Efficient Implementation Research Articles

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

Optimizing Web-Based Survey Applications with Laravel and Cloud Computing

This study aims to optimize the performance of a web-based survey application through the implementation of the Laravel framework and cloud computing technology. Survey applications often face challenges regarding response speed and scalability as the number of users increases. In this research, performance testing of the application was conducted both before and after optimization. The test results show that the application's response time significantly decreased after the implementation of Laravel and cloud technology. Before optimization, the average response time reached 4.5 seconds at 1,000 users, while after optimization, it dropped to 2.2 seconds. This performance improvement was achieved through efficient caching implementation, database query optimization using Eloquent ORM, and balanced load distribution via load balancers. Additionally, the application's availability increased to 99.9% thanks to cloud features such as auto-scaling and data replication. The conclusion of this study indicates that the combination of Laravel and cloud computing technology is effective in enhancing the performance and reliability of web-based survey applications, providing practical guidance for other web application developers facing similar challenges.

Ultrafast diffraction algorithms for light-in-flight holography

Digital light-in-flight (LIF) holography is an ultrafast imaging technique capable of single-shot simultaneous 3D and femtosecond time resolution acquisitions of light pulse propagation. However, the numerical diffraction algorithms used to model light on femtosecond timescales are currently limited in scope, accuracy, and efficiency. We derive an analytical model capable of modeling LIF hologram formation for various optical setup configurations, able to model 3D objects and precisely account for the limited temporal coherence of the signal. We design an efficient algorithmic implementation and validate the system in numerical simulations and with an experimental LIF holographic recording setup. We report ultrafast numerical diffraction over 10,000 times faster than the reference technique, with higher accuracy and capable of modeling 3D samples, thereby broadening its application domain.

A Model-Driven Approach to Developing Scalable Educational Software for Adaptive Learning Environments

This research presents a model-driven approach to the development of scalable educational software tailored to adaptive learning environments. With the increasing demand for personalized education, adaptive learning systems play a crucial role in meeting diverse student needs by adjusting instructional content dynamically. This paper proposes a software engineering framework that integrates model-driven development (MDD) techniques with scalability principles, allowing for the efficient design and implementation of educational applications that can handle varying workloads and user demands. The framework emphasizes modular architecture, reusability, and flexibility to ensure that software can evolve with emerging educational requirements. Key components include the design of a learning content management system (LCMS) and the application of adaptive algorithms to personalize learning pathways. Additionally, this study explores the integration of cloud technologies to enhance the scalability and performance of educational platforms. A prototype system was developed and tested in a controlled environment, showing significant improvements in scalability, system performance, and student engagement compared to traditional static e-learning platforms. The results indicate that the model-driven approach not only improves software development efficiency but also offers a robust solution for creating adaptive educational systems that can scale to meet the growing needs of learners and institutions. This research contributes to the field of educational software development by providing a systematic methodology for building scalable and adaptive learning environments using advanced software engineering techniques.

An efficient hardware implementation of SHA-3 using 3D cellular automata for secure blockchain-based IoT systems

Abstract This paper introduces an innovative hardware implementation of the Secure Hash Algorithm (SHA-3) using three-dimensional cellular automata (3D-CA) for secure blockchain-based IoT systems. Our approach optimizes the SHA-3 algorithm by leveraging the parallel processing capabilities of 3D-CA, addressing the computational challenges in resource-constrained Internet of Things (IoT) environments. Our proposed design is implemented on various Field Programmable Gate Arrays (FPGA) circuits. The obtained results demonstrate the potential of our design to address the demands of blockchain-based IoT systems effectively. Furthermore, a comparative analysis reveals that our hardware implementation offers competitive performance compared to previous works. To ensure security and validate output randomness, the design undergoes rigorous evaluation using the Strict Avalanche Criterion (SAC) and the NIST Statistical Test Suite (STS). The results demonstrate that the proposed architecture exhibits high sensitivity to input changes while maintaining robust resistance against cryptanalytic attacks.

Development of a fuzzy analytic hierarchy process model, sensitivity analysis, and addressing barriers to additive manufacturing implementation in small- and medium-sized enterprises (SMEs)

Additive manufacturing (AM) is gaining popularity worldwide due to its potential to revolutionize manufacturing processes, improve product customization, and reduce waste. Nevertheless, small- and medium-sized enterprises (SMEs) encounter major challenges when implementing AM technologies. The objective of this research is to utilize the fuzzy analytic hierarchy process (FAHP) to identify and prioritize the critical challenges that impede the implementation of AM for SMEs. The study employs a two-stage methodology. First, a thorough literature review and expert discussions identify 15 major barriers to AM adoption. Second, a systematic model is proposed to establish a ranking of these barriers, which is based on the FAHP. The results are validated using sensitivity analysis to ensure the robustness of the findings. Additionally, potential solutions to overcome these barriers are discussed. The research indicates that the two most significant barriers for SMEs are the high costs of equipment and the scarcity of skilled designers. The model that has been presented is a valuable resource for SME managers and practitioners, as it allows them to make strategic and informed decisions that facilitate the efficient implementation of AM. This research is groundbreaking in its comprehensive analysis of the prioritization and ranking of AM implementation barriers, with a particular emphasis on SMEs. It offers essential insights for future research and practical applications.

Digital Technologies for Promoting Construction and Demolition Waste Management: A Systematic Review

The increasing concern about the environment has led to the necessity of ensuring efficient Construction and Demolition Waste Management (C&DWM) in the built environment. Despite the extensive research on C&DWM, the industry still faces significant challenges, including inefficiencies, high costs, and environmental impacts. Meanwhile, incorporating digital technologies (DTs) has emerged as a way to eradicate the challenges of C&DW. In response to the knowledge gap, in this research, we conducted a systematic literature review (SLR), incorporating bibliometric, text-mining, and content analysis to meet the research objectives. In total, 126 papers were retrieved from the Scopus database and transferred into VOSviewer to conduct the bibliometric analysis. The findings identified seven specific DTs, namely, blockchain, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Robotics, Computer Vision (CV), and Building Information modeling (BIM). This study demonstrates that these technologies play a significant role in promoting efficient C&DWM in the construction industry. The study’s implication lies in its potential to guide industry stakeholders and policymakers in promoting the use of DTs and overcoming the barriers to their adoption, thereby facilitating more efficient and sustainable C&DWM practices. Finally, the findings of our research indicate possible future research directions for promoting DTs for C&DWM and eradicating the barriers to efficient implementation.

Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice

Following a large-scale radiological event, timely collection of samples from all potentially exposed individuals may be precluded, and high-throughput bioassays capable of rapid and individualized dose assessment several days post-exposure will be essential for population triage and efficient implementation of medical treatment. The objective of this work was to validate the performance of a biomarker panel of radiosensitive intracellular leukocyte proteins (ACTN1, DDB2, and FDXR) and blood cell counts (CD19+ B-cells and CD3+ T-cells) for retrospective classification of exposure and dose estimation up to 7 days post-exposure in an in-vivo C57BL/6 mouse model. Juvenile and adult C57BL/6 mice of both sexes were total body irradiated with 0, 1, 2, 3, or 4 Gy, peripheral blood was collected 1, 4, and 7-days post-exposure, and individual blood biomarkers were quantified by imaging flow cytometry. An ensemble machine learning platform was used to identify the strongest predictor variables and combine them for biodosimetry outputs. This approach generated successful exposure classification (ROC AUC = 0.94, 95% CI: 0.90–0.97) and quantitative dose reconstruction (R2 = 0.79, RMSE = 0.68 Gy, MAE = 0.53 Gy), supporting the potential utility of the proposed biomarker assay for determining exposure and received dose in an individual.

Fast and Optimal Extraction for Sparse Equality Graphs

Equality graphs (e-graphs) are used to compactly represent equivalence classes of terms in symbolic reasoning systems. Beyond their original roots in automated theorem proving, e-graphs have been used in a variety of applications. They have become particularly important as the key ingredient in the popular technique of equality saturation, which has notable applications in compiler optimization, program synthesis, program verification, and symbolic execution, among others. In a typical equality saturation workflow, an e-graph is used to store a large number of equalities that are generated by local rewrites during a saturation phase, after which an optimal term is extracted from the e-graph as the output of the technique. However, despite its crucial role in equality saturation, e-graph extraction has received relatively little attention in the literature, which we seek to start addressing in this paper. Extraction is a challenging problem and is notably known to be NP-hard in general, so current equality saturation tools rely either on slow optimal extraction algorithms based on integer linear programming (ILP) or on heuristics that may not always produce the optimal result. In fact, in this paper, we show that e-graph extraction is hard to approximate within any constant ratio. Thus, any such heuristic will produce wildly suboptimal results in the worst case. Fortunately, we show that the problem becomes tractable when the e-graph is sparse, which is the case in many practical applications. We present a novel parameterized algorithm for extracting optimal terms from e-graphs with low treewidth, a measure of how “tree-like” a graph is, and prove its correctness. We also present an efficient Rust implementation of our algorithm and evaluate it against ILP on a number of benchmarks extracted from the Cranelift benchmark suite, a real-world compiler optimization library based on equality saturation. Our algorithm optimally extracts e-graphs with treewidths of up to 10 in a fraction of the time taken by ILP. These results suggest that our algorithm can be a valuable tool for equality saturation users who need to extract optimal terms from sparse e-graphs.

Automatically Reducing Privilege for Access Control Policies

Access control policies are programs used to secure cloud resources. These polices should only grant the necessary permissions that a given application needs. However, it is challenging to write and maintain policies as applications and their required permissions change over time. In this paper, we focus on the Amazon Web Services (AWS) IAM policy language and present an approach that, given a policy, synthesizes a modified policy that is more restrictive and better abides to the principle of least privilege. Our approach looks at the actual access history (e.g., access logs) used by an application and computes the least permissive local modification of the user-given policy that still provides the same permissions that were observed in the access history. We treat the problem of computing the least permissive policy as a generalization problem in a lattice of possible policies (i.e., the set of local modifications). We show that our synthesis algorithm comes with correctness guarantees and is amendable to an efficient implementation that is easy to parallelize. We implement our algorithm in a tool IAM-PolicyRefiner and evaluate it on policies attached to AWS roles with access logs. For each role, IAM-PolicyRefiner can compute easy-to-inspect refined policies in less than 1 minute, and the refined policies do not overfit to the requests in the log---i.e., the policies also allow requests in a left-out test set of requests.

Fine Granularity Is Critical for Intelligent Neural Network Pruning.

Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) can remove more total network parameters before reaching a given degree of accuracy loss, while methods that preserve some or all of a network's structure (coarser granularity, such as pruning channels from a CNN) take better advantage of hardware and software optimized for dense matrix computations. We compare intelligent iterative pruning using several different criteria sampled from the literature against random pruning at initialization across multiple granularities on two different architectures and three image classification tasks. Our work is the first direct and comprehensive investigation of the relationship between granularity and the efficacy of intelligent pruning relative to a random-pruning baseline. We find that the accuracy advantage of intelligent over random pruning decreases dramatically as granularity becomes coarser, with minimal advantage for intelligent pruning at granularity coarse enough to fully preserve network structure. For instance, at pruning rates where random pruning leaves ResNet-20 at 85.0% test accuracy on CIFAR-10 after 30,000 training iterations, intelligent weight pruning with the best-in-context criterion leaves it at about 90.0% accuracy (on par with the unpruned network), kernel pruning leaves it at about 86.5%, and channel pruning leaves it at about 85.5%. Our results suggest that compared to coarse pruning, fine pruning combined with efficient implementation of the resulting networks is a more promising direction for easing the trade-off between high accuracy and low computational cost.

A Design Review for Biomedical Wireless Power Transfer Systems with a Three-Coil Inductive Link through a Case Study for NICU Applications

This paper outlines a design approach for biomedical wireless power transfer systems with a focus on three-coil inductive links for neonatal intensive care unit applications. The relevant literature has been explored to support the design approach, equations, simulation results, and the process of experimental analysis. The paper begins with a brief overview of various power amplifier classes, followed by an in-depth examination of the most common power amplifiers used in biomedical wireless power transfer systems. Among the traditional linear and switching amplifier classes, class-D and class-E switching amplifiers are highlighted for their enhanced efficiency and straightforward implementation in biomedical contexts. The impact of load variation on these systems is also discussed. This paper then explores the basic concepts and essential equations governing inductive links, comparing two-coil and multi-coil configurations. In the following, the paper discusses foundational coil parameters and provides theoretical and experimental analysis of both two-coil and multi-coil inductive links through step-by-step measurement techniques using lab equipment and addressing the relevant challenges. Finally, a case study for neonatal intensive care unit applications is presented, showcasing a wireless power transfer system operating at 13.56 MHz for powering a wearable device on a patient lying on a mattress. An inductive link with a transmitter coil embedded in a mattress is designed to supply power to a load at distances ranging from 4 cm to 12 cm, simulating the mattress-to-chest distance of an infant. the experimental results of a three-coil inductive link equipped with a Class-E power amplifier are reported, demonstrating power transfer efficiency ranging from 75% to 25% and power delivery to a 500 Ω-load varying from 340 mW to 25 mW over various distances.

A short-list of pairing-friendly curves resistant to the Special TNFS algorithm at the 192-bit security level

For more than two decades, pairings have been a fundamental tool for designing elegant cryptosystems, varying from digital signature schemes to more complex privacy-preserving constructions. However, the advancement of quantum computing threatens to undermine public-key cryptography. Concretely, it is widely accepted that a future large-scale quantum computer would be capable to break any public-key cryptosystem used today, rendering today's public-key cryptography obsolete and mandating the transition to quantum-safe cryptographic solutions. This necessity is enforced by numerous recognized government bodies around the world, including NIST which initiated the first open competition in standardizing post-quantum (PQ) cryptographic schemes, focusing primarily on digital signatures and key encapsulation/public-key encryption schemes. Despite the current efforts in standardizing PQ primitives, the landscape of complex, privacy-preserving cryptographic protocols, e.g., zkSNARKs/zkSTARKs, is at an early stage. Existing solutions suffer from various disadvantages in terms of efficiency and compactness and in addition, they need to undergo the required scrutiny to gain the necessary trust in the academic and industrial domains. Therefore, it is believed that the migration to purely quantum-safe cryptography would require an intermediate step where current classically secure protocols and quantum-safe solutions will co-exist. This is enforced by the report of the Commercial National Security Algorithm Suite version 2.0, mandating transition to quantum-safe cryptographic algorithms by 2033 and suggesting to incorporate ECC at 192-bit security in the meantime. To this end, the present paper aims at providing a comprehensive study on pairings at 192-bit security level. We start with an exhaustive review in the literature to search for all possible recommendations of such pairing constructions, from which we extract the most promising candidates in terms of efficiency and security, with respect to the advanced Special TNFS attacks. Our analysis is focused, not only on the pairing computation itself, but on additional operations that are relevant in pairing-based applications, such as hashing to pairing groups, cofactor clearing and subgroup membership testing. We implement all functionalities of the most promising candidates within the RELIC cryptographic toolkit in order to identify the most efficient pairing implementation at 192-bit security and provide extensive experimental results.

Quantum Equation-of-Motion Method with Single, Double, and Triple Excitations.

The quantum equation-of-motion (qEOM) method with single and double excitations (qEOM-SD) has been proposed to study electronically excited states, but it fails to handle states dominated by double excitations. In this work, we reformulate the qEOM method within the effective Hamiltonian framework that satisfies the killer condition, and then present an efficient implementation incorporating single, double, and triple excitations. To reduce computational complexity, we employ point-group symmetry and perturbation theory to screen triple excitations, effectively reducing the scaling from No6Nv6 to No5Nv5, where No and Nv are the numbers of occupied and virtual spin orbitals, respectively. Furthermore, we account for the effect of neglected triple excitations by introducing a perturbative correction to the excitation energy. We apply this method to challenging cases where the qEOM-SD method exhibits significant errors, such as the 2 1Δ state of CH+ and the 2 1Σ state of HF. Our new method achieves energy errors below 0.18 eV while incorporating less than 8.2% of triple excitations. Additionally, we extend the operator screening technique to the quantum subspace expansion method for the efficient inclusion of selected triple excitations.

Far-field sound field estimation using robotized measurements and the boundary elements method

Sound Field Estimation (SFE) is a numerical technique widely used to identify and reconstruct the acoustic fields radiated by unknown structures. In particular, SFE proves to be useful when data is only available close to the source, but information in the whole space is required. However, the practical implementation of this method is still hindered by two major drawbacks: the lack of efficient implementation of existing numerical methodologies, and the time-consuming and tedious roll-out of acoustic measurements. This paper aims to provide a solution to both issues. First, the measurements step is fully automated by using a robotic arm, able to accurately gather geometric and acoustic data without any human assistance. In this matter, a particular attention has been paid to the impact of the robot on the acoustic pressure measurements. The sound field prediction is then tackled using the Boundary Element Method (BEM), and implemented using the FreeFEM++ BEM library. Numerically simulated measurements have allowed us to assess the method accuracy, and the overall solution has been successfully tested using actual robotized measurements of an unknown loudspeaker.

Robotic arm, 3D vision and geometric matching for an efficient implementation of inverse Patch Transfer Function method

The inverse patch transfer function method was developed to reconstruct the acoustic fields (velocity, pressure, intensity) at the surface of a vibrating source with a complex geometry using the concept of a virtual acoustic domain. This method can be used to manage sources with complex shapes in a non-anechoic acoustic environment, even in the presence of masking objects. However, this method is quite demanding experimentally as it requires a large number of measurement positions with good spatial positioning accuracy. This article presents a dedicated experimental platform developed to simplify and automate the entire measurement process. First, a 3D camera placed on a robotic arm constructs a point cloud characterising the source and its environment. Using a geometric matching algorithm, the CAD of the object under test is detected in the cloud of points, enabling the different coordinate systems (CAD, robot and physical part) to be matched. Secondly, the measurement points required to apply iPTF can be defined directly in the CAD and reproduced in near-real time on the test rig, regardless of the type of antenna used, providing an efficient means of applying iPTF and opening the way to many advanced applications.

Compensation of staircase-induced boundary absorption errors in FDTD simulation

Finite-difference time-domain (FDTD) methods are popular for wave-based simulations because their regular grid enables highly efficient implementation, especially on parallel processing architectures. On the flip side, these methods exhibit errors caused by the staircase approximations that result when arbitrary boundary geometries are approximated on the fixed grid. Of particular concern in room acoustics is the staircasing of absorptive boundaries, as this has been shown to cause errors as large as 40% in computed reverberation times that do not reduce when the grid is refined. This paper analyses the effectiveness of a previously proposed compensation method that consists of modifying the admittance boundary conditions based on the staircased surface area. A theoretical analysis shows that for special boundary orientations the numerical reflection coefficient converges to the exact result at low frequencies. Numerical tests complement this analysis to show that the compensation yields accurate results at intermediate orientations as well. A spherical resonator case further demonstrates that a close match between numerically and analytically computed relaxation times is also obtained for curved boundaries.

Comprehensive management of gestational diabetes mellitus: practical efficacy of exercise therapy and sustained intervention strategies.

Gestational Diabetes Mellitus (GDM) affects 14.0% of pregnancies globally, with a 35% post-pregnancy relapse and a 60% risk of Type 2 Diabetes (T2D) within 5-10 years. Challenges in long-term management, especially postpartum, include adherence and follow-up difficulties. This study, based on a systematic review and meta-analysis, examined the practical effects of exercise therapy in the prevention, treatment, and prevention of progression from Gestational Diabetes Mellitus (GDM) to Type 2 Diabetes (T2D). Relevant research and clinical practices were retrieved from six major databases (PubMed, Scopus, Web of Science, Cochrane Library, MEDLINE, Science Direct). After analyzing the intervention effects of exercise therapy at different stages, factors favorably influencing the effectiveness of exercise intervention were identified during the more effective stages. Finally, a long-term and efficient exercise implementation plan for the comprehensive management of GDM was proposed. In GDM prevention, exercise reduced the post-intervention risk by 37% compared to the control group (Relative Risk (RR)=0.63; 95% Confidence Interval (CI): 0.54 to 0.72; p=0.01). Studies on GDM treatment showed improved glucose control in the exercise group post-intervention (Mean Difference (MD)=-0.10; 95% CI: -0.16 to -0.04; p=0.04/MD=-0.27; 95% CI: -0.36 to -0.19; p<0.0001). However, exercise therapy didn't significantly affect the incidence of T2D post-GDM (RR=0.88; 95% CI: 0.69 to 1.11; p=0.39) due to challenges in quantified exercise prescriptions and the complexity of postpartum programs. To enhance exercise therapy effectiveness in GDM management, the study recommends adopting an integrated model emphasizing personalized pregnancy plans, postpartum strategies, and long-term support. Leveraging frequent healthcare contact during pregnancy can establish and sustain exercise habits, fostering a lifelong pattern. While the study acknowledges limitations, this approach holds potential for improving glycemic metabolism and developing healthy exercise habits in subsequent generations. Future research should include longer follow-ups to validate the practical efficacy of this approach in preventing T2D after GDM. https://www.crd.york.ac.uk/prospero, identifier CRD42023463617.

Simulation-based effective comparative analysis of neuron circuits for neuromorphic computation systems

The spiking neural networks (SNN) which are inspired by the human brain offers wider scope for application in the growth of neuromorphic computing systems due to their brain level computational capabilities, reduced power consumption, minimal data movement cost, and among other advantages. Spike-based neurons and synapses are the essential building blocks of SNN, and their efficient implementation is vital to its performance enhancement. In this regard, the design and implementation of spiking neurons have been the major focus among the researchers. In this paper, functioning of different leaky integrate fire (LIF)-based spiking neuron circuits like frequency adaptable CMOS-based LIF, resistor-capacitor-based (RC) LIF, and volatile memristor-based LIF are subjected to comparison. The work mainly focuses on revealing analysis of spike duration and amplitude, number of spikes produced during excitation period, threshold operation, field of application, and various other significant parameters of aforementioned neuron circuits. Extensive simulations of these circuits are carried out utilizing the Cadence Virtuoso simulation environment in order to validate their behavior. Further, a brief comparative analysis is executed considering into account the attributes like circuit complexity, supply voltage, firing rate, membrane capacitance, nature of input/output, refractory mechanism, and energy consumption per spike. This work seeks to assist researchers in selecting an appropriate LIF model to efficiently construct memristors and/or non-memristors based SNN for certain application.

Mesh Convolution With Continuous Filters for 3-D Surface Parsing.

Geometric feature learning for 3-D surfaces is critical for many applications in computer graphics and 3-D vision. However, deep learning currently lags in hierarchical modeling of 3-D surfaces due to the lack of required operations and/or their efficient implementations. In this article, we propose a series of modular operations for effective geometric feature learning from 3-D triangle meshes. These operations include novel mesh convolutions, efficient mesh decimation, and associated mesh (un)poolings. Our mesh convolutions exploit spherical harmonics as orthonormal bases to create continuous convolutional filters. The mesh decimation module is graphics processing unit (GPU)-accelerated and able to process batched meshes on-the-fly, while the (un)pooling operations compute features for upsampled/downsampled meshes. We provide an open-source implementation of these operations, collectively termed Picasso. Picasso supports heterogeneous mesh batching and processing. Leveraging its modular operations, we further contribute a novel hierarchical neural network for perceptual parsing of 3-D surfaces, named PicassoNet++. It achieves highly competitive performance for shape analysis and scene segmentation on prominent 3-D benchmarks. The code, data, and trained models are available at https://github.com/EnyaHermite/Picasso.