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Relevant bibliographies by topics / Smart sensors; Fault detection; Thermocouples / Journal articles

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Journal articles on the topic 'Smart sensors; Fault detection; Thermocouples'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Martinez, Dominique, and Daniel Estève. "Adaptive quantization and fault detection in smart sensors." Sensors and Actuators A: Physical 47, no. 1-3 (1995): 530–33. http://dx.doi.org/10.1016/0924-4247(94)00956-i.

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Ji, Yongzhi. "Application of fault detection using distributed sensors in smart cities." Physical Communication 46 (June 2021): 101182. http://dx.doi.org/10.1016/j.phycom.2020.101182.

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Trothe, Max Emil S., Hamid Reza Shaker, Muhyiddine Jradi, and Krzysztof Arendt. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings." Energies 12, no. 9 (2019): 1601. http://dx.doi.org/10.3390/en12091601.

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Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To t

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N.S.Kalpana, R., and Dr P.Nallathai. "Survey on Fault Detection and Diagnosis Using Neural Network in WBAN." International Journal of Engineering & Technology 7, no. 2.20 (2018): 346. http://dx.doi.org/10.14419/ijet.v7i2.20.16731.

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Wireless Body Area Networks (WBAN) is the sensor network used for monitoring health information in e-health systems. WBAN is a combination of sensors used to obtain vital information from the body. It is a special type of WSN. WBAN technology should handle the data in a smart way by reacting to the monitored data and to evaluate the data. It requires fault detection and diagnosis methods for sensors used in WBAN. This paper gives a survey of different types of neural-network approaches for faults detection and diagnosis in WBAN using neural network.

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Hadi, Mohammed H., Abbas H. Issa, and Atheer A. Sabri. "Design and FPGA Implementation of Intelligent Fault Detection in Smart Wireless Sensor Networks." Engineering and Technology Journal 39, no. 4A (2021): 653–62. http://dx.doi.org/10.30684/etj.v39i4a.1951.

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In this paper, both the design and hardware of Fault Detection (FD) in Wireless Sensor Network (WSN) was implemented using FPGA NI myRIO kit, wireless temperature sensors network with small size, low cost, and low power consumption. Work data processing was performed using pattern recognition methods to detect residual generation. LabVIEW software environment was employed for system performance. In this paper. The design of the hardware circuit NI myRIO kit received temperature from the sensors. The examined system showed an ability to monitor and track any fault or fire that may occur; based

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Bae, Jangsik, Meonghun Lee, and Changsun Shin. "A Data-Based Fault-Detection Model for Wireless Sensor Networks." Sustainability 11, no. 21 (2019): 6171. http://dx.doi.org/10.3390/su11216171.

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With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optim

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Omar, Khaldoon Ammar, Ahmed Dhahir Malik, Ansar Jamil, and Hasan Muwafeq Gheni. "Faulty sensor detection using multi-variate sensors in internet of things (IoTs)." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1391. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1391-1399.

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IoT devices are lightweight and have limited computational capabilities often exposed to harsh environments, which can cause failure on the IoT devices [1, 2]. The failure on the IoT devices is also caused due to limited battery life, hardware failure or human mistakes. Sensor faults can be categorized under one type of hardware failure, such as sensor burn, reduced sensor sensitivity and malfunctioned sensors. Any faulty on the IoT devices can cause a problem on the overall operation of the IoT system. Traditional ways in the management of IoT devices is a maintenance officer require to check

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Kolbadi Nejad, M., A. Selk Ghafari, and A. Zabihollah. "Fault Detection in a Cracked Pipeline Embedded with Piezoelectric Sensors/Actuators Employing Bond Graph Approach." Advanced Materials Research 476-478 (February 2012): 1015–19. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.1015.

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The main scope of this article is to simulate a cracked pipeline embedded with piezoelectric sensors and actuators utilizing bond graph approach. Piezoelectric sensors/actuators are becoming very popular in various applications such as health monitoring, active vibration control or noise reduction, and as a part of the systems called smart structures. The proposed bond graph structure in this study, graphically illustrates the power flow between the electrical and mechanical frameworks included in the system. In addition, the proposed framework makes it possible to utilize a modular structure

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Malik, Ahmed Dhahir, Ansar Jamil, Khaldoon Ammar Omar, and Mohd Helmy Abd Wahab. "Implementation of Faulty Sensor Detection Mechanism using Data Correlation of Multivariate Sensor Readings in Smart Agriculture." Annals of Emerging Technologies in Computing 5, no. 5 (2021): 1–9. http://dx.doi.org/10.33166/aetic.2021.05.001.

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Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers, and crops regardless of their geographical differences. Faulty sensor detection is critical in IoT. When a sensor becomes faulty, missing data and/or bad data is provided to the control and management systems, which may lead to potential malfunction or even system failures. Because of this, a sensor fault detection mechanism must be implemented in an IoT system to eliminate this potential fault. This paper focuses on the implementation of a faulty sensor detection

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Liu, Jinfu, Zhenhua Long, Mingliang Bai, Linhai Zhu, and Daren Yu. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems." Energies 14, no. 2 (2021): 389. http://dx.doi.org/10.3390/en14020389.

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As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at t

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Li, Anyi, Xiaohui Yang, Huanyu Dong, Zihao Xie, and Chunsheng Yang. "Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM." Sensors 18, no. 12 (2018): 4430. http://dx.doi.org/10.3390/s18124430.

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An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) al

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Wang, Shiqiang, Jianchun Xing, Ziyan Jiang, and Juelong Li. "A decentralized sensor fault detection and self-repair method for HVAC systems." Building Services Engineering Research and Technology 39, no. 6 (2018): 667–78. http://dx.doi.org/10.1177/0143624418775881.

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This study proposes a novel decentralized sensor fault detection and self-repair method for heating, ventilation and air-conditioning systems. From the perspective of network structure, sensor fault diagnosis in heating, ventilation and air-conditioning systems is distributed to the updated smart sensors without the monitoring host, which is necessary in the traditional centralized method. A fully distributed flat sensor network is established based on fundamental physical equations. Similar to the structure, mechanism and characteristics of biological communities, a smart sensor needs only to

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Liu, Xiaoyang, Haizhou Huang, and Jiawei Xiang. "A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine." Sensors 20, no. 2 (2020): 420. http://dx.doi.org/10.3390/s20020420.

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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks

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Li, Shuheng, Dezhi Hong, and Hongning Wang. "Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4683–90. http://dx.doi.org/10.1609/aaai.v34i04.5900.

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Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensor

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Shahbazi, Zeinab, and Yung-Cheol Byun. "Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches." Applied Sciences 11, no. 8 (2021): 3535. http://dx.doi.org/10.3390/app11083535.

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The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from senso

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Tosi, Daniele, Edoardo Gino Macchi, and Alfredo Cigada. "Fiber-Optic Temperature and Pressure Sensors Applied to Radiofrequency Thermal Ablation in Liver Phantom: Methodology and Experimental Measurements." Journal of Sensors 2015 (2015): 1–22. http://dx.doi.org/10.1155/2015/909012.

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Radiofrequency thermal ablation (RFA) is a procedure aimed at interventional cancer care and is applied to the treatment of small- and midsize tumors in lung, kidney, liver, and other tissues. RFA generates a selective high-temperature field in the tissue; temperature values and their persistency are directly related to the mortality rate of tumor cells. Temperature measurement in up to 3–5 points, using electrical thermocouples, belongs to the present clinical practice of RFA and is the foundation of a physical model of the ablation process. Fiber-optic sensors allow extending the detection o

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Sravani, Annareddy, P. Malarvezhi, and R. Dayana. "Design and implementation of dimmer based smart street lighting system using raspberry Pi and IoT." International Journal of Engineering & Technology 7, no. 2.8 (2018): 524. http://dx.doi.org/10.14419/ijet.v7i2.8.10514.

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In olden days street lights were not operated in an automatic way. Automation of street lights has become apparent these days. But we can notice that we do not require high intensity light during night hours, i.e. when there is no traffic, no people in the streets or on roads and even in the early mornings. As per requirement, the light intensity can be reduced using dimmer circuit. Light dependent resistor (LDR) sensors are used to sense the darkness and Passive Infrared (PIR) sensors are to detect the objects. Raspberry Pi (Master node) and Arduino (Slave node) will communicate each other an

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Farooq, Aqeel, Wadee Alhalabi, and Sara M. Alahmadi. "Traffic systems in smart cities using LabVIEW." Journal of Science and Technology Policy Management 9, no. 2 (2018): 242–55. http://dx.doi.org/10.1108/jstpm-05-2017-0015.

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Purpose The purpose of this research work is to design and apply LabVIEW in the area of traffic maintenance and flow, by introducing improvements in the smart city. The objective is to introduce the automated human–machine interface (HMI) – a computer-based graphical user interface (GUI) – for measuring the traffic flow and detecting faults in poles. Design/methodology/approach This research paper is based on the use of LabVIEW for designing the HMI for a traffic system in a smart city. This includes considerable measures that are: smart flow of traffic, violation detection on the signal, faul

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19

Zamudio-Ramírez, Israel, Roque Alfredo Osornio-Ríos, Jose Alfonso Antonino-Daviu, and Alfredo Quijano-Lopez. "Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis." Sensors 20, no. 5 (2020): 1477. http://dx.doi.org/10.3390/s20051477.

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Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the

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Gaddam, Anuroop, Tim Wilkin, Maia Angelova, and Jyotheesh Gaddam. "Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions." Electronics 9, no. 3 (2020): 511. http://dx.doi.org/10.3390/electronics9030511.

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The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, lea

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ME, M. Ambika, and Madhunisha M. "Smart Internet of Things Based Induction Motor Parameter Monitoring and Control System." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 253–56. http://dx.doi.org/10.35940/ijrte.f5346.039621.

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Automation is the use of various control systems for operating equipment such as machinery, processes in industries such as boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications with minimal or reduced human intervention. This research paper presents advanced approaches using wireless monitoring system for induction motor based on Internet of Things (IoT) for safe and economic data communication. n the first approach, state of the art fault detection strategy is exhibited for induction motor. This research paper d

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Chander, Bhanu, Prem Kumar.B, and Kumaravelan . "A Analysis of Machine Learning in Wireless Sensor Network." International Journal of Engineering & Technology 7, no. 4.6 (2018): 185. http://dx.doi.org/10.14419/ijet.v7i4.6.20460.

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Advances in hardware as well as wireless network tools have positioned us at the doorstep of a new-fangled era where undersized wireless devices will endow with access to information every time, everyplace plus enthusiastically contribute in constructing smart atmosphere. The sensors in WSN’s assemble information regarding the substances they are exploited to sense. Nevertheless these sensors are restricted in their performance by restrictions of power plus bandwidth. Machine Learning methods can facilitate them in overcoming such restrictions. During the past decade, WSNs have seen progressiv

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Burgas, Llorenç, Joan Colomer, Joaquim Melendez, Francisco Ignacio Gamero, and Sergio Herraiz. "Integrated Unfold-PCA Monitoring Application for Smart Buildings: An AHU Application Example." Energies 14, no. 1 (2021): 235. http://dx.doi.org/10.3390/en14010235.

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This paper presents a complete methodology, together with its implementation as a web application, for monitoring smart buildings. The approach uses unfold-Principal Component Analysis (unfold-PCA) as a batch projection method and two statistics, Hotelling’s T-squared (T2) and the squared prediction error (SPE), for alarm generation resulting in two simple control charts independently on the number of variables involved. The method consists of modelling the normal operating conditions of a building (entire building, room or subsystem) with latent variables described expressing the principal co

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Ali, A. Mohamed Syed. "Helmet Deduction Using Image Processing." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 342. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp342-344.

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The project aim is to build a smart, little power bicycle dashboard with advanced vision sensor to find whether the rider is wearing a helmet or not and allows start/stop the bike ignition system. Even while riding the bicycle, the system continues the monitoring process, prevents the fire and sends alert messages to traffic authorities if the helmet not worn at any point in time. An alcohol sensor is built-in within the dashboard to prevent the drunk driving situation. GPS and motion sensors assist the system identifies accident scenario and sends alert SMS to traffic authorities and family m

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Takadama, Keiki. "Selected Papers from i-SAIRAS 2010." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (2011): 1139. http://dx.doi.org/10.20965/jaciii.2011.p1139.

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This special issue features the selected papers from i-SAIRAS 2010 (The 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space) at Sapporo, Japan on August 29 - September 1, 2010), which explores the technology of Artificial Intelligence (AI), Automation and Robotics, and its application in space. In the AI domain, in particular, i-SAIRAS focuses on the following issues: (1) spacecraft autonomy (e.g., inboard software for mission planning and execution, resource management, fault protection, science data analysis, guidance, navigation and control, smart senso

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Nayyar, Anand, Pijush Kanti Dutta Pramankit, and Rajni Mohana. "Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions." Scalable Computing: Practice and Experience 21, no. 3 (2020): 347–48. http://dx.doi.org/10.12694/scpe.v21i3.1568.

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Internet of Things (IoT) is regarded as a next-generation wave of Information Technology (IT) after the widespread emergence of the Internet and mobile communication technologies. IoT supports information exchange and networked interaction of appliances, vehicles and other objects, making sensing and actuation possible in a low-cost and smart manner.  On the other hand, cyber-physical systems (CPS) are described as the engineered systems which are built upon the tight integration of the cyber entities (e.g., computation, communication, and control) and the physical things (natural

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Nayyar, Anand, Rudra Rameshwar, and Piyush Kanti Dutta. "Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies." Scalable Computing: Practice and Experience 20, no. 2 (2019): iii—vi. http://dx.doi.org/10.12694/scpe.v20i2.1558.

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Recent Trends and Future of Fog and Edge Computing, Services, and Enabling Technologies Cloud computing has been established as the most popular as well as suitable computing infrastructure providing on-demand, scalable and pay-as-you-go computing resources and services for the state-of-the-art ICT applications which generate a massive amount of data. Though Cloud is certainly the most fitting solution for most of the applications with respect to processing capability and storage, it may not be so for the real-time applications. The main problem with Cloud is the latency as the Cloud dat

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K K A, Abdullah, Robert A B C, and Adeyemo A B. "August 2016 VOLUME 5, ISSUE 8, AUGUST 2016 5th Generation Wi-Fi Shatha Ghazal, Raina S Alkhlailah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5801 ECG Arrhythmia Detection Using Choi-Williams Time-Frequency Distribution and Artificial Neural Network Sanjit K. Dash, G. Sasibhushana Rao Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5802 Data Security using RSA Algorithm in Cloud Computing Santosh Kumar Singh, Dr. P.K. Manjhi, Dr. R.K. Tiwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5803 Detection Algorithms in Medical Imaging Priyanka Pareek, Pankaj Dalal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5804 A Review Study on the CPU Scheduling Algorithms Shweta Jain, Dr. Saurabh Jain Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5805 Healthcare Biosensors - A Paradigm Shift To Wireless Technology Taha Mukhtar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5806 Congestion Control for Peer to Peer Application using Random Early Detection Algorithm Sonam Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5807 Quantitative and Qualitative Analysis of Milk Parameters using Arduino Controller Y.R. Bhamare, M.B. Matsagar, C.G. Dighavkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5808 Ardunio Based Security and Safety using GSM as Fault Alert System for BTS (Base Transceiver Station) Umeshwari Khot, Prof. Venkat N. Ghodke Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5809 Automatic Single and Multi Topic Summarization and Evolution to Generate Timeline Mrs. V. Meenakshi, Ms. S. Jeyanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5810 Data Hiding in Encrypted HEVC/AVC Video Streams Saltanat Shaikh, Prof. Shahzia Sayyad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5811 A Study of Imbalanced Classification Problem P. Rajeshwari, D. Maheshwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5812 Design of PTL based Area Efficient and Low Power 4-bit ALU Saraabu Narendra Achari, Mr. C. Pakkiraiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5813 The Design of Driver Safety Awareness and Assistance System through Sleep Activated and Auto Brake System for Vehicle Control D. Sivabalaselvamani, Dr. A. Tamilarasi, L. Rahunathan and A.S. Harishankher Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5814 Parameters Selection, Applications & Convergence Analysis of PSO Algorithms Sachin Kumar, Mr. N.K. Gupta Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5815 Effective Pattern Deploying Model for the Document Restructuring and Classification Niketa, Jharna Chopra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5816 Cataloging Telugu Sentences by Hidden Morkov Techniques V. Suresh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5817 Biometrics for Cell Phone Safety Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5818 Digital Image Watermarking using Modified DWT&DCT Combination and Bi Linear Interpolation Yannam .Nagarjuna, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5819 Comparative Study and Analysis on the Techniques of Web Mining Dipika Sahu, Yamini Chouhan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5820 A Review of MIL-STD-1553 Bus Trends and Future K. Padmanabham, Prabhakar Kanugo, Dr. K. Nagabhushan Raju, M. Chandrashekar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5821 Design of QPSK Digital Modulation Scheme Using Turbo Codes for an Air Borne System D. Sai Brunda, B. Geetha Rani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5822 An Efficient Locally Weighted Spectral Cluster for Automatic Image Segmentation Vishnu Priya M, J Santhosh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5823 An Efficient Sliding Window Based Micro Cluster Over Data Streams Nancy Mary, A. Venugopal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5824 Comparative Analysis of Traditional Frequency Reuse Techniques in LTE Network Neelam Rani, Dr. Sanjeev Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5825 Score Level Integration of Fingerprint and Hand Geometry Biometrics Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5826 CHARM: Intelligently Cost and Bandwidth Detection for FTP Servers using Heuristic Algorithm Shiva Urolagin Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5827 Image Enhancement Using Modified Exposure Based Histogram SK. Nasreen, N. Anupama Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5828 Human Gesture Based Recognition and Classification Using MATLAB Suman, Er. Kapil Sirohi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5829 Image Denoising- A Novel Approach Dipali D. Sathe, Prof. K.N. Barbole Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5830 Design of Low Pass Digital FIR Filter Using Nature Inspired Technique Nisha Rani, Balraj Singh, Darshan Singh Sidhu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5831 Issues and Challenges in Software Quality Assurance Himangi, Surender singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5832 Hybridization of GSA and AFSA to Detect Black Hole Attack in Wireless Sensor Network Soni Rani, Charanjit Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5833 Reversible Watermarking Technique for Data Hiding, Accurate Tamper Detection in ROI and Exact Recovery of ROI Y. Usha Madhuri, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5834 Fault Tolerance and Concurrency Control in Heterogeneous Distributed Database Systems Sagar Patel, Meghna Burli, Nidhi Shah, Prof. (Mrs.) Vinaya Sawant Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5835 Collection of Offline Tamil Handwriting Samples and Database Creation D. Rajalakshmi, Dr. S.K. Jayanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5836 Overview of Renewable Energy in Maharashtra Mr. Sagar P. Thombare, Mr. Vishal Gunjal, Miss. Snehal Bhandarkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5837 Comparative Analysis of Efficient Image Steganographic Technique with the 2-bit LSB Algorithm for Color Images K. S. Sadasiva Rao, Dr A. Damodaram Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5838 An Efficient Reverse Converter Design for Five Moduli Set RNS Y. Ayyavaru Reddy, B. Sekhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5839 VLSI Design of Area Efficient High Performance SPMV Accelerator using VBW-CBQCSR Scheme N. Narasimharao, A. Mallaiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5840 Customer Retention of MCDR using 3SCDM Approaches Suban Ravichandran, Chandrasekaran Ramasamy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5841 User Privacy and Data Trustworthiness in Mobile Crowd Sensing Ms. T. Sharadha, Dr. R. Vijaya Bhanu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5842 A Safe Anti-Conspiracy Data Model For Changing Groups in Cloud G. Ajay Kumar, Devaraj Verma C Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5843 Scope and Adoption of M-Commerce in India Anurag Mishra, Sanjay Medhavi, Khan Shah Mohd, P.C. Mishra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5844 A Secure Data Hiding Scheme For Color Image Mrs. S.A. Bhavani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5845 A Study of Different Content Based Image Retrieval Techniques C. Gururaj, D. Jayadevappa, Satish Tunga Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5846 Cache Management for Big Data Applications: Survey Kiran Grover, Surender Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5847 Survey on Energy Efficient Protocols and Challenges in IOT Syeda Butool Fatima, Sayyada Fahmeeda Sultana, Sadiya Ansari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5848 Educational Data Mining For Evaluating Students Performance Sampreethi P.K, VR. Nagarajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5849 Iterative Pareto Principle for Software Test Case Prioritization Manas Kumar Yogi, G. Vijay Kumar, D. Uma Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5850 Localization Techniques in Wireless Sensor Networks: A Review Abhishek Kumar, Deepak Prashar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5851 Ensemble Averaging Filter for Noise Reduction Tom Thomas Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5852 Survey Paper on Get My Route Application Shubham A. Purohit, Tushar R. Khandare, Prof. Swapnil V. Deshmukh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5853 Design and Implementation of Smart Car with Self-Navigation and Self-Parking Systems using Sensors and RFID Technology Madhuri M. Bijamwar, Prof. S.G. Kole, Prof. S.S. Savkare Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5854 Comparison Study of Induction Motor Drives using Microcontroller and FPGA Sooraj M S, Sreerag K T V Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5855 A Survey on Text Categorization Senthil Kumar B, Bhavitha Varma E Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5856 Multirate Signal Reconstruction Using Two Channel Orthogonal Filter Bank Sijo Thomas, Darsana P Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5857 The Multi-keyword Synonym Search for Encrypted Cloud Data Using Clustering Method Monika Rani H G, Varshini Vidyadhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5858 A Review on Various Speech Enhancement Techniques Alugonda Rajani, Soundarya .S.V.S Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5859 A Survey on Various Spoofing Attacks and Image Fusion Techniques Pravallika .P, Dr. K. Satya Prasad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5860 Non-Invasive Vein Detection using Infra-red Rays Aradhana Singh, Dr. S.C. Prasanna Kumar, Dr. B.G. Sudershan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5861 Boundary-Polygons for Minutiae based Fingerprinst Recognition Kusha Maharshi, Prashant Sahai Saxena Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5862 Image Forgery Detection on Digital Images Nimi Susan Saji, Ranjitha Rajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5863 Enhancing Information Security in Big Data Renu Kesharwani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5864 Secure Multi-Owner Data Sharing for Dynamic Groups in Cloud Ms. Nilophar M. Masuldar, Prof. V. P. Kshirsagar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5865 Compact Microstrip Octagonal Slot Antenna for Wireless Communication Applications Thasneem .H, Midhun Joy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5866 ‘Aquarius’- Smart IOT Technology for Water Level Monitoring System Prof. A. M. Jagtap, Bhaldar Saniya Sikandar, Shinde Sharmila Shivaji, Khalate Vrushali Pramod, Nirmal Kalyani Sarangdhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5867 Future of Results in Select Search Engine Peerzada Mohammad Iqbal, Dr. Abdul Majid Baba, Aasim Bashir Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5868 Semantic Indexing Techniques on Information Retrieval of Web Content." IJARCCE 5, no. 8 (2016): 347–52. http://dx.doi.org/10.17148/ijarcce.2016.5869.

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Khorgade, Dr Manisha, Anjali Jaiswal, Mrunal Karmore, and Vidisha Kamble. "Smart Street Light." International Journal of Advanced Research in Science, Communication and Technology, June 4, 2021, 152–54. http://dx.doi.org/10.48175/ijarsct-1368.

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This paper is about a smart lighting system that can control the street light efficiently by using sensors to dim and brighten whenever it is required. This system is based on the concept of IoT. It helps to reduce the number of accidents that takes place on the roads and also saves a lot of power and electricity. It increases safety and provides more efficiency. It also provides a safe environment for the pedestrians during the night time by lighting up the place. The system also maintains database to store useful information from each street light like power consumption, total number of burn

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Techane, Asheber Wagshum, Yu-Fu Wang, and Bereket Haile Weldegiorgis. "Rotating Machinery Prognostics and Application of Machine Learning Algorithms." Annual Conference of the PHM Society 10, no. 1 (2018). http://dx.doi.org/10.36001/phmconf.2018.v10i1.253.

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The internet of things (IOT) enabled presence of abundant sensors on smart machineries and the recent advance in deep learning is accelerating the development of predictive maintenance in production systems with less time and fair amount of effort. In this work a Deep learning Neural Networks (DNN) based bearing health monitoring system with index of similarity check is developed and tested for its effectiveness. The assessment procedure followed in here trains a DNN model on a time series data segmented to a vector size equal to number of data points per cycle as training and test data sets.

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Valinejadshoubi, Mojtaba, Osama Moselhi, and Ashutosh Bagchi. "Integrating BIM into sensor-based facilities management operations." Journal of Facilities Management ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/jfm-08-2020-0055.

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Purpose To mitigate the problems in sensor-based facility management (FM) such as lack of detailed visual information about a built facility and the maintenance of large scale sensor deployments, an integrated data source for the facility’s life cycle should be used. Building information modeling (BIM) provides a useful visual model and database that can be used as a repository for all data captured or made during the facility’s life cycle. It can be used for modeling the sensing-based system for data collection, serving as a source of all information for smart objects such as the sensors used

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