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Parking management has grown to be a significant issue with the rise in vehicle traffic in urban regions. The conventional parking method causes traffic congestion and environmental pollution in addition to being time-consuming. These issues are resolved by the IoT-based smart parking system. It gives drivers and parking administrators access to real-time parking information through sensors and communication networks. To forecast parking availability and maximize parking spot use, the system uses data provided by the IR sensor. Additionally, it provides functions like web booking and payment. By decreasing the amount of time and effort needed to locate a parking space, the smart parking system improves the user experience. Additionally, it aids in lowering fuel consumption, carbon emissions, and traffic congestion, making it an ecologically friendly choice

The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.

Integration of the latest technological advancements such as Internet of Things (IoT) and Computational Intelligence (CI) techniques is an active research area for various industrial applications. The rapid urbanization and exponential growth of vehicles has led to crowded traffic in cities. The deployment of IoT infrastructures for building smart and intelligent traffic management system greatly improves the quality and comfort of city dwellers. This work aims at building a cost effective IoT enabled traffic forecasting system using deep learning techniques. The case study experimentation is done in a real time traffic environment. The main contributions of this work include: (i) deploying road side sensor station built with ultrasonic sensor and Arduino Uno controller for obtaining traffic flow data (ii) building an IoT cloud system based on open source Thingspeak cloud platform for monitoring real time traffic (iii) performing short term traffic forecast using Recurrent Neural Network (RNN) models such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of the prediction model is compared with the traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) and Convolutional Neural Network (CNN). The results show good performance metrics with RMSE of 5.8, 7.9, 10.2 for LSTM model and 6.7, 8.6, 10.9 for GRU model for three different scenarios such as whole day, morning congested hour and evening congested hour datasets.

Vibration of a smart beam is being controlled. This smart beam setup is comprised of actuators and sensors placed at the root of a cantilever beam. Numerous factors, including human action and nearby motorized equipment, can result in vibrations. In this instance, the actuator signal is used to create the disturbance. The piezoelectric sensors are used to detect the vibration. The PID(s) controller simultaneously transmits a control signal through dSPACE to the actuator through which the vibration can be reduced. Using Ziegler-Nichols, a PID controller is designed for flexible beam structure vibration suppression in order to maximize outcomes. The model of the system which includes the dynamics of the structure together with the sensor/actuator dynamics is obtained through on line system identification technique. The performance of the PID(s) controller is evaluated experimentally using dSPACE controller board.

Our project aims to provide secure file storage for users on the cloud (specifically on AWS S3) by utilizing a hybrid cryptography approach. This involves encrypting files using both AES and RSA algorithms, with the user receiving the encryption key through email. To further enhance security, the key will be hidden behind an image or within a PDF document using steganography techniques. Additionally, the files stored in the S3 bucket will also be encrypted, but instead of encrypting the contents of the file, we will encrypt the file format itself. The file format can be decrypted using the GHE decryption key and software, which will be made available to end-users. Overall, our project aims to provide a robust and secure solution for cloud-based file storage. &nbsp

Nanoparticles are solid colloidal particles ranging from 1 to 100 nm in size, they consist of macromolecular materials in which the active ingredients is dissolved, entrapped, or encapsuled, or absorbed. For environmental remediation the recent studies indicate the potential of iron nanoparticles. Biological resources such as bacteria, algae, fungi and plants have been used for the production of low-cost, energy-efficient and nontoxic environmentally friendly products. Nano scaled irons were synthesized from the plant Clitoria ternatea extract of under environmental condition at room temperature by using 0.01M FeCl3 (ferric chloride) at 30 oC in 30 min. The zero valent iron nanoparticles are synthesized in oxidation state. Box Behnken design optimization tool was used to predict the nanoparticles yield with three variables. The reductants present in the plant extracts act as reducing and stabilizing agent. A systematic characterization of nano scaled iron particles was performed using UV-Vis, pH, FT-IR and SEM studies. The diameter of iron nanoparticles was found within the range 50-100 nm. In the green nanotechnology the valuable approach of synthesis of nanoparticles by the exploitation of different biomaterials. This study focuses on various studies outlining the novel routes for biosynthesis of these nanoparticles by plant resources along with outlining the future scope of work in this area

Shampoos are most probably used as cosmetics. In general, hair care products are used for cleaning scalp and also used for beautifying agents. It contains suitable additives preservatives and active ingredients. The purpose of using shampoo is to remove dirt that is build up on the hair without stripping out much of this bum. The herbal shampoo popularized due to formulated from herbal sources which is safer with milder effect. In this study, herbal shampoo was formulated containing suitable ingredient such as Lawsonia inermis (L) Henna), Azadirachta indica A. Juss. (Neem), Hibiscus rosa-sinensis L (Hibiscus), Ficus benghalensis L. (Banyan), Phyllanthus emblica L. (Amla), Sapindus mukorossi Gaertn. (Ritha), Senegalia rugata (Lam.) Britton & Rose. (Shikakai) and Aloe vera (L.) Burm.f. (Aloe vera) different proportions to formulate and evaluate its physicochemical properties. Marketed crude products of the above-mentioned herbal drugs were used in the formulation of herbal shampoo. Ritha and Shikakai when shaken with water create rich foam impact. Amla fruit and Hibiscus rosa-sinensis L. flowers are used to promote hair growth, anti-dandruff agent, strengthen hairs, and prevent hair fall. Dried roots of Ficus benghalensis (Banyan) produce Lustrous effects on hairs. H. rosa-sinensis flower and Aloe vera (L) used as a conditioning agent and also moisturizing agent. The formulated herbal shampoo was evaluated for its physical and chemical stability.

An epithelium in a living organism seldom develops in isolation. Rather, most epithelia are tethered to other epithelial or non-epithelial tissues, necessitating growth coordination between layers. We investigated how two tethered epithelial layers of theDrosophilalarval wing imaginal disc, the disc proper (DP) and the peripodial epithelium (PE), coordinate their growth. DP growth is driven by the morphogens Hedgehog (Hh) and Dpp, but regulation of PE growth is poorly understood. We find that the PE adapts to changes in growth rates of the DP, but not vice versa, suggesting a “leader and follower” mechanism. Moreover, PE growth can occur by cell shape changes, even when proliferation is inhibited. While Hh and Dpp pattern gene expression in both layers, growth of the DP is exquisitely sensitive to Dpp levels, while growth of the PE is not; the PE can achieve an appropriate size even when Dpp signaling is inhibited. Instead, both the growth of the PE and its accompanying cell shape changes require the activity of two components of the mechanosensitive Hippo pathway, the DNA-binding protein Scalloped (Sd) and its co-activator (Yki), which could allow the PE to sense and respond to forces generated by DP growth. Thus, an increased reliance on mechanically-dependent growth mediated by the Hippo pathway, at the expense of morphogen-dependent growth, enables the PE to evade layer-intrinsic growth control mechanisms and coordinate its growth with the DP. This provides a potential paradigm for growth coordination between different components of a developing organ.

Autonomous transportation is a new paradigm of an Industry 5.0 cyber-physical system that provides a lot of opportunities in smart logistics applications. The safety and reliability of deep learning-driven systems are still a question under research. The safety of an autonomous guided vehicle is dependent on the proper selection of sensors and the transmission of reflex data. Several academics worked on sensor-based difficulties by developing a sensor correction system and fine-tuning algorithms to regulate the system’s efficiency and precision. In this paper, the introduction of vision sensor and its scene terrain classification using a deep learning algorithm is performed with proposed datasets during sensor failure conditions. The proposed classification technique is to identify the mobile robot obstacle and obstacle-free path for smart logistic vehicle application. To analyze the information from the acquired image datasets, the proposed classification algorithm employs segmentation techniques. The analysis of proposed dataset is validated with U-shaped convolutional network (U-Net) architecture and region-based convolutional neural network (Mask R-CNN) architecture model. Based on the results, the selection of 1400 raw image datasets is trained and validated using semantic segmentation classifier models. For various terrain dataset clusters, the Mask R-CNN classifier model method has the highest model accuracy of 93%, that is, 23% higher than the U-Net classifier model algorithm, which has the lowest model accuracy nearly 70%. As a result, the suggested Mask R-CNN technique has a significant potential of being used in autonomous vehicle applications.

The Zeeman effect is used for measurement of magnetic fields in astrophysical and laboratory plasmas. Magnetic fields in atmospheres of magnetic white dwarf stars are in the range 40 kG-1 GG. The quadratic Zeeman effect results in the additional split and shift of lines for magnetic fields2 MG. Hydrogen Balmer lines were studied in magnetic fields delivered by a 1 MA pulse power generator. The magnetic field was generated by rod loads 0.8-1 mm in diameter. A droplet of CH oil on the load center was the source of hydrogen. A low ionized oil layer was backlit by blackbody emission from the rod with a temperature of 0.5-0.6 eV. Zeeman splitting of H-alpha and H-beta absorption lines were with a grating spectrometer. A spectral shift of the central component of the triplet indicated the quadratic Zeeman effect in hydrogen lines.

The Zinc oxide nanoparticles were successfully synthesized using Zinc nitrate as the precursor and extract of Illicium verum was used as the reducing and stabilizing agent. Synthesized Zinc oxide particles in nano size were characterizedby FT-IR, Ultraviolet-visible SEM & XRD techniques. The formed nanoparticles were confirmed as zinc oxide nanoparticles by UV- Visible, FT-IR spectroscopic techniques. XRD data gave the results regarding structure and size of the prepared nano-sized particles. According to the results of XRD, the size was calculated as 27.01 nm. SEM analysis results sphere and flake like shape and morphology of Zinc oxide nanoparticles.

The aim of the project is to replace cement with fly ash and course aggregate with Ethyl Vinyl Acetate (EVA) in paver block. In this thesis paver block design is by using cement concrete mixture of mix design M30 which is composed of 10mm coarse aggregate cement and fine aggregate (M-sand).In this thesis the cement is partially replaced with fly ash and partial replacement of EVA with coarse aggregate in paver block at various level of 5, 10, 15, and 20 percentage of its weight. The paver block curing process is done for 7days and 28days. After curing it is checked for its compression strength, water absorption test and densitytest.

Women's participation in agriculture is increasing, but their role in decision-making is still limited. Using a household survey of wheat producers in Haryana, Bihar, and Madhya Pradesh, this paper studies women's involvement in wheat production and compares it to that of men; analyses their role in decision-making; and examines the socio-economic influences on their participation. Overall, the paper concludes that although the share of time spent by women on agricultural activities is increasing, their role in decision-making is still highly dependent on the socio-economic characteristics of the farm household.

Physiotherapy may be a technique for relaxing the stresses in muscles in several parts of the body. This system needs the physiotherapist supervision and a daily visit to the hospitals to be at this system we came with an answer by using simple mechanisms like slider crank mechanism, wiper and rack and pinion mechanism. With the assistance of simple robot, physio treatment is completed automatically and maybe done reception with none assistance. Moreover, Home traction devices have also meant that patients once advised by the therapist regarding the weights and duration of traction treatment are ready to apply traction themselves.

In the present paper, impedance measurements of the battery configuration, Anode│lithium borophosphate glass electrolyte│LiCoO2 cathode, has been carried out to throw some light on the electrochemi...

Polymers are the long chain organic materials that are held together by directional covalent bonds. These kind of organic materials are either synthesized or naturally obtained and they have wide r...

In this experimental work, a mixture of Hydroxyapatite (HAp)+yttrium stabilized zirconia (YSZ) is deposited on a layer of Ti6Al7Nb alloy by electron-beam deposition method, which finds its ...

A routing algorithm plays a major role in Data communication across an inter-network. Software-Defined Networking (SDN) is a new era of computer networking that separates Data plane from Control plane which controls, changes and manages the network behavior dynamically via open interfaces with the help of SDN Controllers. In SDN, the networking devices route packets in the form of flows with the help of flow rules given by the controllers and the flow rules are stored in the corresponding flow tables of the networking devices. The Box-Covering (BC) algorithm is an existing algorithm used in SDN for achieving renormalization of networks by calculating the fractal dimension of networks and covering the network with the minimum possible number of boxes and Dijkstra’s algorithm is used for calculating shortest paths. The proposed work focuses on Path Optimization in the Box-Covering-Based Routing (BCR) algorithm, which is an existing algorithm used in large-scale SDN. In the proposed work, a Link-Weight system has been used for bounding the Link Utilization of the shortest path between the Source and the Destination to minimize the Average Packet Delay in the Network. The results show that the proposed work reduces the Average Packet Delay compared to the existing algorithms such as Dijkstra’s algorithm, Graph Compression algorithm and BCR algorithm and also improves the performance of the network.

This article provides the overview and conceptual explanation of a robotic course using a peer learning activity. In this approach, the demo-based peer teaching education activity was performed to improve the engineering education system. Strategies that relate to student experiences and engage their interests can be difficult to glean and execute in a short duration. The introduction of a demo-based learning method reduces complexity and time frame to complete for students. Initially, the prototype was developed using a 3D printer and it is used to explain the mobile robot concepts through peer teaching techniques to undergraduate students. The activity results were used to study the peer teaching impact among the student's group through weighted average and correlation coefficient method. Through demo assignments and assessment the results justify the improvement of students self-confidence, interest towards the course, perspective thinking, student to student interaction and teamwork.