Smart cities utilize digital technologies for the improvement of its services’ quality and performance by reducing resources’ cost and consumption, with a commitment of action and efficiency to its citizens. The increased urban migration has led to many problems in cities, such as traffic congestion, waste management, noise pollution, energy consumption, air pollution, etc., as nowadays COVID-19 pandemic has seized the whole world. So, it is necessary to carry out its standard operating procedures (SOPs), including less human interaction. Thus, technology plays a vital role via Internet-of-Things (IoT) based systems. In this paper, a lightweight security mechanism (LSM) is proposed to enrich the IoT based systems. Blockchain technology is integrated, and its completely decentralized peer-to-peer (P2P) technology enables the users’ authentication and authorizes legitimate procedures. The IoT based management system is developed to monitor some of the aforementioned problems and solve solid waste, air, and noise monitoring systems. The Ethereum blockchain is used to implement a smart contract based framework for the system’s security and access control. The evaluation of performance of the LSM demonstrates that it is an efficient and lightweight tool in terms of cost, resources, and computation and superior over related security studies.

Self-driving cars are an active area of interdisciplinary research spanning Artificial Intelligence (AI), Internet of Things (IoT), embedded systems, and control engineering. One crucial component needed in ensuring autonomous navigation is to accurately detect vehicles, pedestrians, or other obstacles on the road and ascertain their distance from the self-driving vehicle. The primary algorithms employed for this purpose involve the use of cameras and Light Detection and Ranging (LiDAR) data. Another category of algorithms consists of a fusion between these two sensor data. Sensor fusion networks take input as 2D camera images and LiDAR point clouds to output 3D bounding boxes as detection results. In this paper, we experimentally evaluate the performance of three object detection methods based on the input data type. We offer a comparison of three object detection networks by considering the following metrics - accuracy, performance in occluded environment, and computational complexity. YOLOv3, BEV network, and Point Fusion were trained and tested on the KITTI benchmark dataset. The performance of a sensor fusion network was shown to be superior to single-input networks.

Energy conservation has been the most popular topic of the modern world. Heating, Ventilation and Air Conditioning (HVAC) systems consume approximately 10 % of the total energy of world. In order to improve the efficiency of HVAC systems, two dimensional (2D) room with inlet, outlet and heat source has been modeled. ANSYS Fluent has been used for numerical analysis of air flow in a 2D room. User Defined Functions (UDFs), which are coded in C language and hooked in ANSYS fluent, have been used for recording temperature variations and for heat generation within 2D room. Besides studying velocity fields and temperature distributions within indoor environment under specified boundary conditions, reference region for comparative analysis is also selected during Steady State (SS) numerical simulations. During transient analysis, temperature variations of a selected location are recorded for four different scenarios under varying inlet velocity profiles i.e. three for 0°, 30°, 60° angle with 1.3661 m/s velocity and fourth 0° with 2.7322 m/s velocity. Temperature profile of reference region after 1500 sec of transient simulations are compared with the steady state. Temperature profile of the scenario once the air is injected at 30° closely matched with the steady state temperature profile of the selected region. Time for attainment of SS temperature is also measured and compared after transient simulations. SS temperature value was attained twice, first at 240 seconds when the air was injected at 0° with 2.7322 m/s and secondly at 522 seconds when inlet air entered at velocity of 1.3361 m/s at 30°. The power consumption by increasing the fan speed is much higher as compared to the power consumed for changing direction only.

In social media platforms, crowd-users extensively interact and contribute information related to software applications. Usually, crowd-users discuss software features or hot issues and record their opinions about the software applications under discussion either in textual form or via end-user votes. Such requirements-related information is considered a pivotal alternative source for requirements engineers to the already existing in-house stakeholders in order to illustrate decision-making. Also, requirements decision-making for Crowd requirements engineering is a difficult task, as it is always based on incomplete knowledge and requires trade-offs from multi-perspectives. However, existing requirements models and associated tools are still lacking, which enable requirements engineers to make informed decision-making and capture conflicting requirements knowledge. This paper elaborates the interaction among the crowd-users about the Google Map mobile application in the Reddit forum to recover conflicting requirements-related information using the goal modeling approach. For this purpose, we extracted critical arguments from a crowd-users conversation in user forums regarding a given design; built a graphical argumentation model based on the extracted information; aligned types of arguments with goal-oriented modeling constructs in the non-functional requirements framework; conducted exiting goal-model analysis to the requirements model to reach consensus based on argumentation and reasoning, such as supporting, attacking, undefined, and conflicting. The proposal is described with illustrative example models and the associated evaluation processes of design decision-making situation for Google Map interface design.

Finger vein recognition technology is a novel biometric technology with multiple features such as live capture, stability, difficulty in stealing and imitating, and more in the field of information security that has been utilized in a wide range of applications. In this proposed method, the finger region is separated from the background using a Sobel Edge detector and a Poly ROI which helps shape the finger. The background separation enhancement of low contrast using dual contrast limited adaptive histogram equalization which works on the visual characteristics of the finger-vein image dataset. When dual CLAHE is applied, the finger-vein histogram intensity is separated all across the image. Following the implementation of DCLAHE, an enhanced 2D-CNN model is utilized to recognize objects with the updated dataset. By maximizing the values of a preprocessed dataset, the 2D CNN model learns features. This model has a 94.88% accuracy rate.

The thyristor is a power electronics device that is widely used in various electrical appliances due to its lower on-conduction losses, easyavailability, lower switching loss, greater efficiency and cost-benefit. Mostly a thyristor is used in rectifiers and variable-speed drives. Almost 70% of loads used in the world consists of induction motors in various types and forms. In this work, a thyristor-based controller is used to control the speed of a single phase induction motor by adjusting the firing angle for the gate terminal of the thyristor. Depending upon the firing angle, the output voltage, output current, speed, power factor and the total harmonic distortion are varied which is analyzed through MATLAB/Simulink. Further curve fitting technique is used to formulate the mathematical relationships between varying parameters concerning thyristor’s firing angle. The findings of this work are helpful to achieve the best curve fit model for varying parameters concerningthe thyristor firing angle.

A substantial amount of research has been done to develop improved Intelligent Transportation Systems (ITS) to alleviate traffic congestion problems. These include methods that incorporate the indirect impact on traffic flow such as weather. In this paper, we studied the impact of weather conditions on traffic congestion along with more spatial and temporal factors, such as weekdays/time and location, which is a different approach to this problem. The proposed solution uses all these indicators to estimate the flow of traffic. We evaluate the level of congestion (LOC) based on the traffic volume grouped in certain regions of the city. The index for the defined LOC indicates the traffic flow from “free -flowing” to “traffic jam”. The data for the traffic volume count is collected from the Department of Transportation (DOT) for NYMTC. Weather conditions along with special and temporal information have an essential role in predicting the congestion level. We used supervised machine learning for this purpose. The prediction models are based on certain factors such as the volume count of the traffic at the entry and exit point of each street pair, particular days of the week, timestamp, geographical location, and weather parameters. The study is done on the major roadways of each of the four prominent boroughs in New York. The results of the traffic prediction model were established by using the Gradient Boosting Regression Tree (GBRT) which showed an accuracy of 97.12%. Moreover, the calculation speed was relatively fast, and it has stronger applicability to the prediction of congestion conditions.

With the increase in the usage of electromagnetic devices, electromagnetic interference increased many folds. Frequency Selective Surface (FSS) provide effective shielding from unwanted frequency ranges. A thin, conformal band-stop FSS is presented in this research that provides effective electromagnetic shielding properties in X-band. The FSS acts as a band stop filter at 10 GHz. The proposed FSS has 54.7% fractional bandwidth. The design is of the dimensions 6.79 x 6.79 x 0.127 milimeter cube, employing Rogers RT 5880 substrate with 0.0009 dielectric constant. It has an attenuation of at least -57.97 dB. The proposed FSS shows oblique incidence angle independence for both TE and TM modes, up to 60o scan angle. The incidence angle independence makes the FSS response stable for both normal and varying angles of the incident waves. The design has a copper cladding of 0.018 mm, making the overall FSS thickness of 0.145 mm. The thin substrate makes the design flexible and easily bendable for curved surfaces. Its thin structure makes it easily applicable on buildings, vehicles and military aircrafts for electromagnetic shielding purposes. The conformability and shielding properties make the design suitable for various other applications.

In this paper, we learn about the control of open channel water glide under the flood routing conditions. Generally, for flood routing in rivers, the Saint-Venant equations will be used which can be solved by finite distinction method. Saint-Venant equations will be converted into nonlinear equations and will be solved using the Preissmann scheme in the finite difference method. Using the Newton Raphson method, the set of equations will be changed into linear equations and will be solved by the space method. Our aims are to the estimated zones of Saint-Venant equations for flood routing by using the finite difference method with over bank unsteady flow in an open channel. The effectiveness of this method to optimize the choice of finite difference method is more accurate than other methods having adequate space and time steps.

An Automatic Vehicle Number Plate Recognition System (AVNPR) is a key research area in image processing. Various techniques are developed and tested by researchers to improve the detection and recognition rate of AVNPR system but faced problems due to issues such as variation in format, lighting conditions, scales, and colors of number plates in different countries or states or even provinces of a country. Douglas Peucker Algorithm for shape approximation has been used in this research to detect the rectangular contours and the most prominent rectangular contour is extracted as a number plate (NP) and the connected component analysis is used to segment the characters followed by optical character recognition (OCR) to recognize the number plate characters. A custom dataset of 210 vehicle images with different colors at various distances and lighting conditions was used for the proposed method captured on my smart phone Galaxy J7 Model SM-j700F at roads and parking. The dataset contains various types of vehicles (i.e. Trucks, motorcars, mini-buses, tractors, pick-ups etc). The proposed method shows an average result of 95.5%. The novelty used in this method is that it works for different colors simultaneously because in Pakistan, several colors are used for vehicle NPs.

Skin cancer is an uncontrolled development of abnormal skin cells potentially due to excessive exposure to sun, history of sunburns, less melanin, Precancerous skin lesions, moles, etc. This occur when unrepaired DNA damages the cells of the skin. It is one of the diseases that are viewed on its quick evolution and the most common type of cancer that endangers life. Researchers have implemented several machine learning and deep learning techniques for classification of skin cancer. In this research paper, different cancer categories are classified using significant attributes. We have used International Skin Imaging Collaboration (ISIC) dataset for classification purposes. This dermoscopic attributes dataset includes 1000 images and 10016 instances, seven categories, 5 features and 2 Meta attributes. We implemented K-Nearest Neighbor, Logistic Regression, Convolutional Neural Network, Naïve Bayes, and Decision Tree for classification and compared their performance. In order to implement classification algorithm, we used Orange which is an open-source machine learning, data mining, and data visualization toolkit. The models are evaluated based on matrices that include Accuracy, C. Automation, F1 score, Precision, Recall, and AUC. Furthermore, frequency of features is visualized using graphical method and the ROC analysis is also performed for the classifiers. It is observed that CNN technique provided the highest accuracy of 89% and the mentioned results are the highest results of classification with the state of the art techniques. For future, the improved and recent dataset and ensemble modelling techniques based on deep learning can used to enhance classification results. The research can also be extended for other cancer types using CNN.

Heat exchangers are employed in numerous applications of industry, automotive and air conditioning systems. The efficacy of heat exchangers depends upon various factors e.g., Reynolds number (Re) of the fluids, geometry of heat exchanging surfaces, and the Prandtl number of the cooling air. In this paper, the working of a crossflow heat exchanger with elliptical tubes is simulated numerically for 5000 < Re < 20000 at its maximum operating temperature of 323K. The tubes were arranged in a staggered way. The radical investigations were done at one-of-a-kind relative humidity ranges within the cooling air ranging from 0% to 80%. The relative humidity was modeled in the shape of mass fractions of water vapors in the air. The thermos-physical properties of dry and moist air were employed for the analysis. The impact of this changing of relative humidity on forced convection heat transfer of heat exchangers is examined in the form of percentage change in Nusselt number. With the increase in moisture content in the air, the Nusselt number was observed increased up to 4.5%. The paper provides a tool to analyze the Nusselt number of the elliptical-shaped heat exchanger while operating in moist atmospheric conditions.

Finite Impulse Response (FIR) filter model emulates the Inter Symbol Interference (ISI) in a wireless communication channel. An equalizer, typically an Infinite Impulse Response (IIR) filter, behaves as an inverse filter to the FIR filter to remove the effects of the ISI. IIR filters are generally avoided due to tractability issues, and an FIR filter, with an adaptive signal processing algorithm to minimize the error due to the ISI, is deployed at the receiver. However, the filter is observed to quickly reach a steady state where further iterations do not yield a reduction in the error. This can be attributed to relatively slow variations in the steady state error which prevent further reduction of the errors. This work focuses on converting the low frequency error variations to high frequency variations by the use of multirate signal processing. As such, the steady state error can be damped as well, providing further reduction in the error and an enhanced adaptive filter performance.

Pulmonary diseases are very severe health complications in the world that impose a massive worldwide health burden. These diseases comprise of pneumonia, asthma, tuberculosis, Covid-19, cancer, etc. The evidences show that around 65 million people undergo the chronic obstructive pulmonary disease and nearly 3 million people pass away from it each year that make it the third prominent reason of death worldwide. To decrease the burden of lungs diseases timely diagnosis is very essential. Computer-aided diagnostic, are systems that support doctors in the analysis of medical images. This study showcases that Report Generation System has automated the Chest X-Ray interpretation procedure and lessen human effort, consequently helped the people for timely diagnoses of chronic lungs diseases to decrease the death rate. This system provides great relief for people in rural areas where the doctor-to-patient ratio is only 1 doctor per 1300 people. As a result, after utilizing this application, the affected individual can seek further therapy for the ailment they have been diagnosed with. The proposed system is supposed to be used in the distinct architecture of deep learning (Deep Convolution Neural Network), this is fine tuned to CNN-RNN trainable end-to-end architecture. By using the patient-wise official split of the OpenI dataset we have trained a CNN-RNN model with attention. Our model achieved an accuracy of 94%, which is the highest performance.