Improvement In Execution Time Research Articles

Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

Multi-Object Detection and Tracking (MODT) are essential in many application fields. Still, many enhancements in the speed of detection and tracking were required to overcome the challenges during implementation. This paper presents a new algorithm system for (MODT) to improve the execution time to be robust in real-time applications. A background subtraction detection algorithm with a Kalman filter was used to track and predict the object position and speed parameters. To improve the processing time, its needs to reduce some frames in a way that does not affect the detection accuracy too much and instead use the prediction and the estimated value obtained based on the Kalman filter for the tracked object. This work uses a single video camera to show how effectively to compute and detect multiple objects concurrently; it is applied for daytime preprocessing in an automated traffic surveillance system. Preliminary testing findings show that the suggested algorithm for this vehicle monitoring system is feasible and effective. It illustrates that using the suggested algorithm with a single video camera can simultaneously watch, detect, and track several vehicles and improve execution time. Simulation results on the built system demonstrate that the proposed system reduced the execution time to approximately 41.5% compared to the standard background subtraction algorithm. Results indicate the proposed algorithm has an approximate error for the position and speed of detected and tracked objects compared with the standard background subtraction algorithm. Index Terms— multi-object detection, tracking, background subtraction, Kalman filter, morphological.

너비 우선 탐색 기반 비재귀 트리 탐색 알고리즘의 효율적 구현과 성능 비교

The objective of this study is to present an efficient tree traversal algorithm in terms of execution time and implementation complexity. Trees, defined as acyclic connected graphs, are widely used in various fields of computer science. Unlike traditional recursive methods or depth first search implementations, this research proposes an implementation based on breadth first search. The algorithm improves execution time using a non-recursive approach and reduces implementation complexity by using only one-dimensional arrays and loops. Additionally, the algorithm has been optimized through a preprocessing phase. To evaluate the performance of the proposed algorithm compared to existing ones, trees with random structures were generated and their execution times were measured. The results demonstrated that the proposed algorithm exhibits efficient performance.

GPU-accelerated full-field modelling of highly dispersive ultrafast optical parametric oscillators

We demonstrate GPU-accelerated modelling of ultrafast optical parametric oscillators (OPOs) via the χ(2) nonlinear envelope equation with 1265× improvement in execution time compared with a CPU-based approach. Incorporating an adaptive step-size algorithm and absorbing boundary conditions, our model is capable of simulating OPOs containing long (>10 mm) nonlinear crystals or significant intracavity dispersion with outputs generated in less than 1 minute, allowing the investigation of systems that were previously computationally prohibitive to explore. We implement real-world parameters such as optical coatings, material absorption, and non-ideal poling domains within quasi-phase matched nonlinear crystals, producing excellent agreement with the spectral tuning behaviour and average power from a previously reported prism-based OPO. Our digital twinning approach provides a low-cost iterative development platform for ultrafast OPOs.

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

DBSCAN inspired task scheduling algorithm for cloud infrastructure

Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling.

An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE

Providing end-to-end security is vital for most networks. Emerging quantum computers make it necessary to design secure crypto-systems against quantum attacks. Binary Ring Learning With Error (Ring-Bin LWE) is a Lattice-based cryptography that is hard to solve by quantum computers. Also, this algorithm does not have costly operations in terms of area, making Ring-Bin LWE a suitable algorithm for resource-constraint devices. This work presents a lightweight hardware implementation of Ring-Bin LWE. In the proposed design, a new multiplication method and design for Ring-Bin LWE is introduced which results in latency reduction by a factor of two. Using column-based multiplication, our design processes two consecutive coefficients in each cycle. The architecture is designed based on the proposed multiplication and contains one specific register bank with two sub-bank registers. The design is implemented on the FPGA platforms. The implementation results show an impressive improvement in execution time and Area-Time metrics over previous similar works.

Communication-hiding pipelined BiCGSafe methods for solving large linear systems

Recently, a new variant of the BiCGStab method, known as the pipelined BiCGStab, has been proposed. This method can achieve a higher degree of scalability and speed-up rates through a mechanism in which the communication phase for the computation of the inner product can be overlapped with the computation of the matrix-vector product. Meanwhile, several generalized iteration methods with better convergence behavior than BiCGStab exist, such as ssBiCGSafe, BiCGSafe, and GPBi-CG. Among these methods, ssBiCGSafe, which requires a single phase of computing inner products per iteration, is best suited for high-performance computing systems. As described herein, inspired by the success of the pipelined BiCGStab method, we propose pipelined variations of the ssBiCGSafe method in which only one phase of inner product computation per iteration is required and this phase of inner product computation can be overlapped with the matrix-vector computation. Through numerical experimentation, we demonstrate that the proposed methods engender improvements in convergence behavior and execution time compared to the pipelined BiCGStab and ssBiCGSafe methods.

Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework.

Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown impressive performance for the video analytics task. However, these newly introduced methods either exclusively focus on model performance or the effectiveness of these models in terms of computational efficiency, resulting in a biased trade-off between robustness and computational efficiency in their proposed methods to deal with challenging HAR problem. To enhance both the accuracy and computational efficiency, this paper presents a computationally efficient yet generic spatial-temporal cascaded framework that exploits the deep discriminative spatial and temporal features for HAR. For efficient representation of human actions, we propose an efficient dual attentional convolutional neural network (DA-CNN) architecture that leverages a unified channel-spatial attention mechanism to extract human-centric salient features in video frames. The dual channel-spatial attention layers together with the convolutional layers learn to be more selective in the spatial receptive fields having objects within the feature maps. The extracted discriminative salient features are then forwarded to a stacked bi-directional gated recurrent unit (Bi-GRU) for long-term temporal modeling and recognition of human actions using both forward and backward pass gradient learning. Extensive experiments are conducted on three publicly available human action datasets, where the obtained results verify the effectiveness of our proposed framework (DA-CNN+Bi-GRU) over the state-of-the-art methods in terms of model accuracy and inference runtime across each dataset. Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.

Fuzzy based optimized itemset mining in high dimensional transactional database using adaptable FCM

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.

Design and Development of a Fog-Assisted Elephant Corridor over a Railway Track

Elephants are one of the largest animals on earth and are found in forests, grasslands and savannahs in the tropical and subtropical regions of Asia and Africa. A country like India, especially the northeastern region, is covered by deep forests and is home to many elephants. Railroads are an effective and inexpensive means of transporting goods and passengers in this region. Due to poor visibility in the forests, collisions between trains and elephants are increasing day by day. In the last ten years, more than 190 elephants died due to train accidents. The most effective solution to this collision problem is to stop the train immediately. To address this sensitive issue, a solution is needed to detect and monitor elephants near railroad tracks and analyze data from the camera trap near the intersection of elephant corridors and railroad tracks. In this paper, we have developed a fog computing-based framework that not only detects and monitors the elephants but also improves the latency, network utilization and execution time. The fog-enabled elephant monitoring system informs the train control system of the existence of elephants in the corridor and a warning light LED flashes near the train tracks. This system is deployed and simulated in the iFogSim simulator and shows improvements in latency, network utilization, and execution time compared to cloud-based infrastructures.

XNOR-Bitcount Operation Exploiting Computing-In-Memory With STT-MRAMs

This brief presents an energy-efficient and high-performance XNOR-bitcount architecture exploiting the benefits of computing-in-memory (CiM) and unique properties of spin-transfer torque magnetic RAM (STT-MRAM) based on double-barrier magnetic tunnel junctions (DMTJs). Our work proposes hardware and algorithmic optimizations, benchmarked against a state-of-the-art CiM-based XNOR-bitcount design. Simulation results show that our hardware optimization reduces the storage requirement (–50%) for each XNOR-bitcount operation. The proposed algorithmic optimization improves execution time and energy consumption by about 30% (78%) and 26% (85%), respectively, for single (5 sequential) 9-bit XNOR-bitcount operations. As a case study, our solution is demonstrated for shape analysis using bit-quads.

HLS-based High-throughput and Work-efficient Synthesizable Graph Processing Template Pipeline

Hardware systems composed of diverse execution resources are being deployed to cope with the complexity and performance requirements of Artificial Intelligence (AI) and Machine Learning (ML) applications. With the emergence of new hardware platforms, system-wide programming support has become much more important. While this is true for various devices ranging from CPUs to GPUs, it is especially critical for specific neural network accelerators implemented on FPGAs. For example, Intel’s recent HARP platform encompasses a Xeon CPU and an FPGA, which requires an intense software stack to be used effectively. Programming such a hybrid system will be a challenge for most of the non-expert users. High-level language solutions such as Intel OpenCL for FPGA try to address the problem. However, as the abstraction level increases, the efficiency of implementation decreases, depicting two opposing requirements. In this work, we propose a framework to generate HLS-based, FPGA-accelerated, high-throughput/work-efficient, synthesizable, and template-based graph-processing pipeline. While a fixed and clock-wise precisely designed deep-pipeline architecture, written in SystemC, is responsible for processing graph vertices, the user implements the intended iterative graph algorithm by implementing/modifying only a single module in C/C++. This way, efficiency and high performance can be achieved with better programmability and productivity. With similar programming efforts, it is shown that the proposed template outperforms a high-throughput OpenCL baseline by up to 50% in terms of edge throughput. Furthermore, the novel work-efficient design significantly improves execution time and power consumption by up to 100×.

Enhancement of OPTICS’ time complexity by using fuzzy clusters

Density-Based clustering are the main clustering algorithms because they can cluster data with different shapes and densities, but some of these algorithms have high time complexity like OPTICS (Ordering Points to Identify Clustering Structure) and DBSCAN (Density-based spatial clustering of applications with noise) where the time complexity up to O(n2). In this paper, we use an approach to reduce this complexity by providing fuzzy clusters to OPTICS, which make the process of finding neighbours within an only narrow region (fuzzy group) instead of searching all the state space making the algorithm faster than the original one with keeping almost the same accuracy of the innovative algorithm. The results show that there is an improvement in execution time using some synthetic and real datasets with high dimensions.

An Optimal Control Method for Storage Systems With Ramp Constraints, Based on an On-Going Trimming Process

We propose an optimal control method for storage systems that are affected by the power grid’s ramp constraints. Such problems are becoming increasingly important in recent years due to the integration of renewable energy sources, which often leads to duck curve effects. The main idea of the proposed method is to limit the number of computations in the forward recursion stage by applying an ongoing trimming process, which reduces the number of discrete energy values being scanned. As a result, the practical range of values is significantly reduced, which leads to a lower computational burden. To verify our results, the proposed method is compared to a classic solution method from the literature, showing a significant improvement in overall execution time and memory requirements when considering a typical test case.

Improved secure dynamic bit standard technique for a private cloud platform to address security challenges

The numerous technologies and procedures used to safeguard private cloud infrastructure are private cloud security. Despite multitenant public cloud environments, resources are dedicated to individual companies in a private cloud. Cloud computing uses several data security mechanisms, including authentication and identification, access control, encryption, secure deletion, integrity checking, and data masking. Cloud computing relies heavily on security and traffic management. Because data must be transferred from a local computer to a remote computer when using a cloud computing service, data protection is becoming an increasingly important security problem. Research on cloud computing-based secure dynamic bit standard (CC-SDBS) technology has increased data security. This technique’s cryptographic algorithms (CA) improve cloud computing security. As a result of the private cloud security level algorithm (PCSLA), the client’s private data can be easily accessed in the cloud. For the client’s private cloud data, PCSLA provides dynamic security measures. Cloud computing applications can use the tests’ better data security in the future. The proposed algorithm’s testing results have shown a noticeable improvement in cipher size and execution time compared with other commonly used cloud computing. Numerical results revealed that CC-SDBS improved average energy consumption by 20%, average network latency by 25%, average key generation time analysis by 4.3%, and average network use by 15%.

Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.

A novel approach for handling semantic trajectories on data warehouses

A trajectory is a set of traces left by a moving object. It contains spatio-temporal information about where and when that object was, as well as other semantical relevant information. It is described by a continuation of movement. Data concerning moving objects and their trajectories can be stored in a Trajectory Data Warehouses for organization, managing, and analysis purposes. This work is dedicated to semantic trajectory data warehouses. A logical schema is proposed, called S-TrODW, where an object relational framework is used. The main novelty of the S-TrODW model is the integration of trajectories and their segments in the fact table by means of a nested relation. An algorithm is presented for transforming the flat star schema (with non-nested trajectory segments) to the S-TrODW schema. The proposal is validated through a case study dealing with freight transportation. A more natural modelling and queries formulation, as well as the improvement of query execution time are among the contributions of this paper compared to other works.

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of the hottest server components (i.e., cores and cache). Previous methods focused on either improving energy efficiency by adopting new hybrid-cache architectures including the resistive random-access memory (RRAM) and static random-access memory (SRAM) at the hardware level, or exploring tradeoffs between lifetime limitation and performance of multicore processors under stable workloads conditions. Therefore, no work has so far proposed a co-optimization method with hybrid-cache-based server architectures for real-life dynamic scenarios taking into account scalability, performance, lifetime reliability, and energy efficiency at the same time. In this article, we first formulate a reliability model for the hybrid-cache architecture to enable precise lifetime reliability management and energy efficiency optimization. We also include the performance and energy overheads of cache switching, and optimize the benefits of hybrid-cache usage for better energy efficiency and performance. Then, we propose a runtime <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -learning-based reliability management and performance optimization approach for multicore microprocessors with the hybrid-cache architecture, jointly incorporated with a dynamic preemptive priority queue management method to improve the overall tasks’ performance by targeting to respect their end time limits. Experimental results show that our proposed method achieves up to 44% average performance (i.e., tasks execution time) improvement, while maintaining the whole system design lifetime longer than five years, when compared to the latest state-of-the-art energy efficiency optimization and reliability management methods for computing servers.

Arbitrarily Parallelizable Code: A Model of Computation Evaluated on a Message-Passing Many-Core System

The number of processing elements per solution is growing. From embedded devices now employing (often heterogeneous) multi-core processors, across many-core scientific computing platforms, to distributed systems comprising thousands of interconnected processors, parallel programming of one form or another is now the norm. Understanding how to efficiently parallelize code, however, is still an open problem, and the difficulties are exacerbated across heterogeneous processing, and especially at run time, when it is sometimes desirable to change the parallelization strategy to meet non-functional requirements (e.g., load balancing and power consumption). In this article, we investigate the use of a programming model based on series-parallel partial orders: computations are expressed as directed graphs that expose parallelization opportunities and necessary sequencing by construction. This programming model is suitable as an intermediate representation for higher-level languages. We then describe a model of computation for such a programming model that maps such graphs into a stack-based structure more amenable to hardware processing. We describe the formal small-step semantics for this model of computation and use this formal description to show that the model can be arbitrarily parallelized, at compile and runtime, with correct execution guaranteed by design. We empirically support this claim and evaluate parallelization benefits using a prototype open-source compiler, targeting a message-passing many-core simulation. We empirically verify the correctness of arbitrary parallelization, supporting the validity of our formal semantics, analyze the distribution of operations within cores to understand the implementation impact of the paradigm, and assess execution time improvements when five micro-benchmarks are automatically and randomly parallelized across 2 × 2 and 4 × 4 multi-core configurations, resulting in execution time decrease by up to 95% in the best case.