Limited Processing Time Research Articles

Feedback Beamforming in the Time Domain.

Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

STEP TOWARDS INTELLIGENT TRANSPORTATION SYSTEM WITH VEHICLE CLASSIFICATION AND RECOGNITION USING SPEEDED-UP ROBUST FEATURES

<p>Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.</p>

BICE: Exploring Compact Search Space by Using Bipartite Matching and Cell-Wide Verification

Subgraph matching is the problem of searching for all embeddings of a query graph in a data graph, and subgraph query processing (also known as subgraph search) is to find all the data graphs that contain a query graph as subgraphs. Extensive research has been done to develop practical solutions for both problems. However, the existing solutions still show limited query processing time due to a lot of unnecessary computations in search. In this paper, we focus on exploring as compact search space as possible by using three techniques: (1) pruning by bipartite matching, (2) pruning by failing sets with bipartite matching, and (3) cell-wide verification. We propose a new algorithm BICE, which combines these three techniques. We conduct extensive experiments on real-world datasets as well as synthetic datasets to evaluate the effectiveness of the techniques. Experiments show that our approach outperforms the fastest existing subgraph search algorithm by up to two orders of magnitude in terms of elapsed time to process a query. Our approach also outperforms state-of-the-art subgraph matching algorithms by up to two orders of magnitude.

A genetic algorithm based decision support system for forecasting security prices in stock index

Recent studies in finance argue that technical analysis has the ability to predict stock prices. Though a variety of systems are used for market assessment and timing, past research has shown very little interest in optimizing the parameters of these systems. Genetic Algorithms (GA) are a soft computing based optimization procedure that optimizes a rule or parameters of a rule where search space is very large and it is not practically possible to test each and every parameter combination due to limited processing power and time. In this research we have used a GA based approach to optimize parameters of a pre-defined rule set that predicts the next-day’s stock price. Results obtained from our experiments are promising and encouraging enough to lead us to believe that Genetic Algorithm (GA) is an appropriate way of addressing these types of NP hard problems.

A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams.

Search for Optimal 2D Models of the U-net Neural Network for Solving the Problem of Semantic Segmentation of Tomographic Images of Hydrate-Containing Samples

The task of semantic segmentation of 2D-tomographic scans of hydrate-containing rocks is a multi-class classification of pixels of each input image in a set according to the classes “Granule”, “Fluid”, “Hydrate”. Now this is implemented in the form of segmentation by the “Granule” class using the convolutional architecture of the U-Net neural network and classification of pixels unclassified as “Granule” into the “Fluid” and “Hydrate” classes by the threshold value of pixel intensity.Considering the limited processing time of a large volume of tomographic data, it is necessary to find a compromise between the complexity of the model and the quality of segmentation. On the other hand, it is also required to propose a second, simpler implementation of the network, to extend it to a 3D segmentation model.The solution of these optimization problems is achieved by tuning the hyperparameters of the U-Net model. To determine which set of network hyperparameters is the best in a particular case, a partial search was performed over the hyperparameter grid, limited by the variables responsible for:1) the number of trained filters in convolution operations;2) learning the biases vector for output channels from convolutional operations;3) choosing an algorithm to increase the resolution in the network decoder part.This article describes the process of finding optimal models and provides an assumption about the possibilities for their improvement.

ESENSI PROKRASTINASI FUNGSIONAL MEMBENTUK INFORMATION LITERACY SELF-EFFICACY MAHASISWA TINGKAT AKHIR PROGRAM STUDI ILMU PERPUSTAKAAN DI INDONESIA

The process of writing a thesis for final year students of the library science study program provides a new platform for developing the ability to manage information to become new knowledge. It is often mentioned that writing a thesis presents its own challenges for students at every stage. The emergence of functional procrastination activity is no exception. The purpose of this study is to describe the essence of functional procrastination activities in the thesis writing process, both individually and in groups. The research method used is descriptive with a qualitative approach involving library science students from two universities as informants. The results showed that functional procrastination activity is an unavoidable but can be minimized part. This condition occurs because students have difficulty in analyzing and determining information based on their information needs. In general, functional procrastination activities have a positive essence on perfecting the results of the thesis, but it should be understood that these conditions need to be minimized considering the limited processing time and the expiration duration of the data held. In addition, the results of the study also found that a series of processes for completing thesis writing by students is a platform for developing information literacy self-efficacy.

Exploring vitamin-C as a retarder for calcium sulfoaluminate cement

Vitamin-C (VC), also known asascorbic acid,is a water solvable vitamin and can be found in various plants. Its function as an essential nutrient for human being is well known and has been extensively studied. This study explores a novel application of VC as a retarder for calcium sulfoaluminate cement (CSA). CSA has gained significant research interest for its potential as a low-carbon alternative of ordinary Portland cement (OPC). However, CSA sets very fast, leaving very limited processing time. Experimental studies carried out in this study show that the setting times of CSA can be extended over five times by adding VC at only 0.5% of the weight of the cementitious material. More importantly, this addition of VC doesn’t reduce the compressive strength of the produced mortar samples at 28d because their pore structure is refined by the added VC. The retarding mechanisms of VC on CSA can be attributed to both the surface adsorption and complexation with Ca2+.

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> RF chains and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M &lt; N$ </tex-math></inline-formula> . Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

Fuzzy Relational Scattered Distance Based Clustering Method for Sparsely Distributed High Dimensional Data Objects

Clustering is one of the most significant ideas in data mining. It is an unsupervised learning model. Clustering technique in handling high dimensional data is more complex due to intrinsic sparsity nature of high dimensional data. Though, existing methods to reduce immaterial clusters were based on spectral clustering algorithm and graph-based learning algorithm, whose lack of sparsity and polynomial time complexity compromises their efficiency when applied to sparse high dimensional data. This paper concentrates to cluster the sparsely distributed high dimensional data objects. Fuzzy Relational Scattered Distance Based Clustering (FRSDBC) method is developed with three models such as Geometric Median Based Fuzzy model, Scattered Distance measure model, Grid based clustered sparse data representation model. Geometric Median Based Fuzzy model calculates the geometric median of similar sparse data and then the non similar sparse data objects to fitting the relational fuzziness across data points. It involves in the subspace reduction of data objects. Scattered Distance measure model is used to measure the distance between the inner and outer object. Grid based clustering is used to calculate the area of the cluster in FRSDBC method. The main idea of the FRSDBC method is to clustering data points over sparsely distributed data within limited processing time. The Clustering Time, Clustering Accuracy and Space Complexity of each method is analyzed. The result of the FRSDBC method is compared with other techniques, the results obtained are more accurate, easy to understand and the clustering time was substantially low in FRSDBC method. It is widely used in many practical applications such as weather forecast, share trading, medical data analysis and aerial data analysis.

Component-Based Interactive Framework for Intelligent Transportation Cyber-Physical Systems.

While emerging technology for self-driving automation in vehicles progresses rapidly, the transition to an era of roads full of fully connected and automated vehicles (CAVs) may take longer than expected. Until then, it is inevitable that CAVs should coexist and interact with drivers of non-autonomous vehicles (NAVs) in urban roads. During this period of transition, it is critical to provide road safety with the mixed vehicular traffic and uncertainty caused by human drivers. To investigate the issues caused by the coexistence and interaction with humans, we propose to build a component-based and interactive intelligent transportation cyber-physical systems (ITCPS) framework. Our design of the interactive ITCPS framework aims to provide a standardized structure for users by defining core components. The framework is specified by behavior models and interfaces for the desired ITCPS components and is implemented as a form of human and hardware-in-the-loop system. We developed an intersection crossing assistance service and an automatic emergency braking service as an example of practical applications using the framework. To evaluate the framework, we tested its performance to show how effectively it operates while supporting real-time processing. The results indicate that it satisfies the timing requirements of vehicle-to-vehicle (V2V) communication and the limited processing time required for performing the functions of behavior models, even though the traffic volume reaches the road capacity. A case study using statistical analysis is conducted to assess the practical value of the developed experimental environment. The results of the case study validate the reliability among the specified variables for the experiments involving human drivers. It has shown that V2V communication support has positive effects on road safety, including intersection safety, braking events, and perception-reaction time (PRT) of the drivers. Furthermore, V2V communication support and PRT are identified as the important indicators affecting road safety at an un-signalized intersection. The proposed interactive framework is expected to contribute in constructing a comprehensive environment for the urban ITCPS and providing experimental support for the analysis of human behavior in the coexistence environment.

Large-Small Sorting for Successive Cancellation List Decoding of Polar Codes

The successive cancellation list (SCL) decoding is used to achieve good error-correcting performance for practical finite-length polar codes. However, the metric sorting that is repeatedly performed in SCL decoding increases the overall decoding latency as the list size increases, making it difficult to apply the SCL decoding to the applications with limited processing time. To reduce the latency of the metric sorting, this paper proposes a new sorting method derived by analyzing path extension cases encountered in SCL decoding. The proposed method can avoid unnecessary sorting operations by adaptively determining the sorting size, and can be combined with other metric sorting methods. Simulation results show that the proposed method significantly reduces the decoding latency for various list sizes and code rates without degrading the error-correcting performance. The proposed method also becomes more effective as the code rate or the list size increases. Combined with the state-of-the-art sorting method, the proposed method reduces the average SCL decoding latency for a (1024, 512) polar code by 33.1% when the list size is 8.

Determination of Wide Burnishing Energy-Force Parameters based on Constructing the Kinematically Admissible Velocity Field

Processing technology using productive surface plastic deformation (SPD) was developed and is known as “wide burnishing” (WB). The mechanics of new WB technology differs from classic SPD technologies (rolling or burnishing). For example, force, applied during burnishing, is equal to 150–300 N, but for WB it is equal to 2500–5000 N due to implementing this process in mass production, characterized with limited processing time (3–4 turns of the work piece). WB also has a high degree of deformation due to a multiple deformation passes. An analytical study of burnishing with the upper bound approach method energy-force parameters was carried out. Its results were compared with the results of finite elements analysis in Deform 2D and with experimental data. Field of reasonable burnishing parameters, assuring minimum surface roughness, was determined experimentally.

Flow micropillar array electroporation to enhance size specific transfection to a large population of cells

Despite serving as a popular non-viral delivery approach, electroporation carries several drawbacks in its current configurations. We developed a Flow Micropillar-array Electroporation (FME) system to wisely regulate an important transmembrane-determining factor, namely cell size variations among individual cells, to achieve effective transfection. In FME, cells flow through a slit-type microfluidic channel on which carbon electrodes with well-patterned micropillar array texture are integrated as the top and bottom wall. Gravity helps bring cells to the micropillar array surface so that the permeable area on cells in different size populations is specified by their size regardless their random location fact. Without sacrificing cell viability, we demonstrate this FME concept by delivering DNA plasmids to several mammalian cell lines with obvious transfection enhancement when compared to a commercial system (K562: 3.0 folds; A549: 3.3 folds; HeLa: 1.8 folds, COS7: 1.7 folds; 293T: 2.9 folds; mES: 2.5 folds). Moreover, carbon-based electrodes are less expensive, more durable, and convenient for integration with a microfluidic setup which enables rapid and massive transfection capability that many therapeutic application needs. The success of FME may benefit many emerging biological studies and clinical practice that requires effective transfection to a large population of cells in limited processing time.

Optimal calibration strategy for a five-axis machine tool accuracy improvement using the D-optimal approach

An optimisation process is proposed to design a measurement strategy for the indirect calibration of a five-axis machine tool using the SAMBA (Scale and Master Balls Artefact) method. The DETMAX algorithm is applied to select optimised artefact positions and lengths, and rotational machine axes angular positions, based on a comparison of maximised observability indices values. Simulation outcome produces probing sequences that offer a better estimation of axis location errors and error motions within a limited processing time. Different optimised calibration scenarios are then conducted simultaneously in a nested experimental test to ensure a fair selection of the probing strategy within the same operational conditions. Observability index results supported by uncertainty analysis suggest that a one master ball, for axis location errors, and four master balls, for axis location errors and error motions, probing sequences are the most efficient strategies for a number of measurement depending on the number of machine error model unknown parameters.

CubeDMA – Optimizing three-dimensional DMA transfers for hyperspectral imaging applications

Onboard computing is one of the principal needs in space-related technology in the recent years. In particular, onboard hyperspectral imaging (HSI) processing has advanced significantly. Due to advances in sensor technology, onboard HSI processing continuously meets new challenges related to increasing dataset size, limited processing time and limited communication links. High throughput and data reduction are crucial for satisfying real-time constraint and for preserving transmission bandwidth. For systems capable of accommodating a wide range of processing algorithms, there is a need for a flexible communication infrastructure that can provide fast access to/from memory in different access patterns. In this paper, existing FPGA-related Direct Memory Access (DMA) solutions have been evaluated, and a new DMA solution tailored for hyperspectral images has been proposed. Results show that the proposed DMA core, CubeDMA, handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient.

An Efficient Real-Time FPGA Implementation of the CCSDS-123 Compression Standard for Hyperspectral Images

Hyperspectral imaging (HSI) can extract information from scenes on the earth surface acquired by airborne or spaceborne sensors. On-board processing of HSI is characterized by large datasets on one side and limited processing time and communication links on the other. The CCSDS-123 algorithm is a compression standard assembled for space-related application that allows compacted data transmission via a transmission link. In this paper, a low-complexity high-throughput field-programmable gate array (FPGA) implementation of CCSDS-123 compression algorithm with band interleaved by pixel ordering is presented. Hardware accelerators implemented in FPGAs are increasingly used for custom tasks due to their efficiency, low power, and reconfigurability. The proposed implementation of CCSDS-123 compression standard has been tested on ZedBoard development board containing a Zynq-7020 FPGA. The results are verified against an existing software implementation. The synthesized design can perform on-the-fly processing of hyperspectral images with maximum operating frequency of 147 MHz. The achieved throughput of 147 Msamples/s (2.35 Gb/s) is higher when compared with the throughput reported in recent state-of-the-art FPGA implementations.

Merit-guided dynamic feature selection filter for data streams

Learning from ephemeral data streams has garnered the interest of both researchers and practitioners towards adaptive learning techniques. Despite the convincing results obtained thus far, most of the current research still overlooks that the relevance of features may change throughout the learning process. Scenarios where features become - or cease to be - relevant to the learning task are called feature drifting data streams, and the identification of which features are relevant becomes even more challenging when the feature space is high-dimensional. To select relevant features during the progress of data streams, we propose a merit-guided and classifier-independent dynamic feature selection algorithm named DynamIc SymmetriCal Uncertainty Selection for Streams (DISCUSS). We evaluate our proposal on both synthetic and real-world datasets and show that DISCUSS can boost kNN and Naive Bayes classifiers’ accuracy rates on high-dimensional data streams, while at the expense of limited processing time and memory space. Finally, the drawbacks of the proposed method are assessed, and possible future works on the topic are also discussed.

A Cloud-Based Parallel Space-Saving Algorithm for Big Networking Data

As the network continues to evolve, completely analyzing the traffic requires immeasurable resources. In situations of processing enormous streaming data, the most significant k items (Top-k) are more interesting, and some streaming algorithms are deployed due to relatively limited memory and also limited processing time per item. Space-saving is such one of the most popular algorithms for computation of frequent and Top-k elements in data streams. In this paper, this algorithm is implemented in the cloud for analyzing big networking data, and an empirical formula of the counter number is derived for efficiently maintaining Top-k items. Meanwhile, easily understandable proof manner is presented to prove the merging ability of Space-saving algorithm, and some experiments are conducted to affirm the effectiveness of the algorithm.

Benchmarking worker nodes using LHCb productions and comparing with HEPSpec06

In order to estimate the capabilities of a computing slot with limited processing time, it is necessary to know with a rather good precision its “power”. This allows for example pilot jobs to match a task for which the required CPU-work is known, or to define the number of events to be processed knowing the CPU-work per event. Otherwise one always has the risk that the task is aborted because it exceeds the CPU capabilities of the resource. It also allows a better accounting of the consumed resources.The traditional way the CPU power is estimated in WLCG since 2007 is using the HEP-Spec06 benchmark (HS06) suite that was verified at the time to scale properly with a set of typical HEP applications. However, the hardware architecture of processors has evolved, all WLCG experiments moved to using 64-bit applications and use different compilation flags from those advertised for running HS06. It is therefore interesting to check the scaling of HS06 with the HEP applications.For this purpose, we have been using CPU intensive massive simulation productions from the LHCb experiment and compared their event throughput to the HS06 rating of the worker nodes. We also compared it with a much faster benchmark script that is used by the DIRAC framework used by LHCb for evaluating at run time the performance of the worker nodes.This contribution reports on the finding of these comparisons: the main observation is that the scaling with HS06 is no longer fulfilled, while the fast benchmarks have a better scaling but are less precise. One can also clearly see that some hardware or software features when enabled on the worker nodes may enhance their performance beyond expectation from either benchmark, depending on external factors.