (H2S), formaldehyde (HCHO), hydrogen cyanide (HCN), and phosphine (PH3). Our study suggests that nitrogen doping in GQDs can significantly increase the adsorption strength for these hazardous gases. Our in-depth analysis proposes that adsorption is governed by the presence of hydrogen bonds between the hydrogen in the adsorbates and the nitrogen in the adsorbents. These findings should persuade scientists to rationally and methodically modify surfaces to serve as sensors for the detection of hazardous gases.
Journal Article
Co-deposition of CuO:ZnO Nanocomposite on n-Type Si Substrate by Chemical Bath Deposition (CBD) Technique for Photovoltaic Application
In the present study, CuO:ZnO nanocomposite (NC)-based heterojunction photovoltaic devices have been successfully fabricated on n-type Si ( ) wafers, utilizing a simple and cost-efficient chemical bath deposition (CBD) method. The primary focus of our research was to explore the optimal weight percentage of the co-deposited CuO and ZnO in the NCs and to investigate their material, chemical, and electronic properties. For this purpose, the synthesis processes were conducted with different stoichiometric ratios of CuO and ZnO, namely 1:2, 1:1, and 2:1, which we designated as CZ1, CZ2, and CZ3, respectively. The material properties of the fabricated devices were extensively studied with a synergetic approach of relevant experimental techniques, including field-emission scanning electron microscopy (FESEM), energy dispersive x-ray spectroscopy (EDX), and x-ray diffraction (XRD) measurements. A bandgap variation from 2.0 eV to 2.52 eV was measured for the samples using spectroscopic ellipsometry. Electrochemical impedance spectroscopy (EIS) analysis revealed that the CZ2 sample exhibited ~ 32% enhancement in recombination resistance in comparison to CZ1, indicating a reduction in non-radiative recombination at its heterointerface. Comparative photovoltaic analysis indicates that the highest efficiency of ~ 1.3% was achieved for the CZ2 device due to its superior crystalline quality, relatively higher absorbance in the visible region, and fewer interfacial defects. Therefore, this study explores the CBD technique for analyzing electronic and optoelectronic properties of the dual-oxide-based nanocomposite for its possible photovoltaic applications.
Journal Article
Low-Frequency Resistorless Electronically Tunable Quadrature Oscillator Based on an EX-CCCII
An approach for the implementation of a resistorless, electronically tunable quadrature oscillator (RETQO) is proposed in this paper. The proposed circuit consists of only one EX-CCCII block and two capacitors with a ground connection. Non-ideality analyses are performed to prove the suitability of the RETQO design. The frequency of oscillation expression is obtained. The oscillator independently controls the condition of oscillation (CO) and the frequency of oscillation (FO). The acceptability of the circuit is confirmed using CMOS-based TSMC 180-nanometer technology. Also, numerous simulations based on the SPICE circuit simulator are accomplished. Moreover, experimentation has also been tested using IC AD844 which is commercially available. There is good agreement of the proposed work with existing reports.
Journal Article
Nitrogen-Doped Graphene Quantum Dots for Efficient Detection of Toxic Gas
Owing to their adjustable optoelectronic and adsorption characteristics, graphene quantum dots (GQDs) have become a potential material to manufacture effective gas sensors. Our present work deals with the study of interaction of a pristine graphene quantum dot (GQD) and a pyridinic nitrogen-doped defective graphene quantum dot (4N-GQD) with various toxic gases such as sulfur dioxide (SO2), hydrogen fluoride (HF), hydrogen chloride (HCl), methane (CH4), and carbon monoxide (CO). The 4N-GQD system has revealed strong interaction with these toxic gases in comparison to their pristine counterpart, which is because of the formation of an active region due to charge accumulation at the nitrogen-doped site. The adsorbate–adsorbent interaction in these systems has been studied at the molecular level employing density functional theory (DFT). Our findings suggest that the 4N-GQD system would be an effective adsorbent for a variety of toxic gases. These findings will help to steer the development of gas sensors with desirable features.
Journal Article
Leaky Integrate-and-Fire Neuron Model-Based SNN Latency Estimation Using FNS
The use of neural modeling tools is becoming increasingly common in the exploration of human brain behavior, enabling effective simulations through event-driven methodologies. As a result, years of study and advancements in the field of neurotechnology have led to the creation of several artificial neural network approaches that mimic biological neural networks. The event-driven approach provides an effective method for mimicking large-scale spiking neural networks (SNNs), by taking advantage of the brain’s sparse processing. This paper investigates SNN employing a leaky integrate-and-fire neuron model with latency estimation through FNS. A three-layer feedforward network (FFN) is constructed, incorporating design parameters from Config Wizard. Notably, our study sheds light on the impact of synchrony within a simple FFN. Through the incorporation of biologically plausible delay effects, our model offers novel insights that complement the existing literature. Neural activity is organized in CSV format files, facilitating the reconstruction of electrophysiological-like signals. FNS enables a comprehensive exploration of interactions within and between populations of spiking neurons. In the near future, we intend to use these findings in situations where this particular class of neural networks and digital signal processing (DSP) applications can be combined to create potent nonlinear DSP techniques.
Journal Article
Strain- and Temperature-Modulated Growth of Mn3Ga Films
Antiferromagnetic (AF) and ferrimagnetic (FiM) thin films have burgeoning significance in memory and computing applications due to their robustness and ultrafast and energy-efficient switching dynamics. Mn3Ga features a multitude of spin orders that can be meticulously controlled with stoichiometry, temperature, and strain modulations. In this work, we have carefully designed three suitable stacks of Mn3Ga thin films on MgO (111), STO (111) and STO (111)/Ta substrates deposited across varying substrate temperatures up to 500°C. The delicate interplay of strain and temperature tuning is examined by characterizing their magnetic, crystallographic, and morphological properties. The FiM tetragonal τ-Mn3Ga and AF hexagonal ε-Mn3Ga phases display relatively low saturation magnetizations of 10–60 and ≤ 20 kA/m, respectively. No preferential in-plane or out-of-plane magnetic anisotropy is observed for both τ- and ε-Mn3Ga phases. Critically, we observed that the STO strain-regulated τ-phase is stabilized over a wider temperature window and provides more compact, uniformly dispersed grains with average grain size of ~ 100 nm. This work establishes a sturdy methodology in understanding Mn3Ga thin film growth for eventual AF- and FiM-based memory and computing applications.
Journal Article
Low-Light Image Restoration Using a Convolutional Neural Network
The accurate diagnosis of medical conditions from low-light images, particularly black-and-white x-rays, is impeded by challenges such as noise, constrained visibility, and a lack of detail. Existing enhancement methods often exacerbate these issues by introducing detail loss, color oversaturation, or higher noise levels. This paper proposes a novel U-Net-based Convolutional Neural Network (CNN) specifically developed to address these challenges in low-light black-and-white medical images. Our designed architecture employs skip connections within the U-Net framework to effectively balance noise reduction with detail information preservation. This makes it possible for the network to learn hierarchical image representations while retaining important features for diagnosis. The trained network accomplishes real-time image enhancement, enabling immediate visual improvement during diagnosis and perhaps assisting radiologists in making faster and more accurate findings. Our approach illustrates a significant improvement in image quality and outperforms traditional methods in terms of noise reduction and detail preservation. This study holds significant potential to improve medical image analysis and diagnosis, potentially leading to enhanced patient care and earlier interventions.
Journal Article
Optimal Design of 2.4 GHz ISM Band CMOS LNA Using the Cat Swarm Optimization Algorithm
This article presents the optimal design of a high-gain, low-noise-figure (NF), highly linear complementary metal–oxide–semiconductor (CMOS) cascode low-noise amplifier (LNA) with inductive source degeneration for a 2.4 GHz ISM band. The evolutionary technique adopted here is the cat swarm optimization (CSO) algorithm. CSO is utilized to minimize the NF. The optimal transistor sizing of the LNA circuit is obtained through the CSO algorithm. Additionally, CSO helps to determine the other component values for the LNA. These parameters are applied to construct the LNA in CADENCE software. The post-layout simulation for the 2.4 GHz LNA results in an IIP3 of −2.44 dBm, NF of 0.303 dB, gain of 17.95 dB, and layout area of 0.66 × 0.65 mm2. The CSO-based design of LNA produces better results as compared to the previous literature regarding NF, gain, and figure of merit (FOM). The designed LNA is useful in Bluetooth, Zigbee, Wi-Fi, and wireless body area network (WBAN) applications.
Journal Article