Face Mask Detection Using Deep Learning and Computer Vision

COVID-19 pandemic has rapidly affected our day-to-day life the world trade and movements. Wearing a face mask is very essentials for protecting against virus. People also wear mask to cover themselves in order to reduce the spread of covid virus. The corona virus covid-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask in public area according to the world health organization (WHO). The covid-19 pandemic forced government across the world to impose lockdowns to prevent virus transmission report indicates that wearing face mask while at work clearly reduce the risk of transmission .we will use the dataset to build a covid-19 face mask detector with computer vision using python,opencv,tensorflow,keras library and deep learning. Our goal is to identify whether the person on image or live video stream is wearing mask or not wearing face mask this can help to society and whole organization to avoid the transfer of virus one person to antother.we used computer vision and deep learning modules to detect a with mask image and without mask image. Keywords: face detection, face recognition, CNN, SVM, opencv, python, tensorflow, keras.

Traditional and current-prospective methods of agricultural plant diseases detection: A review

As it is known, a significant part of the yield of agricultural crops is lost due to harmful organisms, including diseases. The article reveals the data on the widespread types of plant diseases (rot, wilting, deformation, the formation of tumors, pustules, etc.) and their symptoms. Early identification of the pathogen type of plant infection is of high significance for disease control. Various methods are used to diagnose pathogens of disease on plant. This article discusses the review of the literature data on traditional methods for diagnosis of plant pathogens, such as visual observation, microscopy, mycological analysis, and biological diagnostics or the use of indicator plants. Rapid and reliable detection of plant disease and identification of its pathogen is the first and most important stage in disease control. Early identification of the cause of the disease allows timely selection of the proper protection method and ensures prevention of crop losses. There are a number of traditional methods for identifying plant diseases, however, in order to ensure the promptness and reliability of diagnostics, as well as to eliminate the shortcomings inherent in traditional diagnostics, in recent years, new means and technologies for identifying pathogens have been developed and introduced into practice. As well as the article provides information on such innovative methods of diagnosis of diseases and identification of their pathogens, which are used widely in the world today, such as immunodiagnostics, molecular-genetic (and phylogenetic) identification, mass spectrometry, etc.

Fault Analysis and Protection of Low-Voltage DC Microgrid Equipped by Renewable Energy Resources

This chapter consists of two sections, ‘Modelling of DC Microgrids' and ‘Protection of DC Microgrids'. In the first section, the new developments in DC Microgrids are discussed. Then, the Modelling of renewable energy resources-based DC Microgrid using characteristics and mathematics equations of each component are presented and then they are simulated by MATLAB. Afterward, the fault analysis and fault current behavior of the studied DC Microgrid are investigated. In the second section, a method of protecting the DC Microgrid and locating the fault in different parts of the system is proposed. The proposed method protects DC Microgrid using localized protection devices. And, the effectiveness of the proposed protection method is validated in a DC Microgrid with ring configuration.

Development of New Protection Scheme in DC Microgrid Using Wavelet Transform

The demand for a low voltage direct current (LVDC) microgrid is increasing by the increase of DC-based digital loads and renewable resources and the rapid development of power electronics technology. For the stable operation of an LVDC microgrid, it is necessary to develop a protection method. In this paper, the new protection scheme considering the fault section is proposed using wavelet transform (WT) in an LVDC microgrid. The fault sections are classified into DC side of the alternating current (AC)/DC converter, DC/DC converter connected to photovoltaic (PV) system, DC line, and DC bus. The characteristics of fault current at each fault section are analyzed. Based on these analyses, the new protection scheme including the fault section estimation is proposed using WT. The proposed scheme estimates the fault section using the detail component after performing WT and sends the trip signal to each circuit breaker according to the fault section. The proposed protection scheme is verified through various simulations according to the fault region and fault current using electromagnetic transient program (EMTP)/ATPDraw and MATLAB. The simulation results show that the fault section is accurately determined, and the corresponding circuit breaker (CB) operations are performed.

Non-Global Privacy Protection Facing Sensitive Areas in Face Images

Face images, as an information carrier, are naturally weak in privacy. If they are collected and analyzed by malicious third parties, personal privacy will leak, and many other unmeasurable losses will occur. Differential privacy protection of face images is mainly being studied under non-interactive frameworks. However, the ε-effect impacts the entire image under these frameworks. Besides, the noise influence is uniform across the protected image, during the realization of the Laplace mechanism. The differential privacy of face images under interactive mechanisms can protect the privacy of different areas to different degrees, but the total error is still constrained by the image size. To solve the problem, this paper proposes a non-global privacy protection method for sensitive areas in face images, known as differential privacy of landmark positioning (DPLP). The proposed algorithm is realized as follows: Firstly, the active shape model (ASM) algorithm was adopted to position the area of each face landmark. If the landmark overlaps a subgraph of the original image, then the subgraph would be taken as a sensitive area. Then, the sensitive area was treated as the seed for regional growth, following the fusion similarity measurement mechanism (FSMM). In our method, the privacy budget is only allocated to the seed; whether any other insensitive area would be protected depends on whether the area exists in a growing region. In addition, when a subgraph meets the criterion for merging with multiple seeds, the most reasonable seed to be merged would be selected by the exponential mechanism. Experimental results show that the DPLP algorithm satisfies ε-differential privacy, its total error does not change with image size, and the noisy image remains highly available.