Enhanced Disease Detection through Image Fusion in Solanum Tuberosum L.
An Improved Technique for Enhancement of Satellite Image
Magnetic Resonance and Computer Tomography Image Fusion using Novel Weight Maps Obtained by using Median and Guided Filters
Thresholding Techniques in Computer Vision Applications
Advancement in Brain Tumour Detection using Deep Learning Technique
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
The novel coronavirus disease (COVID-19), with a start line in China, has spread hastily amongst human beings dwelling in other international locations, and is coming near approximately 34,986,502 instances worldwide in line with the facts of Edith Cowan University (ecu) Centre for disorder prevention and control. There are a restrained number of COVID-19 test kits to be had in hospitals due to the growing cases day by day. Consequently, it is important to implement an automated detection machine as a brief opportunity diagnosis choice to prevent COVID-19 spreading among human beings. Fusion was considered as a concatenation between the two-person vectors on this context. Speckle-affected and coffee-fine X-ray images along with top first-class pictures have been utilized in our test for carrying out exams. If training and trying out are done with best selected right fine X-ray photos in a super situation, the output accuracy can be observed higher. However, this doesn't constitute a real-existence situation, wherein the photo database would be a mixture of each appropriate- and poor-first-rate pictures. Therefore, this technique of the use of different excellent snap shots could test how nicely the machine can react to such real-lifestyles situations. A modified anisotropic diffusion filtering technique become hired to take away multiplicative speckle noise from the test photographs. The software of these techniques ought to successfully conquer the restrictions in enter photograph quality. Subsequent, the function extraction changed into finished on the test photographs. Ultimately, the Convolutional Neural Network (CNN) classifier accomplished a type of X-ray photographs to pick out whether or not it changed into COVID-19 or until now. Pneumonia, an interstitial lung sickness, is the main reason of loss of life in children under the age of five. It accounted for approximately 16% of the deaths of kids below the age of 5, killing around 880,000 kids in 2016 according to a look at conducted with the aid of United Nations International Children's Emergency Fund (UNICEF). Affected children were mostly much less than two years old. Well timed detection of pneumonia in youngsters can assist to the technique of restoration. This paper gives convolutional neural community fashions to accurately hit upon pneumonic lungs from chest X-rays, which can be utilized inside the actual global by using medical practitioners to treat pneumonia. Experimentation was conducted on Chest X-Ray images dataset to be had on Kaggle.
In a Counter Insurgency/Counter Terrorism environment, where the life of a soldier is of paramount importance at times of uncertainty while carrying out house clearing drills. It is imperative that they would come across situations wherein there is a presence of a hostile target inside a closed room/house. This research paper encompasses “Through the wall target detection, classification, and range estimation”. Using a two port Vector Network Analyzer (VNA) as a power source, which was initially in the frequency domain was converted to time domain format for better assimilation of target detection and classification. One port of VNA utilized as transmitter and other as receiver, where two dual ridged horn antennas were placed. Also, by carrying out screen mirroring of the VNA with the laptop, the display as well as control of VNA, could be done using a laptop. The experimental results achieved were detection of stationary object/ stationary human being, moving human being, differentiation between stationary human & moving human and also differentiation between armed and unarmed personnel. These results achieved were based on the peak detection and display on the VNA screen/laptop. To achieve algorithm-based detection and classification, signal processing is required to be done depending on the peak detected and also an algorithm for real-time data transfer from VNA to the laptop.
Remote sensing techniques play an important role in exploring and management of water resources on the surface of the earth. Water bodies' identification from multispectral imagery is mainly done using several water indices. Water indices evolved based on the reflectance variations in multispectral imagery from different water bodies. Normalized Difference Water Index (NDWI) is mostly popularly used water index for detection of water bodies. The NDWI water index is used to classify the clear water bodies from non-water bodies and mixed water bodies. Modified Normalized Difference Water Index (MNDWI) is used to classify the clear water bodies along with mixed water bodies from non-water bodies. The selection of appropriate indices significantly affects the performance accuracy in extraction of water bodies. This paper aims in proposing simple and effective water index based on multi bands, Blue, Green, NIR and SWIR2.The objective of Modified Water Index (MWI) is better handling classification of the mixed water pixels. The Blue, Green bands has high reflectance values and Near Infrared (NIR), Short Wave Infrared (SWIR2) bands has low reflectance from water bodies. The significant reflectance variance feature of SWIR2 from clear water to mixed water bodies is useful in the classification of the mixed water pixels in the MWI. The performance of the Modified Water Index is analyzed and compared with the existing several water indices. The performance metrics such as Water Spread Area, mean and standard deviation are used to compare the effectiveness of water body indices.
Maps of land use and land cover are necessary for studying how the earth's surface changes over time and how human activities affect their surroundings the expanding pool of available resources of remote sensing, particularly the wellarchived Sentinel-2B images with resolution of 10 metres were used to conduct the research. For land use and land cover maps, supervised categorization is used. However, ground truth is required to attain high classification accuracy. There is a need for high-quality samples in big quantities. It takes time and effort to collect ground truth samples. When it comes to ground truth, it might be costly and sometimes impossible to obtain. This work presented a method for using Google Earth Engine products as ground truth instead of manually labelling ground truth samples and proceeding with classification with region of interest to produce land use and land cover maps for 2021 in this study area, Amravati, on the Google Earth Engine platform in this paper. The accuracy test is carried out on randomly generated samples from various places, with an overall accuracy of roughly 83.4 percent.
Attendance is an important task in any institution. Manual methods generally waste a lot of time. They are also not the safest and fastest option. In schools and colleges, the most common technique for attendance is calling the students' names, and in industries, the most common method is signing a register which generally needs extra time to do the attendance. A "Smart Attendance System with Protection" is proposed to solve these problems. This project is mainly divided into 3 parts according to its applications which includes, attendance, thermal screening and ultrasonic sensors. For the automatic attendance part, the Local Binary Pattern Histogram (LBPH) method is used for the face recognition of the person. LBPH is considered the most useful technique for face recognition. For the second part of the project, thermal screening, a sensor (MLX90614) is used to take the temperature of the person and check if it is normal or not. In the third part of the project, ultrasonic sensors are used to detect the hands of the person. Under the sanitization section, and after detection, a small pump motor is used to pour the sanitizer into the hands of the person for the sanitization of their hands. Manual records can be easily extracted from the Smart Attendance System when needed. Attendance record manipulation is not possible in the system. The proposed device uses the Raspberry Pi, IRT, Ultrasonic Sensor, Pi Camera, and Pump Motor. An attendance record is generated and stored in excel format.
