Discussion on Excavation Monitoring Schemes Based on Image Processing

2011 ◽  
Vol 71-78 ◽  
pp. 4317-4320
Author(s):  
Mao Heng Sun ◽  
Wei Jiang ◽  
Chen Hao Hu ◽  
Tian Tian Feng
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Wireless Sensor Network ◽  
Digital Signal ◽  
Wireless Sensor ◽  
Monitoring Methods ◽  
Sensing Technology ◽  
Processing Platform ◽  
Internet Of Things Technology ◽  
First Time

This study breaks through traditional displacement and inclination monitoring methods in excavation engineering, and proposes for the first time that combining excavation engineering with subjects such as Iconology, Signal Processing and Wireless Sensor Network, use Digital Signal Processing, Digital Image Processing, and Micro-Displacement Sensing technology for monitoring and processing various types of displacement. Then collect all monitoring points’ information through Internet of Things technology into database processing platform for processing, in order to achieve automatic real-time monitoring.

scholarly journals In-Situ Pipeline Monitoring System Design for Identifying and Locating Damaging Activities Based on Wireless Sensor Network

2020 ◽  
pp. 33-46
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Monitoring Methods ◽  
Pipeline Monitoring

Due to the recent advancements in the fields of Micro Electromechanical Sensors (MEMS), communication, and operating systems, wireless remote monitoring methods became easy to build and low cost option compared to the conventional methods such as wired cameras and vehicle patrols. Pipeline Monitoring Systems (PMS) benefit the most of such wireless remote monitoring since each pipeline would span for long distances up to hundreds of kilometers. However, precise monitoring requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. This Paper presents the design and implementation of In-situ pipeline monitoring system for locating damaging activities based on wireless sensor network. The system built upon a WSN of several nodes. Each node contains high computational 1.2GHz Quad-Core ARM Cortex-A53 (64Bit) processor for In-Situ data processing and equipped in 3-axis accelerometer. The proposed system was tested on pipelines in Al-Mussaib gas turbine power plant. During test knocking events are applied at several distances relative to the nodes locations. Data collected at each node are filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.

Approximate Compressor-Based Multiplier Design Methodology for Error-Resilient Digital Signal Processing

2020 ◽  
Vol 29 (14) ◽  
pp. 2050233
Author(s):  
Zhixi Yang ◽  
Xianbin Li ◽  
Jun Yang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Resource Utilization ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Electrical Performance ◽  
Approximate Computing ◽  
Dsp Applications

As many digital signal processing (DSP) applications such as digital filtering are inherently error-tolerant, approximate computing has attracted significant attention. A multiplier is the fundamental component for DSP applications and takes up the most part of the resource utilization, namely power and area. A multiplier consists of partial product arrays (PPAs) and compressors are often used to reduce partial products (PPs) to generate the final product. Approximate computing has been studied as an innovative paradigm for reducing resource utilization for the DSP systems. In this paper, a 4:2 approximate compressor-based multiplier is studied. Approximate 4:2 compressors are designed with a practical design criterion, and an approximate multiplier that uses both truncation and the proposed compressors for PP reduction is subsequently designed. Different levels of truncation and approximate compression combination are studied for accuracy and electrical performance. A practical selection algorithm is then leveraged to identify the optimal combinations for multiplier designs with better performance in terms of both accuracy and electrical performance measurements. Two real case studies are performed, i.e., image processing and a finite impulse response (FIR) filter. The design proposed in this paper has achieved up to 16.96% and 20.81% savings on power and area with an average signal-to-noise ratio (SNR) larger than 25[Formula: see text]dB for image processing; similarly, with a decrease of 0.3[Formula: see text]dB in the output SNR, 12.22% and 30.05% savings on power and area have been achieved for an FIR filter compared to conventional multiplier designs.

Parameter-Induced Stochastic Resonance in Wireless Sensor Network Signal Processing

2010 ◽  
Vol 121-122 ◽  
pp. 646-650
Author(s):  
Zi Kai Zhao ◽  
Guo Hua Hui
Keyword(s):  
Signal Processing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Stochastic Resonance ◽  
Noise Intensity ◽  
System Parameter ◽  
Wireless Sensor ◽  
Potential Well ◽  
Processing Level ◽  
Well Model

Parameter-induced stochastic resonance (PSR) using double potential well model was focused in this paper. Based on the former stochastic resonance study, system parameter µ was used to explore the resonance characteristics. A bluetooth-based wireless sensor network (WSN) was adopted to obtain the experimental data for parameter-induced stochastic resonance simulating. Under fixed noise intensity range, the changes of system parameter µ led to a systematic output resonance. Simulating results demonstrated that the systematic parameter µ could lead to stochastic resonance at signal processing level.

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