Efficient Computer Research Articles

A Review of the Present Cryptographic Arsenal to Deal with Post-Quantum Threats

The looming threat of quantum attacks on the digital infrastructure protected by conventional cryptographic protocol has generated urgency in identifying and deploying countermeasures that can mitigate the threat. There is a need for stronger cryptographic schemes that combine the strengths of both classic and quantum technologies. Post-Quantum Cryptography (PQC) has emerged as a potential solution that can withstand the challenges posed by advances in quantum computing. Owing to the increasing importance of PQC, the present research is an attempt to assess the existing research done so far so that the existing gaps can be identified which can then strengthen the existing literature. The systematic literature review presented in this work has outlined six key categories of PQC, namely, lattice-based, code-based, hash-based, multivariate, isogeny-based, symmetric-key-based cryptosystems. The study concluded that the advances made in quantum computing will result in the development of quantum computers with superior computational power. Such highly efficient quantum computers will have the ability to break the currently available cryptography schemes most used in a variety of practical applications. With the advent of quantum computing, the computational capabilities of the potential cyber attackers would grow exponentially, which would render the traditional cyber security measures inadequate. This calls for techniques like PQC to be developed and strengthen.

Optoelectronic neuromorphic devices and their applications

Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing. Owing to the unique advantages such as high parallelism and ultralow power consumption, bioinspired neuromorphic computing can have the capability of breaking through the bottlenecks of conventional computers and is now considered as an ideal option to realize the next-generation artificial intelligence. As the hardware carriers that allow the implementing of neuromorphic computing, neuromorphic devices are very critical in building neuromorphic chips. Meanwhile, the development of human visual systems and optogenetics also provides a new insight into how to study neuromorphic devices. The emerging optoelectronic neuromorphic devices feature the unique advantages of photonics and electronics, showing great potential in the neuromorphic computing field and attracting more and more attention of the scientists. In view of these, the main purpose of this review is to disclose the recent research advances in optoelectronic neuromorphic devices and the prospects of their practical applications. We first review the artificial optoelectronic synapses and neurons, including device structural features, working mechanisms, and neuromorphic simulation functions. Then, we introduce the applications of optoelectronic neuromorphic devices particularly suitable for the fields including artificial vision systems, artificial perception systems, and neuromorphic computing. Finally, we summarize the challenges to the optoelectronic neuromorphic devices, which we are facing now, and present some perspectives about their development directions in the future.

Modelling and Analysis of Virotherapy of Cancer Using an Efficient Hybrid Soft Computing Procedure

Modelling and Analysis of Virotherapy of Cancer Using an Efficient Hybrid Soft Computing Procedure

EFFICIENT CALIBRATION FOR HIGH-DIMENSIONAL COMPUTER MODEL OUTPUT USING BASIS METHODS

Calibration of expensive computer models with high-dimensional output fields can be approached via history matching. If the entire output field is matched, with patterns or correlations between locations or time points represented, calculating the distance metric between observational data and model output for a single input setting requires a time intensive inversion of a high-dimensional matrix. By using a low-dimensional basis representation rather than emulating each output individually, we define a metric in the reduced space that allows the implausibility for the field to be calculated efficiently, with only small matrix inversions required, using projection that is consistent with the variance specifications in the implausibility. We show that projection using the $L_2$ norm can result in different conclusions, with the ordering of points not maintained on the basis, with implications for both history matching and probabilistic methods. We demonstrate the scalability of our method through history matching of the Canadian atmosphere model, CanAM4, comparing basis methods to emulation of each output individually, showing that the basis approach can be more accurate, whilst also being more efficient.

Energy Management in Cloud Through Green Cloud Technologies

To address the issue of storage and processing of large-scale data, Cloud Computing has gained swift progression in the field of technology. Though offering solutions to many problems, cloud computing is still in the early stage of research and implementa-tion. It suffers from many challenges namely security, standardization and energy con-sumption. In this paper, we focus on the issue of energy consumption through cloud computing and the technology known as Green Cloud Computing to tackle the said issue. Green Cloud Computing plans to lessen the immense energy utilization, the re-quirement of physical equipment, destructive fossil fuel byproducts and so on. To shield our current circumstances from cloud technology’s adverse consequences, the cloud framework should be updated toward green registering. Green cloud computing broad-ly centers around the planning of effective clouds with green qualities like efficient en-ergy management, virtualization, load balancing, green servers, reusability, grid com-puting and recyclability.

Online Rail Fastener Detection Based on YOLO Network

Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5 (YOLOv5) network, and completes the real-time classification of fastener states. The improved YOLOv5 network proposed contains five sections, which are Input, Backbone, Neck, Head Detector and a read-only Few-shot Example Learning module. The main purpose of this project is to improve the detection precision and shorten the detection time. Ultimately, the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms. This model achieves on-line fastener detection by completing the “sampling-detection-recognition-warning” cycle of a single sample before the next image is sampled. Specifically, the mean average precision of model reaches 94.6%. And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU.

SPICE Study of STDP Characteristics in a Drift and Diffusive Memristor-Based Synapse for Neuromorphic Computing

Neuromorphic hardware is a system with massive potential to enable efficient computing by mimicking the human brain. The novel system processes information using neuron spikes (Action Potentials) and the synaptic connections between neurons are trained using biologically plausible methods like spike-timing-dependent plasticity (STDP). Memristor is one of the promising candidates to implement such neuromorphic hardware. Two types of memristors, diffusive and drift, have been proposed to form a synapse showing faithful emulation of STDP, where the diffusion effect is used to trace the spike timing history crucial for STDP and the drift memristor keeps the weight information in a longer time scale. The purpose of this paper is to systematically investigate STDP characteristics in such a synapse with serially connected two memristors using SPICE models. The results show that STDP properties are strongly dependent on device parameters and even the shape of STDP curves is modified. Different shapes of the STDP curve were identified. The results and analysis could support the design of emerging device-based synapses, which can faithfully mimic biological STDP characteristics for future neuromorphic systems.

Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction

The identification of tumorous tissues in the brain based on Magnetic Resonance Images (MRI) analysis is a challenging and time consuming task that highly depends on radiologists expertise. As prompt diagnosis of tumors can often be inherent to the patient's survival, it is however crucial to decrease the amount of time spent on the manual analysis of MRI while increasing the accuracy of the detection process. To tackle these issues, many research works have already investigated efficient computer vision systems. They offer new opportunities to assist health care providers in the establishment of fast and more accurate tumor detection, classification and segmentation. However, often based on deep learning methods, the development and tuning of these solutions remains time and energy consuming while inducing a lack of explainability in the decision making system. In this study, we respond to these issues by solving a brain tumor detection task using the Selective Search (SS) algorithm coupled with a simplified Pulse-Coupled Neural Network (PCNN) for visual feature extraction and detection validation. The performed experiments showed promising results in terms of computational cost and detection accuracy. This leads to the development of a light-weight brain tumor detection system.

Synthesis, quantum-chemical calculations and virtual screening of the alkaloid cytisine derivatives

The synthesis of some cytisine derivatives was carried out in the work. The article provides the data of quantum-chemical calculation and virtual screening of the alkaloid cytisine derivatives synthesized. At the same time, the reaction centers of the cytisine derivatives molecules were determined. In order to study the reactivity of the derivatives obtained (namely cinnamoylcytisine, lipoylcytisine, and cytisinylisoalantholactone) the quantum-chemical calculations were conducted to determine the energy and charge characteristics of the molecules. The results indicate a sufficient thermodynamic stability of the cinnamoylcytisine and lipoylcytisine molecules. The cytisinylisoalantholactone molecule is not stable according to the results of quantum chemical calculations. The data on the energy values of the frontier molecular orbitals show that, in general, all molecules exhibit electrophilic properties. A bioprediction was implemented using PASS (Prediction of Activity Spectra for Substances) as one of the most efficient and well-known computer program with the aim of detailed study and the probable establishment of the biological activity of the synthesized cytisine derivatives. Based on the results of virtual screening, promising types of alkaloid cytisine derivatives were identified, which are potential sources of original drugs

Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors

As a fundamental component of biological neurons, dendrites have been proven to have crucial effects in neuronal activities. Single neurons with dendrite structures show high signal processing capability that is analogous to a multilayer perceptron (MLP), whereas oversimplified point neuron models are still prevalent in artificial intelligence algorithms and neuromorphic systems and fundamentally limit their efficiency and functionality of the systems constructed. In this study, we propose a dual-mode dendritic device based on electrolyte gated transistor, which can be operated to generate both supralinear and sublinear current–voltage responses when receiving input voltage pulses. We propose and demonstrate that the dual-mode dendritic devices can be used as a dendritic processing block between weight matrices and output neurons so as to dramatically enhance the expression ability of the neural networks. A dual-mode dendrites-enhanced neural network is therefore constructed with only two trainable parameters in the second layer, thus achieving 1000× reduction in the amount of second layer parameter compared to MLP. After training by back propagation, the network reaches 90.1% accuracy in MNIST handwritten digits classification, showing advantage of the present dual-mode dendritic devices in building highly efficient neuromorphic computing.

A Highly Efficient Computer Method for Solving Polynomial Equations Appearing in Engineering Problems

A highly efficient two-step simultaneous iterative computer method is established here for solving polynomial equations. A suitable special type of correction helps us to achieve a very high computational efficiency as compared to the existing methods so far in the literature. Analysis of simultaneous scheme proves that its convergence order is 14. Residual graphs are also provided to demonstrate the efficiency and performance of the newly constructed simultaneous computer method in comparison with the methods given in the literature. In the end, some engineering problems and some higher degree complex polynomials are solved numerically to validate its numerical performance.

The Problem of Determining Discount Rate for Integrated Investment Projects in the Oil and Gas Industry

The main aim of this paper was to examine specific approaches to determining the discount rate for comprehensive computation of investment projects efficiency in the oil and gas industry. The objective of the study was to develop a scientific approach for determining the discount rate for integrated oil and gas projects. The authors analyze dynamic methods for determining the efficiency of investment projects in the oil and gas industry and conclude that they are advisable for oil and gas projects due to the high capital intensity of the projects and their long payback period. Regarding the need to implement dynamic indicators of efficiency, the authors set the task of deter-mining the proper discount rate as a factor having a significant impact on effectiveness evaluation. The discount rate is proposed to be evaluated by solving the equation and finding the break-even point where the NPV (net present value) of the integrated project will be equal to 0 (taking into account the revenue of the subprojects included in the complex). The practical implementation of methodological approaches to assessing the discount rate for integrated projects is relevant due to the execution of large, systemically important and integrated projects. As a result of the study, the authors put forward a methodological algorithm for determining the discount rate of an integrated project which assumes an assessment of cash flows for the subprojects included in the complex; determination of the target rate of return for subprojects; and calculation of prices for products at which a complex project become break-even. The practical implementation of methodological approaches to assessing the discount rate for integrated projects is relevant due to the execution of large systemically important integrated projects.

Using analog computers in today's largest computational challenges

Abstract. Analog computers can be revived as a feasible technology platform for low precision, energy efficient and fast computing. We justify this statement by measuring the performance of a modern analog computer and comparing it with that of traditional digital processors. General statements are made about the solution of ordinary and partial differential equations. Computational fluid dynamics are discussed as an example of large scale scientific computing applications. Several models are proposed which demonstrate the benefits of analog and digital-analog hybrid computing.

А Gpu-based Orthogonal Matrix Factorization Algorithm that Produces a Two-Diagonal Shape


 
 
 With the development of the Big Data sphere, as well as those fields of study that we can relate to artificial intelligence, the need for fast and efficient computing has become one of the most important tasks nowadays. That is why in the recent decade, graphics processing unit computations have been actively developing to provide an ability for scientists and developers to use thousands of cores GPUs have in order to perform intensive computations. The goal of this research is to implement orthogonal decomposition of a matrix by applying a series of Householder transformations in Java language using JCuda library to conduct a research on its benefits. Several related papers were examined. Malaschonok and Savchenko in their work have introduced an improved version of QR algorithm for this purpose [4] and achieved better results, however Householder algorithm is more promising for GPUs according to another team of researchers – Lahabar and Narayanan [6]. However, they were using Float numbers, while we are using Double, and apart from that we are working on a new BigDecimal type for CUDA. Apart from that, there is still no solution for handling huge matrices where errors in calculations might occur.
 The algorithm of orthogonal matrix decomposition, which is the first part of SVD algorithm, is researched and implemented in this work. The implementation of matrix bidiagonalization and calculation of orthogonal factors by the Hausholder method in the jCUDA environment on a graphics processor is presented, and the algorithm for the central processor for comparisons is also implemented. Research of the received results where we experimentally measured acceleration of calculations with the use of the graphic processor in comparison with the implementation on the central processor are carried out. We show a speedup up to 53 times compared to CPU implementation on a big matrix size, specifically 2048, and even better results when using more advanced GPUs. At the same time, we still experience bigger errors in calculations while using graphic processing units due to synchronization problems. We compared execution on different platforms (Windows 10 and Arch Linux) and discovered that they are almost the same, taking the computation speed into account. The results have shown that on GPU we can achieve better performance, however there are more implementation difficulties with this approach.
 
 

An Energy Efficient UAV-Based Edge Computing System with Reliability Guarantee for Mobile Ground Nodes.

An edge computing system is a distributed computing framework that provides execution resources such as computation and storage for applications involving networking close to the end nodes. An unmanned aerial vehicle (UAV)-aided edge computing system can provide a flexible configuration for mobile ground nodes (MGN). However, edge computing systems still require higher guaranteed reliability for computational task completion and more efficient energy management before their widespread usage. To solve these problems, we propose an energy efficient UAV-based edge computing system with energy harvesting capability. In this system, the MGN makes requests for computing service from multiple UAVs, and geographically proximate UAVs determine whether or not to conduct the data processing in a distributed manner. To minimize the energy consumption of UAVs while maintaining a guaranteed level of reliability for task completion, we propose a stochastic game model with constraints for our proposed system. We apply a best response algorithm to obtain a multi-policy constrained Nash equilibrium. The results show that our system can achieve an improved life cycle compared to the individual computing scheme while maintaining a sufficient successful complete computation probability.

Efficient low-scaling computation of NMR shieldings at the second-order Møller-Plesset perturbation theory level with Cholesky-decomposed densities and an attenuated Coulomb metric.

A method for the computation of nuclear magnetic resonance (NMR) shieldings with second-order Møller-Plesset perturbation theory (MP2) is presented which allows to efficiently compute the entire set of shieldings for a given molecular structure. The equations are derived using Laplace-transformed atomic orbital second-order Møller-Plesset perturbation theory as a starting point. The Z-vector approach is employed for minimizing the number of coupled-perturbed self-consistent-field equations that need to be solved. In addition, the method uses the resolution-of-the-identity approximation with an attenuated Coulomb metric and Cholesky decomposition of pseudo-density matrices. The sparsity in the three-center integrals is exploited with sparse linear algebra approaches, leading to reduced computational cost and memory demands. Test calculations show that the deviations from NMR shifts obtained with canonical MP2 are small if appropriate thresholds are used. The performance of the method is illustrated in calculations on DNA strands and on glycine chains with up to 283 atoms and 2864 basis functions.

A population-based game-theoretic optimizer for the minimum weighted vertex cover

Toward higher solution efficiency and faster computation, this paper addresses the minimum weighted vertex cover (MWVC) problem by introducing game theory into iterated optimization and proposing a population-based game-theoretic optimizer (PGTO). A group of candidate solutions are iterated through the specially designed procedures of swarm evolution (SE), learning in games (LIG), and local search (LS) successively. Within the framework of potential game theory, we prove that LIG computes in finite time Nash equilibria that represent vertex cover solutions where no redundant nodes exist. Moreover, theoretical analysis is presented that by exchanging the actions of certain neighbours, LS is capable of generating better results upon the input Nash equilibrium. Through intensive numerical experiments, we show that while enlarging the population size could boost the global objective, a mutation probability between 0.05 and 0.2 is more likely to provide the best performance. Comparison experiments against the state of the art demonstrate the superiority of the presented methodology, both in terms of solution quality and computation speed.

Leakage Reuse for Energy Efficient Near-Memory Computing of Heterogeneous DNN Accelerators

The exploration of custom deep neural network (DNN) accelerators for highly energy constrained edge devices with on-device intelligence is gaining traction in the research community. Despite the superior throughput and performance of custom accelerators as compared to CPUs or GPUs, the energy efficiency and versatility of state-of-the-art DNN accelerators is constrained due to a) the storage and movement of a large volume of data and b) the limited scope of monolithic architectures, where the entire accelerator executes only a single model at any given time. In this paper, a multi-voltage domain heterogeneous DNN accelerator is proposed that executes multiple models simultaneously with different power-performance operating points. The proposed architecture concurrently implements near-memory computing and leakage reuse, where the leakage current of idle memory banks within each processing element is utilized to deliver current to the adjacently placed multiply-and-accumulate (MAC) units. The proposed architecture and circuit techniques are evaluated with SPICE simulation in a 65 nm CMOS technology. The simulation results indicate that the proposed heterogeneous architecture with leakage reuse improves the energy efficiency to 3.27 tera-operations per second per watt (TOPS/W) as compared to a conventional monolithic and single voltage domain architecture that exhibits an energy efficiency of 0.0458 TOPS/W. In addition, the proposed accelerator that applies the leakage reuse technique to only half of the memory elements storing the weights reduces the power consumption of the sub-arrays of processing elements by 100 mW as compared to an accelerator that does not apply leakage reuse.

An Augmented Reality Periscope for Submarines with Extended Visual Classification.

Submarines are considered extremely strategic for any naval army due to their stealth capability. Periscopes are crucial sensors for these vessels, and emerging to the surface or periscope depth is required to identify visual contacts through this device. This maneuver has many procedures and usually has to be fast and agile to avoid exposure. This paper presents and implements a novel architecture for real submarine periscopes developed for future Brazilian naval fleet operations. Our system consists of a probe that is connected to the craft and carries a 360 camera. We project and take the images inside the vessel using traditional VR/XR devices. We also propose and implement an efficient computer vision-based MR technique to estimate and display detected vessels effectively and precisely. The vessel detection model is trained using synthetic images. So, we built and made available a dataset composed of 99,000 images. Finally, we also estimate distances of the classified elements, showing all the information in an AR-based interface. Although the probe is wired-connected, it allows for the vessel to stand in deep positions, reducing its exposure and introducing a new way for submarine maneuvers and operations. We validate our proposal through a user experience experiment using 19 experts in periscope operations.

HSA-SPC: Hybrid Spectrum Access with Spectrum Prediction and Cooperation for Performance Enhancement of Multiuser Cognitive Radio Network

The spectrum sensing (SS) is employed to identify the status of licensed channel in the cognitive radio/user (CR/CU). Arbitrarily choosing the licensed channel for sensing in high traffic intensity network results into reduced throughput of cognitive user due to wait state problem in interweave mode. However, the wait state problem is overcome with hybrid spectrum access, resulting into improvement in throughput of CU. In addition, the sensing decisions without cooperation increases the sensing error and affect the sensing performance of CU resulting into reduction in throughput of CU. In this paper, we have proposed new approach that is Hybrid Spectrum Access with Spectrum Prediction and Cooperation (HSA-SPC) of cognitive users in which we have collectively employed spectrum prediction, cooperation and hybrid spectrum access to improve the sensing performance and throughput of CU. Further, for the proposed approach we have considered a new frame structure in multiuser environment and derived the closed form expression for the throughput. In addition to this, we have also shown the computation of energy efficiency for our proposed approach. Further, with the help of simulation results, we have shown the improvement in CU throughput with our proposed approach over reported literature. Comparison of various approaches with the proposed method HSA-SPC is also illustrated.