Information Detection and Data Denoising Method Using Nano Electrode Array Sensor

2021 ◽  
Vol 16 (2) ◽  
pp. 303-311
Author(s):  
Cheng Le

Computer technology and sensor technology can be combined. The technology set can be used to monitor the concentration of heavy metals in soil, which can help to prevent the occurrence of heavy metal pollution in time. First, nanotechnology, electrode polarization and the advantages of gold nanoparticles modified electrode are studied, and the design method of the nano electrode array is further analyzed. Also, the internal parameters of the three-electrode equivalent circuit are studied, and the model of the three-electrode equivalent circuit is derived. On this basis, a heavy metal monitoring circuit based on the nano electrode array sensor is designed. While the information monitoring based on this circuit is performed, wavelet domain denoising technology is studied in data processing. In view of the defects of the general hard threshold in practical application, the threshold is improved to recognize the depth of denoising. In the experiment, gold nanoparticles modified mercury electrode is used as working electrode. According to the principle that the precipitation time is inversely proportional to the detection current, 0.01 mol/L HCl is selected as the solution environment; moreover, it is set that pH=4 and the precipitation time is 4 min. The results show that for the same kind of ions, with the increase of the concentration of ions to be measured, the scanning potential range remains unchanged, while the peak current increases significantly. Metal ions can be effectively identified based on the potential corresponding to peak value. In the data processing of the detection circuit, the improved signal denoising method is compared with the default threshold wavelet domain denoising technology. The results show that the improved wavelet domain denoising method has less signal error, and the denoising effect of heavy metal detection is obvious.

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 475
Author(s):  
Mohammad Nishat Akhtar ◽  
Abdurrahman Javid Shaikh ◽  
Ambareen Khan ◽  
Habib Awais ◽  
Elmi Abu Bakar ◽  
...  

With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration.


2009 ◽  
Vol 48 (29) ◽  
pp. 5313-5315 ◽  
Author(s):  
J. C. González ◽  
J. C. Hernández ◽  
M. López-Haro ◽  
E. del Río ◽  
J. J. Delgado ◽  
...  

1995 ◽  
Vol 30 (5) ◽  
pp. 347-353 ◽  
Author(s):  
Haruya Sakai ◽  
Hideki Ichihashi ◽  
Hiroyuki Suganuma ◽  
Ryo Tatsukawa

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1394 ◽  
Author(s):  
Marsha Putri ◽  
Chao-Hsun Lou ◽  
Mat Syai’in ◽  
Shang-Hsin Ou ◽  
Yu-Chun Wang

The application of multivariate statistical techniques including cluster analysis and principal component analysis-multiple linear regression (PCA-MLR) was successfully used to classify the river pollution level in Taiwan and identify possible pollution sources. Water quality and heavy metal monitoring data from the Taiwan Environmental Protection Administration (EPA) was evaluated for 14 major rivers in four regions of Taiwan with the Erren River classified as the most polluted river in the country. Biochemical oxygen demand (6.1 ± 2.38), ammonia (3.48 ± 3.23), and total phosphate (0.65 ± 0.38) mg/L concentration in this river was the highest of the 14 rivers evaluated. In addition, heavy metal levels in the following rivers exceeded the Taiwan EPA standard limit (lead: 0.01, copper: 0.03, and manganese: 0.03) mg/L concentration: lead-in the Dongshan (0.02 ± 0.09), Jhuoshuei (0.03 ± 0.03), and Xinhuwei Rivers (0.02 ± 0.02) mg/L; copper: in the Dahan (0.036 ± 0.097), Laojie (0.06 ± 1.77), and Erren Rivers are (0.05 ± 0.158) mg/L; manganese: in all rivers. A total 72% of the water pollution in the Erren River was estimated to originate from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research demonstrated that applying PCA-MLR and cluster analysis on long-term monitoring water quality would provide integrated information for river water pollution management and future policy making.


1997 ◽  
Vol 58 (2-3) ◽  
pp. 203-207 ◽  
Author(s):  
Torill Eidhammer Sjøbakk ◽  
Bjørn Almli ◽  
Eiliv Steinnes

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