Key Optimization Techniques Research Articles

Mastering Real-Time Data Processing Applications : Optimization Strategies for Peak Performance

This comprehensive article explores strategies for optimizing real-time data processing applications in the era of big data and distributed systems. It examines the growing demand for real-time analytics across industries and organizations' challenges in maintaining low latency and scalability. The article discusses key optimization techniques, including leveraging the Akka framework's actor model and supervision strategies, implementing efficient data ingestion through stream processing and partitioning, utilizing caching strategies, employing robust monitoring and auto-scaling mechanisms, ensuring fault tolerance through checkpointing and graceful degradation, and adopting asynchronous communication patterns. Throughout the article, real-world case studies and performance metrics demonstrate the significant improvements these strategies can bring to system throughput, latency, and resilience in various sectors such as finance, e-commerce, telecommunications, and manufacturing.

Real-time FPGA Implementation of an Optic Flow Algorithm Suitable for Background Oriented Schlieren Technique

Abstract To meet the demands of real-time displacement detection in embedded systems, a real-time optical flow calculation method suitable for background oriented schlieren (BOS) technology was transplanted and implemented on an Field Programmable Gate Array(FPGA) system. Firstly, through the analysis of different optical flow operation characteristics and the comparison of processing results in MATLAB with actual experimental data, an optical flow algorithm suitable for real-time processing of background shadow images was selected. Subsequently, by using hardware pipelining and multimodule parallel processing, the single-layer L&K optical flow algorithm was transplanted onto the FPGA and algorithm optimization was performed to achieve real-time optical flow calculation based on adjacent frame images. The key optimization techniques in this paper mainly include cleverly designing Double-Data-Rate Three Synchronous Dynamic Random Access Memory (DDR3) memory partitioning read and write logic to avoid image read and write conflicts; ensuring the timing alignment and data synchronization of consecutive frames by using burst read arbitration modules and two independent First In First Out (FIFO) buffers; and in optical flow calculation, constructing a 3×3 window using a shifting FIFO method to effectively save cache resources and improve computational speed. Experimental results show that the system can achieve real-time processing speeds of not less than 60fps for 1024×768 resolution images, meeting the requirements of practical applications.

Harnessing the power of floating photovoltaic: A global review

As the global demand for energy continues to increase, floating photovoltaic (FPV) power is gaining more attention as a promising clean energy source. This paper summarizes the unique advantages of FPV, such as its freedom from land restrictions, higher energy output, and potential integration with other forms of energy. However, FPV also faces challenges due to complex and harsh environmental conditions such as wind, waves, corrosion, and biological fouling. Therefore, the integrated mechanism design and the use of lightweight hydrophobic materials are crucial for ensuring the safety and stability of FPV systems under such conditions. This article also highlights certain key design points and optimization techniques that can improve the structural reliability of FPV systems. With continuous technological advancements and the accumulation of experience, the production efficiency of FPV has improved, leading to the expansion of FPV projects and a reduction in investment costs. As a result, the development of FPV has been accelerating globally, with numerous high-capacity projects being constructed. Building upon the previous literature reviews, this paper provides a concise review of the latest FPV case studies, innovative technologies, challenges in marine environments, economic costs, and market prospects from various perspectives. The primary objective is to encourage further research and application in the field of FPV.

Stabilizing the training of deep neural networks using Adam optimization and gradient clipping

The field of neural network training and optimization has seen significant advancements in recent years, with new techniques and algorithms being proposed to improve the efficiency and effectiveness of training. In this paper, we review several key optimization techniques and their impact on training neural networks, with a focus on long-term dependencies and the difficulties that can arise during training. We begin by discussing the challenges of learning long-term dependencies with gradient descent, as highlighted in the 1994 paper by Bengio et al. We then introduce Adam, a method for stochastic optimization proposed by Kingma and Ba in 2014. We also explore the difficulties of training recurrent neural networks, as discussed in the 2013 paper by Pascanu, Mikolov, and Bengio. We also review recent advances in optimization techniques such as Convergence of Adam and Beyond by Jianmin et al. (2017), Yogi by Dong et al. (2018), AdaBound by Zhang et al. (2018) and On the Variance of the Adaptive Learning Rate and Beyond by Liu et al. (2019). We will highlight the advantages and disadvantages of each technique and discuss their potential impact on the field. Overall, this paper provides a comprehensive overview of recent advancements in neural network optimization and their implications for training and performance. Keywords: Deep neural networks, optimization, Adam, gradient clipping, training, stabilization, overfitting, generalization, recurrent neural networks, non-convex loss landscapes.

Retraction Note to: Architectural framework and simulation of quantum key optimization techniques in healthcare networks for data security

Retraction Note to: Architectural framework and simulation of quantum key optimization techniques in healthcare networks for data security

FgSpMSpV: A Fine-grained Parallel SpMSpV Framework on HPC Platforms

Sparse matrix-sparse vector (SpMSpV) multiplication is one of the fundamental and important operations in many high-performance scientific and engineering applications. The inherent irregularity and poor data locality lead to two main challenges to scaling SpMSpV over high-performance computing (HPC) systems: (i) a large amount of redundant data limits the utilization of bandwidth and parallel resources; (ii) the irregular access pattern limits the exploitation of computing resources. This paper proposes a fine-grained parallel SpMSpV (fgSpMSpV) framework on Sunway TaihuLight supercomputer to alleviate the challenges for large-scale real-world applications. First,fgSpMSpVadopts an MPI\( + \)OpenMP\( +X \)parallelization model to exploit the multi-stage and hybrid parallelism of heterogeneous HPC architectures and accelerate both pre-/post-processing and main SpMSpV computation. Second,fgSpMSpVutilizes an adaptive parallel execution to reduce the pre-processing, adapt to the parallelism and memory hierarchy of the Sunway system, while still tame redundant and random memory accesses in SpMSpV, including a set of techniques like the fine-grained partitioner, re-collection method, and Compressed Sparse Column Vector (CSCV) matrix format. Third,fgSpMSpVuses several optimization techniques to further utilize the computing resources.fgSpMSpVon the Sunway TaihuLight gains a noticeable performance improvement from the key optimization techniques with various sparsity of the input. Additionally,fgSpMSpVis implemented on an NVIDIA Tesal P100 GPU and applied to the breath-first-search (BFS) application.fgSpMSpVon a P100 GPU obtains the speedup of up to\( 134.38\times \)over the state-of-the-art SpMSpV algorithms, and the BFS application usingfgSpMSpVachieves the speedup of up to\( 21.68\times \)over the state-of-the-arts.

LEOPARD: Parallel Optimal Deep Echo State Network Prediction Improves Service Coverage for UAV-Assisted Outdoor Hotspots

Unmanned aerial vehicle (UAV) base stations (BSs) can help meet the dynamic traffic demand of flash mobile crowds, but user movements also pose a significant challenge on fast-tracking for avoiding service interruption. This paper presents a novel paral <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LE</b> l <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ptimal dee <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> echo st <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> te netwo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> k pre <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> iction (LEOPARD) approach that can fast and accurately learn the movement of a user equipment (UE) to reduce its impact on the link performance from the UE to the UAV-BS. Improving the current learning technique of deep echo state network (ESN), LEOPARD consists further three key optimization and learning techniques. First, we develop a Bayesian-Optimization Algorithm (BOA)-based hyper-parameters adjustment method for improving movement prediction accuracy. Secondly, the Message Passing Interface (MPI) technique is integrated into the design of LEOPARD to reduce the time complexity caused by BOA. Last, we design a Kuhn-Munkres (KM)-based matching algorithm to save the re-positioning energy consumption of multiple UAV-BSs. As shown in our simulation results, the prediction accuracy of the proposed LEOPARD, combining DeepESN, BOA, and MPI techniques, is 78% and 67% better than the state-of-the-art shallow ESN and the original deep ESN, respectively.

RETRACTED ARTICLE: Architectural framework and simulation of quantum key optimization techniques in healthcare networks for data security

Healthcare systems are heterogeneous by nature, where each device is running on different architectures, platform and operating system. This heterogeneity affects the communication performance in-terms of delay and security threats. An optimized quantum key management technique has been proposed for the purpose of managing the keys with little overhead, which enhances the health care information security by reducing the threats. And also the communication with the key authority is simulated with the quantum channel. The generated key is distributed via a dedicated quantum channel in terms of quantum bits which decreases the eavesdropping rate, transmission error, and leakage to maximum extent which improves the security further. Healthcare User group and content server communicate with the key server via a quantum channel sending them photons. Then they discuss results using a public channel. After getting an encryption key from the key server via the quantum channel, the content server can encrypt their healthcare content and send them by any public channel to the healthcare user groups. This proposed research work investigates group secret key generation problems for different types of Healthcare networks and also addresses the quantum key distribution which enhances the key security in healthcare networks. The analysis shows that the two algorithms yield optimal group key rates in healthcare networks. Numerical results are also provided to validate the performance of the proposed key generation, optimization and quantum distribution. The key generation, optimization and healthcare content encryption and decryption using those keys enhances the security of patient data and quantum simulation shows that about 90% of eavesdropping rate is reduced in healthcare network.

Linear Algebra and Learning From Data [Bookshelf

This book contains the key linear algebra and optimization techniques at the forefront of active data-science and machine learning practice today. This is an appropriate choice of content because while state-of-the-art machine learning applications can change each month (as in reinforcement learning, language translation, game playing, or image classification), the underlying mathematical concepts and algorithms do not. The text is an offspring of a current Massachusetts Institute of Technology (MIT) mathematics course, Matrix Methods in Data Analysis and Signal Processing, which, in turn, was greatly influenced by a current University of Michigan electrical engineering and computer science course, Matrix Methods for Signal Processing, Data Analysis, and Machine Learning. Both courses are aimed at advanced undergraduate and graduate students in science and engineering. The book is broken down into seven parts. Part 1 presents highlights of linear algebra and covers a variety of basic matrix concepts, starting with matrix multiplication and moving up to singular values, principal components, low-rank approximations, and matrix/tensor factorizations. Part 2 discusses computation with large matrices, focusing on matrix factorizations, iterative methods, and recent powerful algorithms for approximating matrix problem solutions using randomization and projection. Due to the writing style, this is one of those books that you can open to almost any page and find a topic you didn’t know about or understand. After reading a few pages, it is quite likely that you will learn something new and useful. To me, such a resource has great value above and beyond its use solely as a textbook. I believe this textbook would be valuable for a matrix methods class oriented toward signal processing and machine learning. It would also be useful as a reference for someone seeking to be self-taught or review material learned years ago (but not used recently). Given that there is no discussion of system identification or recurrent networks for learning systems or automata, the book may be slightly less appealing to control theorists. Overall, a book of this kind is a very ambitious project. It covers many topics, several of which are typically reserved for dedicated graduate courses. Consequently, some depth must be sacrificed when such breadth is attempted. To help readers delve deeper, a more coherent approach to presenting references would be useful. As it stands, some references are embedded in the main text, while others are listed at the end of a section or chapter, but with inconsistent cross references. Some sections simply do not reference anything for further reading. Although one might argue that this is less of a problem (because Google Scholar and other web searches can help find original articles quite efficiently) it would still be helpful to get pointers about where to start. As this textbook becomes more widely used and the material continues to evolve, I believe a second edition that combines Jupyter Notebook with Matlab, Python, or TensorFlow examples would be a very welcome addition, consistent with the spirit of how machine learning practitioners actually work. In summary, this text is a valuable foundational asset for teaching, learning, and reference and is surely worth considering for your classroom and/or personal library.

Financial Fraud Detection using Bio-Inspired Key Optimization and Machine Learning Technique

Financial Fraud Detection using Bio-Inspired Key Optimization and Machine Learning Technique

The New Non-Volatile Based On STT-MRAM Lies in The Design and Implementation of High-Speed Cache of Magnetic Random Memory Material

With the development of semiconductor technology, the buffer of processor integration is getting larger and larger. The high-speed design of storage capacity of traditional memory devices has an important connection with cache. How to design new non-volatile magnetic random memory has become an important step. To solve these problems, researchers at home and abroad have discussed a large number of new non-volatile memory technologies, which have excellent characteristics such as non-volatility, low power consumption and high memory density. The key optimization techniques for the disadvantages of high write power, limited write life and long write latency of the new non-volatile memory are analyzed. Finally, the possible research directions of new non-volatile memory devices in future cache optimization are discussed.

Accelerating DynEarthSol3D on tightly coupled CPU–GPU heterogeneous processors

DynEarthSol3D (Dynamic Earth Solver in Three Dimensions) is a flexible, open-source finite element solver that models the momentum balance and the heat transfer of elasto-visco-plastic material in the Lagrangian form using unstructured meshes. It provides a platform for the study of the long-term deformation of earth's lithosphere and various problems in civil and geotechnical engineering. However, the continuous computation and update of a very large mesh poses an intolerably high computational burden to developers and users in practice. For example, simulating a small input mesh containing around 3000 elements in 20 million time steps would take more than 10 days on a high-end desktop CPU. In this paper, we explore tightly coupled CPU–GPU heterogeneous processors to address the computing concern by leveraging their new features and developing hardware-architecture-aware optimizations. Our proposed key optimization techniques are three-fold: memory access pattern improvement, data transfer elimination and kernel launch overhead minimization. Experimental results show that our proposed implementation on a tightly coupled heterogeneous processor outperforms all other alternatives including traditional discrete GPU, quad-core CPU using OpenMP, and serial implementations by 67%, 50%, and 154% respectively even though the embedded GPU in the heterogeneous processor has significantly less number of cores than high-end discrete GPU.

Throughput of the Magnetic Induction Based Wireless Underground Sensor Networks: Key Optimization Techniques

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Recently, a magnetoinductive (MI) waveguide technique has been proposed to cope with the very harsh propagation conditions in WUSNs. This relay-based approach allows for an extension of the transmission range, which can be quite limited if relays are not deployed. In this paper, tree-based WUSNs are considered. The objective of our work is to determine the optimal system parameters, topology, and deployment strategy in order to avoid bottlenecks in the system and achieve optimal network throughput. We compare two different deployment schemes: MI waveguides and direct MI transmission (no relays deployed) based connections between sensors. The two schemes are different in nature and propagation characteristics. Therefore, different optimization techniques are utilized. The optimal set of system parameters is chosen to maximize the channel capacity of the worst link and therefore optimize the available data rate. The bottleneck throughput of the direct MI transmission based network can be then compared with the respective results of the MI waveguides based network. In several cases, we observe a better performance of the direct MI transmission based networks.

Efficient Mode Decision Schemes for HEVC Inter Prediction

The emerging High Efficiency Video Coding (HEVC) standard reduces the bit rate by almost 40% over the preceding state-of-the-art Advanced Video Coding (AVC) standard with the same objective quality but at about 40% encoding complexity overhead. The main reason for HEVC complexity is inter prediction that accounts for 60%-70% of the whole encoding time. This paper analyzes the rate-distortion-complexity characteristics of the HEVC inter prediction as a function of different block partition structures and puts the analysis results into practice by developing optimized mode decision schemes for the HEVC encoder. The HEVC inter prediction involves three different partition modes: square motion partition, symmetric motion partition (SMP), and asymmetric motion partition (AMP) out of which the decision of SMPs and AMPs are optimized in this paper. The key optimization techniques behind the proposed schemes are: 1) a conditional evaluation of the SMP modes; 2) range limitations primarily in the SMP sizes and secondarily in the AMP sizes; and 3) a selection of the SMP and AMP ranges as a function of the quantization parameter. These three techniques can be seamlessly incorporated in the existing control structures of the HEVC reference encoder without limiting its potential parallelization, hardware acceleration, or speed-up with other existing encoder optimizations. Our experiments show that the proposed schemes are able to cut the average complexity of the HEVC reference encoder by 31%-51% at a cost of 0.2%-1.3% bit rate increase under the random access coding configuration. The respective values under the low-delay B coding configuration are 32%-50% and 0.3%-1.3%.

User-defined consistency sensitive cache invalidation strategies for wireless data access

In order to fulfill users’ insatiable interests in accessing Internet services and information wirelessly, one of the key optimization techniques is caching frequently accessed data items in a local cache. A strong consistency is implicitly assumed in most caching schemes but it may cause a long query delay. In this paper, we propose a consistency-sensitive cache invalidation scheme, called ConSens, based on the existing Invalidation Report (IR) and Updated IR (UIR) based cache invalidation frameworks. In the ConSens scheme, each user is able to set its own consistency level with a server independently. This user-defined cache consistency can support diverse consistency requirements of applications. We also propose both lazy request and opportunistic data access techniques to effectively balance the data accessibility and query delay. In addition, we enhance the IR-based cache invalidation mechanism and propose a multiple data transmission scheme, called MDT, to further reduce the query delay. Extensive performance evaluation studies show that the proposed strategies can effectively balance the data accessibility, reduce the query delay, and significantly increase the number of opportunistic accesses according to the user-defined consistencies.

Enhanced Dynamic Hierarchical Replication and Weighted Scheduling Strategy in Data Grid

The Data Grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to the limitation of available resources in the grid as well as production of large volumes of data, efficient use of the Grid resources becomes an important challenge. Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner. Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available. In this paper two strategies are proposed, first a novel job scheduling strategy called Weighted Scheduling Strategy (WSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in a queue, the location of the required data for the job and the computing capacity of the sites Second, a dynamic data replication strategy, called Enhanced Dynamic Hierarchical Replication (EDHR) that improves file access time. This strategy is an enhanced version of the Dynamic Hierarchical Replication strategy. It uses an economic model for file deletion when there is not enough space for the replica. The economic model is based on the future value of a data file. Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time. So, it is considered in this paper. The proposed strategies are implemented by OptorSim, the European Data Grid simulator. Experiment results show that the proposed strategies achieve better performance by minimizing the data access time and avoiding unnecessary replication.

Job scheduling and dynamic data replication in data grid environment

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper, two algorithms are proposed: first, a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that considers the number of jobs waiting in queue, the location of required data for the job, and computational capability; second, a dynamic data replication strategy called Dynamic Hierarchical Replication Algorithm (DHRA) that improves file access time. DHRA stores each replica in an appropriate site, i.e., appropriate site in the requested region that has the highest number of access for that particular replica. Also, it can minimize access latency by selecting the best replica when various sites hold replicas of datasets. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.

Combination of data replication and scheduling algorithm for improving data availability in Data Grids

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper two algorithms are proposed, first a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in queue, the location of required data for the job and the computing capacity of sites. Second a dynamic data replication strategy, called the Modified Dynamic Hierarchical Replication Algorithm (MDHRA) that improves file access time. This strategy is an enhanced version of Dynamic Hierarchical Replication (DHR) strategy. Data replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement. MDHRA replaces replicas based on the last time the replica was requested, number of access, and size of replica. It selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.

MUSer2: An Efficient MUS Extractor1

Algorithms for extraction of Minimally Unsatisfiable Subformulas (MUSes) of CNF formulas find a wide range of practical applications, including product configuration, knowledge-based validation, hardware and software design and verification. This paper describes the MUS extractor MUSer2. MUSer2 implements a wide range of MUS extraction algorithms, integrates a number of key optimization techniques, and represents the current state-of-the-art in MUS extraction.

GPU Acceleration in a Visual Servo System

In this paper we present our novel work of using the Graphic Processing Unit (GPU) to improve the performance of a homography-based visual servo system. We propose a GPU accelerated Efficient Second-order Minimization (GPU-ESM) algorithm to ensure a fast and stable homography solution, approximately 20 times faster than its CPU implementation. To enhance the system stability, we adopt a GPU accelerated Scale Invariant Feature Transform (SIFT) algorithm to deal with those cases where GPU-ESM algorithm performs poor, such as large image differences, occlusion and so on. The combination of both GPU accelerated algorithms is described in detail. The effectiveness of our GPU accelerated system is evaluated with experimental data. The key optimization techniques in our GPU applications are presented as a reference for other researchers.