scholarly journals Audio Signal Identification and Search Approach for Minimizing the Search Time in Audio Fingerprinting Using Template Matching

2016 ◽  
Vol 3 (2) ◽  
pp. 23-33
Author(s):  
Mohammed Ahmed ◽  
Swaleha Shaikh ◽  
Hera Ansari ◽  
Arshi Siddiqui
Keyword(s):  
Template Matching ◽  
Search Time ◽  
Audio Signal ◽  
Signal Identification ◽  
Audio Fingerprinting ◽  
Search Approach

Parallel Scatter Search Approach for the MinMax Regret Location Problem

2018 ◽  
Vol 9 (2) ◽  
pp. 1-17
Author(s):  
Sarah Ibri ◽  
Mohammed EL Amin Cherabrab ◽  
Nasreddine Abdoune
Keyword(s):  
Location Problem ◽  
Search Algorithm ◽  
Discrete Event ◽  
Scatter Search ◽  
Search Time ◽  
Computation Time ◽  
Minmax Regret ◽  
Search Approach ◽  
Two Stages

In this paper we propose an efficient solving method based on a parallel scatter search algorithm that accelerates the search time to solve the minmax regret location problem. The algorithm was applied in the context of emergency management to locate emergency vehicles stations. A discrete event simulator was used to test the quality of the obtained solutions on the operational level. We compared the performance of the algorithm to an existing two stages method, and experiments show the efficiency of the proposed method in terms of quality of solution as well as the gain in computation time that could be obtained by parallelizing the proposed algorithm.

scholarly journals A flexible method to create wave file features

2021 ◽  
Vol 11 (2) ◽  
pp. 1311
Author(s):  
Ghazi M. J. Qaryouti
Keyword(s):  
Audio Signal ◽  
Digital Audio ◽  
Human Knowledge ◽  
Signal Identification ◽  
Important Data ◽  
Recognition Time ◽  
Audio File ◽  
Voice Identification ◽  
Small Set ◽  
The Voice

Digital audio signal is one of the most important data type at present, it is used in various vital applications, such as human knowledge, security and banking applications, most applications require signal identification and recognition, and to increase the efficiency of these applications we must seek a method to represent the audio file by a small set of values called a features vector. In this paper research we will introduce an enhanced method of features extraction based on k-mean clustering. The method will be tested and implemented to show how the proposed method can reduce the efforts of voice identification, and can minimize the recognition time a set of voice extracted features must be used instead of using the voice wave file.

scholarly journals Query Optimization in a Blockchain-Based Land Registry Management System

2021 ◽  
Vol 26 (1) ◽  
pp. 13-21
Author(s):  
Amrendra Singh Yadav ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Network Architecture ◽  
Hash Table ◽  
Search Time ◽  
Consensus Algorithm ◽  
Decentralized System ◽  
Blockchain Technology ◽  
Time Required ◽  
Technical Discussion ◽  
Search Approach ◽  
Land Records

Digitization of land records is not sufficient for preventing fraud cases, time delay, and brokers' involvement. Distributed Ledger Technology (DLT) is used for making this digitized record more secure and process it in a decentralized way, and reduces paperwork in selling and buying of land. Blockchain technology has come to the fore in recent years and is the center of technical discussion, with intuitive applications driven by its network architecture. It has been firmly established as one of the most important emerging technologies. This article aims to implement a land registry mechanism using blockchain technology and optimize searching of land records in blockchain. Interplanetary File System (IPFS) provides an infrastructure that offers a precise portrayal of all the members' roles. The application interacts with the blockchain network, which is built using IPFS. This paper will help in providing a secure and decentralized system for the land registry process. The proposed consensus algorithm MRRCM achieves less time required to generated a block on an average by 3.06% round-robin and 96.48% PoW approach. The proposed modified hash table search approach requires less time to search the blockchain's land record block than the extensive liner searches and hash table search approach. A search for a land record in the blockchain reduces the search time on an average by 59.5% compared to the traditional extensive liner search approach and by 18.68% as compared with the hash table search approach.

scholarly journals AutoShrink: A Topology-Aware NAS for Discovering Efficient Neural Architecture

2020 ◽  
Vol 34 (04) ◽  
pp. 6829-6836
Author(s):  
Tunhou Zhang ◽  
Hsin-Pai Cheng ◽  
Zhenwen Li ◽  
Feng Yan ◽  
Chengyu Huang ◽  
...  
Keyword(s):  
Cell Structure ◽  
Search Time ◽  
Building Blocks ◽  
Search Space ◽  
Directed Acyclic Graphs ◽  
Neural Architecture ◽  
Cell Structures ◽  
Acyclic Graphs ◽  
Search Approach ◽  
Network Patterns

Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including both cell-based and node-based approaches, the search process is time consuming and the performance of found architecture may be sub-optimal. To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search (NAS) for searching efficient building blocks of neural architectures. Our method is node-based and thus can learn flexible network patterns in cell structures within a topological search space. Directed Acyclic Graphs (DAGs) are used to abstract DNN architectures and progressively optimize the cell structure through edge shrinking. As the search space intrinsically reduces as the edges are progressively shrunk, AutoShrink explores more flexible search space with even less search time. We evaluate AutoShrink on image classification and language tasks by crafting ShrinkCNN and ShrinkRNN models. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34% Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy of state-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1.5 GPU hours, which is 7.2× and 6.7× faster than the crafting time of SOTA CNN and RNN models, respectively.

Studi Perilaku Anggota Unit Pengambil Keputusan Dalam Pembelian Organisasional: Sebuah Review Literatur

2017 ◽  
Vol 24 (1) ◽  
pp. 71-86
Author(s):  
Amin Wibowo
Keyword(s):  
Decision Making ◽  
Information Search ◽  
Search Time ◽  
Integrated Approach ◽  
Review Literature ◽  
Buying Behavior ◽  
Organizational Buying ◽  
Decision Making Unit ◽  
Behavior Based ◽  
Sharing Responsibility

Up to now, organizational buying is still interesting topic discussed. There are divergences among the findings in organizational buying researches. Different perspectives, fenomena observed, research domains and methods caused the divergences. This paper will discusse organizational buying behavior based on literature review, focused on behavior of decision making unit mainly on equipment buying. From this review literatures, it would be theoritical foundation that is valid and reliable to develop propositions in organizational buying behavior. Based on review literature refferences, variables are classified into: purchase situation, member of decision making unit perception, conflict among the members, information search, influences among members of decision making unit. Integrated approach is used to develop propositions relating to: purchasing complexity, sharing responsibility among the members, conflict in decision making unit, information search, time pressure as moderating variable between sharing responsibility and conflict in decision making unit, the influence among the members inside decision making unit and decision making outcome

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