Real-time System Research Articles

A Comprehensive Review on Image Encryption Techniques using Memristor based Chaotic System for Multimedia Application

Image encryption is one of the most widely used techniques for securing images in trusted and unrestricted public media. However, the drawback is weak security in chaotic encryption algorithms, small key space and hardware implementation complexities. Various appropriate encryption algorithms have been carried out for secure image transmission; it becomes a critical issue to protect image integrity, confidentiality and authenticity. To overcome these issues, this article examines several existing memristor-based image encryption algorithms with various chaotic maps. As a result, this research aims to deliver comprehensive literature on image encryption techniques through memristor to help the researchers. Finally, this survey provides all vital literature on the current image encryption techniques with their benefits, drawbacks, developments, and future directions. We also give a general guideline about cryptography. We conclude that all methods are helpful for real-time image encryption. A comparison has been made between many image processing approaches (conventional and memristor) based on metrics such as the number of pixels change rate (NPCR) with the value of 0.9986, histogram, entropy (7.9993), unified average varying intensity (UACI) achieves the value as 0.4996, energy consumption as 0.32pJ and time complexity as 0.9s. The experimental results of various approaches, key sensitivity and statistical analysis revealed that the survey of memristor-based image encryption schemes provides an effective way to secure image transmission in real-time application.

Compressed point cloud classification with point-based edge sampling

3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and processing, especially in real-time systems. Instead of decoding the compressed data stream and subsequently conducting downstream tasks on the decompressed data, analyzing point clouds directly in their compressed domain has attracted great interest. In this paper, we dive into the realm of compressed point cloud classification (CPCC), aiming to achieve high point cloud classification accuracy in a bitrate-saving way by ensuring the bit stream contains a high degree of representative information of the point cloud. Edge information is one of the most important and representative attributes of the point cloud because it can display the outlines or main shapes. However, extracting edge points or information from point cloud models is challenging due to their irregularity and sparsity. To address this challenge, we adopt an advanced edge-sampling method that enhances existing state-of-the-art (SOTA) point cloud edge-sampling techniques based on attention mechanisms and consequently develop a novel CPCC method “CPCC-PES” that focuses on point cloud’s edge information. The result obtained on the benchmark ModelNet40 dataset shows that our model has superior rate-accuracy trade-off performance than SOTA works. Specifically, our method achieves over 90% Top-1 Accuracy with a mere 0.08 bits-per-point (bpp), marking a remarkable over 96% reduction in BD-bitrate compared with specialized codecs. This means that our method only consumes 20% of the bitrate of other SOTA works while maintaining comparable accuracy. Furthermore, we propose a new evaluation metric named BD-Top-1 Accuracy to evaluate the trade-off performance between bitrate and Top-1 Accuracy for future CPCC research.

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.

Intelligent Health Care Data Analysis System Using Swarm based Optimization Algorithm

Large amounts of clinical data present both opportunities and challenges in modern healthcare. Effective data analysis improves patient care, early disease detection, and personalised treatment. This paper proposes an Intelligent Health Care Data Analysis System employing PSO and neural networks to improve disease detection. Missing values, normalisation, and feature selection ensure high-quality analysis inputs. Disease classification and prediction from clinical data are improved by PSO optimising neural network weights and biases. This method works on Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease datasets with and without missing values. PSO-optimized neural networks outperform regular neural networks in accuracy and robustness. Scalable and adaptable for real-time diagnostic applications, this intelligent system may improve healthcare delivery by enabling more precise and timely interventions. Using neural networks and advanced swarm-based optimisation, medical data analytics has improved patient outcomes and healthcare system efficiency. Particle Swarm Optimization-based Neural Network (IPSONN) diagnoses clinical illnesses better. IPSONN was created using K-means clustering and IPSO-trained Neural Networks. K-means clusterings optimal cluster number determines the hidden layers neurones. Cluster approximation error determines best clusters. Mean square error calculates hidden and output layer weights in QPSO. The classifier was tested with four clinical datasets from the University of California, Irvine (UCI) machine learning repository: Pima Indian Diabetes, Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease.

Skin mucosal immune parameters and expression of the immune-relevant genes in Danio rerio treated by white button mushroom (Agaricus bisporus).

This study evaluates using different levels of the white button mushroom powder (WBMP) on some mucosal innate immune parameters (lysozyme, protease, esterase, alkaline phosphatase activities, and total immunoglobulin levels), and the relative expression of some principal immune-relevant genes (lysozyme, TNF-α, and IL-1β) in the zebra danio intestine. Zebrafish specimens (1.75 ± 0.25g) were divided into experimental units based on the additives to a diet including 5, 10, and 20g of WBMP per kilogram of food weight, alone or in conjunction with the antibiotic (10mg/kg BW), and the AGRIMOS (1g/kg food weight). Following the 11-day experimental duration, the skin mucus and intestine were sampled. To assess the immune gene expression, the real-time PCR detection system was conducted according to the ΔΔCt method using the IQ5 software (Bio-RAD). Results showed that all groups had a significant increase in terms of mucosal lysozyme activity compared to the control group. Examination of total immunoglobulin, protease, esterase, and ALP activity in fish under experimental treatment showed that there was no significant difference between the trial groups and the control groups. The most expression of the lysozyme gene was related to the group that was separately taken the lower concentration (5g per kg of FW) of WBMP. In conclusion, the amount of 1% mushroom powder in the diet can improve its immune function. Our recommendation is that given the positive effects that mushroom powder added on the diet alone, avoid taking antibiotics for this purpose.

Real-time LEO satellite clock estimation with predicted LEO satellite orbits constrained

Low Earth Orbit (LEO) satellites can augment the traditional GNSS-based positioning, navigation and timing services, which require real-time high-precision LEO satellite clock products. As the complicated systematic effects contained in the LEO satellite clock estimates limit their high-precision mid- to long-term prediction, high-frequency LEO satellite clocks need to be estimated within a Kalman filter, resulting in a short prediction time for real-time applications. Compared to the clock estimation using Batch Least-Squares (BLS) adjustment, filter-based clock estimation experiences a lower precision. Increasing the model strength by introducing external orbital information, thus, de-correlating the orbital and clock parameters, will benefit real-time clock precision. In this contribution, reduced-dynamic LEO satellite orbits are first estimated using BLS adjustment in near real-time and predicted in the short term. The predicted orbits are then constrained during the Kalman-filter-based clock estimation process. The variance–covariance matrix of the introduced orbital errors is tested for different sets of values in the radial, along-track and cross-track directions when constraining orbits of different prediction times. One week of GPS data from the Sentinel-3B satellite in 2018 was used for validation of the proposed method. When weakly constraining high-accuracy predicted orbits within a prediction time of 20 min, i.e., with a standard deviation of the constraint set to 2–3 dm in the radial and cross-track directions, and 4–6 dm in the along-track direction, the estimated clock accuracy can be improved from about 0.27 to 0.23 ns, with a 13.4% improvement. Depending on the prediction period of the introduced orbits, the Signal-In-Space Range Error (SISRE) of the LEO satellite to Earth can also be improved, from about 9.59 cm without constraints, to 7.38–8.07 cm after constraining the predicted orbits, with an improvement of 16–23%. The improvements in the SISRE also indicate a better consistency between the real-time clock and orbital estimates.

Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms

The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the “SensorNetGuard” dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The “SensorNetGuard” dataset is available on the IEEE data port and Kaggle platform.

StrawSnake: A Real-Time Strawberry Instance Segmentation Network Based on the Contour Learning Approach

Automated harvesting systems rely heavily on precise and real-time fruit recognition, which is essential for improving efficiency and reducing labor costs. Strawberries, due to their delicate structure and complex growing environments, present unique challenges for automated recognition systems. Current methods predominantly utilize pixel-level and box-based approaches, which are insufficient for real-time applications due to their inability to accurately pinpoint strawberry locations. To address these limitations, this study proposes StrawSnake, a contour-based detection and segmentation network tailored for strawberries. By designing a strawberry-specific octagonal contour and employing deep snake convolution (DSConv) for boundary feature extraction, StrawSnake significantly enhances recognition accuracy and speed. The Multi-scale Feature Reinforcement Block (MFRB) further strengthens the model by focusing on crucial boundary features and aggregating multi-level contour information, which improves global context comprehension. The newly developed TongStraw_DB database and the public StrawDI_Db1 database, consisting of 1080 and 3100 high-resolution strawberry images with manually segmented ground truth contours, respectively, serves as a robust foundation for training and validation. The results indicate that StrawSnake achieves real-time recognition capabilities with high accuracy, outperforming existing methods in various comparative tests. Ablation studies confirm the effectiveness of the DSConv and MFRB modules in boosting performance. StrawSnake’s integration into automated harvesting systems marks a substantial step forward in the field, promising enhanced precision and efficiency in strawberry recognition tasks. This innovation underscores the method’s potential to transform automated harvesting technologies, making them more reliable and effective for practical applications.

Data‐driven observability analysis and enhancement for radome slope estimation with constraints on impact angle and terminal acceleration

AbstractThe authors address the problem of observability analysis and enhancement for missiles equipped with strapdown seekers, especially considering the line‐of‐sight angle‐only measurements and the constraints on impact angle and terminal acceleration. First, based on the generalised polynomial chaos expansion, a novel data‐driven observability analysis method is proposed to quantitatively analyse the observability of the radome aberration estimation system in real time, including the observability condition, the observability degree of each state and the effect of stochastic noises. The results show that under conventional proportional navigation guidance, the radome aberration estimation system is weakly observable with line‐of‐sight angle‐only measurements. To address this problem, a novel manoeuvre algorithm, the observability‐enhanced guidance law, is introduced to enhance the system observability within specified constraints, thereby improving the estimation performance. This guidance law incorporates a bias term to meet terminal constraints, and optimises the navigation gain using the proposed observability indices. Simulations are conducted to verify the performance of the proposed guidance law in conjunction with a target state estimator.

Enhancing the performance of extreme learning machine technique using optimization algorithms for embedded workload characterization

Embedded devices are used in many domains, including healthcare, industries, and home automation, all of which entail significant workloads. As a direct consequence, the embedded devices require retrieval and processing of data of large volume, which occupy large memory space in the embedded devices. Compression along with workload characterization is an effective technique for minimizing memory usage and to the improvement of endurance of memory in such devices. This paper investigates the embedded workload characterization using Extreme Learning Machine (ELM) that is particularly suitable for large-scale datasets and real-time applications. Though ELM is single-layer feedforward network, its input weight randomization has a considerable effect on the accuracy of the classification. In this paper, the authors have proposed a hybrid algorithm for the optimization of the randomization of ELM. The Particle Swarm Optimizer (PSO), the Genetic Algorithm (GA), the Ant Colony Optimizer (ACO), and the Whale Optimization Algorithm (WOA) were used in the optimization of the classification process in ELM to increase the accuracy of the results. The input data must be categorized based on the energy consumed by each workload to proceed with further processing according to the system requirements. This paper explores and analyses the performance of hybridized ELM-Genetic algorithm (ELM-GA), ELM-Particle Swarm Optimization (PSO), ELM-Ant Colony Optimizer (ELM-ACO), and ELM- Whale Optimization Algorithm (ELM-WOA) optimization algorithms. The embedded benchmarks Internet of Medical Things (IoMT), Mi Benchmark (MiBench), and Embedded Microprocessor Benchmark Consortium (EEMBC) have been used as a dataset for classification in this paper. The extensive experimental study shows that the hybridized ELM-WOA provides a 98.5 % classification accuracy, 98.1 % specificity, and 98 % sensitivity compared to the other optimization methods discussed in this paper.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

The Design and Real-Time Optimization of an EtherCAT Master for Multi-Axis Motion Control

To address the issues of low bandwidth, weak real-time performance, and poor synchronization in traditional fieldbuses for multi-axis motion control, a solution for the implementation of an EtherCAT master based on the IgH EtherCAT Master open-source software framework and an embedded hardware platform is proposed. On a hardware platform centered around the AM64x Sitara processor, a Linux real-time operating system based on the Xenomai real-time kernel is constructed, and the IgH master framework is ported to realize a high-performance EtherCAT master. The configuration process of the EtherCAT bus is detailed, a master application program is developed, and methods for the real-time performance optimization of the master—such as exclusive CPU usage by the master process and the optimization of the network card driver—are proposed. Finally, experiments are conducted on a six-axis servo control platform, with the packet analysis of the periodic EtherCAT data frames sent by the master. The experimental results show that the optimized master, under a high-speed communication cycle of 500 microseconds, maintains maximum jitter within 20 microseconds and average jitter within 1 microsecond, meeting the requirements for high-precision multi-axis motion control.

Interactive Mesh Sculpting with Arbitrary Topologies in Head-Mounted VR Environments

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system’s real-time performance and universality, highlighting its user-centric design.

Reducing Motion Artifacts in Craniocervical Background Subtraction Angiography with Deformable Registration and Unsupervised Deep Learning

Abstract Background In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact due to voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent post-contrast images rely on key point registration using iterative optimization, which has limited real-time application. Purpose Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts. Methods We extend HyperMorph, an open source deep learning deformable registration framework, to reduce motion artifacts in DSA. Novel image similarity loss functions with vessel layer estimation were introduced to optimize background registration, making it robust to the variable presence of intravascular iodinated contrast. Results A total of 516 studies with 5,240 angiographic series were collected and divided into training (5046 series) and hold-out test (194 series) sets. Blinded algorithm rankings and Likert scores on five-point scales (1 = worst, 5 = best) were generated by three practicing interventional neuroradiologists using 50 series randomly selected from the hold-out test set. Compared to traditional DSA, our learning-based background subtraction angiography (BSA) significant improved vascular fidelity (2.4 ± 0.6 for DSA vs. 3.6 ± 0.5 for BSA), subtraction artifacts (2.0 ± 0.4 for DSA vs. 3.9 ± 0.3 for BSA), and overall quality (2.1 ± 0.5 for DSA vs. 3.9 ± 0.4 for BSA) (p < 0.0001). Learning-based BSA also significantly outperformed affine registration-based BSA (p < 0.0001). The average inference time for learning-based BSA was 30 milliseconds per frame on our hardware. Conclusion The results demonstrate that deep learning deformable registration, combined with an appropriate loss function, can significantly reduce the motion artifacts that degrade DSA.

A real-time working memory evaluation system for macaques in microwave fields.

With the development and widespread application of electromagnetic technology, the health hazards of electromagnetic radiation have attracted much attention and concern. The effect of electromagnetic radiation on the nervous system, especially on learning, memory, and cognitive functions, is an important research topic in the field of electromagnetic biological effects. Most previous studies were conducted with rodents, which are relatively mature. As research has progressed, studies using non-human primates as experimental subjects have been carried out. Compared to rodents, non-human primates such as macaques not only have brain structures more similar to those of humans but also exhibit learning and memory processes that are similar. In this paper, we present a behavioral test system for the real-time evaluation of the working memory (WM) of macaques in a microwave environment. The system consists of two parts: hardware and software. The hardware consists of four modules: the operation terminal, the control terminal, the optical signal transmission, and detection module and the reward feedback module. The software program can implement the feeding learning task, the button-pressing learning task, and the delayed match-to-sample task. The device is useful for the real-time evaluation of the WM of macaques in microwave environments, showing good electromagnetic compatibility, a simple and reliable structure, and easy operation.

Multiple factors shape social contacts in dairy cows

Cattle develop preferential relationships with other individuals in the herd. These social interactions between individuals have a significant impact on both animal welfare and production. Given the relevance of social behaviour in dairy cattle, scientific studies have focused on understanding social interactions among cattle. These may also be influenced by individual area preferences, particularly when animals are housed in confined spaces. Therefore, investigating the relationship between individual area preferences and social interactions is essential for understanding social behaviour in dairy cattle. Real-time location systems provide the opportunity to monitor individual area preferences and social contacts at the same time. This study aims to assess the impact of dairy cows' area preferences on their daily social contacts and to determine the potential implications of overlooking individual area preferences in social behaviour studies. The individual position of the lactating cows was automatically collected once per second for two months on a Swedish commercial farm housing dairy cows inside a free-stall barn. The location data of 243 lactating cows was used to construct the social networks and to estimate the similarity of the area utilisation distributions between these individuals. The effect of utilisation distribution similarity in social networks was investigated by applying separable temporal exponential random graph mixed models. The role of different cow characteristics in the similarity of the utilisation distributions was assessed through a linear mixed model. Our analyses stressed the importance of similarity of area preference, parity, kindergarten effect, and filial relatedness in shaping daily social contacts in dairy cattle. The kindergarten effect refers to the effect on cow behaviour of being grouped together in the early stages of their lives. Similarity of area preference was influenced by the kindergarten effect and relatedness by pedigree, which favoured interactions between these individuals. The described approach allowed to disassociate the area preference from the social contacts between cows, providing more accurate results of the importance of the cow’s characteristics on their social behaviour.

Research on Real-Time Groundwater Quality Monitoring System Using Sensors around Livestock Burial Sites

Research on Real-Time Groundwater Quality Monitoring System Using Sensors around Livestock Burial Sites

Grey modelling and real-time forecasting for the approximate non-homogeneous white exponential law BDS clock bias sequences

Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting process. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards, a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy’s prediction accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP.

Wafer scale WS2 based ultrafast photosensing and memory computing devices for neuromorphic computing

Integration of optical sensors with memristors can establish the bridge between photosensing and memory devices for Internet of Things (IoT) based applications. This paper presents the realization of integrated sensing and computing memory (ISCM) devices using tungsten disulfide (WS2) and their application for neuromorphic computing. The ISCM device fabrication process is scalable as microfabrication steps followed on 2″ wafer, ISCM device testing and image classification for neuromorphic computing. The photosensing/memory tests were conducted using electrical and optical stimulations (broadband spectrum). The fabricated photosensing device offers a higher responsivity (8 A W−1), higher detectivity (2.85 × 1011 Jones) and fast response speed (80.2/78.3 μs) at 950 nm. The memory device has shown a set/reset time of 51.6/73.5 μs respectively. Further, the repeatability, stability and reproducibility tests were conducted by stimulating the device with different modulating frequencies. The frequency modulation tests confirm that the ISCM devices are stable and perfect candidate for real-time IoT applications. Moreover, the device’s potentiation and depression results were used for image classification with the accuracy of 98.27%. These demonstrated device’s test results provide possibilities to fabricate the smart sensors with integrated functionalities.

Detection of DDoS Attack Based on Deep Neural Network with various Number of Features

Distributed Denial of Service (DDoS) attacks have become an effective threat to the reliability and availability of the services of the internet in last decades. The effectiveness of utilizing Deep Neural Networks (DNNs) for DDoS attack detection is investigated in this paper. We implemented a reliable detection system that analyzes network traffic data to spot any DDoS activities. A multi-layer perceptron model trained on a dataset containing five different forms of DDoS attacks and normal traffic is used in this method. Also, three cases of varous number of features was investigated to extract the optimal number of features that can be used for detection of DDoS attacks. To improve accuracy, a great deal of testing was done on the model's architecture using various hyperparameters and training procedures. With a 96.5% detection rate, the DNN results showed a high degree of accuracy. This demonstration highlights the ability of deep neural networks to identify DDoS attacks in the midst of regular traffic. The six-category classification enhances detection granularity and facilitates the application of more specialized and successful mitigation techniques. Given the great precision attained, DNNs have the potential to be an essential part of real-time detection systems, providing a major advancement over conventional techniques.