##### International Journal of Image, Graphics and Signal Processing (IJIGSP)

IJIGSP Vol. 12, No. 5, Oct. 2020

#### MECS Press Journal

REGULAR PAPERS

##### Design and Implementation of Optimal PID Controller Using PLC for Al-Tahady ESP

DOI: https://doi.org/10.5815/ijigsp.2020.05.01, Pub. Date: 8 Oct. 2020

The electrostatic precipitator (ESP) is an extensively used system in metallurgical industries and the generation of power to decrease the release of dust in the flue gas. In the design of the Electrostatic precipitator unit, gas emission uniform distribution is expected to fulfil its best aggregation performance. Programming Logic Controller (PLC) is a controller for industrial process automation and self-monitoring. A lot of industries utilized PLC to automatically control the entire process with less involvement from the human and to evade errors. In this paper, A mathematical model for Electrostatic precipitator from physical parameters and analysis has been developed. The controller is built depending on this model using the basic principle of a well-known A Proportional Integral Derivative (PID) controller to control the high voltage of the Electrostatic precipitator (ESP) by adjusting the opening of voltage and current by applying analogue signals (4-20 mA) from output cards of the PLC. The simulation results paved the way to build a practical system. building the mathematical model by using the Identification Toolbox of MATLAB® Version 9.6. The system was built using Allen Bradley PLC. The effect of control parameters (PID) in the case of voltage or current has been studied to demonstrate the efficiency of the model for the precipitator and observer in the case of the control system for the Al-Tahady ESP. The PID controller was built and the best values for the Electrostatic Precipitator controller are (KP=2.3904, KI=3.5382, KD=0.3). PID controller reduces steady-state errors.

##### Contrast Enhancement of Images through Skewness and Mode Based Bi-Histogram Equalization

DOI: https://doi.org/10.5815/ijigsp.2020.05.02, Pub. Date: 8 Oct. 2020

In this paper, skewness and mode-based histogram equalization algorithm have been proposed for contrast enhancement of digital images. The present method gives a novel idea for histogram clipping and histogram bifurcation. The prior is done with the skewness value and the latter is done with help of mode values of the intensity level random data set. The pixel intensity levels are random and thus a stochastic approach has been used and found to yield improved figure of merits. The image histogram has been clipped with the help of a pre-assigned threshold value computed from skewness value to restrict the rate of over enhancement. The clipped histogram is subdivided into two parts, using the histogram subdivision limit which is calculated on the basis of the mode value of the image. Histogram of individual sub-image is equalized independently and then integrated to form the final enhanced image. The simulation results have shown that the proposed skewness and mode based bi-histogram equalization algorithm enhances the contrast of the image in a better manner compared with the other histogram equalization methods in terms of FSIM, PSIM, SFF, VSI, HaarPSI, and GMSD.

##### A Fingerprint Template Protection Scheme Using Arnold Transform and Bio-hashing

DOI: https://doi.org/10.5815/ijigsp.2020.05.03, Pub. Date: 8 Oct. 2020

Fingerprint biometric is popularly used for protecting digital devices and applications. They are better and more reliable for authentication in comparison to the usual security tokens or password, which make them to be at the forefront of identity management systems. Though, they have several security benefits, there are several weaknesses of the fingerprint biometric recognition system. The greatest challenge of the fingerprint biometric system is theft or leakage of the template information. Also, each individual has limited and unique fingerprint which is permanent throughout their lifespan, hence, the compromise of the fingerprint biometric will cause a lifetime threat to the security and privacy of such an individual. Security and privacy risk of fingerprint biometric have previously been studied in the context of cryptosystem and cancelable biometric generation. However, these approaches do not obviously address the issue of revocability, diversity and irreversibility of fingerprint features to guard against the wrong use or theft of fingerprint biometric information.  In this paper, we proposed a model that harnesses the strength of Arnold transform and Bio-hashing on fingerprint biometric features to overcome the limitations commonly encountered in sole fingerprint biometric approaches. In the experimental analysis, the result of irreversibility showed 0% False Acceptance Rate (FAR), performance showed maximum of 0.2% FAR and maximum of 0.8% False Rejection Rate (FRR) at different threshold values. Also, the result of renewability/revocability at SMDKAB SMKADKB and SMKBDKA showed that the protection did not match each other. Therefore, the performance of the proposed model was notable and the techniques could be efficiently and reliably used to enforce protection on biometric templates in establishments/organizations so that their information and processes could be secured.

##### Variance Analysis Based Mango Recognition Using Correlation Distance

DOI: https://doi.org/10.5815/ijigsp.2020.05.04, Pub. Date: 8 Oct. 2020

Mango plays a major role in the Agro industry and it is a very popular fruit to most of the people due to its flavor and taste. There are many varieties of mangoes that are differentiable based on their various characteristics. Sometimes it is difficult and time consuming for general people or farmers to categorize the mango into different types due to intra-class variation among various types of mangoes. This paper has proposed an automatic system to recognize mangoes thus it becomes convenient to identify various types of mangoes. In this method, mangoes are recognized into different categories based on variance analysis or data dispersion measures. Measures include five number summary, variance, mean deviation, skewness, coefficient of variation which are used as features. From both training and query images, feature vectors are created. Correlation is used to recognize mangoes into various categories. The proposed method shows better result than some existing methods.