The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific f...
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The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.
Challenges of farming are getting worse due to pests, other environmental issues, and changes in the climate. Several novel technologies have been developed to improve plant growth and productivity, yet there are stil...
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Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. Howe...
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Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo
In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in data sharing due to privacy concerns. Federated learning (FL) offers a ...
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With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the...
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With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid *** solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency ***,a threephase unbalanced network’s positive sequence fast estimation model is ***,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss ***,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).
Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and atte...
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Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T
Recently,wireless security has been highlighted as one of the most important techniques for 6G mobile communication *** researchers have tried to improve the Physical-Layer Security(PLS)performance such as Secrecy Out...
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Recently,wireless security has been highlighted as one of the most important techniques for 6G mobile communication *** researchers have tried to improve the Physical-Layer Security(PLS)performance such as Secrecy Outage Probability(SOP)and Secrecy Energy-Efficiency(SEE).The SOP indicates the outage probability that the data transmission between legitimate devices does not guarantee a certain reliability level,and the SEE is defined as the ratio between the achievable secrecy-rate and the consumed transmit *** this paper,we consider a Multi-User Multi-Input Single-Output(MU-MISO)downlink cellular network where a legitimate Base Station(BS)equipped with multiple transmit antennas sends secure information to multiple legitimate Mobile Stations(MSs),and multiple potential eavesdroppers(EVEs)equipped with a single receive antenna try to eavesdrop on this *** potential EVE tries to intercept the secure information,i.e.,the private message,from the legitimate BS to legitimate MSs with a certain eavesdropping *** securely receive the private information,each legitimate MS feeds back its effective channel gain to the legitimate BS only when the effective channel gain is higher than a certain threshold,i.e.,the legitimate MSs adopt an Opportunistic Feedback(OF)*** such eavesdropping channels,both SOP and SEE are analyzed as performance measures of PLS and their closed-form expressions are derived *** on the analytical results,it is shown that the SOP of the OF strategy approaches that of a Full Feedback(FF)strategy as the number of legitimate MSs or the number of antennas at the BS ***,the trade-off between SOP and SEE as a function of the channel feedback threshold in the OF strategy is *** analytical results and related observations are verified by numerical simulations.
The objective of this paper is to implement an effective and robust lane detection system for autonomous vehicles. This can be achieved by generating road images using MATLAB, converting them into a bird's-eye vie...
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The GNSS signal used for vehicle localization exhibits a shaded area such as a tunnel. To accurately estimate the location in the shaded area, the accumulated position error must be compensated with information obtain...
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This study presents an AI-based optimized energy management model for a hydrogen refueling station with a PV system and fuel cell (FC). Using the Time-series Dense Encoder (TiDE) model for day-ahead PV generation and ...
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