Journal Punjab Academy of Sciences

Mode Division Multiplexing Enabled Security Enhanced VLC system Employing 2-D WMZCC codes

2024-01-29T14:10:36+00:00

Optical code division multiplexing (OCDMA), a popular multiple access technique in VLC systems, offers excellent security while supporting several users. The nonadjacent weights (W), time (t) dimension based optical coding,two dimensional (2D) Weight Managed Zero Cross Correlation (WMZCC), and better security are all introduced in this work. In a 5 user×100 Gbps OCDMA system using 2DWMZCC codes, Laguerre-Gaussian (LG) modes are investigated in terms of Q factor, log symbol error rate (SER), and bit error rate (BER). In proposed 2D-WMZCC codes, it has been shown that HG modes exhibit greater data eavesdropping resistance than LG modes.

High Speed 100 Gbps Visible Light Communication Link Employing Single Channel PDM-QPSK and DSP

2024-01-29T16:25:35+00:00

With the tremendous increase in the high capacity demands in sixth generation (6G) wireless networks, Visible light communication (VLC) is turned up as efficient and appealing candidate. In this work, a single channel 100 Gbps VLC system is presented by incorporating polarization enabled multilevel modulation and digital signal processing (DSP). Effects of different VLC link lengths, and transmitter half angles are explored in terms of Q factor, log symbol error rate (SER), log bit error rate (BER) and error vector magnitude percentage (EVM%). Results revealed that proposed system can cover 14 m link distance using lower transmitter half angle and DSP for multifarious functions such as nonlinear and linear compensations, normalization, and phase error compensation.

INTEGRATED ENCRYPTION SCHEME IN FOG COMPUTING FOR E-HEALTHCARE APPLICATION

2024-02-29T05:02:07+00:00

E-Health is a system of providing health services by making use of electronic technologies. They provide improved, flexible, and affordable health services to a large population, especially in situations like a pandemic. The IoT is helping them to connect, generate and share a huge amount of data. This system of online sharing of health records and advice is generating data at rapid growth. This large data is to be managed in terms of storage, processing, transmission, privacy, and security. The cloud came up as a source of big data management but when it came to latency and bandwidth consumption, a system of processing near the edge came as a Fog Computing with distributed processing, reduced delay, and less bandwidth. The Fog is near the generation of data that provides benefits like location awareness, heterogeneity, real-time access, wireless access, and scalability. Here the security of sensitive data is a prime factor. Security can be achieved with cryptography. The paper implemented RSA, BLOWFISH, AES, and Integrated Encryption Scheme of (RSA+BLOWFISH) and (RSA+AES) combination security algorithms in the Fog-enabled E-Healthcare system. The comparison of performance is done on the basis of key size, encryption-decryption time, and throughput. It is found that BLOWFISH has the least encryption-decryption time and more throughput in a single encryption scheme. In the Integrated Encryption Scheme, the (RSA+AES) combination has improved performance among the two combinations. So, to achieve the security of the E-Health system in the Fog computing environment it is more efficient to use Integrated Encryption Scheme.

Non-Fungible Token (NFT): Survey, Analysis, Benefits, and Provocation

2024-02-29T05:11:45+00:00

The Non-Fungible Token (NFT) marketplace has been growing rapidly in recent years, inspired by the Ethereum token. At the time of writing, the total cash used on finished NFT income has reached 34,530,649.86 USD. However, the improvement of the NFT environment remains in its early stage, and the technology of NFT is premature. This technical report provides a top-level view of contemporary NFT solutions, technical components, protocols, standards, and favored proprieties, as well as a safety evolution.

TRIPLE BAND THZ MIMO ANTENNA FOR SHORT-RANGE COMMUNICATIONS

2024-01-31T08:02:49+00:00

For wireless applications, a single-patch antenna having a shape slot is suggested along with a 1×8 Multiple Input Multiple Output (MIMO) antenna array. The substrate is made of polyimide material, which has a 0.04 mm thickness and a dielectric constant of 3.5. The proposed MIMO antenna array uses a remarkably small rectangular microstrip patch antenna with dimensions of 0.075mm by 0.111mm by 0.04mm. Performance measures such as return loss, bandwidth, antenna gain, antenna efficiency, and VSWR ECC are computed for resonating frequency. The designed MIMO antenna array resonates for three different frequencies of 0.7487 THz, 0.8015 THz, and 1.1558THz and has a return loss of less than -10 dB. The designed array has an antenna efficiency of 96% and a maximum achievable antenna gain of 16.5 dB for resonating frequency. Due to its improved properties, the designed antenna array might be suitable for a range of THz applications.

Optical Multi-carrier Generation Using Cascaded Three Stage Modulators

2024-01-31T08:21:24+00:00

High-capacity optical communication networks can be efficiently implemented using an optical multi-carrier generator (OMCG). A serial combination of three intensity modulators, such as the amplitude modulator (AM), single drive Mach-Zehnder modulator (SP-MZM), and AM, is suggested for the synthesis of the largest number of tones with a high tone to noise ratio (TNR) and the lowest amplitude difference (AD). The presented OMCG designs employ a single RF source; no additional fibre Bragg gratings or specialized techniques are used. The presented system is investigated for different radio frequencies (RF) and input powers in terms of AD and TNR respectively. Results revealed that 25 GHz RF frequency and 40 dB extinction ratio are optimal for the proposed system. Total 65 tones with a maximum bandwidth of 640 GHz, 0.96 dB AD, and the greatest TNR of 48.58 dB are offered by proposed system.

HANDOVER OPTIMIZATION IN 5G NETWORKS USING ANFIS

2024-01-31T08:30:08+00:00

The Corona Virus Disease (COVID-19) pandemic has upended the lives of people all over the world, and people have realized that the demand for new technologies, such as Internet of Things (IoT)-based devices, Vehicle Adhoc Networks (VANETS), and other upcoming technologies is increasing nowadays. These technologies are mostly based on controlling objects from a remote location, and some of them avoid close contact with others. So, a 5G network with more bandwidth is beneficial but the higher frequency signals will have more collisions with obstacles in the air, and thus it tends to reduce its energy more quickly. As a result, 5G networks are segregated into small cells to alleviate this issue but at the same time it is difficult to manage the handover process in small cells. To optimize the handover process, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is adopted in this research. The structure, as well as the input and output variables have been developed, and the rule base mathematical model, which describes all the possible handover scenarios based on these parameters, has been evaluated. A MATLAB simulation was used to test the effectiveness of the proposed handover ANFIS.

100 GHz Radio Signal Over 160 Gbps-160 km ROF link Using Polarization Division Multiplexing Based 256-QAM and Matched Filter

2024-01-31T10:07:40+00:00

High capacity communication systems are essential to meet the demand of fifth generation (5G) networks due to the sudden increase in bandwidth-hungry applications over the past few years. Radio over fiber (RoF) incorporating polarization division multiplexing (PDM) is a spectrally efficient and predominant option for high speed and prolonged reach RF enabled optical communication systems. Higher level quadrature amplitude modulations (QAMs) have narrow spectrums and perfect candidate for RoF. However, integration of PDM supported higher QAMs are not explored extensively in RoF technology. Therefore, in this research article, a 160 Gbps-100 GHz RoF system is presented over 160 km link distance by employing PDM-256-QAM and matched filter. Moreover, the eradication of dispersion, time skews, phase compensation and nonlinear effects have been performed by employing different algorithms such as blind phase search (BPS), constant modulus Algorithm (CMA), and viterbi phase estimation (VPE).The presented system is investigated for discerning the effects of different RF frequencies, input powers, matched filter bandwidths on polarization X and y in terms of log bit error rate (log BER), symbol error rate (SER), Q factor, and error vector magnitude (EVM). Results revealed that polarization exhibited better performance as compared to polarization X.

Microstrip patch antenna loaded with ferrite films for UMTS applications

2024-01-31T10:16:18+00:00

This paper presents the novel design of a microstrip antenna loaded with ferrite films as a substrate material. The antenna is designed to operate in the frequency range of upto 2.1 GHz which is suitable for UMTS applications. The antenna is composed of a rectangular-shaped patch antenna with thin ferrite films as a substrate. Miniaturization is achieved by using ferrite films with a high dielectric constant to increase antenna gain and decrease antenna size.High-Frequency Structural Simulator (HFSS) is used to simulate the antenna design and the results are compared with the measured values.

EMOTION RECOGNITION USING TWITTER DATA: AN ENSEMBLE MACHINE LEARNING TECHNIQUE

2024-01-31T10:23:33+00:00

Due to the expansion of world of the internet and the quick acceptance of platforms for social media, information is now able to exchange in ways never previously imagined in history of mankind. A social networking site like Twitter offers a forum where people may interact, discuss, as well as respond to specific issues via short entries, like tweets of 140 characters and fewer. Users may engage by utilizing the comment, like and share tabs on texts, videos, images and other content.Although platforms for social media are now so extensively utilized, individuals are creating as well as sharing so much information than shared before, which can be incorrect or unconnected to reality. It is difficult to identify erroneous or inaccurate statements in textual content autonomously and find emotions of people. In this paper, we suggest an Ensemble method for sentiment and emotion analysis. Different textual features of actual and Emotion and sentiment have been utilized. We used a publicly accessible dataset of twitter sentiment analysis that included total 48,247 authenticated tweets out of 23,947 of which were authentic positive texts labelled as binary 0s and 24,300 of which were negative texts labelled as binary 1s. In order to assess our approach, we used well-known (ML) machine learning models such as Logistic Regression (LR), Decision Tree (DT), AdaBoost, SGD, XG-Boost, and Naive Bayes. In order to get more accurate findings, we created a multi-model sentiment and emotion analysing system utilizing the ensemble approach and the classifiers stated above. Our recommended ensemble learner method outperforms individual learners, according to an experimental study.

SIMULATION METHODOLOGY FOR ANALYZING THE PERFORMANCE OF TFET THROUGH THM-TFET MODEL FILE IN CADENCE

2024-01-31T10:28:22+00:00

MOSFETs are used to create the majority of real-time devices at present due to their fast-switching times. MOSFET subthreshold swing scaling cannot be 60mV/decade lowered. Because of its low subthreshold swing and low leakage current, tunnel FET (TFET) is a viable MOSFET substitute. This paper shows the simulation of the TFET inverter using cadence virtuoso by using the model file called THM-TFET, it is Verilog-A coded and used for the DC and AC simulations of Tunnel FET(TFET)

Green Cloud Computing: A Comprehensive Review of Eco-Friendly Computing Strategies

2024-01-31T10:34:18+00:00

As the demand for cloud computing continues to increase, the environmental impact of data centers is becoming a growing concern. Green cloud computing has emerged as a solution to address this issue, reducing the carbon footprint of cloud computing while maintaining its performance and functionality. This paper comprehensively reviews eco-friendly computing strategies for Green Cloud Computing, such as virtualization, consolidation, dynamic resource allocation, energy-efficient hardware design, Renewable energy sources, and energy-efficient cooling systems. Green computation algorithms, which are an integral component of the green cloud computing movement that aims to reduce the carbon footprint of computing systems, are explored. The algorithms are designed to optimize energy efficiency, reduce power consumption, and ensure the efficient use of resources while maintaining performance and functionality. The algorithms are essential to widely adopt eco-friendly computing strategies and create a more sustainable future for cloud computing.

50G-PON-based DP-QPSK Technique by Utilizing the Millimeter Frequency Spectrum

2024-01-31T10:39:45+00:00

For the fifth generation (5G) broadband fronthaul network, the architecture of the 50 Gbps passive optical network (50G-PON) is provided. This research makes use of a quadrature phase shift keying (QPSK) structure with a coherent receiver network and the dual polarization (DP) idea. In order to maximize band usage, the millimeter wave at 32 GHz is modulated over 50G DP-QPSK that spans a 60 km fiber span. A more effective technique to utilize millimeter wave in the high spectrum region for mobile broadband is through passive optical network solutions. The performance is assessed using the constellation figure and signal spectra.

Comparative Analysis of Silver and Graphene based Metasurface Unit-Cell Structure for Wideband THz Antenna

2024-02-05T10:06:55+00:00

The rapid growth of wireless communication is accelerating like never before. It is propelled by the introduction of the devices that uses 5G and beyond. It is foreseeable that the antennas in THz range, smart antennas and reconfigurable intelligent surfaces (RIS) are going to be vital in the future. A lot of traction is happening in the THz frequency range with the amalgamation of the THz antennas based off of metasurface integrations. In this paper a metasurface based THz antenna is designed using 2 different materials and their performance is evaluated and compared. The unit cell structure is made up of silver and graphene materials and geometries are modified for the patch antenna. The improved performance of graphene over silver is obtained and the results are presented in a novel and comparably comprehensible manner.

Recent Machine Learning Advances in Agriculture

2024-02-05T10:12:12+00:00

The economy of India is significantly influenced by agriculture. The majority of Indians are either directly or indirectly dependent on agriculture. Therefore, it cannot be denied that agriculture plays an important role in the nation. So it is important for former to make best decision while growing any crop based on multiple factors. In this era of advancement there are number of technologies which can be used in agriculture field one of these is Machine Learning (ML). In this paper we have summarized different ML algorithms that are implemented by different researchers in their studies for Crop recommendation, yield prediction. We have concluded that Random Forest, Support Vector Machine, Naïve Bayes, Neural Network, Decision Tree, Knearest neighbor, XGBoost(eXtreme Gradient boosting), Multivariate Linear Regression, Logistic Regression, Chi-Square Automatic Interaction Detection (CHAID) and Sliding Window non-Linear Regression comes with maximum accuracy. This prediction has been done based on various factors such as soil type, temperature, rainfall, pH value of soil, Nitrogen Phosphorus Potassium(NPK) content of soil , sowing season, porosity of soil ,erosion ,water holding, drainage, crop yield, crop consumed , humidity and location parameters. Furthermore in future ML algorithms can be utilized in various sectors of Agriculture like disease detection in crops, soil suitability recommendation and prediction of retail prices.

Study of Classifiers for the Identification of Fake News

2024-02-05T10:18:40+00:00

A significant source of local and international news for millions of individuals, online social networking websites are rapidly growing. OSNs, on the contrary side, have two disadvantages. Despite the numerous benefits they provide, such as endless, simple communication and instant access to data and news, they can also come with a number of drawbacks and concerns. The spread of misleading information is one of their toughest obstacles. Fake news detection is a difficult & unsolved issue. Nevertheless, designing a solution is anything but straightforward given the unique characteristics and challenges of identifying bogus news on OSNs. Artificial intelligence (AI) techniques, on the other hand, are still incapable of overcoming this difficult obstacle. Even worse, by creating and disseminating false information, artificial intelligence (AI) systems like deep learning and machine learning are being abused to trick customers. Because the material is meant to closely mirror reality, it can occasionally be difficult to assess its reliability using AI alone and without support from outside sources. As a result, it's still very difficult to recognise bogus news automatically. This research seeks to offer a fundamental review of the methods currently used to recognise false news and prevent it from spreading over OSNs in addition to classifying fake news using several classifiers.

A COMPREHENSIVE STUDY OF THE IOT ATTACKS ON DIFFERENT LAYERS

2024-02-29T11:14:16+00:00

A key innovation in utilizing the enormous capacity of wireless media in the physical world is the internet of things. The internet of things' (IoT) recent explosive expansion is a result of its versatility, adaptability, and intelligence. Most Internet of Things (IoT) apps run tasks automatically, without interacting with people or real-world items. By connecting with a variety of smart programs that are running independently on various platforms, practically everywhere in the globe, we may manage our surroundings. The potential threats in the five layers of IoT are specifically reviewed in this paper. In this study, we will outline various IoT components i.e. (architecture, IoT Security attacks, Attacks and Targeted Layer of IoT) and the general description of IoT-layered security. In this paper, author discuss the IoT attacks which are performed on different layers of IoT architecture. We have discussed security attacks in detail in this paper with relation to various IoT layer types. We recently presented a few of the IoT's uses. This study will help academics and manufacturers assess and narrow the attack surface area on IoT devices.

SMART ENERGY METER WITH THEFT DETECTION

2024-02-07T08:11:47+00:00

On a monthly basis, conventional electricity meters given by government power supply organizations monitor the energy use of an entire residence or enterprise. This implies that customers have no way of tracking individual appliance energy use. In addition, consumers are unaware of their everyday energy use habits. Smart meters may be created by incorporating IOT into our traditional electrical meters. The primary purpose of IOT-based smart meters is to improve the existing system's performance, reliability, and features through an active customer approach. Smart energy meters acquire all energy-related data at a low cost. It tackles power quality concerns while also highlighting the potential to improve Demand Side Management (DSM).

Multi-index transportation problem: An overview of its variants, solution techniques and applications

2024-02-05T10:26:38+00:00

Multi-index Transportation Problem (MTP) and its variants have originated to model one of the complex scenarios of transportation problem where cost and/or time of transportation also depend upon type of transported commodity and/or freight vehicle used in transportation along with given sources and destinations. In almost last six and half decades, researchers have extensively studied MTP and its variants from different perspectives, developed many solution approaches and established its applicability by modeling manyproblems of engineering and management as MTP. However, a comprehensive review of the problem domain is surprisingly absent from the concerned literature, therefore, this paper strives to fill this void by systematically examining 59published articlesfiltered through a well-defined search criterion. Methodologically, the examined articles arecategorizedinto various classes based on two, possibly exhaustive but certainly not exclusive, attributes of classification, one is, approach used to study the problem and, another is, contribution of the study. Classification is presented in a two-way tabular form that will serve as a guiding map to young researchers who are intending to study MTP, its variants, and/or applications in engineering and management.This paper also provides an indication for the future work. It concludes that, since its inception, MTP has evolved as one of the very important problem domains in logistics research and metaheuristic approach has contributed most in its study.

A Review on Multi-band optical communication system

2024-02-05T10:32:51+00:00

Multiband optical communication has attracted a lot of interest since it is difficult to increase capacity per optical fibre within traditional, constrained bandwidths. Our goal is to increase bandwidth from the currently popular C or L bands to other bands like the S, E, and O bands. Multiband optical communication is the promising technique to extend the lifetime of currently deployed networks. In this paper, we review the components, potentialities and challenges of Multi-band optical communication system over single mode fibers, compared to transmission solutions such as multi-fiber (MF), multi-mode fiber (MMF) and multi-core fiber (MCF) .

Breast Cancer Prediction via Grid Search Hyperparameter Optimization

2024-02-05T10:42:56+00:00

Breast Cancer is a serious health issue worldwide, and early detection is crucial in preventing deaths. Machine learning can help identify tumors efficiently, and this paper introduces the Grid Search Hyperparameter Optimization (GSHPO) method to optimize the parameters of six existing models, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest. The best parameters were applied to predict outcomes in six datasets, including OWBCD, WDBC, Coimbra, BRCA, Haberman, and SEER. The results show that tuning the hyperparameters of models has a significant positive impact on prediction accuracy.

An Overview of Advanced Technologies Applied to Identified Printed and Handwritten Text in Gurmukhi Script: A Review

2024-02-07T06:46:36+00:00

Handwriting recognition of Gurmukhi is an area of research which has gained traction in recent years. This paper reviews the state of the art of handwriting recognition for Gurmukhi. A brief overview of the various approaches used for Gurmukhi handwriting recognition, their benefits and drawbacks, the current challenges and the future research directions is presented. Specifically, the review will focus on the recent advances in deep learning models, feature extraction techniques, and preprocessing strategies. It further examines the challenges and potential areas of improvement in the field of Gurmukhi handwriting recognition. Finally, it provides insights into the potential applications of this technology and the opportunities for future research in the area. The paper concludes by highlighting the importance of handwriting recognition for Gurmukhi and the need for further research and development in the field.

A Survey on Crop Yield Prediction Using Machine Learning

2024-02-07T06:51:51+00:00

Agriculture is the science and practice of raising plants and animals. India is the second-largest agricultural nation in the world, with 60.45% of its land used for farming. In India, agriculture is one of the most common and least-paid professions. Being an agricultural nation, India's economy is heavily dependent on rising agricultural yields and agro-industrial goods. Machine learning can bring a boom in the agriculture field by changing the income scenario for optimal crop. This paper focuses on predicting the yield of the crop by applying various machine learning algorithms. Machine learning algorithms' predictions will assist farmers in selecting the best crops for their farms based on factors including soil type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer, and months. This paper focuses on a concise comparative work of several papers that discuss many methods for assessing crop yield. It seeks to predict agricultural yield through consideration and investigation of the datasets of previous years of the crop. The study also describes various current methods for auditing crop yield. It also includes a comparison of several algorithms and their advantages and disadvantages.

Handling Missing Values in Numeric dataset Using Machine Learning Techniques:A Review

2024-02-07T06:55:21+00:00

Data mining is essential for pre-processing task to ensure the quality of the finalproduct. These tasks include data preparation, cleaning, integration, transformation, reduction, and discretization. Missing values are a common problem that regularly occurs throughout the data cleaning process in various research fields.To complete missing values, eliminate noise and remove inconsistencies is an important process in the preparation of the data. This paper focuses on a review of several classification methods, including their benefits and shortcomings.It is used in a variety of industries, including internet marketing, healthcare, social networking, finance, and insurance. The accuracy of data imputation for machine learning classifiers such as Bayesian Networks, Decision Trees and K-Nearest Neighbors (KNN), as well as Support Vector Machines, is compared in this paper. Based on the findings, Bayesian appears to provide the most promising results when compared to the other classifiers.

MODELLING AND ANALYSIS OF FULLY INTEGRATED 3:2 SWITCHED CAPACITOR DC-DC CONVERTER

2024-02-07T07:00:12+00:00

A type of DC-DC power converter known as a switched-capacitor converter efficiently changes one voltage level to another by employing switches and capacitors. They provide high-performance point-of-load voltage regulation for digital systems. This paper investigates general steady-state performance model of 3:2 series-parallel switched capacitor DC-DC converter. The existing basic SC converter analysis is extended to derive minimum achievable power loss for efficiency enhancement that suffers from both conduction and switching losses. The method relies on charge-multiplier approach and analysis is done to achieve 48mA load current realized on a 22nm technology node. According to the findings, a unit cell's switch size equates to 1Ω on-resistance, and a switching frequency of at least 200MHz is needed to achieve the target efficiency of more than 85% and an output voltage more than 0.72V.

Theoretical Investigation of the Compact Dielectric Polymer using PVDF-HFP

2024-02-07T07:04:53+00:00

Reducing the size of the dielectric material is a big problem to prepare next-generation dielectric gadget. In this paper, we proposedsolution of this problem that affects the size of the dielectric particles. Due to the large size of 140nm, a good permittivity has been detected. This has been explained by the property of a material like structure, model, lattice size, or shape and having a very large permittivity. We observed the permittivity fortheapprox size of about 1.1µm, due to the domain – wall role and boundary effect. The proposed work will highlight necessity to make devices that have a very small size and may give very large production with the help of PVDF-HFP.

UNLOCKING INDUSTRY 5.0 POST-COVID 19 CATASTROPHE

2024-02-07T07:08:35+00:00

A new industrial revolution has erupted amid the Covid-2019 catastrophe. Covid-2019 has certainly pushed the swift development of Industry 4.0 but has also further led to the fast evolution to a new worker-centricstyle termed Industry 5.0. It can be said firmly that, unlike all the previous four revolutions which were based on technological advancements the new revolution is people-centric. The European countries are leading from the front in the implementation of this new revolution to empower employees instead of replacing them with new technologies.Industry 4.0 had been onaboom in the pastdecade but had a major shortcoming of over-dependence on industrialization. The dire need was felt to collaborate workers with their smart industries for optimization of business productivity.This paper is an attempt to understand the new human-centric revolution termed Industry 5.0and a few challenges being identified to be effective in competitive businesses. Lastly, it is found that the execution of Industry 5.0 could reducethe over-dependence on technology and would emphasize the associationbetween workers and industries.

A REVIEW ON IMPROVEMENT OF DRONE DETECTION AND TRACKING SYSTEM USING ARTIFICIAL INTELLIGENCE

2024-02-07T07:16:19+00:00

The development of the drone industry has led to an increase injudicious, unauthorized, and illegal drone use, which has resulted in significant harm to society and the economy. They look at some major incidents involving drones around the world and identify essential characteristics for upcoming anti-drone systems. According to this study, the drone industry has made drones that can be used in everyday life available to the general public because of the widespread interest in them. However, as the use of drones became more widely available, the likelihood of accidents has increased, raising concerns regarding safety and security: slipping out of control, colliding with others, or breaking into secured properties. It is fundamental for the two eyewitnesses and the robot to know about an oncoming robot for wellbeing reasons. This paper presents a comprehensive drone detection system based on machine learning as a result of a literature review of various studies and the various issues encountered. All of these issues will be addressed by future research.

CLASSIFICATION OF MODULATION TECHNIQUES USING CONVOLUTIONAL NEURAL NETWORK: A REVIEW

2024-02-07T07:21:57+00:00

A method to classify the required modulations among the various kinds of distributed systems by using the Convolutional neural network (CNN). A supervised Machine learning (ML) algorithm, Support vector machine (SVM) is used to classify the required modulation among all the modulations due to its advantages of majorly low complexity. In this paper, different researchers’ research work is studied and different problems are faced like CNNs are a regularised version of multilayer perceptrons that were motivated by the biological process of neuronal connection. They are efficiently used in a variety of classification problems because, in contrast to other classification methods, they require less preparation. The CNN is explained in simple terms with relevant mathematical analysis. For a better understanding of the classification of modulated techniques, some analog modulation techniques such as Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK), 8-ary phase shift keying (8-PSK), 16-ary Quadrature amplitude modulation (16-QAM), 64-ary Quadrature amplitude modulation (64-QAM), 4-ary pulse amplitude modulation (PAM4), Gaussian frequency shift keying (GFSK), Continuous phase frequency shift keying (CPFSK) and digital modulations such as Broadcast FM (B-FM), Double sideband amplitude modulation (DSB-AM), Single sideband amplitude modulation (SSB-AM) are considered for review.

Reduction of Blocking Probability in GMPLS Optical Networks Using Novel Hybrid Optimization Algorithm

2024-02-07T07:27:42+00:00

In this paper, an optimized algorithm hybridizing Minimum Execution Time (MET), Weighted Round Robin (WRR) and Particle Swarm Optimization (PSO) is presented to minimize the blocking probability in Generalized Multi-Protocol Label Switched (GMPLS) optical networks to improve Quality of Service (QoS). MET is used for distributing the bandwidth in a well-organized approach to further reduce the latency in the network. The deviation in blocking probability is calculated depending on demanding traffic load and number of wavelengths accessible at the instant when user demand occurs in the GMPLS optical network. WRR is used to increase the flexibility in computing the path between the nodes. The overheads are also optimized using PSO with increasing number of iterations. The consequences exposed that the blocking probability reduces with growing number of accessible wavelengths. The value of blocking probability obtained is <1% by using the proposed hybrid optimization technique for bandwidth allocation and path computation.

Facial Expression Recognition with Local Binary Pattern in Machine Learning :A Review

2024-02-07T07:34:03+00:00

Automaticallyanalyzingfacialexpressionsisanintriguingandchallengingproblemwhichhassignificantapplications in numerous fields, including data-driven animationand human–computer interaction. An essential step in successfullyrecognizingfacialexpressionsisobtaininganaccuratefacialrepresentation from original face image. For person-independentfacial expression recognition, in this paper we have local feature-based facial representation and local binary patterns(LBP) techniques. On a number of databases, comprehensive analysis hasbeen presented on various machine-learning techniques for facialrecognition.LBPFeatureextractiontechniquehasbeendemonstrated to recognize facial expressions.Further, to extractthe most discriminating localbinary pattern features a boosted-LBP feature with support vector machine classifiers has been suggested which result sin best face recognition performance accuracy.Additionally,we have describedthe investigations oflow-resolution facial expression recognition withLBP feature extraction which is a crucial issue which have not received earlier consideration.

A MILLIMETER WAVE OVER NEXT GENERATION PASSIVE OPTICAL NETWORK STAGE 2: AN IDEA FOR FUTURE GENERATION NETWORK

2024-02-07T07:49:49+00:00

The potential of millimeter wave to support next-generation passive optical network (NGPON2) technology is covered in this research. Millimeter wave offers optical access networks a number of benefits, including higher capacity, better spectrum usage, decreased latency, and improved security. The study goes on to look at the difficulties that need to be overcome in order to fully utilizemillimeter wave for NGPON2, such as the requirement for higher data rates, expanded transmission range, and enhanced antenna performance. The report concludes with a summary of the various millimeter wave enabled NGPON2 components and the ongoing research to improve their performance.

Performance Assessment of III-V Heterojunction FinFETs at 14nm Gate Length

2024-02-07T08:03:48+00:00

Fin shaped field effect transistors have become prominent candidate in high-performance processors of leading semiconductor industries. It is owing to the fact that thin fin of structure and presence of gates on multiple sides increase electrostatic integrity. Objective of the present work is to design FinFET for gate length of 14nm and analyse performance parameters for different channel materials. Fin width and fin height of device is set as 7nm and 12nm; 25nm and 30nm respectively. Channel of FinFET has been structured with III-V type materials such as AlGaAs, InGaAs and InP. Cogenda Visual TCAD tool has been used for accurate design and extensive simulations of FinFET. Performance has been investigated in terms of on-current (Ion), leakage current (Ioff) and subthreshold swing (SS) for applied drain voltage and gate voltage. These parameters have been extracted from current-voltage characteristics of the device. Ion/Ioff is of the order 107, Ioff is of the order 10-14 and SS is around 80mV/dec for InP material. Based on performance comparison, it is noted that superior performance is achieved in case of InP material as compared to other materials.

GOVERNMENT POLICIES' INFLUENCE ON REAL ESTATE CUSTOMER PURCHASING BEHAVIOUR

2024-02-07T08:22:08+00:00

With the liberalization of the market, the realty sector in India acquired importance, as more business possibilities, as well as labour relocation, led to the growing demand for both residential and commercial space. This research is on the Indian real estate business. This study was primarily concerned with consumer purchasing behaviour concerning residential housing. The principal goal of this study was to examine the impact of government policies on several types of buyers' property purchasing behaviour in different residential areas of Punjab and to determine whether government policies will inspire a person to buy real estate property and whether customers are becoming more confident. The paper is helpful for both buyers and real estate agents. It helps buyers understand the factors that go into making a purchase decision, and it helps real estate agents understand what buyers want and how they think.

DETECTION OF MENTAL STRESS USING BIOSIGNALS THROUGH MACHINE LEARNING- A BRIEF SURVEY

2024-02-07T08:26:20+00:00

This paper explores the classification of various existingbiosignals used forstressdetection and evaluates their effectiveness. Out of all the biosignals reviewed, ECG was chosenasthebestbiosignals.Furtherreviewwasconductedto determine the most suitable machine learning model for the chosen ECG signal. Results have shown that KNN gives the best accuracy of96.41% followed bySVM witha considerably high accuracy of 90.10%. The best ML models were then evaluated on other biosignals for comparison of effectiveness ondifferentdatasignals.Thefindingsofthisstudyofferuseful insights into the selection of optimal biosignals and machine learning algorithms for detecting stress, which can contribute to development of personalized stress management technologies and improving mental health.

Review of Mode Division Multiplexed passive optical networks and its hybrid with TDM and WDM systems

2024-02-07T08:18:19+00:00

We review the use of Mode Division Multiplexing (MDM) as a multiplexing technology for the realization of passive optical networks. We start with approaches for MDM realization – with the use of complex MIMO digital signal processing or without complex DSP using few mode fibers. We proceed to explain basic architecture of MDM PON, and its hybrid with time division multiplexing - TDM MDM PON and hybrid of time, wavelength and mode division multiplexing – MDM TDM WDM PON. We then proceed to discuss recent progress in the field of Mode Division Multiplexing.

PERFORMANCE INVESTIGATIONS OF 40 GB/S ORBITAL ANGULAR MOMENTUM MULTIPLEXED FREE SPACE OPTICS TRANSMISSION SYSTEM UNDER WEATHER TURBULENCE

2024-03-07T04:37:24+00:00

In this paper, a design of 40 Gb/s free space optics (FSO) transmission link is proposed employing orbital angular momentum (OAM) multiplexing technique under the different weather turbulence such as clear sky, rain, fog, haze and dust. The system has four channels comprised of OAM multiplexed laser beams such as LG0,0, LG0,13, LG0,40, LG0,80, for realization free space transmission. It has been observed that Interchannel interference is a performance deteriorating constraint is in multi-channel OAM multiplexed FSO systems. To suppress of inter-channel interference a transmitter diversity NRZ/RZ formatting technique has been incorporated. A graphically representation of performance comparison is established considering return-to-zero (RZ), alternate mark inversion (AMI), non-return-to zero (NRZ) and transmitter Diversity- (TD)-NRZ/RZ modulation signal encodings in terms of bit error rate (log BER). The Results revealed that the system performance is better with TD-NRZ/RZ encoding compared to other techniques and it covers a transmission range from 67 to 1200 m depending upon the weather conditions.

A SURVEY OF CONVOLUTIONAL NEURAL NETWORKS AND VISION TRANSFORMER FRAMEWORK BASED ON OBJECT DETECTION

2024-03-07T04:44:08+00:00

Deep Learning methods have given computers the power to imagine and create new things, something not possible just a few years ago. Convolutional Neural Network (CNN) due to the powerful ability of feature learning and transfer learning, has turned to be the state of the art for object detection task of computer vision. However, much research today focuses on new architecture based on attention. Self-Attention mechanism has been shown to be very useful in diverse application domains. In this context, Vision Transformer (ViT) have been introduced and have demonstrated similar accuracy and runtime performance compared with CNN architectures in vision tasks. In this paper, we have reviewed CNN and ViT framework based on object detection. We have made a comprehensive description of their architecture, reviewed some popular state-of-the-art models, and finally, we have also drawn a comparison among those models.

PERFORMANCE EVALUATION OF INTER-SATELLITE OPTICAL WIRELESS SYSTEM ON NON-GEOSTATIONARY ORBIT USING DIFFERENT QAM TECHNIQUES WITH DSP

2024-03-07T04:47:53+00:00

Information transmission channels are essential for global coverage in space communication. Since radio frequency (RF) communication has been shown to have a significant latency and low data transmission rates, inter-satellite communication has grown in popularity recently and optical wireless communication has made significant strides. Inter Satellite Optical wireless Communication (IsOWC) technology is gaining popularity among academics due to its many benefits, including huge channel bandwidth, high-speed connection, and cost-effective operations. High speed inter-satellite communication cannot be conducted using uniphase modulations like NRZ modulation. With the aid of a matching filter and DSP, PDM 512 QAM at 160 Gbps is shown for conducted study. At wavelengths of 850 nm, the proposed work is explored for PDM 512 QAM, and the results are assessed with consideration of the quality factor, symbol error rate, and error vector magnitude.

RECOMMENDING CLONES FOR REFACTORING USING COMMIT MESSAGES

2024-03-07T04:57:00+00:00

Software can have many clones which are required to be managed. One possible method of clone management is refactoring. But all clones do not require the attention of developers for their management through refactoring. There is a need to develop tools that understand the developers' real intent behind clone management and recommend suitable clones for refactoring. In this paper, we proposed an approach that uses commit message analysis for recommending suitable clones for refactoring.

ADVANCED BACKDOOR USING PYTHON

2024-03-07T05:13:19+00:00

A malware is a piece of code or software whose main functionality is to infect a system to manipulate, steal or destroy data and in turn violate the security of that system. Malwares are heavily used to gain access to systems for the purpose of security breach or vulnerability testing.Backdoor is a type of malware that create a way for hacker to get inside a device and execute commands without knowing of the owner. In this paper we have created a malware using python and different libraries that addresses different types of issues in current backdoors and solves it.