Network Applications Research Articles

Entanglement enhancement of two giant atoms with multiple connection points in bidirectional-chiral quantum waveguide-QED system

We study the entanglement generation of two giant atoms within a one-dimensional bidirectional-chiral waveguide quantum electrodynamics (QED) system, where the initial state of the two giant atoms are ∣e a , g b 〉. Here, each giant atom is coupled to the waveguide through three connection points, with the configurations divided into five types based on the arrangement of coupling points between the giant atoms and the waveguide: separate, fully braided, partially braided, fully nested, and partially nested. We explore the entanglement generation process within each configuration in both nonchiral and chiral coupling cases. It is demonstrated that entanglement can be controlled as needed by either adjusting the phase shift or selecting different configurations. For nonchiral coupling, the entanglement of each configuration exhibits steady state properties attributable to the presence of dark state. In addition, we find that steady-state entanglement can be obtained at more phase shifts in certain configurations by increasing the number of coupling points between the giant atoms and the bidirectional waveguide. In the case of chiral coupling, the entanglement is maximally enhanced compared to the one of nonchiral case. Especially in fully braided configuration, the concurrence reaches its peak value 1, which is robust to chirality. We further show the influence of atomic initial states on the evolution of interatomic entanglement. Our scheme can be used for entanglement generation in chiral quantum networks of giant-atom waveguide-QED systems, with potential applications in quantum networks and quantum communications.

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models

Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagued by the curse of dimensionality. Using physical applications, we show that L0 sparsification prior to Stein variational gradient descent (L0+SVGD) is a more robust and efficient means of uncertainty quantification, in terms of computational cost and performance than the direct application of SGVD or projected SGVD methods. Specifically, L0+SVGD demonstrates superior resilience to noise, the ability to perform well in extrapolated regions, and a faster convergence rate to an optimal solution.

Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.

Deterministic and multiuser quantum teleportation network of continuous-variable polarization states

Quantum teleportation is widely applied as a key ingredient in quantum information science, and quantum network provides unique abilities for upscaling its applications. Polarization state of light is a vital degree of freedom with the advantages of remote transfer and direct light-atom interaction. To construct a multiuser quantum network, deterministic quantum teleportation of polarization state in more users' network remains challenging. Compared with photonic quantum state, the deterministic quantum teleportation network of polarization state is realized with a single set of continuous-variable entangled states. The key techniques include only one set of quadrature entangled network, single quadrature controller, and active polarization controller. Arbitrary polarization state is deterministically and controllably teleported in such a system involving four or three users. This system is deterministic and scalable with user number, which enables the potential application in deterministic metropolitan quantum network. Published by the American Physical Society 2024

Connected Dominating Set Construction Using the Hunger Games Search Optimisation Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is “to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes”. The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications.

Predicting Stock Prices in the Electric Vehicle and Technology Sectors Using Long Short-Term Memory Models

Forecasting stock prices is a complex task because of the volatility and intricate temporal dependencies present in financial markets. Long Short-Term Memory (LSTM) networks, known for their proficiency in handling extended time-series data, offer an effective solution to these challenges. This paper delves into the applications in LSTM networks to forecast stock prices for various companies in the electric vehicle and related sectors, specifically examining data from Tesla, BYD, Panasonic, and NVIDIA. The study assesses the predictive accuracy and performance of these models using RMSE, MSE, MAE, and R metrics. By comparing these metrics across different companies, the paper provides an in-depth analysis of LSTM networks' effectiveness in financial forecasting. The findings indicate that when LSTM networks are optimized through hyperparameter tuning and architectural adjustments, they can accurately identify patterns in stock price data, offering valuable insights for investors and analysts in the electric vehicle, technology, and energy industries.

Vulnerability extraction and prediction method based on improved information gain algorithm.

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened.

Leveraging quantum uncertainty: Quantum randomness through the lens of classical communication networks

The generation of random numbers and the study of its properties have been an elusive field for a fair portion of the century. The application of random numbers is employed in many use cases such as cryptography, neural networks, numerical simulation, and gambling. The performance of each of these use cases is profoundly impacted by the employed random numbers; henceforth, the quality of randomness is of critical importance when it comes to their usage. A poorly generated random number can make a security system vulnerable, or any numerical or statistical evaluation misleading. Although various modes of classical random number generators exist and still function to provide strong cryptographic properties, emergence of quantum mechanical randomness has shed light on a novel path of generating certified randomness which supersedes the classical counterpart in terms of security. Harnessing quantum mechanical phenomena enables generation of true random numbers which can be certified and further implemented to elevate the net quality of the specific use cases. In this work, we generate and analyze random numbers from three different sources — 50: 50 beam splitter (BS), quantum key distribution (QKD) setup with classical post-processing scheme, and a commercially available quantum random number generator (QRNG) (ID Quantique (IDQ)). The quality of the generated random numbers from the various sources is checked in statistical tests and compared. Further on, we have developed a system which implements the QRNG-based random numbers to facilitate message authentication code (MAC) and one time password (OTP) protocols, demonstrating a communication network application. In this manner we discuss about a network which integrates quantum mechanics to the current classical networking approaches to enhance certain aspects of the networking protocol — in this case, the security.

SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications.

Spiking neural networks (SNNs) have received increasing attention due to their high biological plausibility and energy efficiency. The binary spike-based information propagation enables efficient sparse computation in event-based and static computer vision applications. However, the weight precision and especially the membrane potential precision remain as high-precision values (e.g., 32 bits) in state-of-the-art SNN algorithms. Each neuron in an SNN stores the membrane potential over time and typically updates its value in every time step. Such frequent read/write operations of high-precision membrane potential incur storage and memory access overhead in SNNs, which undermines the SNNs' compatibility with resource-constrained hardware. To resolve this inefficiency, prior works have explored the time step reduction and low-precision representation of membrane potential at a limited scale and reported significant accuracy drops. Furthermore, while recent advances in on-device AI present pruning and quantization optimization with different architectures and datasets, simultaneous pruning with quantization is highly under-explored in SNNs. In this work, we present SpQuant-SNN, a fully-quantized spiking neural network with ultra-low precision weights, membrane potential, and high spatial-channel sparsity, enabling the end-to-end low precision with significantly reduced operations on SNN. First, we propose an integer-only quantization scheme for the membrane potential with a stacked surrogate gradient function, a simple-yet-effective method that enables the smooth learning process of quantized SNN training. Second, we implement spatial-channel pruning with membrane potential prior, toward reducing the layer-wise computational complexity, and floating-point operations (FLOPs) in SNNs. Finally, to further improve the accuracy of low-precision and sparse SNN, we propose a self-adaptive learnable potential threshold for SNN training. Equipped with high biological adaptiveness, minimal computations, and memory utilization, SpQuant-SNN achieves state-of-the-art performance across multiple SNN models for both event-based and static image datasets, including both image classification and object detection tasks. The proposed SpQuant-SNN achieved up to 13× memory reduction and >4.7× FLOPs reduction with < 1.8% accuracy degradation for both classification and object detection tasks, compared to the SOTA baseline.

Developing Wireless Communication Technique for Usage in IoT Applications

The Internet of Things (IoT) is another registering worldview that imagines standard ordinary articles being changed into significant items. In view of its benefits over wired advancements, like simpler arrangement, less expensive structures, compact help, flexibility, and straightforwardness of association, remote innovations will be the best option for interfacing IoT gadgets. There are assortments of remote advances that can be utilized for IoT; these innovations cover a wide scope of distances, from a couple of centimeters to numerous kilometers. The Internet Engineering Task Force (IETF) introduced the 6LowPAN convention in this article, and the ZigBee association set up the ZigBee convention over the low-power IEEE802.15.4 standard. Sensors are gathered in remote sensor networks applications to illuminate sensor centers. Commonly, battery power is utilized to drive these center points. In IoT applications, these centers should keep going for quite a long time without waiting be re-energized. IoT helps with settling on choices in view of genuine information gathered from a colossal number of ordinary gadgets that have been improved with information through the expansion of detecting, handling, and correspondence capacities. Remote correspondence is for the most part utilized by IoT gadgets to speak with different gadgets.

Developing Innovative Secured Protocol For M Health Application In Wireless Body Area Networks

In today's technologically advanced society, cell phones and related devices are essential for managing peoples' individual demands. This is a smart device that determines and regulates how we use our free time, communicate, shop, and engage with the outside world. It still requires a lot of fixes to be effective and strong. The development of tiny devices, or sensors, gave mHEALTH greater adaptability and a fresh perspective. The majority of currently available mobile applications are widely accessible by users, who can use them to take vital signs and evaluate their own physical state. In an effort to increase public acceptability, some mobile companies have developed health applications for smartphones. People use these programs to monitor changes in bodily parameters such as body temperature, heart rate, blood pressure, and pulse rate in order to assess their physical conditions. Using the MSE-BAN architecture, this study has created a stable mHEALTH architecture. The scheduling plan for the study project was created by taking the importance of the essential signals into account. The public health care system that has been suggested can shorten hospital stays and enable clinical staff to continuously monitor patients.

CLARA: A cluster-based node correlation for sampling rate adaptation and fault tolerance in sensor networks

Recently, wireless sensor networks (WSNs) have been proven as an efficient and low-cost solution for monitoring various kind of applications. However, the massive amount of data collected and transmitted by the sensor nodes, which are mostly redundant, will quickly consume their limited battery power, which is sometimes difficult to replace or recharge. Although the huge efforts made by researchers to solve such problem, most of the proposed techniques suffer from their accuracy and their complexity, which is not suitable for limited-resources sensors. Therefore, designing new data reduction techniques to reduce the raw data collected in such networks is becoming essential to increase their lifetime. In this paper, we propose a CLuster-based node correlation for sAmpling Rate adaptation and fAult tolerance, abbreviated CLARA, mechanism dedicated to periodic sensor network applications. Mainly, CLARA works on two stages: node correlation and fault tolerance. The first stage introduces a data clustering method that aims to search the correlation among neighboring nodes. Then, it accordingly adapts their sensing frequencies in a way to reduce the amount of data collected in such networks while preserving the information integrity at the sink. In the second stage, a fault tolerance model is proposed that allows the sink to regenerate the raw sensor data based on two methods: moving average (MA) and exponential smoothing (ES). We demonstrated the efficiency of our technique through both simulations and experiments. The best obtained results show that the first stage can reduce the sensor sampling rate, and accordingly the sensor energy, up to 64% while the second stage can accurately regenerate the raw data with an error loss less than 0.15.

Allosteric stem-loop multicomponent DNAzyme: A versatile stimuli-responsive switch for nanotweezer operations and multifunctional biosensing

DNAzyme, possessing the capabilities of both encoding information and catalyzing chemical reactions, holds significant value in advancing the widespread application of dynamic nanotechnology. Particularly, multicomponent DNAzyme (MNAzyme) has attracted considerable attention due to its remarkable versatility and practicality. However, the inevitable signal leakage of MNAzyme constrains the applications in complex nucleic acid reaction networks. Herein, we present an allosteric stem-loop multicomponent DNAzyme (ASLM) that incorporates a blocking domain to mitigate leakage resulting from secondary structures of MNAzyme while simultaneously stabilizing binding affinity during complex formation. As the sequence of blocking domain serves as an extension independent of the functional region in MNAzyme, this modulation structure can be flexibly assembled with conformation-switchable nanotweezer to form a time-responsive molecular device, enabling dynamic and dissipative operations through nucleic acid and RNase H. More importantly, the ASLM structure can be directly incorporated into DNA catalytic circuits for detecting multiple biomolecules (nucleic acid, protease, and small molecule). With its scalability and versatility, this effector-activated allosteric regulator will serve as a universal stimuli-responsive switch in complex and hierarchical DNA networks, expanding the scope of DNA nanotechnology in molecular device construction and diagnostic biosensing.

Security Risk Analysis in Accounting Information Systems Based on Data Dashboard from the Palo Alto Network PA-820 UTM

This study analyzes security risks in accounting information systems using data from the Palo Alto Network PA-820 UTM dashboard, which monitors various aspects such as network activity, user activity, and application usage. The data includes information on data transfers, source and destination IP activities, and system rule usage. The study aims to identify potential threats that could affect the integrity and security of accounting information systems. Keywords: Accounting Information Systems, Security, Risk, Dashboard, Network Activity.

Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks.

With the rapid progress of deep neural network (DNN) applications on memristive platforms, there has been a growing interest in the acceleration and compression of memristive networks. As an emerging model optimization technique for memristive platforms, bit-level sparsity training (with the fixed-point quantization) can significantly reduce the demand for analog-to-digital converters (ADCs) resolution, which is critical for energy and area consumption. However, the bit sparsity and the fixed-point quantization will inevitably lead to a large performance loss. Different from the existing training and optimization techniques, this work attempts to explore more sparsity-tolerant architectures to compensate for performance degradation. We first empirically demonstrate that in a certain search space (e.g., 4-bit quantized DARTS space), network architectures differ in bit-level sparsity tolerance. It is reasonable and necessary to search the architectures for efficient deployment on memristive platforms by the neural architecture search (NAS) technology. We further introduce bit-level sparsity-tolerant NAS (BST-NAS), which encapsulates low-precision quantization and bit-level sparsity training into the differentiable NAS, to explore the optimal bit-level sparsity-tolerant architectures. Experimentally, with the same degree of sparsity and experiment settings, our searched architectures obtain a promising performance, which outperform the normal NAS-based DARTS-series architectures (about 5.8% higher than that of DARTS-V2 and 2.7% higher than that of PC-DARTS) on CIFAR10.

Improving model robustness to weight noise via consistency regularization

Abstract As an emerging computing architecture, the computing-in-memory (CIM) exhibits significant potential for energy efficiency and computing power in artificial intelligence applications. However, the intrinsic non-idealities of CIM devices, manifesting as random interference on the weights of neural network, may significantly impact the inference accuracy. In this paper, we propose a novel training algorithm designed to mitigate the impact of weight noise. The algorithm strategically minimizes cross-entropy loss while concurrently refining the feature representations in intermediate layers to emulate those of an ideal, noise-free network. This dual-objective approach not only preserves the accuracy of the neural network but also enhances its robustness against noise-induced degradation. Empirical validation across several benchmark datasets confirms that our algorithm sets a new benchmark for accuracy in CIM-enabled neural network applications. Compared to the most commonly used forward noise training methods, our approach yields approximately a 2% accuracy boost on the ResNet32 model with the CIFAR-10 dataset and a weight noise scale of 0.2, and achieves a minimum performance gain of 1% on ResNet18 with the ImageNet dataset under the same noise quantization conditions.

A secure scheme based on polar-coded and 3D mixed domain scrambling in W-band MMW-RoF system

With the development of 6G network, the issue of physical layer security in optical fiber communication is becoming more and more significant. In this paper, a polar-coded encryption and 3D mixed domain scrambling secure scheme in W-band millimeter-wave radio-over-fiber (MMW-RoF) system is proposed. The keys are divided into the shared key and the dynamic key, and the shared key is used for constellation-level encryption and 3D mixed domain scrambling with 3D cat mapping, the dynamic key is used for polar code encryption. After RoF system transmission, the scheme can achieve a large key space of 10453, which is powerful enough to resist brute-force. Because the key is randomly selected in the codebook, this encryption scheme has the effect of resisting chosen-plaintext attacks. In terms of reliability, compared to traditional polar-OFDM and 3D QAM polar-OFDM, this scheme can achieve ∼0.4 dB and ∼0.45 dB gains in receiver sensitivity, respectively. So, the proposed scheme has good security performance and reliability performance, which has a significant potential for application in future optical networks.

Wearable Devices based on Wireless Sensor Network and Speech Synchronization Overlay Algorithm Application in Sports Training Data Simulation

Wearable Devices based on Wireless Sensor Network and Speech Synchronization Overlay Algorithm Application in Sports Training Data Simulation

Design of Microstrip Patch Antenna for WSN Application

Abstract: This paper focuses on the design and simulation of a microstrip patch antenna for Wireless Sensor Network (WSN) applications, utilizing HFSS (High-Frequency Structure Simulator) software and an FR-4 epoxy substrate. The objective is to develop an efficient and cost-effective antenna tailored for the 2.4 GHz frequency band, commonly used in WSNs due to its favourable propagation characteristics. The choice of FR-4 epoxy substrate, with a relative dielectric constant (ࡾࢿ (of 4.4 provides a balance of performance, availability, and cost-effectiveness. These properties are crucial in determining the antenna's resonant frequency and overall efficiency. The design process involves optimizing the dimensions of the rectangular microstrip patch antenna to achieve the desired frequency and performance metrics. HFSS simulation tools are employed to model the antenna's behaviour, allowing for precise adjustments and performance predictions. Key parameters analysed include return loss (S11), gain, radiation pattern, and bandwidth.

Construction of Optimal Two-Dimensional Optical Orthogonal Codes with at Most One Pulse per Wavelength.

Two-dimensional optical orthogonal codes have important applications in optical code division multiple access networks. In this paper, a generic construction of two-dimensional optical orthogonal codes with at most one pulse per wavelength (AM-OPPW 2D OOCs) is proposed. As a result, some optimal AM-OPPW 2D OOCs with new parameters can be yielded. The new AM-OPPW 2D OOC may support more subscribers and heavier asynchronous traffic compared with known constructions.