scholarly journals Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

2021 ◽  
Vol 17 (02) ◽  
pp. 37
Author(s):  
Maryam A. Yasir ◽  
Yossra Hussain Ali
Keyword(s):  
Real Time ◽  
High Performance ◽  
Performance Metrics ◽  
Computation Time ◽  
Background Extraction ◽  
Accurate Performance ◽  
Real Time Applications ◽  
Conventional Models ◽  
Evaluation Approaches ◽  
Extraction Model

<p>In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.</p><p>In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. </p><p>In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.</p>

scholarly journals EOBAA: Enhanced Ontology Based Alignment Algorithm for Mining Frequent Patterns

2018 ◽  
Vol 7 (3.12) ◽  
pp. 157
Author(s):  
D Srinivasa Rao ◽  
V Sucharitha ◽  
K V.V Satyanarayana
Keyword(s):  
Real Time ◽  
High Performance ◽  
Computation Time ◽  
Search Space ◽  
Search Tree ◽  
Frequent Itemsets ◽  
Alignment Algorithm ◽  
Frequent Patterns ◽  
Pruning Strategy ◽  
Real Time Applications

Mining frequent patterns are most widely used in many applications such as supermarkets, diagnostics, and other real-time applications. Performance of the algorithm is calculated based on the computation of the algorithm. It is very tedious to compute the frequent patterns in mining. Many algorithms and techniques are implemented and studied to generate the high-performance algorithms such as Prepost+ which employees the N-list to represent itemsets and directly discovers frequent itemsets using a set-enumeration search tree. But due to its pruning strategy, it is known that the computation time is more for processing the search space. It enumerates all item sets from datasets by the principle of exhaustion and they don’t sort them based on utility, but only a statistical proof of most recurring itemset. In this paper, the proposed Enhanced Ontologies based Alignment Algorithm (EOBAA) to identify, extract, sort out the HUI's from FI's. To improve the similarity measure the proposed system adopted Cosine similarity. The experiments conducted on 1 real datasets and show the performance of the EOBAA based on the computation time and accuracy of the proposed EOBAA.  

scholarly journals HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

2018 ◽  
Vol 7 (12) ◽  
pp. 467 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Wenze Luo ◽  
Luo Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Buffer Analysis ◽  
Data Volume ◽  
Time Buffer ◽  
Real World Datasets ◽  
Spatial Indexes

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

scholarly journals Approximate Adder: Lower-Part or Adder

2020 ◽  
Vol 9 (7) ◽  
pp. 1176-1180
Keyword(s):  
Real Time ◽  
Power Dissipation ◽  
High Performance ◽  
Building Blocks ◽  
Cmos Technology ◽  
Approximate Computing ◽  
Computing Technique ◽  
Ripple Carry Adder ◽  
Real Time Applications ◽  
Dsp Systems

High-performance VLSI systems are essential in real-time applications, in order to increase the performance of the VLSI systems, an approximate computing technique is followed where the performance of the circuit is enhanced by trading off it with a slight loss in the accuracy. These approximate circuits are used in error-tolerant applications, where output need not be accurate. This paper concentrates mainly on approximate adders, as they are major building blocks of DSP systems. The analysis of the Lower-part OR Adder for 4-bit addition and comparison of it with the precise adder i.e., Ripple Carry Adder using the mentor graphics tool in 90 nm CMOS technology are presented in this paper. Our experimental results show that there is 17%-70% savings in power dissipation, 4%-32% saving in the area, and 19%-84% savings in time due to approximate adder. As the LOA-2 and LOA-3 are performing optimally these two adders can be used for error-tolerant applications and based on the requirement LOA-2 or LOA-3 can be selected.

Novel prioritized LRU circuits for shared cache in computer systems

2020 ◽  
Vol 34 (23) ◽  
pp. 2050242
Author(s):  
Yao Wang ◽  
Lijun Sun ◽  
Haibo Wang ◽  
Lavanya Gopalakrishnan ◽  
Ronald Eaton
Keyword(s):  
Real Time ◽  
High Performance ◽  
Cmos Technology ◽  
Cache Replacement ◽  
Worst Case ◽  
Shared Cache ◽  
Cache Access ◽  
Worst Case Execution Time ◽  
Real Time Applications ◽  
Time Systems

Cache sharing technique is critical in multi-core and multi-threading systems. It potentially delays the execution of real-time applications and makes the prediction of the worst-case execution time (WCET) of real-time applications more challenging. Prioritized cache has been demonstrated as a promising approach to address this challenge. Instead of the conventional prioritized cache schemes realized at the architecture level by using cache controllers, this work presents two prioritized least recently used (LRU) cache replacement circuits that directly accomplish the prioritization inside the cache circuits, hence significantly reduces the cache access latency. The performance, hardware and power overheads due to the proposed prioritized LRU circuits are investigated based on a 65 nm CMOS technology. It shows that the proposed circuits have very low overhead compared to conventional cache circuits. The presented techniques will lead to more effective prioritized shared cache implementations and benefit the development of high-performance real-time systems.

Review of Imbalanced Data Classification and Approaches Relating to Real-Time Applications

2021 ◽  
pp. 1-22
Author(s):  
Anjali S. More ◽  
Dipti P. Rana
Keyword(s):  
Real Time ◽  
Performance Metrics ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Classification ◽  
Disease Diagnosis ◽  
Skewed Data ◽  
Data Imbalance ◽  
Imbalanced Data Classification ◽  
Real Time Applications

In today's era, multifarious data mining applications deal with leading challenges of handling imbalanced data classification and its impact on performance metrics. There is the presence of skewed data distribution in an ample range of existent time applications which engrossed the attention of researchers. Fraud detection in finance, disease diagnosis in medical applications, oil spill detection, pilfering in electricity, anomaly detection and intrusion detection in security, and other real-time applications constitute uneven data distribution. Data imbalance affects classification performance metrics and upturns the error rate. These leading challenges prompted researchers to investigate imbalanced data applications and related machine learning approaches. The intent of this research work is to review a wide variety of imbalanced data applications of skewed data distribution as binary class data unevenness and multiclass data disproportion, the problem encounters, the variety of approaches to resolve the data imbalance, and possible open research areas.

scholarly journals Impact of Channel Bonding on Network Performance in Real-Time Applications

2019 ◽  
Vol 9 (1) ◽  
pp. 4361-4366
Keyword(s):  
Real Time ◽  
Performance Metrics ◽  
Local Area Network ◽  
Network Throughput ◽  
Area Network ◽  
File Transfer ◽  
Ieee 802.11N ◽  
Channel Bonding ◽  
Real Time Applications ◽  
The Impact

This paper investigates the impact of channel bonding property provided in wireless technology on performance in real-time applications. IEEE 802.11n is an amendment to the IEEE 802.11 Wireless Local Area Network (WLAN) standard, which aims to extensively improve network throughput over legacy WLANs. This new network technology provides a better performance for general Internet applications such as web service and file transfer. However, the recent network measurements show that real-time application traffic is consistently increasing in the Internet. Real-time applications such as Voice over IP (VoIP) or video conferencing requires distinct performance metrics compared to the general Internet services in that they prioritize delay, latency, and delay jitter rather than network throughput. This paper investigates how such real-time applications perform in IEEE 802.11n WLANs. Our indoor experiments show that 802.11n basically supports better service than the previous WLAN standards. The channel bonding technique in 802.11n further improves the performance even under mobile conditions.

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