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Drones are used as helpful machine for the use of human in various mission to successfully complete the required work in less time .The swarm of drones is the advancement of the mission which are time typical and time complexity centered problems. Typical application with swarm drones are civil security ,distant and extended area, observation of hostile and other wide applications. While the Paper, used the lightweight and economical way to use swarm drone. Ad-hoc network is used as medium for the communication of multi drone for the exploration in confined areas. This paper the main point is that the swarm technology in the time complex missions and use it other than commercial purpose. The required data is collected form the real time flight of the drones and interpreted to find the successful path in completion of the required work. Predefined path defined to the multi drone in ordered for exploration in desired path. The purpose of this paper, acquisition and pattern formation in efficient manner of the successful path finding in confined areas for better exploration strategy. This work is developed with in framework to support robot operating system.

A fire outbreak is a hazardous act that leads to numerous consequences. Detecting a fire at an early stage and extinguishing it can aid in prevention of various accidents. Till now we rely on human resource. This often leads to risking the life of that person. Therefore, fire security becomes an important aspect to save human lives. In this a fire extinguishing robot has been proposed and designed which detects the fire location and extinguish fire by using sprinklers on triggering the pump. This robot uses flame sensors for accurate fire detection. This proposed model of Fire Extinguishing Robot using Arduino used to detect presence of fire and extinguishing it automatically without any human interference. It contains gear motors and motor driver to control the movement of robot when it detects any presence of fire and will automatically start the water pump to extinguish that fire breakout. This model robot has a water ejector which is capable of ejecting water at the fire breakout place. The water ejector pipe can be move towards the required direction using servo motor. The whole operation is controlled by an Arduino UNO. Fire-fighting is an important but dangerous occupation. A fire-fighter must be able to reach the situation quickly and safely extinguish the fire, preventing further damage and reduce fatalities. Technology has come to rescue this issue, fire-fighters and machines are now having more efficient and effective method of fire fighting. This is gives you the design idea of fire fighting robot using autonomous operation. The robotic vehicle is loaded with fire extinguisher and a water pump which is controlled over solenoid valve to throw water. An Arduino UNO is a is used for the desired operation. A water tank and fire extinguisher setup along with water pump is mounted on the robot body and its operation is carried out from the output through appropriate signal from the sensor. The whole operation is controlled by an Arduino UNO.

At the beginning of the 21st century, many consumers show interest in purchasing safe, healthy, and nutritious foods. The intent requirement of end-users and many food product manufacturers are trying to feature a new processing technique for the healthy food supply. The non-thermal nature of cold plasma treatment is one of the leading breakthrough technologies for several food processing applications. The beneficial response of cold plasma processing on food quality characteristics is widely accepted as a substitution technique for new food manufacturing practices. This review aims to elaborate and offer crispy innovative ideas on cold plasma application in various food processing channels. It highlights the scientific approaches on the principle of generation and mechanism of cold plasma treatment on rheological properties of foods. It provides an overview of the behavior of cold plasma in terms of viscosity, crystallization, gelatinization, shear stress, and shear rate. Research reports highlighted that the cold plasma treated samples demonstrated a pseudoplastic behavior. The published literatures indicated that the cold plasma is a potential technology for modification of native starch to obtain desirable rheological properties. The adaptability and environmentally friendly nature of non-thermal cold plasma processing provide exclusive advantages compared to the traditional processing technique.

Audio Signals are the portrayal of sounds. It changes with respect to frequencies rather than time, and it shows more information in the frequency domain. So it is much appropriate to evaluate in the frequency domain rather than the time domain. By using different transforms like DFT, DST, DCT, MDCT, Integer MDCT, the time domain audio signal can be converted into a frequency domain signal. The signal is reconstructed to analyze the features like mean square error, Signal to noise ratio, Peak signal to noise ratio between the original and reconstructed signal. Other features like energy, entropy, zero crossing rates (ZCR) were also considered for the evaluation. In this paper, different audio file formats were taken for interpretation. It includes wave file, mp3 file, m4a file, aac file, where wave file is in uncompressed format and mp3, m4a, aac are in compressed format. These compressed files come under lossy compression. The above-mentioned features are used for applications like music information retrieval (MIR). MIR includes onset detection, pitch detection and to measure the noise and loudness of the music.

Transportation plays a major role of a person’s life. A life without transportation is unimaginable. People focus on four major types of transportation i.e. Roadways, Railways, Airways and Waterways. Among these transportation majority of people prefer railways as it helps connect distant locations and the comfort it provides. But the problem in railways is it needs more of monitoring and thereby the person travelling across areas can be at ease. In order to ensure safety in railway transportation, monitoring of tracks is very crucial. Hence, this research article focuses on monitoring the tracks present in railways through a novel image processing technique devised. The name of the technique we devised is known as CRACK TRACKER. Crack Tracker does the role of monitoring the tracks by capturing the images of the tracks and it is sent to the nearby station so that the cracks can be patched in the future. The cracks are clearly detected and intimated instantly to the station master. A reliable solution for continuous and cost efficient tracking is processed and depicted in this research article.

To assess and determine the various aspects of the interrelationship between social and digital media and orthodontics from the patient's as well as orthodontist’s perspective. Determining where the patient looks for Orthodontist’s information online and where the orthodontist is posting or promoting their practice 2. Differentiating between patient interests on web page/social media and orthodontic posts 3. Comfort level of patients to visit an orthodontist based on digital media popularity/reviewsIn our 2-part survey, 2 separate google forms were created to assess the various aspects of practice promotion and sent to Orthodontists and patients separately. Demographic data and informed consent was taken.The survey comprises of two groups: A) Orthodontists (212 participants) B) Patients (200 participants)From the orthodontist’s perspective, patients visit them based on internet popularity. They promote their practice using their own website which is available on Google. Social media is used commonly to promote themselves by posting photos of patients and clinic. Facebook was most used. Orthodontists believe that 15-20% visit them based on internet popularity.From the patients perspective, they choose orthodontists based on popularity on internet with google search being the most used. They review orthodontists on social media on popular sites such as Instagram, WhatsApp, Facebook and Twitter. Word of mouth referrals was the most preferred old school method of finding orthodontists based on experience. With vast improvement in digital technology, youngsters prefer to review orthodontists based on digital media reviews as well. The largest discrepancy found between social media sites used by the patients/ parents and orthodontists was with Facebook and Instagram. This opportunity is being vastly ignored by most orthodontic practices and should be considered as a potential marketing tool for current and prospective patients.

Multiple and heterogenous Earth observation (EO) platforms are broadly used for a wide array of applications, and the integration of these diverse modalities facilitates better extraction of information than using them individually. The detection capability of the multispectral unmanned aerial vehicle (UAV) and satellite imagery can be significantly improved by fusing with ground hyperspectral data. However, variability in spatial and spectral resolution can affect the efficiency of such dataset's fusion. In this study, to address the modality bias, the input data was projected to a shared latent space using cross-modal generative approaches or guided unsupervised transformation. The proposed adversarial networks and variational encoder-based strategies used bi-directional transformations to model the cross-domain correlation without using cross-domain correspondence. It may be noted that an interpolation-based convolution was adopted instead of the normal convolution for learning the features of the point spectral data (ground spectra). The proposed generative adversarial network-based approach employed dynamic time wrapping based layers along with a cyclic consistency constraint to use the minimal number of unlabeled samples, having cross-domain correlation, to compute a cross-modal generative latent space. The proposed variational encoder-based transformation also addressed the cross-modal resolution differences and limited availability of cross-domain samples by using a mixture of expert-based strategy, cross-domain constraints, and adversarial learning. In addition, the latent space was modelled to be composed of modality independent and modality dependent spaces, thereby further reducing the requirement of training samples and addressing the cross-modality biases. An unsupervised covariance guided transformation was also proposed to transform the labelled samples without using cross-domain correlation prior. The proposed latent space transformation approaches resolved the requirement of cross-domain samples which has been a critical issue with the fusion of multi-modal Earth observation data. This study also proposed a latent graph generation and graph convolutional approach to predict the labels resolving the domain discrepancy and cross-modality biases. Based on the experiments over different standard benchmark airborne datasets and real-world UAV datasets, the developed approaches outperformed the prominent hyperspectral panchromatic sharpening, image fusion, and domain adaptation approaches. By using specific constraints and regularizations, the network developed was less sensitive to network parameters, unlike in similar implementations. The proposed approach illustrated improved generalizability in comparison with the prominent existing approaches. In addition to the fusion-based classification of the multispectral and hyperspectral datasets, the proposed approach was extended to the classification of hyperspectral airborne datasets where the latent graph generation and convolution were employed to resolve the domain bias with a small number of training samples. Overall, the developed transformations and architectures will be useful for the semantic interpretation and analysis of multimodal data and are applicable to signal processing, manifold learning, video analysis, data mining, and time series analysis, to name a few., This research was partly supported by the Hebrew University of Jerusalem Intramural Research Found Career Development, Association of Field Crop Farmers in Israel and the Chief Scientist of the Israeli Ministry of Agriculture and Rural Development (projects 20-02-0087 and 12-01-0041).

As cities become smart cities, the number of vehicles has grown significantly over time, causing critical issues including traffic congestion, accidents, and many other issues. This system will be based on a number of contemporary technologies, including wireless sensor networks (WSNs), surveillance cameras, and IoT. For monitoring both the flow of traffic and the number of parking spaces in a smart city which is proposed in this article. The proposed system also includes cutting-edge features, such as the ability to remotely view traffic flow and the number of nearby parking spaces via an Android mobile application, that make it easier for drivers to locate free parking spots and cut down on unnecessary trips. Drivers can take alternate routes and avoid traffic jams thanks to this. Our solution combines three interconnected smart subsystems—crossroad management, parking space management, and a mobile application—to connect residents to a smart city.

In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions. Inspired by the intrinsic difficulty of extracting expert knowledge and distilling it back into AI models and by observations of human behavior in real-world experimental design, our algorithm lets the human expert take the lead in the experimental process. The human expert can use their domain expertise to its full potential, while the AI plays the role of a muse, injecting novelty and searching for areas of weakness to break the human out of over-exploitation induced by cognitive entrenchment. With mild assumptions, we show that our algorithm converges sub-linearly, at a rate faster than the AI or human alone. We validate our algorithm using synthetic data and with human experts performing real-world experiments., Comment: 34 Pages, 7 Figures and 5 Tables

The present study was taken up in Sivagangai district to examine the issues of trend in climate change, association between climate change and area, production and productivity of crops and impact of climate change on production of crops. The climate trend in Sivagangai district was studied using the descriptive statistics of skewness, kurtosis, coefficient of variation and compound growth rate of climatic variables of maximum temperature, minimum temperature and rainfall over the past 50 years from 1971 to 2020. Pearson correlation technique was used to analyze the degree of association between climatic variables and area, production and productivity of principal crops namely paddy, black gram and groundnut. Cobb-Douglas production function model was fitted to study the quantitative relationship between the average production of principal crops grown in the district and the climatic variables of temperature and rainfall and cropped area. Maximum temperature and minimum temperature were negatively skewed and rainfall was positively skewed. Kurtosis for maximum temperature was platykurtic and for the minimum temperature and rainfall of Sivagangai district, the kurtosis was Leptokurtic. The maximum temperature and minimum temperature were less variable and rainfall was highly variable. Pearson correlation coefficient revealed that maximum temperature, minimum temperature and rainfall had significant association with area, production and productivity of crops in Sivagangai district of Tamil Nadu. Cobb- Douglas production function analysis revealed that annual rainfall had a positive relationship with production of paddy. On the other hand, climatic variables of temperature and rainfall were not influencing the groundnut and black gram production.

In this article, two popular linear empirical methods viz., log-ratio model (LRM) and log-linear model (LLM) are used to derive water depths in shallow nearshore waters from Sentinel-2 multi-spectral images. Based on these empirical models, a multi-scene ensemble and a non-linear Support Vector Regression (SVR) machine learning approaches are applied to improve the accuracies from these traditional methods. In this analysis, firstly the best scene is selected from a set of six Sentinel-2 A&B images using noise-equivalent reflectance NEΔRrs (sr−1), optimal bands for LRM and LLM are selected using Optimal band ratio analysis (OBRA) and by computing Pearson Correlation Coefficient (R) between each band respectively. A total of 80% depth data points obtained from JetSki based echo-sounding measurements are used for training and the remaining 20% are used for testing each approach. The overall errors during the test phase for the range of depth 0–12 m are 0.79 m (0.67 m), 0.94 m (0.66 m) and 0.57 m (0.39 m) using traditional empirical LRM (LLM) methods from the best image, empirical-based ensemble and SVR approaches respectively. Irrespective of the approach, the LLM produced smoother and relatively accurate bathymetry as compared to the LRM. The LLM based SVR ML approach provides the best performance over the entire depth range as compared to all the methods considered in this study. Therefore, this method can be used for efficiently estimating nearshore depths and produce updated high-resolution bathymetry maps for many coastal applications.