Traditional Computer Research Articles

Deep Neural Network Assisted Quantum Chemistry Calculations on Quantum Computers.

The variational quantum eigensolver (VQE) is a widely employed method to solve electronic structure problems in the current noisy intermediate-scale quantum (NISQ) devices. However, due to inherent noise in the NISQ devices, VQE results on NISQ devices often deviate significantly from the results obtained on noiseless statevector simulators or traditional classical computers. The iterative nature of VQE further amplifies the errors in each loop. Recent works have explored ways to integrate deep neural networks (DNN) with VQE to mitigate iterative errors, albeit primarily limited to the noiseless statevector simulators. In this work, we trained DNN models across various quantum circuits and examined the potential of two DNN-VQE approaches, DNN1 and DNNF, for predicting the ground state energies of small molecules in the presence of device noise. We carefully examined the accuracy of the DNN1, DNNF, and VQE methods on both noisy simulators and real quantum devices by considering different ansatzes of varying qubit counts and circuit depths. Our results illustrate the advantages and limitations of both VQE and DNN-VQE approaches. Notably, both DNN1 and DNNF methods consistently outperform the standard VQE method in providing more accurate ground state energies in noisy environments. However, despite being more accurate than VQE, the energies predicted using these methods on real quantum hardware remain meaningful only at reasonable circuit depths (depth = 15, gates = 21). At higher depths (depth = 83, gates = 112), they deviate significantly from the exact results. Additionally, we find that DNNF does not offer any notable advantage over VQE in terms of speed. Consequently, our study recommends DNN1 as the preferred method for obtaining quick and accurate ground state energies of molecules on current quantum hardware, particularly for quantum circuits with lower depth and fewer qubits.

Doświadczenie w wirtualnej rzeczywistości a pamięć wzrokowa

The number of studies investigating different aspects of the use of virtual reality and VR goggles is constantly growing. However, there is currently a lack of analyses related to the visual memory of resources learned in immersive VR. Therefore, a study was conducted to determine the level of memorisation of visual elements presented using VR headsets or a traditional computer. The study involved 120 students divided into three equal groups. In the first group, the participants used VR goggles and the presentation was supervised by a lecturer who drew attention to key elements and monitored the pace of the action. In the second group, users worked entirely on their own with the VR headsets, and in the third group they watched a narrated presentation on a computer screen. The main research objective was to determine the differences in the amount of elements memorised between the study groups. The study used both quantitative and qualitative data. The ANOVA analysis of variance showed that there was a statistically significant difference between all groups. The highest level of memorisation of visual elements was recorded in the group using VR goggles and supervised by a guide. The lowest was in the group that used VR headsets alone. Participants in the study noted that immersive VR is so fascinating that it is difficult for them to supervise their own time and activity in a way that ensures success. As a result, 70% of people working alone with VR goggles did not manage to familiarise themselves with all the material.

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Quantum computing applications for Internet of Things

AbstractThe rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum‐secured smart lock etc.

Fused filament fabrication in CAD education: A closed-loop approach

Integrating low-cost fused filament fabrication 3D printing as a foundation for learning 3D modelling is explored. This method blends traditional computer aided design (CAD) instruction with additive manufacturing possibilities. Experimental results demonstrate increased comprehension speed and reduced learning time. This hands-on approach empowers students by enabling direct engagement with the modelling process. Analogous to reverse engineering, the strategy instructs engineering students from final product to model creation, closing the gap between theory and practice. Incorporating 3D printing bridges this divide, enhancing understanding, creativity and problem-solving. The study underscores technology's influence on learning strategies, aligning with the surge of 3D printing in education. Results link advanced design technology usage to improved student performance, with 3D-printed materials yielding 45% higher grades and 30% faster task completion. This study advocates curricular advancement for design-focused careers through enhanced technology integration and favourable 3D printing model reception.

Analysis of the principle and state-of-art applications for quantum neural network

The Neural Network is a well-known computational model that widely applied in machine learning (ML) inspired by human brains, which can perform the ML tasks including classification and feature extraction. Contemporarily it has been succeeded in all areas functioning as a powerful tool. Quantum computing is an emerging field based on quantum computers, which is a different calculation logic in the context of quantum dynamic theory providing an exponential computation power edge over traditional computers. Quantum Neural Network (QNN) is an intersection of the two areas, leveraging the advantage of quantum computing in the neural network, providing a strikingly powerful algorithm with promising potential. On this basis, this paper will demonstrate the state-of-art of QNN, which briefly explains the basic principle of QNN and an introduction of several typical QNN models. In addition, the current defects and drawbacks will also be discussed simultaneously. Overall, these results serve as a preliminary introduction to the topic, which shed light on guiding further exploration of quantum computing algorithms.

Ultra-high density and nonvolatile nanophotonic convolution processing unit

Photonic convolution processing units are the core components of photonic neural networks (PNNs). Traditionally, for an N × N photonic convolution processing unit, it typically requires O(N2) or O(Nlog2N) cascaded photonic devices, which leads to larger sizes and lower density. Therefore, nanophotonic neural networks (N-PNN) with high density are highly attractive. In this work, we propose an ultra-high density and nonvolatile N-PNN scheme, requiring only a compact photonic device to represent all weight values in the N × N convolution kernel, which results in a computational density of more than 5 petaflop operations per second per square millimeter (POPS/mm2) under ideal conditions. The footprints of the proposed 2 × 2 and 3 × 3 nanophotonic convolution processing units are only 4 × 4 μm2 and 6 × 6 μm2, respectively. Moreover, based on these two types of units, the N-PNN achieves image classification and recognition capabilities comparable to traditional computers. In addition, Owing to its lower insertion loss, our approach holds great significance for the scalability and on-chip integration of large-scale N-PNNs. This facilitated the integration of N-PNNs with existing electronic systems.

A Comprehensive Review on Object Detectors for Urban Mobility on Smart Traffic Management

This comprehensive review explores the landscape of object detectors in the context of urban mobility for smart traffic management. With the increasing complexity of urban environments and the integration of intelligent transportation systems, the demand for accurate and efficient object detection algorithms has surged. This paper provides a thorough examination of state-of-the-art object detectors, evaluating their performance, strengths, and limitations in the specific context of urban mobility. The review encompasses a wide range of detectors, including traditional computer vision methods and modern deep learning approaches, discussing their applicability to real-world urban traffic scenarios. By synthesizing insights from diverse methodologies, this review aims to guide researchers, practitioners, and policymakers in selecting suitable object detectors for enhancing smart traffic management systems in urban settings.

Algorithm efficiency and hybrid applications of quantum computing

With the development of science and technology, it is difficult for traditional computers to solve cutting-edge problems due to the lack of computing power, and the importance of quantum computers is increasing day by day. This article starts with the simple principle of quantum computing, introduces the most advanced quantum computing instruments and quantum computing algorithms, and points out the application prospects in medicine, chemistry and other fields. This paper explains the basic principles of quantum computing algorithms, their efficiency over traditional algorithms, and focuses on the Shor algorithm and its variations. In terms of applications, the quantum computer Zu Chongzhi and its contribution to the sampling problem of quantum random circuits are introduced. This paper makes a certain analysis of the limitations of quantum computing, and gives the future development goals in a targeted manner. Besides, we summarize popular quantum computing algorithms and applications, and make contributions to the promotion and development of quantum computing. Overall, these results shed light on guiding further exploration of how to improve the computational efficiency of quantum computers.

Development of an automatic reading recognition system for liquid-in-glass thermometers based on deep learning

The reading recognition system based on computer vision technology can improve the reading efficiency and accuracy of liquid-in-glass thermometers (LiGTs), and avoid the error caused by the human-eye reading method. Due to the requirement of a large field of view in the reading recognition process, the targets of LiGTs in the image are small and the accuracy of traditional computer vision (TCV) methods is undesirable. In addition, the potential shadow lines and the meniscus bottom on the image of LiGTs can also affect the robustness of the accuracy of the TCV methods. Around these issues, this paper develops an automatic reading recognition system for LiGTs based on deep learning, which aims to improve the accuracy of automatic reading recognition. Specifically, the cameras in our system are assisted in acquiring ideal images of LiGTs through the back-light illumination and lifting mechanism. Then a multi-task attention network and a general alignment processing module are designed for reading recognition on images of LiGTs. Experimental results on two self-built thermometer datasets prove that the system designed in this study can accurately recognize the temperature reading of LiGTs, and the performance of the proposed method outperforms other reading recognition methods.

A Deep Learning-Based Lightweight Model for the Detection of Marine Fishes

The species and population size of marine fish are important for maintaining the ecological environment and reflecting climate change. Traditional fish detection methods mainly rely on manual or traditional computer vision, which has disadvantages such as complex design, low detection accuracy, and poor generalization. The widespread use of ocean observation systems has accumulated a large number of images and videos, which makes the application of deep learning on marine fish detection possible. In this paper, a real-time high-precision fish detection algorithm based on YOLOv5s is constructed. Considering the enhancement of the channel representation and spatial interaction ability of the model, the attention mechanism and gated convolution are introduced, respectively, and GhostNet is introduced to lighten the model. Through a series of model comparisons, two improved models, S-Head-Ghost-Fish9 and S-SE-HorBlock-Head-Ghost-Fish9, are finally obtained. Compared with the original model, in terms of model size, the former reduces by 19% and the latter increases by 9.5%; in terms of computation, the former reduces by 15.7% and the latter reduces by 3.1%; in terms of detection speed, both take about 17 ms to detect a single image, and both can meet the real-time detection requirements; in terms of detection accuracy, the former improves by 3% and the latter by 3.6%. Compared with the latest detection algorithms of YOLOv6 and YOLOv8, the detection accuracy is slightly lower than 1%, but the model size and computation amount are only 1/3 to 1/2 of them. The improved models can help assess the population size and growth of the fish, which is of great significance in maintaining the stability of the fish population.

A Hand-Held Platform for Boar Sperm Viability Diagnosis Based on Smartphone.

The swine fever virus seriously affects pork production, and to improve pork production, pig breeding efficiency needs to be improved, and the detection of boar sperm activity is an important part of the pig breeding process. Traditional laboratory testing methods rely on bulky testing equipment, such as phase-contrast microscopes, high-speed cameras, and computers, which limit the testing scenarios. To solve the above problems, in this paper, a microfluidic chip was designed to simulate sperm in the oviduct with a channel thickness of 20 um, which can only accommodate sperm for two-dimensional movement. A miniature microscope system which can be used in combination with a smartphone is designed that is only the size of the palm of the hand and has a magnification of about 38 times. An intelligent diagnostic app was developed using Java language, which can automatically identify and track boar sperm with a recognition rate of 96.08% and an average tracking rate of 86%. The results show that the proposed smartphone-based hand-held platform can effectively replace the traditional microscope compound computer to diagnose sperm activity. In contrast, the platform is smaller, easier to use and is not limited by the usage scenarios.

Image encryption algorithm based on hyperchaos and DNA coding

AbstractA novel image encryption algorithm based on hyperchaos and DNA encoding is proposed in this paper. Unlike the traditional DNA‐based encoding method, a more fine‐grained encryption approach is adopted, which enhances the security of the system. To address the inherent binary arithmetic limitation of traditional DNA computing, the authors develop a novel symmetric and reversible DNA subtraction operation that fully utilizes the encryption advantage of DNA computing. To enhance security, the authors introduce a DNA crossover mechanism based on pixel, which can generate different ciphertexts even for the same pixel values. In the algorithm, the authors propose a diffusion strategy that combines left diffusion, right diffusion, and XOR parameters. This strategy not only ensures the avalanche effect, where even a single pixel change in the plaintext image will cause a drastic change in the ciphertext image, but also greatly enhances the security of the algorithm. Experimental results and performance analysis indicate that the authors’ proposed system has good encryption performance and the ability to resist noise attacks and clipping attacks.

Phase-based motion estimation and SVR smooth for target-free 3D deformation measurement using stereophotogrammetry

Non-contact measurement methods based on computer vision have shown great potential for measuring structural surface displacement and strain due to their low cost and high efficiency. However, the traditional computer vision technology based on digital image correlation (DIC) is limited by its dependence on natural texture features of the surface and its susceptibility to environmental conditions, making it challenging to obtain effective and robust measurements of surface deformation. Additionally, these methods typically only focus on the structural response in the image plane, which makes it difficult to perform a comprehensive health assessment of complex structural components with asymmetry. Therefore, a time-domain visual measurement technique based on image phase information is proposed for measuring three-dimensional (3D) deformation of structures using stereophotogrammetry. The method involves extracting image phase information using 2D Gabor filters and applying it to dense optical flow estimation based on polynomial approximation and semi-global block matching (SGBM) for obtaining robust 3D displacement measurements. The support vector regression (SVR) is then used to perform hyperplane fitting of the original displacements for improving the smoothness of the 3D displacements. Finally, an intuitive strain conversion method is introduced to convert the 3D displacements into 3D strains. The performance of the proposed technique has been validated through numerical simulation experiments based on a physics-based graphics model (PBGM) in a synthetic environment. The robustness of the proposed method has been further proved through free attenuation vibration experiments on a steel ruler. The initial evaluation of the expected performance of the measurement plan has been enabled by performing a 1:1 reduction of the field measurement process in a synthetic environment. The results indicate that the proposed method is a promising method for accurately and reliably measuring structural deformation in both synthetic and real-world environments, especially for complex structural components with asymmetry.

System for PCB Defect Detection Using Visual Computing and Deep Learning for Production Optimization

With the growing competition between the various manufacturers of electronic products, the quality of the products developed and the consequent confidence in the brand are fundamental factors for the survival of companies. To guarantee the quality of the products in the manufacturing process, it is crucial to identify defects during the production stage of an electronic device. This study presents a system based on traditional visual computing and new deep learning methods to detect defects in electronic devices during the manufacturing process. A prototype of the proposed system was developed and manufactured for direct use in the production line of electronic devices. Tests were performed using a particular smartphone model that had 22 critical components to inspect and the results showed that the proposed system achieved an average accuracy of more than 90% in defect detection when it was directly used in the operational production line. Other studies in this field perform measurements in controlled laboratory environments and identify fewer critical components. Therefore, the proposed method is a real-time high-performance system. Furthermore, the proposed system conforms with the Industry 4.0 goal that process system digitization is essential to improve indicators and optimize production.

Impact of Docker Container Virtualization On Wireless Mesh Network by Using Software-Defined Network

In today’s advanced digital world era, it is extremely difficult for small enterprises or organizations to merge traditional or legacy computer network devices/equipment and wireless mesh networking devices with the latest digital computer network technology with respect to the expense of buying and maintaining expensive branded networking devices. However, today, by applying the neatly Software-defined networking, the OpenFlow protocol along with virtualization such as docker containers, which is a pack of their specific libraries, configured files, and software, provides advantages over proprietary or branded computer networking devices with respect to purchasing expenditure, operational expenditure, and improved performance in computer networking. Redistribution of routing protocol is very essential when using various autonomous systems in wireless mesh networks. Docker containers of frr and quagga give an edge over traditional or branded physical router devices, some docker containers are used as wired and wireless hosts/clients in the wireless mesh network. The novel idea used in this paper is on how to use the different software-defined controllers (Ryu and Pox controller) in a docker containerized wireless mesh network to analyse with respect to packet transfer, jitter in transmission, minimum delay in transmission, maximum delay in transmission, the average delay in transmission, delay standard deviation bit-rate, send packets, average packets drop, dropped packets along-with average loss-burst size in Mininet Wi-Fi testbed at the different scenario and the result shows that by using the docker container virtualization along with software-defined network two different controllers improves the performance and optimize the wireless mesh network. In addition, it shows that by using containerization and virtualization, capital expenditure and operational expenditure can be reduced in designing and developing wireless mesh network topologies.

An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication

The present work argues estimating number of sources in communication system using an integrated model of Principal Component Analysis (PCA) neural network and kernel method to produce Eigenvalue Grads Method (EGM). The essential advantage of this new suggested model is that, PCA neural is used to determine the covariance matrix instead of the traditional computation process which is time consuming. Simulation outcomes of this adopted model demonstrate wonderful responses through effectiveness, fast converge speed for (PCA) neural network, as well as achieving correct number of sources.

Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain.

Dynamic Edge Computation Offloading for Internet of Vehicles With Deep Reinforcement Learning

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.

Effects of Reward Schedule and Avatar Visibility on Joint Agency during VR Collaboration.

Joint agency, a group-level sense of agency, has been studied as an essential social cognitive element while engaging in collaborative tasks. The joint agency has been actively investigated in diverse contexts (e.g., performance, reward schedules, and predictability), yet the studies were mostly conducted in traditional 2D computer environments. Since virtual reality (VR) is an emerging technology for remote collaboration, we aimed to probe the effects of traditional reward schedule factors along with novel VR features (i.e., avatar visibility) on joint agency during remote collaboration. In this study, we implemented an experiment based on a card-matching game to test the effects of the reward schedule (fair or equal) and the counterpart's avatar hand visibility (absent or present) on the sense of joint agency. The results showed that participants felt a higher sense of joint agency when the reward was distributed equally regardless of the individual performance and when the counterpart's avatar hand was present. Moreover, the effects of reward schedule and avatar hand visibility interacted, with a bigger amount of deficit for the absent avatar hand when the reward was distributed differentially according to performance. Interestingly, the sense of joint agency was strongly correlated to the level of collaborative performance, as well as to perceptions of other social cognitive factors, including cooperativeness, reward fairness, and social presence. These results contribute to the understanding of joint agency perceptions during VR collaboration and provide design guidelines for remote collaborative tasks and environments for users' optimal social experience and performance.