Evolution in Signal Processing and Telecommunication Networks

In this article, the use of a 50 Ω microstrip feed to simulate and evaluate the miniaturization and dual-band operation of slot antenna has been explored. The designed antenna achieves the functionality of a slot antenna without the need to extend the slot by introducing several slits. The utilization of several slits in the reference slot antenna results in a dual-band antenna with resonance occurring at 1.48 and 3.48 GHz for each respective frequency. The topology reduces the resonant frequency of the microstrip feed slot by 48.25% due to the presence of the various slits. The simulated efficiency stands at 79.58%, while the measured efficiency is 75.94%. The radiation patterns are examined, and the prototype validation is conducted by means of a simulation study focusing on the suggested antenna configuration.

Elliptical split ring resonator structure antenna has been proposed to get frequency reconfigurability in order to use for different applications. There are several other structures that have already been used to achieve the reconfigurability having complex structures compared to this structure. In this design, bottom and middle layers are made of metal with same design parameters. Also, middle layer is made of dielectric between ground and patch. Here, probe feeding is being used to excite the antenna. To achieve reconfiguration properties, two PIN diodes have been used which are acting as a switch. By using these switches, four gains have been obtained at four different frequencies which represent the frequency reconfiguration property in designed antenna. Top layer is made up of metamaterial in which elliptical split ring resonator structure is being used. All the design has been done using CST microwave studio. Here, reflection coefficient has been achieved less than − 10 dB and VSWR less than 2. All four gains are 2.66 dBi, 2.76 dBi, 2.78 dBi, and 2.67 dBi at 9.91 GHz, 9.94 GHz, 10.27 GHz, and 10.14 GHz respectively. This design is useful for beam steering and phase array to locate the target, etc.

This paper introduces a new two-way half-duplex cooperative communication system based on joint grouping subcarrier-permutation index modulation-based differential chaos shift keying (GSPIM-DCSK), a novel modulation scheme that enhances the existing grouping subcarrier index modulation (GSIM-DCSK) system by utilizing a permutation index to load additional data bits in order to enhance its energy efficiency, data rate, and BER performance. Through a relay, this cooperative system allows two users to communicate. While the input bits are separated into groups consisting of carrier, permutation, and modulated bits, GSPIM-DCSK uses a permutation index to send additional data bits. Within each group, the carrier index bits choose which subcarrier is inactive. The bit error rate (BER) of GSPIM-DCSK under various channel circumstances is calculated as part of the performance evaluation. Furthermore, the performance of the new cooperative system employing GSPIM-DCSK is contrasted with that of a conventional cooperative system using DCSK in terms of BER under different channels. The results and analysis provide valuable insights into the effectiveness and advantages of the proposed GSPIM-DCSK modulation scheme and its cooperative communication application.

In this paper, a class of fractional-order system (FOS) has been modelled in complex $$z$$ z -domain through direct discretization of the fractional-order (FO) operator. Two generating functions, namely Visweswaran–Varshney–Gupta–Schneider (VVGS) transform and reduced Ngo transform, have been employed to obtain the approximate discrete-time models of the FO operator via continued fraction expansion (CFE) method. The stability analysis is performed through determining the pole locations of the discretized fractional-order systems. Finally, the two transforms have been compared based on the simulation outcomes obtained from the frequency responses of the approximated fractional-order systems.

Wireless technologies in intelligent transportation systems are evolving rapidly globally. Dedicated short-range communications standards are insufficient to meet the growing demand for data exchange on roads. Cellular Vehicle-to-Everything (C-V2X) using massive multiple-input and multiple-output (MIMO) technology provides high data rates, massive connectivity, and low latency. However, the broadcasting nature of wireless media poses security risks. This paper addresses the challenge of securing wireless transmission in massive MIMO systems at the physical layer. It calculates the average outage probability based on long-term evaluation (LTE)-V2X link behavior and proposes a two-stage user authentication mechanism to identify suspicious users requesting extra bandwidth. Eavesdroppers are detected using pilot signals, and their information is captured and limited through beacon information. The findings suggest that mobility affects bandwidth requests, making mobile users less likely to be eavesdroppers compared to stationary users.

The effect of shadowing and multipath fading goes hand in hand in wireless communication systems. In this paper, a study on a composite type of fading channel is performed. The ASER analysis is carried out deploying the Fisher-Snedecor F fading channel considering the DF-type relay in the transmission system wherein a direct link is also considered between the source and the receiver. Mathematical statements of PDF, CDF, and OP are hence derived. MGF expression is shown in respect of Meijer’s G function, which is then used to procure the ASER of the system. At the receiver end MRC technique combines the SNR of the signals received. Non-coherent modulation schemes like NCBFSK and DBPSK are used for the analysis purpose of the ASER. The results show that ASER performs better at high values of SNR, which is the desired result. Similarly, DBPSK modulation is seen to give an improved performance of ASER than NCBFSK. Simulation is carried out to verify the ASER statements.

In this paper, we present and analyze the design process for a compact, low-profile patch antenna on an FR-4 glass epoxy microwave substrate. The Tire Pressure Monitoring System, or TPMS, is a system that monitors tire pressure for each individual wheel on a vehicle to ensure optimal tire performance and safety. The system has significantly reduced the number of accidents caused by tire deflation, resulting in the saving of hundreds of lives and billions of dollars. The antenna is the main component that sends the tire pressure information to the driver's dashboard, and the driver can monitor the tire pressure constantly through wireless networks. Through a compact antenna, the driver is aware of the pressure on a moving tire. In this paper, a compact microstrip patch antenna is proposed for TPMS applications. The simulated resonant frequency of this antenna is 433.86 MHz, which is applicable for the European Union standard TPMS RF band. The corresponding bandwidth (10 dB) of this antenna is 10.09%. This antenna's reduction in resonant frequency is 81.21% in comparison with the reference antenna. Design details of each proposed topology and the results of both simulations and experiments are outlined and discussed with a parametric study. The radiation characteristics, input impedance, and efficiency are also depicted with a relatively stable measured response.

This paper presents a thorough examination of transparent substrates and conductive materials for antenna fabrication. The study encompasses a detailed analysis of the essential specifications of commonly used transparent substrates. Furthermore, the conductive transparent conductors employed in previous research are investigated, and their properties are discussed. It is worth noting that the majority of transparent conductive inks utilized in literature rely on nanomaterials and thin films. Additionally, the prevalent transparent substrates predominantly comprise glass and PET materials.

The Mobile Ad Hoc NETwork (MANET) is a class of unguided as well as self-configuring network made up of wireless mobile nodes that do not depends upon any existing infrastructure. A node may join network directly with other already connected devices through wireless links. The hop-to-hop routing concept is being used for nodes which are far from other ones. Those which are intersection nodes are known as nodes for relaying. It is varied from a settled network because of a centralized entity. The stabled base stations do not present in the environment. The example of such type of application is an Internet. MANETs are not yet widely employed, research in this particular area is mostly originated on simulation. Mobility and movements of the nodes affects the numbers of linked pathways in this type of network, which as outcome affect the overall performance of routing algorithms. In this paper we will learn simulative experiment of Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), and Ad-hoc On Demand Vector (AODV) routing protocols, the behave of these routing protocols on different speeds was analyzed. The result will be based on some performance metrics. The model which is used for experiment in this paper is random waypoint model. By using this model we will find how these three protocols perform. The impact of mobility (different speeds of a mobile node) is analyzed over MANET routing protocols. The performance metrics like packet overhead, drop rate, packet delivery ratio (PDR), throughput, average end-to-end delay and are used to authenticate the outcomes of mobility model.

Wireless optical communication (WOC) also known as Free Space Optics (FSO), is one of the most promising methods for high information rate point-to-point transmission that has gained substantial attention in recent years. Compared to RF counterparts, WOC offers various advantages, including large modulation bandwidth, license-free operation, ease of installation, and immunity to electromagnetic interferences. WOC systems are based on optical technology, which has the potential to achieve even higher data transmission rates in the future. This makes it a future-proof technology that can meet the increasing demands of data-intensive applications. WOC plays a crucial role in various industries and applications. It is of great importance due to its high-speed data transmission, secure communication, immunity to interference, energy efficiency, line-of-sight communication, wide application range, scalability, and compactness. With the increasing demand for faster and more reliable wireless communication, the importance of wireless optical communication is likely to continue to grow in the future. The performance of the WOC depends upon number of factors like atmospheric condition, distance, receiver sensitivity, etc. In this paper, the main contributions are to improve the WOC system by analysis its important parameters like Q-factor, eye height, transmitter aperture diameter, and receiver aperture diameter. The system performance is evaluated for each unique case in terms of the q-factor, eye height, and bit error rate for a channel length varying between 5 and 35 km. The transmission power and the aperture diameter have a significant role on the overall system performance. Upon changing these parameters, it has been observed from the results obtained that the transmission range is directly influenced by the variations in the transmission power levels and the transmitter and receiver aperture diameters.

In optical communication system, dispersion compensation techniques are used to lessen the effects of chromatic dispersion, that degrade the quality and performance of transmitted signals. Dispersion compensation techniques play a crucial role in maintaining signal quality, extending transmission distances, enhancing system performance, and providing flexibility in network design in optical communication systems. These techniques aim to minimize or eliminate the distortion caused by dispersion, allowing for longer distance transmission and better signal quality. This paper presents an analysis of the role of the dispersion compensating fiber in an optical fiber communication link. The existence of dispersion is detrimental to the whole system performance. The use of compensation schemes can help to reduce dispersion effects that can induce crosstalk and hamper the system performance. The length of the dispersion compensation fiber is varied from 10 to 30 km to study its effect on the system performance in provisions of the Q-value, eye height and the BER.

In this review work, the parameters of a stub-based Chebyshev third-order planar microwave low pass filter (LPF) were measured using a variety of patterns. When compared to a simple filter, the findings reveal that the response is better, with the quality factor and ripple size being the improved characteristics. This means that enhanced quality factor (56.421 to − 76.97851 dB/GHz) and reduced ripple size (− 2.53 to − 2.093353 dB) are recovered as information from the analyzed response, and this comparative analysis is performed not only with single patterns but with multiple patterns in various numbers and sizes at a certain distance. The complementary split-ring resonator (CSRR), which has negative permittivity and defected ground structure (DGS), which is a defective ground structure, is where the patterns employed.

This paper introduces a new design of a varactor-based Digitally Controlled Oscillator (DCO). The hybrid ring-type DCO is designed using three different delay stages. Each delay stage consists of a varactor-based load element which is controlled digitally using an NMOS-based switching network. A 4-bit three-stage DCO and a 4-bit five-stage DCO have been designed, both utilizing the MOS varactor as a load element. The 4-bit three-stage DCO exhibits a frequency variation from 2.092 to 1.750 GHz while consuming 0.643 mW of power. In the case of a 4-bit five-stage DCO, the output frequency is in the range of 1.080–0.920 GHz, accompanied by a power consumption of 1.099 mW. The control bits are systematically changed from [0000] to [1111]. The effects of varying supply voltage on the output frequency and power consumption are also measured and recorded. Results are obtained in TSMC 0.18 µm CMOS process technology.

With the advent of modern wireless communication standards, it becomes a common scenario that images and videos are transmitted more often in our day-to-day applications. However, images and videos consume a large bandwidth if transmitted uncompressed. In many cases after compression and transmission through channels, the quality of the images and videos often gets deteriorated. Now, some applications require a considerable amount of image quality for better understanding and interpretation, e.g., transmission of medical images. The present standards have certain limitations in that, particularly if the noise is associated with images. With this paper we compared four image compression approaches JPEG, autoencoder, VGG, and vision transformer (ViT) for standard images and standard images with introduced Gaussian noise and CIFAR10 datasets. This paper consists of two parts, the first part gives general overview of JPEG which are still in use, being a benchmark for every compression algorithm since they are the foundation of image compression algorithms and then recent trends like compression using deep learning which includes autoencoder, neural network-based compression like VGG and the transformer-based compression like ViT which are trending and are giving more promising results. The second part consists of the comparison of these four approaches, calculating their MSE, PSNR and Compression Ratio using CIFAR10 datasets, standard images and standard image with introduced Gaussian noise to get better and promising results of image compression maintaining its quality. Thus, among all four approaches, ViT and VGG give the best compression ratio for standard images and CIFAR datasets, respectively.

In present era, where the demand for increased bandwidth is constantly rising, achieving higher spectral efficiency is an imperative necessity. A potential answer to the need in this direction is optical communication. Due to their theoretically limitless and orthogonal modes that can be effectively multiplexed, laser light beams carrying orbital angular momentum (OAM) have introduced a new paradigm in data transfers. We examined the intensity profiles and phase structures of Laguerre–Gaussian (LG) beams with various orders of topological charges in this research. The propagation characteristics of LG beams are simulated. The data communication system model simulated for the numerical study uses an OAM carrying LG beam as the carrier, ON–OFF keying for modulation, and an AWGN channel for additive white Gaussian noise. The bit error rates (BER) were calculated using the modelled system for a range of signal-to-noise ratio (SNR), and the outcomes are consistent with the predictions made by theory.

The electromagnetic shielding effectiveness (ESE) of two human head models (HMs) of seven tissues; skin, fat, bone, dura, cerebrospinal fluid (CSF), gray matter (GM), and white Matter (WM) is evaluated. The effect of mobile phone position (MPP) on the RF absorption is modelled in this work. The simulation of ESE of HMs is performed using the Transmission Line Method: in the mobile position absence with oblique angle of incidence (OAI) variation for transverse electric and transverse magnetic polarization (TEP), (TMP); and in the mobile position presence and polarization. Also, repeating the same procedure by transparent silver nanowire Ag-NW/Poly(diallyl dimethylammonium chloride) (PDDA) as with nickel (Ni) lamination forming a laminated shield incorporation. Specific absorption rate (SAR), a radiation absorption metric, is estimated at 3.6 GHz from the obtained ESE for the age-dependent HMs. Out of both the adult and child HMs, a higher SAR is absorbed by the child HM, of about 2.39E−6 W/kg at 30° mobile phone tilt for TEP.

Optical devices based on photonic crystal superiorly concentrated by various investigators turns to be crucial need in various integrated applications. This research proposes a four channel wavelength division de-multiplexer (WDM) based on photonic crystal ring resonators (PhCRR) that are appropriate for various applications. This work uses 4-resonant rings with varied geometrical factors to complete the de-multiplexing task. The de-multiplexer includes two photonic band gap regions, and was designed using a square lattice 2D-photonic crystal structure of dielectric rods. The band gap covers the wavelengths for optical communication. The optimal channel spacing for this de-multiplexer and the transmission efficiency is superior to 95%. The efficiency is substantially higher than other approaches. Based on its major role, various design mechanisms and metrics are analyzed and reported. Moreover, the structural design of the anticipated model captures the attention of various investigators owing to its higher quality factor, design flexibility and low loss which fulfills the major applications requirements. Numerical analysis and simulations are done using Photonics CAD software. Here, the main concept is investigating the drawback of the existing approach and resolve in the proposed model. The comparison is helpful in modeling the structure with better performance.

This paper presents study on a microstrip patch antenna, which resembles like English alphabet “L”. The proposed patch antenna resonates at 9.15 GHz with gain and bandwidth of 4 dBi and 1.51 GHz (16.33%), respectively. The directivity of this patch antenna is 8.390 dBi. Coaxial probe feeding has been used to feed the proposed antenna. This operating band of the proposed antenna is beneficial for X-band applications like Earth Exploration Satellite System (EESS), which is used in several areas such as weather forecast, collecting data about the earth etc. 1.6 mm thick FR4 substrate has been used to fabricate this proposed antenna. The proposed antenna possesses low profile and is easy to fabricate. Simulations are done on commercially available electromagnetic simulator, HFSS, which works on the theory of full wave finite element method.

In this paper, a novel six-element 3 $$\,\times \,$$ × 2 Franklin array antenna is proposed for 5G millimeter-wave applications. Coaxial feed was used to excite the six elements. The parameters of the Franklin array elements are fine-tuned to achieve a high-gain antenna performance as desired. The suggested antenna employs Rogers RT Duroid substrate, which enables a broad frequency range from 25.61 to 34.62 GHz, covering a 5G millimeter-wave frequency band n257/n258/n261. The antenna dimensions are 19 mm $$^3$$ 3 $$\times $$ × 19 mm $$^3$$ 3 $$\times $$ × 1.6 mm $$^3$$ 3 . The proposed antenna has 29.91% fractional bandwidth along with a peak gain of 10.64 dBi at 28 GHz frequency and it is well-suited for wideband and high-gain mm-wave applications in the context of 5G FR-2.

Humans are perpetually in search of ways to enhance their quality of life, yet some of these pursuits inadvertently inflict harm on the environment, affecting land, water, air, and more. Since land is the fundamental resource supporting human existence, it serves as a starting point for our investigation into the alterations that have occurred. Specifically, the Rushikonda region in Andhra Pradesh’s Visakhapatnam district has witnessed noticeable changes in land use based on observations. The objective is to comprehensively study how land use and land cover have evolved in Rushikonda from 2015 to 2021, spanning 6 years. This study sheds light on the far-reaching impacts of shifts in agricultural practices, industrial activities, and land utilization on the environment. The study’s findings underscore the vulnerability of nature, industry, crop patterns, and land use to the scarcity of water resources. Ultimately, the paper concludes that recent analytical approaches have facilitated a broader understanding of land utilization trends, revealing a significant 28% shift in land patterns that can inform construction planning near the Rushikonda area.

Due to the large growth of web technologies and online resources, there is high information made available to everyone. With the growth of technology and resources, people are expressing their opinions and thoughts more comfortably. It impacts the decision-making of everyone, the mindset of the customer is based on the reviews of the other users in the web resources. Hence, going through all the reviews and getting to a final conclusion about the product has become a major issue nowadays. The solution to the problem is provided by sentiment analysis. Sentiment analysis derives the final polarity of the product by analyzing all the reviews of users. The overall sentiment polarity is affected by aspects within the domain, knowing the polarity of every aspect in the considered domain can be done by ABSA (aspect-based sentiment analysis). Aspect-based sentiment analysis is developed using many different solutions. Among them, deep learning and machine-learning methods made a great impact on the development of ABSA. In ABSA, the primary feature to be taken into consideration is aspects of domain is polarity. Choosing the most suitable model from the different models which are available is the difficult part. A comparative analysis is performed between ML (machine learning) and DL (deep learning) models.

In a student’s academic journey, one of the paramount factors is attendance. Attending classes is of utmost importance as it directly correlates with a student’s ability to grasp and comprehend the material presented by educators. To maintain satisfactory academic progress, students are typically required to achieve a minimum attendance threshold, often set at 75%, failure to meet which may result in fines or even the loss of an entire academic year. In some cases, students resort to various methods, including employing proxies, to manipulate their attendance records. To streamline this process, educational institutions have started implementing innovative attendance tracking systems, such as the one discussed in this study. This system utilizes a Convolutional Neural Network (CNN) algorithm, integrated with a Tkinter graphical user interface (GUI) and the Python-based face recognition package. The implementation of this technology not only enhances efficiency but also reduces the time and effort previously spent on manual attendance taking. In this system, a group photograph of students serves as input, and the output provides an accurate count of the classes attended by each student, simplifying attendance monitoring for both students and educators.

High temporal resolution is provided by EEG signals, which is helpful for evaluating and diagnosing youngsters that suffer with ADHD. The goal of this research is to produce a model for ML for identifying youngsters with ADHD and healthy controls 60 youngsters having ADHD and 60 healthy controls provided EEG readings for this investigation who were doing cognitive activities were collected from an open-access database. Three classifiers—AdaBoost, ANN, and RF—used to identify and further test the regional contributions to achieving improved accuracy. 19 channels of EEG data are utilized as input characteristics for classifiers, both individually and in combinatorial groupings. When every channel is considered and the total performance of all the classifiers is evaluated, the Random Forest has the greatest accuracy (80.48%). This study demonstrates the distinct physiological differences between youngsters with ADHD an acronym for attention deficit hyperactivity disorder children who are typically growing and developing present in their brain activity and may to make a diagnosis.

The domain of stock market price forecasting has experienced a significant transformation with the integration of sentiment analysis methods. This study explores the application of the XGBoost algorithm, a robust gradient boosting technique, in the context of stock price prediction enhanced by sentiment analysis. The research leverages historical stock market data and sentiment data from diverse textual sources, including news articles, social media, and financial reports. It encompasses data preprocessing, sentiment analysis, and the integration of sentiment scores with stock data, which serves as the feature set for the XGBoost model. Hyperparameter tuning and cross-validation are used to enhance the model's performance with rigorous evaluation metrics providing insight into its predictive accuracy. The XGBoost algorithm, known for its versatility and predictive power, is revealed as a potent tool in forecasting stock prices, offering the potential for more informed investment decisions. This study serves as an exploration of the fusion between cutting-edge machine learning and the financial world, shedding light on the evolving landscape of stock market price prediction and the substantial role of sentiment analysis coupled with XGBoost in enhancing prediction accuracy.

With the advancement of technology, cyber crimes are increasing more. Many of the cyber crimes are based on the credit cards because with the invention of WIFI credit cards, the frauds are becoming easy with no OTP system. A model is required which can identify the unauthorized or outlier transactions using machine learning approaches. Researchers has implemented traditional and ensemble algorithms for identifying the unauthorized transactions. Few have implemented clustering techniques to recognize the outliers in the transactions but both of them are failed because of the more misclassifications and wrong assumptions of the parametric values in the clustering algorithms. So, in this the proposed model implements two level architecture (Stacking) in which lower level known as “base classifiers” are implemented using the boosting algorithms and at the second level known as “meta classifiers” are designed using the regression models to pick the one with majority voting.

Cervical cancer affects a sizable portion of the female population worldwide across all age categories. As a result, many researchers, pathologists, and academics have offered numerous strategies for detecting this malignancy using Pap smear screening test photos. Large-scale cell proliferation is what is known as cancer. There are various types of cancer. One of the most prevalent diseases among women, cervical cancer, is the subject of this study. Cervical cancer is most frequently diagnosed in women, where it is the second-most prevalent malignancy after breast cancer. The study’s goal is to decrease errors by automatically identifying size, shape, and the texture of the tumour. Segmentation, clustering, feature extraction, and classification techniques serve as the foundation for the proposed study. The test findings display the differentiation between malignant and healthy cells as well as the stages of cancer. The algorithms used in this research are random forest and support vector machines. As a result, the results increase diagnostic accuracy while minimising the workload and human error.

Intelligent video analysis depends on the identification of uncommon events in the video being viewed. A complex element to represent movement and appearance is required for several methods of finding an uncommon event. An exceptionally potent and successful method that might fully satisfy the goals of a neural network model for features delivery of high resolution images. In this paper, local confusion can be found by following convolutional neural network (CNN) features over time. Combining visual flow and CNN’s temporary models allows us to see the sense of location disorientation. The front mask is used to increase the accuracy of the visual flow computation and the visual flow intensity. It is based on the conventional method of visual flow. The technique was rigorously examined using benchmark datasets and video for real-world monitoring. The primary goal of the suggested system is to offer a reliable method of spotting unexpected events in real-time photos that may be used for surveillance. An automated monitoring system that may use neural network techniques to detect and warn different types of security cameras in order to improve image quality and capture efficiency. The suggested system’s major objective is to offer a novel method of tracking and identifying events in low-resolution images without the need of any high-resolution approaches.

In latest existence, Path identifiers (PID) have utilised as inter-domain routing (IDR) things in association. Though, the PIDs utilised in present methods are immobile that creates it simple for attacker to introduce D DoS flooding attacks. To discourse this problem, current a D-PID structure, which make use of PIDs negotiated among neighbouring domains as IDR substance. The PID of the inter-domain connection between two domains in DPID is going to be kept private and can vary periodically. Cryptographic techniques may be employed as well to safeguard the security of information shared over a network. There is a good possibility that DPID’s data-secure technique will stop networking D DoS assaults.

Now wireless sensor networks were first exclusively utilized for military purposes but have now expanded to include a wide range of applications, they have recently drawn much more interest from researchers. A wireless sensor network is completed up of abundant autonomous, disseminated wireless sensor nodes that are dispersed throughout the world at random. The processing power, memory, and battery life of sensor nodes are constrained by their small size. Because of inadequate battery capacity, wireless sensor networks struggle with energy consumption, which shortens the network’s overall lifespan. The problem with energy usage can be solved using clustering approach. Clustering is a notion that has been employed in hierarchical routing protocols. The widely adopted LEACH protocol is a hierarchical routing protocol that tries to lengthen or improve the lifespan of the whole network. Depending on the energy and distance of the sensor nodes organize in the wireless network; this research modified the LEACH protocol, resulting in a new version of LEACH known as modified Energy-Efficient LEACH (IEE—LEACH). To increase lifetime and compare performance with LEACH and E-LEACH, the proposed IEE-LEACH measures residual energy and the distance as of the Cluster head to the Base station in the WSN.

This project work is based on several experiments for lung tuberculosis detection that were conducted implementing Ensemble, Inception, DenseNet, and classification algorithms. The most lethal infectious disease in the world is lung tuberculosis, a bacterial infection. All around the world, there are two billion tuberculosis patients. Mycobacterium tuberculosis, also referred to as Tubercle bacillus, is the bacterium that causes lung tuberculosis. This project effort looks for ways to help patients save a lot of money when they need an additional opinion for a result that has already been determined. Once we have X-ray images, we train and evaluate them before uploading them to classification algorithms. These filters assist in obtaining delicate textural details and in removing unnecessary noise. In order to identify lung tuberculosis, we used KNN, Modified SVM, Decision Tree, and Naive Bayes classification algorithms. According to the project’s results, the modified SVM classifier performs better than the others.

Because of its ability to change current distribution, reconfigurable antennas help control and reverse changes in frequency, polarization, and electrical properties. Pin diode is used as microwave converter in the design. There are two resonances in the antenna, one in the X-band and one in the C-band. The proposed antenna is made of 1.6 mm thick FR4 substrate. High-Frequency Building Simulator (HFSS) software is used in a variety of ways. By adding two transitions between on and off states, the antenna design concept is adapted to operate at multiple frequencies. Selected PIN diodes have different resistance, inductance, and capacitance values. The small 40 mm × 40 mm × 1.6 mm model runs at 9.3 GHz in the X-band and 5.9 GHz in the C-band with the two-pin diode turned on. With the first diode pin on and the second diode pin off, the C-band and X-band are 9 GHz and 10.1 GHz, respectively. With the first diode pin closed and the second diode pin open, it operates at 7.4 GHz in the C-band and 11 GHz in the X-band. With the two-pin diodes off, the device operates in the X-band of 10. C-band from 1 to 5.9 GHz. The rectangular patch antenna concept is small, easy to use, and inexpensive. This paper describes the design and analysis of frequency-reconfigurable antennas for C-band and X-band wireless applications. The antenna allows flexibility of this line thus improving its adaptability and compatibility with various communication systems. The new design can improve the performance of many applications by adapting to changing frequency. Important characteristics of antennas such as bandwidth, power model, and gain have been meticulously analyzed to evaluate their suitability for wireless communication around the world.

Despite the enormous improvement in technology, blood bank systems are either manual or there is no centralised system to maintain the same. A number of problems arise as a result, such as the insufficient blood quality management makes it difficult to track the elements of blood from their collection to consumption, the need to keep blood at a specified temperature, and the potential for transfusion-oriented infections like Malaria, AIDS, Syphilis and Hepatitis B&C. Blood is limited in certain places, yet because of its short shelf life, blood is squandered in other places. Blockchain technology (BCT) is a suitable application in the blood donation domain for management of supply chain due to the traceable and immutable nature of the data maintained in blockchain. Various cryptocurrency-based and non-cryptocurrency-based apps nowadays opt for BCT. The purpose of this study is to outline the architecture for a two-module block chain technology (BCT) cum machine learning (ML) supported blood donation management system. For the efficient administration of blood among various actors of the blood supply system (BSS) such as blood donors, blood-bank, medical centre and patients, the first part is proposed based on BCT. For the finding of blood transfusion transmissible diseases shortly TTI, the second module is proposed based on machine learning. In this paper, the first module of the suggested architecture, which entails collecting blood and storing it under blockchain following determining his eligibility for donation, is implemented. To implement the same, the Hyperledger Fabric tool, a permissioned open-source BC platform with distributed ledger is used in the suggested paradigm. The system users will find it simple to track the blood using the suggested model. TTI is checked twice on the blood that was obtained. One has a blood testing facility, while the other uses a planned ML-based detection. As a result, the blood recipient may reliably get and use the blood since the blood supply is transparent from its collection till consumption. Additionally, donors can receive updates on the status of their blood if used. This helps to motivate them to give blood again in the future by the system.

The segmentation of images is the most essential and basic component of image evaluation and healthcare systems. In image analysis, this is the most difficult process since it determines the efficacy of the results. It is difficult to automatically segment ultrasound images when speckle noise and artefacts are present, which are key components of other medical imaging. Segmentation strategies will vary depending on the level of segmentation and the amount of information needed. In this work, a Spatial Fuzzy C-Mean clustering approach is utilized for segmenting the ultrasound image of the foetal. Foetal images are given as an input to clustering algorithm, which generates feature vectors for each pixel. In clustering, the foetal image is divided into parts based on spatialization. Image quality is improved by applying an anisotropic diffusion filter before segmentation. Based on the results of the experiments, the Spatial Fuzzy C-Means clustering approach yields promising outcomes.

In noisy or unreliable communication networks, data errors are avoided by using error-correction codes (ECCs), a type of error-correction technique. Burst errors are flaws that happen in a succession of bits rather than individual bits. Burst error-correcting codes employ techniques for repairing burst errors that occur one after the other. Many initiatives have been introduced to repair problems that occur at random. The source encodes the message in Hamming code by introducing superfluous bits into the message. Hamming codes can be used to find errors and fix them. CRC is used in transmission, and hamming code is used in memory disks to detect errors. Convolutional codes are transformed into burst error correctors by interleaving from random error correctors. Interleaved codes are used to confuse the receiver's ability to decode the signals. As a result, the interweaver's primary task at the transmitter is to change the incoming symbol sequence. Increasing memory reliability is the goal of decimal matrix coding. The error-correction code and its variants are discussed in this review study. Each code functions with a certain level of efficiency, as seen by its ability to rectify errors as well as how much power and space it uses.

Due to the implementation in uncertain and dangerous environments, wireless sensor networks (WSN) are prone to software, equipment, and system failures. At the sensor level, classifiers are used to classify hardover, spike, drift, data loss, erratic, random, and stuck faults. The data quality from the collected information is determined by the level of participation from all crowd sensor networks (CSN) entities, including service customers, service provider, and data collectors. For CSNs, we propose a blockchain-based incentive methodology. The incentives are being used to entice data collectors to join the network as well as participants. To prevent privacy leakage, advanced encryption standard (AES128) technique is used. Accuracy, precision, F1 score are used to compare the results of the first situation. In this work, simulations show that the extremely randomized trees (ERT) algorithm achieves a higher rate of fault detection. The performance is evaluated by comparing the execution times of all of the consensus mechanisms, while the encryption process is affirmed by correlating the execution times. The incentive system is examined by computing the input string's gas cost and mining time.

VANETs indeed have the potential to revolutionize road safety and transportation efficiency by enabling communication among vehicles and infrastructure. Routing in VANETs is a critical aspect, as it determines how data are transmitted between vehicles and infrastructure nodes. Given the dynamic nature of vehicular networks, with vehicles moving at high speeds and the topology rapidly changing, designing efficient routing protocols is a challenging task. To address these challenges, researchers, and engineers have developed various routing protocols tailored for VANETs. These protocols aim to establish reliable and efficient communication paths, considering factors such as vehicle mobility, network disconnections, and the need for quick adaptation to changes in the network topology. Efficient medium access control (MAC) protocols are also crucial in VANETs. MAC protocols manage how vehicles access the communication medium (i.e., the wireless channel) to avoid collisions and ensure effective communication. Research in VANETs has led to the development of several routing protocols, including position-based, topology-based, and geographic routing protocols. These protocols aim to optimize communication paths, reduce latency, and improve overall network performance. In conclusion, addressing the challenges in VANETs, particularly in routing and MAC protocols, is essential for the successful deployment of intelligent transport systems. The ongoing research and development in this field contribute to creating safer and more efficient transportation systems.

Around millions of people around the world suffer from epilepsy, which is a type of neurological disorder that causes seizures. Early identification is very important to ensure that proper treatment can be provided. Unfortunately, traditional methods, such as electrical stimulation of the brain, are not always accurate. The prevalence of epilepsy is a major cause of disability and death worldwide. Early diagnosis and treatment are very important for the development of effective and efficient therapy. Unfortunately, current methods can only provide inaccurate results. New methods for analyzing epilepsy using electroencephalography (EEG) signals have been developed due to the advances in AI, deep learning, and machine learning. These methods can analyze large amounts of data and identify features and patterns in the signals. The development of new technologies has led to the development of new and accurate methods for diagnosing and treating epilepsy, a chronic condition that affects millions of individuals. Unfortunately, currently, traditional methods such as electrical stimulation cannot provide the best results. Due to the emergence of new technologies, such as deep learning and artificial intelligence, the ability to identify epilepsy using an electroencephalography signal has been greatly improved. In this paper, we present a novel model that can be used to analyze and treat epilepsy using an AI-based approach. We evaluated the performance of this system using various state-of-the-art algorithms, such as the Naïve Bayes, random forest, support vector machine, and CNN. The proposed model is based on a combination of AI and signal processing techniques, which can analyze the signals and identify features and patterns that are indicative of epilepsy. It was evaluated against a large dataset of patients and healthy controls’ signals. The paper presents an AI-based model that can perform an efficient and accurate diagnosis of epilepsy using an EEG signal. Its findings are valuable for the development of new and more accurate methods for treating and identifying epilepsy. According to our results, the proposed model was able to achieve high sensitivity and accuracy in identifying epilepsy using EEG signals. In addition, the CNN algorithm performed well in the evaluation, demonstrating its potential to be used in clinical settings.

In this paper, an iris band-pass filter (BPF) is designed with centre frequency of 97.75 GHz and the fractional bandwidth (FBW) is 4.6%. The design and simulations are carried out using Finite Element Method (FEM)-based Ansys High-Frequency Structure Simulator (HFSS). The proposed iris filter is having pass band ripple of 0.1 dB and return loss < − 15 dB throughout the operating frequency. The design procedure and simulation results for millimetre waveguide BPF are presented in this work.

Human activity recognition (HAR) requires the categorization of sequences of accelerometer data acquired by dedicated equipment or smartphones, in order to recognize discrete motions. Since there is no straightforward method to match accelerometer data to known motions, the challenge is complicated by the high number of observations made per second and the temporal structure of the observations. This paper presents a machine learning-based HAR model for classifying the primary actions such as sitting, standing, lying, walking, walking upstairs, and downstairs. The proposed model reads the data from accelerometer and gyroscope of the smartphone. This data is sent to the principal component analysis (PCA) for dimensionality reduction. PCA reduces the dimension in the input data by retaining the important features and removing the redundancy. The dimensionality reduced data is sent to linear multiclass support vector machine (SVM) for classification. SVM is first trained to identify the best hyperplane for the classification of the data which is then used to classify data in real-time. The proposed model obtained an accuracy of 98.85 during testing.

Hydroponic the power sources for nodes in wireless sensor networks haven’t been able to be recharged because of environmental factors. It makes it hard for WSNs to save energy and keep a network running for a long time. Most of the sensor energy consumes by the data transmission in WSNs that affects the network lifetime. To reduce the communication protocols’ impact on the overall consumption of energy in a network, different types of mechanisms have been proposed. After utilizing low-energy adaptive clustering hierarchy (LEACH) to divide a network into clusters, some nodes were randomly chosen as cluster heads (CHs), and the CH role is rotated among nodes to ensure an even distribution of energy load throughout the network. In this research, a brand-new, modified routing technique is suggested for increasing WSNs’ energy efficiency. This new improved Network lifetime-based LEACH (INL-MLEACH) protocol has been considered the nodes’ residual energy and their average energies. To get the decreased consumption of energy among sensor nodes, INL-MLEACH protocol doesn’t involve the nodes in the formation of a cluster when they are nearer to the Base Station (BS) and considers the optimal CHs. So, the improved delay PSO (DPSO) is employed during the data transfer phase to choose the most energy-efficient relay node based on the cost of the node and the end-to-end delay. This helps keep the network as a whole as energy-efficient as possible. The simulation results are analyzed and proved that the better performance in energy efficiency, network lifetime, and communication quality with the proposed method.