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Response of one-dimensional ionised layer to oscillatory electric fieldsTo provide fundamental insights into the response of laminar flames to alternating current (AC) electric fields, a simplified one-dimensional model using an ionised layer model is formulated with the conservation equations for the ion species with ionisation, recombination, and transport due to molecular diffusion and electric mobility. A parametric study is conducted to investigate the response of the ion layer at different voltages and oscillation frequencies, and the results are examined mainly in terms of the net current–voltage (I–V) characteristics. As the oscillation frequency is increased, a nonmonotonic response in the I–V curve is seen such that the current may exceed the saturation condition corresponding to the steady DC condition. In general the current reaches a peak as the unsteady time scale becomes comparable to the ion transport time scale, which is dictated by the mobility, and eventually becomes attenuated at higher frequencies to behave like a low-pass filter. The extent of the peak current rise and the cut-off frequency are found to depend on the characteristic time scales of the ion chemistry and mobility-induced transport. The simplified model serves as a framework to characterise the behaviour of complex flames in terms of the dominant ionisation and transport processes. The current overshoot behaviour may also imply that the overall effect of the electric field may be further magnified under the AC conditions, motivating further studies of multi-dimensional flames for the ionic wind effects.
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Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge SystemsThe emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad’s Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities.
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Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural NetworkThe detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects. The effectiveness of the proposed method is confirmed by testing its performance in processing an industrial dataset. The intended dataset contains 4479 X-ray images and belongs to six groups: cavity, cracks, inclusion slag, lack of fusion, shape defects, and normal defects. The devised technique achieved an average accuracy of 92%. This indicates that the approach is promising and can be used in contemporary solutions for the automated detection and categorization of welding defects.
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Causal speech enhancement using dynamical-weighted loss and attention encoder-decoder recurrent neural networkSpeech enhancement (SE) reduces background noise signals in target speech and is applied at the front end in various real-world applications, including robust ASRs and real time processing in mobile phone communications. SE systems are commonly integrated into mobile phones to increase quality and intelligibility. As a result, a low-latency system is required to operate in real-world applications. On the other hand, these systems need effi cient optimization. This research focuses on the single-microphone SE operating in real time systems with better optimization. We propose a causal data-driven model that uses attention encoder-decoder long short-term memory (LSTM) to estimate the time-frequency mask from a noisy speech in order to make a clean speech for real-time applications that need low-latency causal processing. The encoder-decoder LSTM and a causal attention mechanism are used in the proposed model. Furthermore, a dynamical-weighted (DW) loss function is proposed to improve model learning by varying the weight loss values. Experi ments demonstrated that the proposed model consistently improves voice quality, intelligi bility, and noise suppression. In the causal processing mode, the LSTM-based estimated suppression time-frequency mask outperforms the baseline model for unseen noise types. The proposed SE improved the STOI by 2.64% (baseline LSTM-IRM), 6.6% (LSTM-KF), 4.18% (DeepXi-KF), and 3.58% (DeepResGRU-KF). In addition, we examine word error rates (WERs) using Google’s Automatic Speech Recognition (ASR). The ASR results show that error rates decreased from 46.33% (noisy signals) to 13.11% (proposed) 15.73% (LSTM), and 14.97% (LSTM-KF).
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Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learningThe Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and “Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are “Fractal Dimension” (FD), “Higher Order Spectra” (HOS), “Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the “Naïve Bayes” (NB), “Support Vector Machine” (SVM), “Random Forest” (RF), and “K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.
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Noise Effects on a Proposed Algorithm for Signal Reconstruction and Bandwidth OptimizationThe development of wireless technology in recent years has increased the demand for channel resources within a limited spectrum. The system's performance can be improved through bandwidth optimization, as the spectrum is a scarce resource. To reconstruct the signal, given incomplete knowledge about the original signal, signal reconstruction algorithms are needed. In this paper, we propose a new scheme for reducing the effect of adding additive white Gaussian noise (AWGN) using a noise reject filter (NRF) on a previously discussed algorithm for baseband signal transmission and reconstruction that can reconstruct most of the signal’s energy without any need to send most of the signal’s concentrated power like the conventional methods, thus achieving bandwidth optimization. The proposed scheme for noise reduction was tested for a pulse signal and stream of pulses with different rates (2, 4, 6, and 8 Mbps) and showed good reconstruction performance in terms of the normalized mean squared error (NMSE) and achieved an average enhancement of around 48%. The proposed schemes for signal reconstruction and noise reduction can be applied to different applications, such as ultra-wideband (UWB) communications, radio frequency identification (RFID) systems, mobile communication networks, and radar systems.
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A complexity efficient PAPR reduction scheme for FBMC-based VLC systemsVisible light communication based on a filter bank multicarrier holds enormous promise for optical wireless communication systems, due to its high-speed and unlicensed spectrum. Moreover, visible light communication techniques greatly impact communication links for small satellites like cube satellites, and pico/nano satellites, in addition to inter-satellite communications between different satellite types in different orbits. However, the transmitted visible signal via the filter bank multicarrier has a high amount of peak-toaverage power ratio, which results in severe distortion for a light emitting diode output. In this work, a scheme for enhancing the peak-to-average power ratio reduction amount is proposed. First, an algorithm based on generating two candidates signals with different peakto-average power ratio is suggested. The signal with the lowest ratio is selected and transmitted. Second, an alternate direct current-biased approach, which is referred to as the addition reversed method, is put forth to transform transmitted signal bipolar values into actual unipolar ones. The performance is assessed through a cumulative distribution function of peak-to-average power ratio, bit error rate, power spectral density, and computational complexity. The simulation results show that, compared to other schemes in literature, the proposed scheme attains a great peak-to-average power ratio reduction and improves the bit the error rate performance with minimum complexity overhead. The proposed approach achieved about 5 dB reduction amount compared to companding technique, 5.5 dB compared to discrete cosine transform precoding, and 8 dB compared to conventional direct current bias of an optical filter bank multicarrier. Thus, the proposed scheme reduces the complexity overhead by 15.7% and 55.55% over discrete cosine transform and companding techniques, respectively
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Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication NetworksThe intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all network administrators. Several existing methods have been proposed for early intrusion detection systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced long-short term memory (ELSTM) technique with recurrent neural network (RNN) (ELSTM-RNN) to enhance security in IDS. Intrusion detection technology has been associated with various problems, such as gradient vanishing, generalization, and overfitting issues. The proposed system solves the gradient-clipping issue using the likely point particle swarm optimization (LPPSO) and enhanced LSTM classification. The proposed method was evaluated using the NSL-KDD dataset (KDD TEST PLUS and KDD TEST21) for validation and testing. Many efficient features were selected using an enhanced technique, namely, the particle swarm optimization. The selected features serve for effective classification using an enhanced LSTM framework, where it is used to efficiently classify and detect the attack data from the normal data. The proposed system has been applied to the UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, and BOT _DATASET datasets for further verification. Results show that the training time of the proposed system is much less than that of other methods for different classes. Finally, the performance of the proposed ELSTM-RNN framework is analyzed using various metrics, such as accuracy, precision, recall, and error rate. Our proposed method outperformed LPBoost and DNNs methods.
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Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding SchemesOn fifth-generation wireless networks, a potential massive MIMO system is used to meet the ever-increasing request for high-traffic data rates, high-resolution streaming media, and cognitive communication. In order to boost the trade-off between energy efficiency (EE), spectral efficiency (SE), and throughput in wireless 5G networks, massive MIMO systems are essential. This paper proposes a strategy for EE 5G optimization utilizing massive MIMO technology. The massive MIMO system architecture would enhance the trade-off between throughput and EE at the optimum number of working antennas. Moreover, the EE-SE tradeoff is adjusted for downlink and uplink massive MIMO systems employing linear precoding techniques such as Multiple - Minimum Mean Square Error (M-MMSE), Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio (MR). Throughput is increased by adding more antennas at the optimum EE, according to the analysis of simulation findings. Next, utilizing M MMSE instead of RZF and ZF, the suggested trading strategy is enhanced and optimized. The results indicate that M-MMSE provides the best tradeoff between EE and throughput at the determined optimal ratio between active antennas and ctive users equipment’s (UE)
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On the inverse power law-normal model for life prediction of organic light emitting diodesIn accelerated life testing analysis with nonthermal accelerating stress, the inverse power law (IPL) is often solely merged with a particular lifetime probability distribution with a shape parameter. Although many fundamental lifetime distributions, such as the normal distribution, are excellent fits to the experimental lifetime data, they have not been considered as they lack the shape parameter. As such, this paper, for the first time, demonstrates that the shape parameter can be replaced by the coefficient of variation, allowing the use of normal distributions in this context. The work further introduces the IPL-normal model in a rigorous mathematical setup that precisely leads to the least squares estimating equations and maximum likelihood estimates of the IPL-normal accelerating parameters and the general coefficient of variation. The proposed model uses accelerated experimental data to successfully predict the lifetime of organic light-emitting diodes (OLEDs) at use conditions. Based on these fundamentals, the predictions are benchmarked with prior works that were validated by market studies.
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Optimizing Energy Efficiencies of IoT-based Wireless Sensor Network Components for Metaverse Sustainable Development using Carry Resist Adder based Booth Recoder (CRABRA)Wireless sensing is now the spine of diverse Internet of Things (IoT) applications. In the Metaverse, the Internet of Things (IoT) can offer wireless and seamlessly integrated immersive digital experiences. Because the Metaverse's enabling technologies are considered to be energy-hungry, questions have been raised concerning the sustainability of its widespread adoption and development. IoT-based wireless sensor networks (WSN) readings are contaminated and distorted by noise. The noise in the signal causes the sensor node's (SN) computations and power consumption to rise, shortening the sensor node's longevity. To reduce noise, an efficient technique is therefore crucial. Finite-impulse response (FIR) filter is commonly employed in IoT-based WSN as a signal pre-processing stage in eliminating noise from the sensor measurements. The multiplication operation's number of adders (logic operators) and the adder steps (logic depths) determine the hardware complexities of FIR filters. The speed of the related application is determined by the multiplier's speed. By reducing the partial product (PP) row, the Booth method speeds up multiplication. The coefficients used by R8BR are ±0,±1 ,±2,±3,and ±4. As a result of the formation of odd multiples ±3, there will be a delay. The adder is required to add ±1 and ±2 for its calculations. This slowdown the multiplication procedures and reduces the recoding performance. To reduce the delay brought on by the creation of odd multiples, a carry resists adder (CRA) is used. CRA was explicitly built to achieve adding of ±2 and ±1 without carry propagation. Theoretically, it is observed that the CRA minimizes delay to 86.26% compared to carry propagation adder (CPA) approaches. Additionally, compared to a typical R8BR multiplier, the experimental findings indicated delay, area, and power reductions of 48.98%, 56.66%, and 31.2%, respectively. Without carry propagating, the CRA does addition faster, with less energy, and occupies less area.
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A Machine learning-driven IoT architecture for predicting the growth and trend of Covid-19 epidemic outbreaks to identify high-risk locationsCovid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology.
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Design and Performance Analysis of Ultra-Wide Bandgap Power Devices-Based EV Fast Charger Using Bi-Directional Power ConvertersA widespread introduction of electric vehicles would require an advanced enriched fast-charging infrastructure and battery technology. Currently used silicon (Si) based power electronic devices limit their efficiencies, power density, and switching frequency. Designing fast-charging stations using these materials is not suitable due to low breakdown potential, less thermal stability, and less power handling abilities. The research will propose an off-board DC high-power density fast charging infrastructure with grid tie application. The EV station is designed by using ultra-wideband gap (UWBG) material-based power electronic devices to charge the EV vehicles in a few minutes up to an acceptable state of charge. The study will analyze the characteristics of Gallium III oxide (Ga2O3) material power devices by modeling them using SPICE and TCAD software tools. The research presents the Simscape physical modeling of electric vehicle chargers based on Ga2O3 power devices. Design analysis of three-phase bidirectional AC/DC converter and DC/DC isolated full bridge converter is present in this paper. Research implements the unity power factor control to improve the power quality requirements of the power grid. The dual active power control of converters provides a wide range of charging power for a variety of EV batteries. The study will provide high current and reliable rapid charging for currently available and upcoming future electric vehicles.
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A Proposed Signal Reconstruction Algorithm over Bandlimited Channels for Wireless CommunicationsIn recent decades, signal reconstruction schemes play an important role in rebuilding the signal again from incomplete information about the original one. In this article, we consider a novel technique for transmitting a signal from its bandpass spectral information. A new recovery algorithm is also used to reconstruct most of baseband signals energy despite receiving a window of its spectrum. This algorithm is tested for many types of signals with different data rates. It is also applied to a human speech signal and showed a good reconstruction status. The performance of the algorithm is evaluated in terms of the normalized mean square error (NMSE) in noiseless and noisy channels. The proposed technique shows its capability to send any signal over a bandlimited channel and reconstruct it again without any need to send most of its spectral power compared to the conventional schemes, thus more bandwidths can be saved and the bandwidth usage can be optimized. However, many problems have been introduced and resolved during the recovery process. The algorithm showed an acceptable recovery status when tested in noisy channels. The proposed scheme may find many applications in high data-rate communications, pulsed radar systems, next generation networking, and OFDM-based wireless communication
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A Survey on Energy Storage: Techniques and ChallengesIntermittent renewable energy is becoming increasingly popular, as storing stationary and mobile energy remains a critical focus of attention. Although electricity cannot be stored on any scale, it can be converted to other kinds of energies that can be stored and then reconverted to electricity on demand. Such energy storage systems can be based on batteries, supercapacitors, flywheels, thermal modules, compressed air, and hydro storage. This survey article explores several aspects of energy storage. First, we define the primary difficulties and goals associated with energy storage. Second, we discuss several strategies employed for energy storage and the criteria used to identify the most appropriate technology. In addition, we address the current issues and limitations of energy storage approaches. Third, we shed light on the battery technologies, which are most frequently used in a wide range of applications for energy storage. The usage and types of batteries are described alongside their market shares and social and environmental aspects. Moreover, the recent advances in battery state estimation and cell-balancing mechanisms are reviewed.
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Floating solar photovoltaic plants in India – A rapid transition to a green energy market and sustainable futureThe 18,000 square kilometers of water reservoirs in India can generate 280 GW of solar power through floating solar photovoltaic plants. The cumulative installed capacity of FSPV is 0.0027 GW, and the country plans to add 10 GW of FSPV to the 227 GW renewable energy target of 2022. The FSPV addition is small related to the entire market for solar energy, but each contribution is appreciated in the renewable energy market. FSPV could be a viable alternative for speeding up solar power deployment in the country and meeting its NDC targets. So far, the country has achieved the world's lowest investment cost for a floating solar installation. Despite the lower costs, generalizations are still premature because FSPV is still in its initial stages of market entry. Continuous innovation and timely adoption of innovative ideas and technology will support India in meeting its solar energy goals and progressing toward a more sustainable future. Governments must establish clear and enforceable policies to assist developers in reducing risks and increasing investor confidence in the sector. Economic and financial feasibility are examined, and various difficulties in technology, design, finances, environment, maintenance, and occupational health that impact the FSPV deployment are discussed. Based on the research, effective and comprehensive FSPV policy suggestions are included to support establishing an appropriate market, fostering competition and innovation, and attracting large-scale investment. This paper aims to stimulate interest among various policy developers, energy suppliers, industrial designers, ergonomists, project developers, manufacturers, health and safety professionals, executing agencies, training entities, and investment institutions of the FSPV plant to implement effective governance planning and help them to participate in their ways to assure sustainable growth.
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Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus imagesIn this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.
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Thermodynamics of the Bardeen Regular Black HoleWe deal with the thermodynamic properties of the Bardeen regular black hole with reference to their respective horizons. It is argued here that the expression of the heat capacity at horizons is positive in one parameter region and negative in the other, and between them the heat capacity diverges where the black hole undergoes the second-order phase transition.
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Response of one-dimensional ionised layer to oscillatory electric fieldsTo provide fundamental insights into the response of laminar flames to alternating current (AC) electric fields, a simplified one-dimensional model using an ionised layer model is formulated with the conservation equations for the ion species with ionisation, recombination, and transport due to molecular diffusion and electric mobility. A parametric study is conducted to investigate the response of the ion layer at different voltages and oscillation frequencies, and the results are examined mainly in terms of the net current–voltage (I–V) characteristics. As the oscillation frequency is increased, a nonmonotonic response in the I–V curve is seen such that the current may exceed the saturation condition corresponding to the steady DC condition. In general the current reaches a peak as the unsteady time scale becomes comparable to the ion transport time scale, which is dictated by the mobility, and eventually becomes attenuated at higher frequencies to behave like a low-pass filter. The extent of the peak current rise and the cut-off frequency are found to depend on the characteristic time scales of the ion chemistry and mobility-induced transport. The simplified model serves as a framework to characterise the behaviour of complex flames in terms of the dominant ionisation and transport processes. The current overshoot behaviour may also imply that the overall effect of the electric field may be further magnified under the AC conditions, motivating further studies of multi-dimensional flames for the ionic wind effects.
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High-performance, energy-efficient, and memory-efficient FIR filter architecture utilizing 8x8 approximate multipliers for wireless sensor network in the Internet of ThingsIoT uses wireless sensor networks (WSN) to deploy many sensors to track environmental and physical parameters. The WSN measurements are frequently contaminated and altered by noise. The noise in the signal increases the sensor node’s computation and energy utilization, resulting in less longevity of the sensor node. The Finite Impulse Response (FIR) filter is commonly employed in WSN to pre-process sensed signals to remove noise from the sensed signals using delay elements, multipliers, and adders. Traditional multiplier-based FIR filter designs result in hardware-intensive multipliers that consume a lot of energy, and area and have low computation speed. These drawbacks make them unsuitable for IoT-based WSN systems with stringent power efficiency necessities. Approximate computing enhances the energy efficiency of an FIR filter. Arithmetic circuits utilizing approximate computing improve the hardware performance, with some loss of accuracy to save energy utilization and boost speed. A novel approximate multiplier architecture employing a fast and straightforward approximation adder is proposed in this study. Approximate multiplier M1 using OR gate and approximate multiplier M2 using proposed approximate adders are compared. The proposed approximate adder is suited for building an adder tree to accumulate partial product (PP) because it is less complicated than traditional adders. Compared to a one-bit-full adder, the critical path delay (CPD) is reduced significantly in the proposed methods. The accuracy comparison of M1. M2 and Wallace tree using the normalized mean error distance (NMED), the mean relative error distance (MRED), the maximum error (ME), and the error rate (ER) with the number of bits utilized for reducing error. For the area (delay) optimized circuit, when the bit used is 4, the delay is 0.4 ns for M1, 0.43 ns for M2, and 1.08 ns for the Wallace tree multiplier. For the delay (area) optimized circuit, when the bit used is 4, the delay is 0.16 ns for M1, 0.16 ns for M2, and 0.40 ns for the Wallace tree multiplier. To more accurately evaluate performance at the circuit level, the PDP and ADP are computed. The NMED, MRED, ME, and ER versus PDP and ADP are computed. The proposed multipliers M1 and M2 are compared with existing approximate multipliers. When an equivalent MRED, NMED, or ER is taken into account, M1 has the smallest ADP and PDP among other multipliers. The very low likelihood of a significant ED occurring is indicated by the small values of NMED and MRED in M1 and M2. The proposed solutions effectively reduce delay, area, and power while maintaining increased accuracy and performance.
