Showing papers by "Vignan University published in 2022"

A LoRaWAN IoT-Enabled Trash Bin Level Monitoring System

Abstract: Municipal solid waste management remains a major problem in urban areas, leading to serious health and environmental issues.Consequently, trash bins are placed in many places to handle the municipal solid waste, but these bins can overflow, spreading around the area, polluting the environment, and causing inconvenience to the public. Therefore, there is a need for a real-time remote monitoring system that alerts the level of garbage in the trash bins to the municipality or a waste management company. To manage the municipal solid waste efficiently, this article presents the development and validation of a self-powered, LoRaWAN Internet-of-Things (IoT)-enabled trash bin level monitoring system. The end nodes of the proposed IoT system are called trash bin level measurement unit (TBLMU) and are installed in each trash bin where the status needs to be monitored. The TBLMU measures the unfilled level and geographical location of a trash bin, processes the data, and transmits it to a LoRaWAN gateway at a frequency of 915 MHz. A LoRaWAN gateway serves as a concentrator for the TBLMUs and relays data between a TBLMU and an IoT trash bin level monitoring server. The users can view and analyze the status of every bin and its geolocation by using a smart graphical user interface. The accuracy of the developed system, wireless range between a TBLMU and a LoRaWAN gateway, average current consumption and life expectancy of the TBLMU, battery charging time, and the cost were studied and are reported here.

Significance of Blockchain Technologies in Industry

Abstract: Blockchain technology is set to extremely affect a wide assortment of enterprises, extending from capital markets to the music business. While some utilization cases may appear glaringly evident, the innovation is as yet encircled by a lot of promotion and vulnerability. As a chief, in what manner would it be a good idea for you to move toward the subject, and when would it be a good idea for you to get the ball rolling and effectively expect to execute blockchain innovation?

Chemistry of magnetic covalent organic frameworks (MagCOFs): from synthesis to separation applications

Abstract: This review aims to explore the design, development and applications of magnetically supported covalent organic frameworks (MCOFs) in extraction of metal ions, nitro explosives and chemical pollutants.

Tunable Bandstop Filter using Graphene in Terahertz Frequency Band

Abstract: In this paper, a planar graphene-based bandstop filter is designed and simulated for the resonant frequency of 1 THz. The transmission line model of the bandstop filter is considered, which consists of open-circuited shunt stubs that are interconnected through unit elements. The bandstop filter design is carried out from the normalized low pass Chebyshev prototype. By using frequency transformation, Richard’s transformation, and Kuroda’s identity, the desired bandstop filter can be designed from the low pass prototype. The graphene layer is introduced between the conductor layer and the dielectric for supporting the propagation of plasmonic waves. The simulation results show that the desired frequency response can be obtained for the designed graphene-based bandstop filter. By varying the chemical potential of the graphene layer, the resonant frequency of the bandstop filter can be tuned over the range of 0.1 THz with the constant absolute bandwidth.

Image Denoising Using an Artificial Neural Network and Genetic Optimization Algorithm Based Dual-Tree Complex Wavelet Transform

Abstract: The main objective of de-noising in images is to remove the unnecessary noise from the image while preserving the essential signal features as much as possible. Different methods are used for eliminating noises from digital images. The major role in de-noising is choosing a suitable method for the denoising process. Thus, various methods are suggested for image de-noising and all methods have its merits and demerits. This approach explores a Neural Network and Nature-Inspired Optimization Algorithm based denoising method implemented using Dual-Tree Complex wavelet Transform (DT-CWT). During this approach, initially the noisy image is converted into various sub-bands by applying DT-CWT. Then, to acquire noise-free wavelet coefficients, a trained Neural Network is applied on every sub-band. On these output wavelet coefficients Thresholding operation is performed with optimized threshold value by Genetic Algorithm. Now denoised image is generated through inverse DT-CWT. Relating to other existed methods this method gives improved results.

Smart Dark Pattern Detection: Making Aware of Misleading Patterns Through the Intended App

Abstract: The significance of dark patterns is to deceive consumers when they are exploring the internet. There were social networking sites such as LinkedIn, Facebook, and others, where users discovered dark patterns whose goal was to steal users’ personal information or get them to click on advertisements. Dark patterns are created by using the domains such as UI/UX. Although, there many kinds in which they trap the users’ attention to focus though the advertisements on the websites where users can be trapped and may lose their money. There are a few security concerns that are required to detect the dark patterns through smart dark pattern detection. The intended theme proposed in this case is designing the application through which browsing could be done where any dark pattern advertisement is identified that could be alerted through a dialog box. For detection, a novel dark pattern detection approach is designed and considered as in-built application activity. The performance and accuracy are the main factors that judge the intended theme is designed as per the expectations.

Nonlinear Integrity Algorithm for Blockchain Based Supply Chain Databases

Abstract: With an ever-growing global supply chain, supply chain management(SCM) is becoming more complex and competitive because of continuous development in global supply chain. In recent years, the blockchain has grown with the promise of becoming a “ trust machine “. However, this technological innovation can foster trust and transparency in many other sectors. In the industrial sector, for example, it could have interesting applications in production ecosystems and in particular in improving supply chain performance, offering the opportunity to increase speed and quality with potential cost savings ranging from 12 to 50%. the computation power required to setup and maintain a blockchain is expensive because of Computing hash for each block added to the blockchain and validating blocks. In this paper we proposed a nonlinear Integrity algorithm which works efficiently in terms of run-time and hash-bit variation with dynamic hash size and data size when compared it with traditional hashing algorithms like whirlpool, SHA, MD5.

Influence of geopolymer aggregates on microstructural and strength characteristics of OPC concrete

Abstract: This paper investigated the physical and microstructural properties of a manufactured fly ash–GGBFS-based geopolymer aggregates. The analysis on geopolymer aggregate included specific gravity, water absorption, crushing value, impact value, attrition, loss angles abrasion, and angularity index. Besides, scanning electron microscopy (SEM) was done to diagnose the microstructure of geopolymer aggregates. In contrast, the compressive strength of geopolymer aggregate-based ordinary Portland cement (OPC) concrete was studied. Besides, the microstructure and the pore structure development of geopolymer aggregate-based concrete at the interfacial transition zone (ITZ) were examined through SEM. In this paper, three types of geopolymer aggregates are prepared by replacing 0, 10, and 20% of fly ash with GGBFS cured under an oven (at 60° for 12 h) and ambient conditions. The experimental data showed that the dry density of manufactured geopolymer aggregate concrete was less than that of natural aggregate-based concrete; it is about 1000 kg/m3 for geopolymer aggregate. However, concrete prepared geopolymer aggregates (80% fly ash and 20% GGBFS) showed higher resistance among all the tests and giving similar results at ambient and oven curing.

A Self-Powered, Real-Time, NRF24L01 IoT-Based Cloud-Enabled Service for Smart Agriculture Decision-Making System

Abstract: Agriculture has been benefited by advanced research and development due to Internet of Things (IoT)-based automation. Environmental and deployment sensors such as DHT11, soil moisture, soil temperature and others are used in agriculture field production and IoT technology is being employed to assess field environment in smart agriculture. Most of the existing systems work only on the air temperature and humidity sensing, for agriculture health monitoring. These systems have limitations to send sensing data from long distances. The approximate range of data communication for these systems is below 100 m which is quite less for agriculture field coverage, in general. As a result of this, agricultural crop production is not up to the mark. We propose an architecture framework to address the above-mentioned shortcomings. This proposed architecture can be applied for long range communications with no data loss and no interference in the information. The system employs an NRF24L01 transceiver module, which works at 2.4 GHz for long-distance communications towards monitoring of agriculture parameters. This research is aimed into a suitable, feasible, and integrated Internet of Things (IoT) technique for smart agriculture. The proposed system saves energy and boosts productivity. This method reduces human effort while evaluating heat index measurement parameters in order to monitor the environment for optimal agriculture growth. The current consumption and life expectancy of the Agriculture Wireless Monitoring Unit (AWMU) are 0.02819 Amperes and 3 days 20 hours 13 minutes and 47 seconds, respectively, according to the experimental analysis. In an open environmental area, the maximum transmission distance for AWMU is up to 200 meters from the wireless access point.

Smart Food Fare Canteen: Automation of Bills and Serving

Abstract: Nowadays, technology is upgrading and advancing in multi-folded ways and dimensions. The real time application considered is the canteen bill system. In the traditional system, the user will take a static bill for the kind of food that may be needed to feed on the bill counter. This leads to many users would experience odd kind of issues such as few may waste the food because that is not tasty, few users may not consume more food because of their potentiality, and few may take more than they deserve. In such cases, in order to set right, the situation that prefers a token for the trail to check up the food is tasty with a certain 10% of fare of that specific food. Manual billing is avoided, and automatic billing is accounted for using debit cards or QR codes through authorized payment gateway apps. Each item in the canteen is assigned a certain price and a scanner and QR code for it; the user has to choose which items to eat after the trail of food items is over. This way avoids wastage of bills that protects trees indirectly and also saves the environment. This automated billing and efficient catering of quality food and its items would be the demanding in the business in the future. In this way, two sections are there in which one is trial session, and the other is real food court session. Both are separated sessions but connected.

Performance Optimization of LANMAR Routing Protocol in Mobile Ad Hoc Network Using DoE

Abstract: A MANET is a team of remote versatile devices or nodes that structure a system to trade data even with the lack of permanent network infrastructure. All the nodes work with the help of battery power. The efficient energy consumption is a main challenge in MANET which indeed affects the operational life of a MANET. So, the aim of this paper is to discover the most significant factor that is responsible for high energy consumption. The routing protocols’ performance is examined using simulation where one-factor-at-a-time (OFAT) approach is promoted. But, in reality, multiple factors affect the performance. But, design of experiments (DOE) approach lets many input factors to be experimented and tested in parallel that affect required output (response). This paper highlights the benefits of using Taguchi design of experiments approach to optimize the Landmark Routing Protocol in MANETs in terms of total energy consumption, control overhead and throughput. MANETs perform subjective to the design of the routing protocols and its configurable parametric values. The specific required network scenarios are simulated using EXata simulator/emulator. To recognize the most significant factor that affects the responses, Analysis of Variance (ANOVA) is applied, and the possible best factor-level combination is found using the signal-to-noise ratio analysis. ANOVA highlighted that the fisheye scope followed by fisheye update interval has the greatest impact on the responses. Later, by fine-tuning the most significant factor in the dynamic network environment, it is observed that LANMAR Routing Protocol performed better.

YOLOV5 and Morphological Hat Transform Based Traffic Sign Recognition

Abstract: This article narrates the traffic sign recognition by using morphological hat transform and YOLOV5. Recognition of traffic signs while driving on the road is necessary to estimate traffic situations and to avoid the accident. Due to bad weather conditions and night time, it is difficult to detect traffic signs in low contrast images. To tackle this problem, in this article, morphological hat transform used to enhance low contrast images and YOLOV5 used for detection. For this traffic sign detection, YOLOV5 network is trained with 4 classes of traffic sign dataset, which contains totally 740 images, in these 592 images used for training and 148 images used as validation images. This YOLOV5 network detected traffic signs with precision 77.9, recall 93.0 and obtained 0.78 mAP.

Microstrip Patch antenna for Wi-Fi and 5G based on MUC

Abstract: Abstract This communication presented the patch antenna functioning at Wi-Fi and 5G bands. To get compactness the side lengths of the patch are calculated with reference to upper-frequency band (3.5 GHz). Lower band (2.4 GHz) is obtained by loading the Mushroom Unit Cell (MUC) along the bottom right corner of the patch. To attain circular polarization (CP) at 5G band the edges of the patch are replaced with Koch fractal curves. This blend of the Double Negative Transmission Lines metamaterials (DNG TL), as well as fractal concepts yielded good compactness suitable for ultra-thin portable gadgets. Measured results have good correlation with simulated data from HFSS. The achieved bandwidths at the lower and upper bands are 2.4% and 5.6%. CP bandwidth of the proposed antenna at 5G band obtained from the measured data is 2.2%.

Quality Assisted Spectrum Allocation in Cognitive NOMA Networks

Abstract: Future communications are developed with new communication standards for multi access technologies, where spectrum sharing and Non-orthogonal multiple access (NOMA) are the two latest approaches. To transfer the data from one end to another end through this wireless communication medium interference take place, which affects the total system performance. To minimize the interference in these communications the efficient spectrum sharing technique was developed. Cooperative relaying in NOMA system with spectrum sharing using threshold modeling were proposed in past. To improve the resource allocation in NOMA with cooperative relaying, instantaneous signal to noise ratio were used in obtaining higher outage throughput in a spectrum sharing cognitive radio-NOMA (CR-NOMA) system. In the previous methods constant thresholding is considered in the energy detection model and the quality of signaling is not observed, which results in lower throughput. In this work, a Quality Assisted Spectrum Allocation (QASA) based on loss probability is proposed and a dynamic threshold modeling is suggested for spectrum sensing under dynamic channel condition. Here the Rayleigh and Rician fading models are considered and used the Lagrange mathematical concept to calculate the bit error and to minimize the distortion. The throughput of the proposed work performs well when compared with the conventional method of Simultaneous wireless information and power transfer (SWIPT).

Pneumonia Prediction Using Swarm Intelligence Algorithms

Abstract: In this chapter, a combination of swarm intelligence algorithms is used to diagnose pneumonia from a patient's x-ray report of lungs conditions. The ability of swarm intelligent algorithms to solve a wide range of problems. For the classification of the disease for this research, a feed forward neural network with swarm intelligent algorithms had been used. The capabilities of global optimization learning algorithms were investigated, along with their training and testing results. In the Chest X-Ray Images (Pneumonia) dataset with categorical and binary data, these optimizations comprise Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Glow worm Swarm Optimization (GSO), and Cuckoo Search Algorithm (CSA). The findings could help researchers quickly find the best algorithm for use in a Pneumonia medical dataset, with final accuracy ranging from 85 to 95 percent after all five final epochs.