Computer Processing Research Articles

An inspired chaos-based estimation-theory optimization for low-density parity-check (LDPC) code decoding

Optimization techniques have emerged as powerful tools for solving complex engineering problems in real-time applications, surpassing traditional heuristic methods. This paper introduces a novel chaos optimization algorithm that integrates the bisection search method to enhance the convergence ability, solution accuracy, and search speed of conventional optimization problems. The proposed algorithm is applied to the design and analysis of a reduced complexity decoding scheme using Low-Density Parity-Check (LDPC) codes. By leveraging the Linear Minimum Mean Square Error (LMMSE) estimation criterion, the proposed method approximates the estimated parameters acquired during the message computation process. This combined approach effectively mitigates error propagation and premature convergence issues. The presented research offers a low-complexity decoding algorithm for LDPC codes, leveraging an optimized chaos bisection search method. The adoption of the LMMSE criterion overcomes data dependency and error propagation concerns during the estimation process. The modified chaos optimization technique, integrated with the bisection search, efficiently determines optimal values from perturbed observations, leading to improved convergence rates and data processing precision by reducing the observation dimension (search space). Simulation results validate the proposed approach, showcasing its ability to mitigate error propagation and premature convergence while maintaining coding gain accuracy. Experimental validation on an FPGA RF-SoC device (model XCZU27DR) demonstrates that the proposed method achieves a maximum throughput of 8.7 Gbps at a clock frequency of 386.72 MHz, with reduced hardware complexity. The experimental verification on an FPGA RF-SoC device highlights the algorithm's minimal hardware complexity, superior decoding throughput, and Bit Error Rate (BER) performance.

An efficient and robust staggered scheme based on adaptive time field for phase field fracture model

The standard staggered scheme in phase field model is computationally slow, so that a large amount of iterations are required. In order to improve the computational efficiency, a novel efficient and robust solution scheme based on adaptive time field is proposed in this paper. This scheme is inspired by the adaptive mesh method for space field, and an error indicator based on phase field and history field variables is established, where the time field is coupled with the phase field and energy field in the space field, so that the time field changes adaptively with the computational iterative process, which improves the convergence rate and reduces the time cost. The numerical results show that the presented novel scheme not only significantly improves the computational efficiency, but also has accurate computational results. Compared with the standard staggered scheme, the time cost of the presented scheme is less than 4% of that of the standard scheme. At the same time, the computational efficiency of the presented solver is faster than the current monolithic BFGS scheme, and it has excellent convergence characteristics.

Nonadiabatic Holonomic Quantum Computation Based on Rydberg Ground State Blockade

Quantum gates are crucial for quantum computation and quantum information processing. However, their effectiveness is often hindered by systematic errors and decoherence. Therefore, achieving resilient quantum gates to these factors is of great significance. We present a method to construct nonadiabatic holonomic single- and two-qubit gates in a Rydberg ground-state-blockade regime. Our approach utilizes a far-off-resonant technique for the single-qubit gate and a modified Rydberg antiblockade for the two-qubit gate. The reduction of the population of single- and two-excitation Rydberg states and the nonadiabatic holonomic process during the construction of the gates ensure robustness to decoherence and systematic errors, respectively. Numerical results demonstrate the fidelity and robustness of our scheme. The proposed scheme holds promise for future applications in quantum computation and quantum information processing tasks.

Alrecon: computed tomography reconstruction web application based on Solara.

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Quality assessment of the nominal attitude model of TOPEX/Poseidon using quaternion data

The altimetry mission TOPEX/Poseidon was operational between 1992 and 2005 and provided innovative insight into global and regional sea level changes and their variability. The correct modelling of the spacecraft orbit in precise orbit determination requires detailed information on the attitude of the spacecraft body and the solar array. Since the availability of attitude observations in form of quaternion data for this mission is limited, the nominal orientation model has to be used mainly. In this study, we compare the nominal and quaternion-based approaches of the TOPEX/Poseidon attitude realisation and validate the nominal model w.r.t. the observed attitude. We found good agreement between both approaches in the spacecraft-related roll, pitch, yaw, and solar array angles with slight differences of up to 0.31° on average. The best accordance is obtained in the yaw angles. A general angular offset of 0.12° and -0.19° on average is found in the pitch and solar array angle differences, respectively. Parameters estimated in the orbit computation process also reflect the good agreement between both orientation models with slightly better results when using the observed attitude. However, in some intervals, the nominal model lacks detailed information about the correct attitude. These can be adjusted using the information from the quaternion data. Such intervals indicate off-nominal spacecraft attitude events like drifts or offsets. Purely geometrically analysed, mispointing in the roll and pitch components of about 0.25° results in a change of the altimeter phase centre position in the radial direction of about 7 mm, which directly impacts the sea-level heights determined from altimetry measurements.

Quantum Anomaly Detection with a Spin Processor in Diamond

AbstractIn the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms cannot only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, the anomaly detection of quantum states encoding audio samples with a three‐qubit quantum processor consisting of solid‐state spins in diamond is experimentally demonstrated. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.

An analysis of attention mechanisms and its variance in transformer

Transformer is a machine learning model based on attention mechanism, which is widely used. When the Transformer model was first proposed, it gradually developed many variants and was promoted and applied in many fields, becoming an important research part in the areas of deep learning. However, the critical attention mechanism of Transformers has issues such as square complexity that affect computational speed and data processing efficiency. In order to meet the needs of data processing and related computing, there have been endless efforts to improve the attention mechanism in Transformers in different work areas. This article mainly provides a brief overview of the recent research progress on the attention mechanism in Transformers. Select representative studies from several directions of attention improvement work to introduce, in order to explore the latest research trends in its improvement work and lay a foundation for pointing out potential research directions for future research work and further improving the performance of Transformers.

Imperative Process Algebra and Models of Parallel Computation

Studies of issues related to computability and computational complexity involve the use of a model of computation. Central in such a model are computational processes. Processes of this kind can be described using an imperative process algebra based on ACP (Algebra of Communicating Processes). In this paper, it is investigated whether the imperative process algebra concerned can play a role in the field of models of computation. It is demonstrated that the process algebra is suitable to describe in a mathematically precise way models of computation corresponding to existing models based on sequential, asynchronous parallel, and synchronous parallel random access machines as well as time and work complexity measures for those models.

Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay.

Cruise ships and other naval vessels include automated Internet of Things (IoT)-based evacuation systems for the passengers and crew to assist them in case of emergencies and accidents. The technical challenges of assisting passengers and crew to safety during emergencies include various aspects such as sensor failures, imperfections in the sound or display systems that are used to direct evacuees, the timely selection of optimum evacuation routes for the evacuees, as well as computation and communication delays that may occur in the IoT infrastructure due to intense activities during an emergency. In addition, during an emergency, the evacuees may be confused or in a panic, and may make mistakes in following the directions offered by the evacuation system. Therefore, the purpose of this work is to analyze the effect of two important aspects that can have an adverse effect on the passengers' evacuation time, namely (a) the computer processing and communication delays, and (b) the errors that may be made by the evacuees in following instructions. The approach we take uses simulation with a representative existing cruise ship model, which dynamically computes the best exit paths for each passenger, with a deadline-driven Adaptive Navigation Strategy (ANS). Our simulation results reveal that delays in the evacuees' reception of instructions can significantly increase the total time needed for passenger evacuation. In contrast, we observe that passenger behavior errors also affect the evacuation duration, but with less effect on the total time needed to evacuate passengers. These findings demonstrate the importance of the design of passenger evacuation systems in a way that takes into account all realistic features of the ship's indoor evacuation environment, including the importance of having high-performance data processing and communication systems that will not result in congestion and communication delays.

FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records

With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.

Study of Trajectory Filtering Methods for ADS-B Based on VSIMM-RSRCKF

In this paper, an advanced ADS-B trajectory filtering method combining Variable Structure Interactive Multi-Modeling (VSIMM) and Reduced Square Root Volume Kalman Filter (RSRCKF) is proposed. After deeply analyzing the operational characteristics of ADS-B system and the application requirements in the field of aviation, this paper aims to improve the accuracy of ADS-B trajectory tracking by this novel filtering method. In order to cope with the tracking performance problems that may be caused by the model set selection in the traditional interacting multi-model algorithm, the Variable Structure Interacting Multi-Model (VSIMM-RSRCKF) algorithm based on the Simplified Square Root Volume Kalman Filtering is adopted in this study for trajectory filtering. By constructing a comprehensive VSIMM model set to describe the dynamic system of maneuvering targets, the filtering method in this paper simplifies the computational process and reduces the computational complexity by squaring the covariance matrix in the iteration, and at the same time ensures the non-negative qualitative nature of the covariance matrix, which effectively avoids the divergence problem that may occur in the filtering process. The goal of this research is to significantly improve the positioning accuracy and reliability of aircraft using the ADS-B system.

ПОСТРОЕНИЕ ЦИФРОВОЙ МОДЕЛИ МЕСТНОСТИ ПРИ ПРОЕКТИРОВАНИИ ТРАСС ЛИНЕЙНЫХ ОБЪЕКТОВ

The paper discusses a method for obtaining a digital terrain model when designing the routes of a linear object (oil pipeline) using a specific example. The relevance of the topic and the current state of the issue being studied are indicated. The sequence of work is described and the necessary equipment is provided. The purpose of the research is to study the means and techniques for creating a digital terrain model (DTM).
 Creating digital terrain models involves converting cartographic information from analogue to digital form. The digital form of storage on electronic media is currently more progressive. It allows you to quickly record and enter into a digital model changes that have occurred on the ground, as well as automate many processes of topographic and geodetic production. Due to the availability of fast computer processing of huge amounts of data, the task of creating a digital model as close to reality as possible becomes feasible. A digital terrain model (DTM) consists of a digital terrain model (DEM) and a digital situation model (DSM).
 The studies have developed a sequence for compiling a digital terrain model using a specific example, which will reduce the cost and time for work, increase the information content of the materials.

Quantifying Soil Erosion in Drought-Impacted Central Odisha, India, through Geospatial Mapping with RUSLE

Soil degradation in Odisha poses a significant conservation concern for the local environment. The present research focused on a region in central Odisha State, India, affected by drought conditions. Several models have emerged to assess soil loss, with the Revised Universal Soil Loss Equation (RUSLE) standing out as the most suitable option. The erosion computation process entails utilizing the digital elevation model (DEM), Landsat-9 imagery, and soil data from several sources accessible in different forms and scales. The present analysis took into account various elements, namely, crop management factor (C), practice management factor (P), slope length factor (LS), steepness factor of the slope (S), and rainfall factor (R). Multiplying these factors yielded the average rate of soil erosion. Areas with a high slope length factor, such as those in Kandhamal, Kalahandi, and Nuapada, have a high erosion rate. The study reveals that 57% of the land in the study area experiences very low to moderate soil erosion at a rate of 2–10 tons per hectare per year, while 43% faces moderately to very severe erosion at a rate of 10–25 tons per hectare per year. Erosion hotspots, covering 32,205 square kilometers, are mainly identified in agricultural and forested hilly areas where slopes exceed 10°, such as those in Kandhamal, Kalahandi, and Nuapada, which have a high erosion rate. These districts are especially vulnerable to soil loss and resulting climate action (Sustainable Development Goals-13) because of frequent and severe rainfall, shifting agricultural practices, a thin surface soil covering, natural erosion, and barren hills. The research emphasizes the urgent need for implementing conservation and management measures to protect high-risk areas from further degradation. In conclusion, the study underscores the effectiveness of the RUSLE-GIS model in conducting quantitative and spatial assessments of soil erosion on a river watershed scale. The model is deemed crucial in formulating conservation strategies to address the identified erosion issues in the tropical highlands of the area.

A New Method to Solve Matrix Game with Interval Payoffs and its MATLAB Code

Game theory is of substantial significance in diverse domains, acting as a potent instrument to comprehend and assess strategic engagements among rational decision-makers. It formulates mathematical models to represent strategic interactions among rational decision-makers in the competitive world. Due to ambiguity in the real-world problems, acquiring the precise payoff values of a matrix game proves challenging. However, in numerous scenarios, these payoffs fluctuate within specific ranges, making them suitable for consideration as interval numbers. This leads to the formulation of a special form of game problem known as the interval valued game problem (IVGP). Some methodologies exist in the literature to find the optimal strategies as well as the value of game for IVGP, but most of them possess some limitations, resulting in the need for proposing a new methodology to find the optimal strategies and value of game. Thus, in this paper, a new solution method for game problems with payoffs represented as interval numbers is presented, utilizing the fuzzy concept. The process begins by transforming the interval payoffs into fuzzy numbers using a ranking function. Subsequently, these fuzzy payoffs are converted into crisp values, leading to the formulation of the crisp matrix game. The resulting crisp matrix game is then solved using linear programming approach. Additionally, MATLAB code for the proposed method is developed and proposed to streamline the computation process, enhancing comparison and decision-making efficiency, particularly when dealing with large payoff matrices. Furthermore, three numerical examples are provided to illustrate the validity of the proposed approach as well as its MATLAB code. A real-life example of IVGP in the realm of tourism industry is also provided. Finally, a comparative analysis is conducted, comparing the proposed method with some existing methods.

DRC-EDI: An integrity protection scheme based on data right confirmation for mobile edge computing

As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance.

A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.

Spectral-temporal-spatial customization via modulating multimodal nonlinear pulse propagation

Multimode fibers (MMFs) are gaining renewed interest for nonlinear effects due to their high-dimensional spatiotemporal nonlinear dynamics and scalability for high power. High-brightness MMF sources with effective control of the nonlinear processes would offer possibilities in many areas from high-power fiber lasers, to bioimaging and chemical sensing, and to intriguing physics phenomena. Here we present a simple yet effective way of controlling nonlinear effects at high peak power levels. This is achieved by leveraging not only the spatial but also the temporal degrees of freedom during multimodal nonlinear pulse propagation in step-index MMFs, using a programmable fiber shaper that introduces time-dependent disorders. We achieve high tunability in MMF output fields, resulting in a broadband high-peak-power source. Its potential as a nonlinear imaging source is further demonstrated through widely tunable two-photon and three-photon microscopy. These demonstrations provide possibilities for technology advances in nonlinear optics, bioimaging, spectroscopy, optical computing, and material processing.

Design of a Convolutional Neural Network Accelerator Based on On-Chip Data Reordering

Convolutional neural networks have been widely applied in the field of computer vision. In convolutional neural networks, convolution operations account for more than 90% of the total computational workload. The current mainstream approach to achieving high energy-efficient convolution operations is through dedicated hardware accelerators. Convolution operations involve a significant amount of weights and input feature data. Due to limited on-chip cache space in accelerators, there is a significant amount of off-chip DRAM memory access involved in the computation process. The latency of DRAM access is 20 times higher than that of SRAM, and the energy consumption of DRAM access is 100 times higher than that of multiply–accumulate (MAC) units. It is evident that the “memory wall” and “power wall” issues in neural network computation remain challenging. This paper presents the design of a hardware accelerator for convolutional neural networks. It employs a dataflow optimization strategy based on on-chip data reordering. This strategy improves on-chip data utilization and reduces the frequency of data exchanges between on-chip cache and off-chip DRAM. The experimental results indicate that compared to the accelerator without this strategy, it can reduce data exchange frequency by up to 82.9%.

QAKA: A novel quantum authentication and key agreement (QAKA) protocol using quantum entanglement for secure communication among IoT devices

AbstractThe constant evolution of the Internet of Things (IoT) concept involves the idea that all elements in the global network communication are reachable without the need for human intervention. In this context, the paramount concern is security, aiming to safeguard IoT communication against unauthorized access. According to the analysis, the security of the IoT communication framework relies on traditional cryptographic schemes like the RSA algorithm. Nevertheless, the latest developments in quantum computing and information processing pose a significant threat to these established schemes. Motivated by this fact, in this article, we proposed a novel quantum authentication and key agreement (QAKA) protocol that provides unconditional security against any classical and futuristic quantum threats. Our protocol ensures secure key agreement and mutual authentication based on quantum hashing with quantum passwords and quantum key distribution. The proposed scheme utilizes quantum teleportation and Greenberger–Horne–Zeilinger states for secure data transfer among entities. Our work is simulated using the Automated Validation of Internet Security Protocols and Applications tool to prove our protocol's formal correctness and security. The Burrows, Abadi, and Needham logic is applied to prove the goal of our protocol. Random oracle model for formal security analysis is presented in our work. Our protocol is also informally analyzed, considering the characteristics such as correctness, security against repudiation and the impossibility of cloning. The proposed protocol is compared with the related protocols in terms of various security features such as replay attacks, man‐in‐middle attacks and futuristic quantum attacks. Finally, the performance of the proposed protocol is presented, revealing superior efficacy when compared with classical authentication schemes and quantum protocols.

A Multiuser, Multisite, and Platform-Independent On-the-Cloud Framework for Interactive Immersion in Holographic XR

Background: The ever-growing extended reality (XR) technologies offer unique tools for the interactive visualization of images with a direct impact on many fields, from bioinformatics to medicine, as well as education and training. However, the accelerated integration of artificial intelligence (AI) into XR applications poses substantial computational processing demands. Additionally, the intricate technical challenges associated with multilocation and multiuser interactions limit the usability and expansion of XR applications. Methods: A cloud deployable framework (Holo-Cloud) as a virtual server on a public cloud platform was designed and tested. The Holo-Cloud hosts FI3D, an augmented reality (AR) platform that renders and visualizes medical 3D imaging data, e.g., MRI images, on AR head-mounted displays and handheld devices. Holo-Cloud aims to overcome challenges by providing on-demand computational resources for location-independent, synergetic, and interactive human-to-image data immersion. Results: We demonstrated that Holo-Cloud is easy to implement, platform-independent, reliable, and secure. Owing to its scalability, Holo-Cloud can immediately adapt to computational needs, delivering adequate processing power for the hosted AR platforms. Conclusion: Holo-Cloud shows the potential to become a standard platform to facilitate the application of interactive XR in medical diagnosis, bioinformatics, and training by providing a robust platform for XR applications.