Data Transfer Research Articles

Into the Data Transfer Thicket: The Dutch Uber Decision and the Relationship Between GDPR Article 3 and Chapter V

Into the Data Transfer Thicket: The Dutch Uber Decision and the Relationship Between GDPR Article 3 and Chapter V

Measurement and performance evaluation of triple band differential integrated extraoral rectifying antenna for data transfer and RF energy harvesting for tongue drive system

Wearable assistive devices are vitally important for tetraplegic individuals to provide valuable insights into their intended directives tailored to tongue motions in wireless healthcare industries. The flexible differentially driven extraoral antenna and rectenna measurement system are developed to enable differential sensing and monitoring of the set of unique tongue gestures for extraoral tongue drive system (eTDS) applications in three frequency bands of Industrial, Scientific, and Medical (ISM) (915.0 MHz, 2400 MHz, and 5800 MHz). The performance analysis is carried out using the heterogeneous human head model. The differential rectifier is coplanarly integrated with the differential extraoral antenna on the same 0.254 mm thin and 9.5 mm wider Rogers RT/ Duroid 6010 LM substrate. The footprint of the fabricated differential rectenna is 0.135 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\varvec{\\lambda\\:}}_{\\varvec{g}}\ imes\\:$$\\end{document} 0.082 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\varvec{\\lambda\\:}}_{\\varvec{g}}$$\\end{document}\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\ imes\\:$$\\end{document} 0.002 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\varvec{\\lambda\\:}}_{\\varvec{g}}$$\\end{document} where the planar size of differential rectifier is 15.75\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:\ imes\\:$$\\end{document} 2.5 mm2. The fabricated systems are situated closely to an artificial head model. The maximum conversion efficiency achieved at 2400 MHz and 5800 MHz is 83.45% and 74.8%, respectively, for 11 dBm of RF input power. Further, the link analysis, including interfacing circuit losses, was carried out theoretically at 915 MHz. Thus, the proposed differential extraoral systems can be employed to acquire and transmit the user intentions in eTDS inspired healthcare applications.

Exploring Built Environment Visual Interactions: A SoftBIM Data-Driven Approach for a Database About the Outdoor View

Windows and glazed facades provide outdoor views, serving as vital sources of visual information that aid navigation and interaction within buildings. These views can trigger psychological and physiological responses, affecting individual well-being. However, optimizing outdoor view quality is challenging due to the complex interplay of factors influencing the building’s experience of vision. Managing the complexity of optimizing outdoor view quality within current digital frameworks for building design presents significant challenges. A key issue lies in the ambiguity of certain visual metrics, which are often difficult to translate into explicit descriptors of spatial configurations. Even when such metrics are available, their practical use as guiding tools in the design process is frequently obstructed by complex data interoperability procedures. These procedures are necessary to enable seamless data transfer across the multiple software environments involved in the design process. This study advocates for the softBIM paradigm, which optimizes workflows by embedding visual analysis results into target geometries. Supported by this process, the calculation of a metric to measure the impact of existing and planned visual obstructions on the vision of the targeted landmarks is proposed and analysed. This metric is specifically applied to assess the visual information incoming to the vertical facades of building envelopes, a context of application that denotes criteria of assessment different from the ones usually applied in the most established frameworks for visual analysis (e.g., isovist analysis). SoftBIM enables effective automation strategies to aid the metric computation and the processing of the results to implement seamless export and data implementation. The visual metric is built upon implementing the Ladybug suite and addresses the different limitations in the target-based visibility calculation supported by the tool.

A secure and efficient data aggregation scheme for cloud–edge collaborative smart meters

Smart meters are part of the Advanced Measurement Infrastructure (AMI) system in the smart grid. It facilitates data transfer between consumers and electricity suppliers (ES). However, the mass deployment of smart meters (SM) brings heavy overhead to grid operation and poses serious privacy threats. To this end, this paper proposes a secure and efficient data aggregation scheme of cloud–edge collaboration smart meters. At first, we standardize the users’ historical electricity load features and use the improved K-Means clustering algorithm to calculate the Euclidean distance between feature vectors to obtain the classification results of users’ load features. On this basis, ES generates relevant parameters to encrypt meter data and protect users’ data privacy based on classification results. The aggregator (Ag) performs the data aggregation, generates the overall signature using the Schnorr aggregation signature method, and sends it to the cloud server (CS). The ES queries the CS to obtain data and parses it to realize the customer billing service. Meanwhile, this paper executes a series of experiments, and the results show that the proposed scheme exhibits significant advantages in privacy protection and system operation efficiency.

Dynamic clustering based risk aware congestion control technique for vehicular network

In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there’s no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study’s conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Cannabis-Based Phytocannabinoids: Overview, Mechanism of Action, Therapeutic Application, Production, and Affecting Environmental Factors

This review provides an overview of cannabis-based phytocannabinoids, focusing on their mechanisms of action, therapeutic applications, and production processes, along with the environmental factors that affect their quality and efficacy. Phytocannabinoids such as THC (∆9-tetrahydrocannabinol), CBD (cannabidiol), CBG (cannabigerol), CBN (cannabinol), and CBC (cannabichromene) exhibit significant therapeutic potential in treating various physical and mental health conditions, including chronic pain, epilepsy, neurodegenerative diseases, skin disorders, and anxiety. The cultivation of cannabis plays a crucial role in determining cannabinoid profiles, with indoor cultivation offering more control and consistency than outdoor methods. Environmental factors such as light, water, temperature, humidity, nutrient management, CO2, and the drying method used are key to optimizing cannabinoid content in inflorescences. This review outlines the need for broader data transfer between the health industry and technological production, especially in terms of what concentration and cannabinoid ratios are effective in treatment. Such data transfer would provide cultivators with information on what environmental parameters should be manipulated to obtain the required final product.

RU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring with PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-art performance on the largest clean dataset, PulseDB. The rU-Net architecture, a fusion of U-Net and ResNet, enhances both generalization and feature extraction accuracy. Accurate multi-scale feature capture is facilitated by short-time Fourier transform (STFT) time-frequency distributions and multi-head attention mechanisms, allowing data-driven feature selection. The inclusion of demographic parameters as supervisory information further elevates performance. On the calibration-based dataset, our model excels, achieving outstanding accuracy (SBP MAE ± std: 4.49 ± 4.86 mmHg, DBP MAE ± std: 2.69 ± 3.10 mmHg), surpassing AAMI standards and earning a BHS Grade A rating. Addressing the challenge of calibration-free data, we propose a fine-tuning-based transfer learning approach. Remarkably, with only 10% data transfer, our model attains exceptional accuracy (SBP MAE ± std: 4.14 ± 5.01 mmHg, DBP MAE ± std: 2.48 ± 2.93 mmHg). This study sets the stage for the development of highly accurate and reliable wearable cuffless blood pressure monitoring devices.

Improving Communication Networks to Transfer Data in Real Time for Environmental Monitoring and Data Collection

Integrated communication networks (CN) have proven successful in tracking environmental activities, wherein several sensors are installed throughout diverse surroundings to gather data or observe certain events. CNs, comprising several interacting detectors, have proven effective in various applications by transmitting data via diverse transmission methods inside the communication system. The erratic and constantly changing surroundings necessitate conventional CNs to engage in frequent conversations to disseminate the latest data, potentially incurring substantial connection expenses through joint data gathering and dissemination. High-frequency communications are prone to failure due to the extensive distance of data transfer. This research presents a unique methodology for multi-sensor environmental monitoring networks utilizing autonomous systems. The transmission system can mitigate elevated communication costs and Single Point of Failing (SPOF) challenges by employing a decentralized method that facilitates in-network processing. The methodology employs Boolean systems, enabling a straightforward verification process while preserving essential details about the dynamics of the communication system. The methodology further simplifies the data collection process and employs a Reinforcement Learning (RL) technique to forecast future events inside the surroundings by recognizing patterns.

DIGITAL TRANSFORMATION AS A TRIGGER FOR MODIFICATION OF DEVELOPMENT STRATEGIES AND BUSINESS MODELS OF COMPANIES IN THE CONTEXT OF THE FORMATION OF GLOBAL DIGITAL PLATFORMS AND HYPERCOMPETITION

The article describes the problem that in the context of digital transformation enterprises, especially those with a traditional business model, face both great opportunities and challenges. The competition is intensifying, turning into hyper-competition, which implies the search for new tools to win customer loyalty and accelerate innovation processes. The article provides a generalization of adequate strategies for the development of enterprises based on the use of information technology, studying the effects of digital transformation on competition and on various forms of digital inequality at the micro and meso levels, including the background of the proliferation of global digital platforms. Digital transformation involves 3 stages - digitization, digitalization, digital transformation itself; it involves changes in enterprise strategies, organizational structure, information technology, supply chains, marketing, development of own digital platforms, market diversification, co-creation. For a successful digital transformation, companies need to build a fundamentally new set of assets, including IT infrastructure, Big Data, AI, IoT and blockchain, digital flexibility, digital network capabilities, cloud solutions, etc. Different types of companies have their own peculiarities of digital transformation. In a hypercompetitive environment, it is more difficult to maintain the company's performance trends due to the change in competition patterns to rivalry patterns, which are characterized by shorter product life cycles and an accelerated pace of innovation. This means that hypercompetition reduces the ability of firms to create sustainable competitive advantages, calling into question, if not the validity, then at least the usefulness of using a resource-based strategy. This thesis is especially important in the context of digital transformation, because it quickly depreciates the strategic assets of traditional companies. A global challenge for all businesses is the development of digital platforms that have evolved into global social and technical intermediaries and business organizations that not only connect users, but also provide the basic infrastructure for doing business. Their control over access to technology allows them to create global networks for the transfer of data and capital, controlling access to important resources, while strengthening their dominance through increased control over technology and data, which digital platforms turn into a key asset that drives the financialization process. Digital platforms are contributing to the globalization of competition between employees and governments and increasing hypercompetitive conditions, which is challenging companies with traditional business models to survive and grow, as the risks of massive bankruptcies increase, which in turn exacerbates digital inequality at both the micro and meso levels.

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BackgroundThe increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.MethodWe analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.ResultsIn many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.ConclusionsWe propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

From data silos to seamless integration and coordination: a data-asset centric approach to smart hospital facility management

PurposeExisting hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.Design/methodology/approachThis study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.FindingsThe capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.Originality/valueThe initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

Cryptography in Network Security: a Mini Review

Network security and cryptography are crucial components in safeguarding data and communication in the digital age. Cryptography plays a vital role in network security by converting plain text into unreadable formats to protect confidentiality and integrity. As organizations generate vast amounts of data daily, ensuring secure data transfer over the internet becomes increasingly important, with network security challenges growing as society transitions to the digital era. Techniques such as encryption are used to protect enterprise information and communication from cyber threats, ensuring the integrity, confidentiality, and accessibility of computer networks and data. Encryption and network security are employed to protect data stored in systems and exchanged over wireless networks, with cryptography utilizing various algorithms and approaches to secure networks and data effectively.

Response-Aided Score-Matching Representative Approaches for Big Data Analysis and Model Selection under Generalized Linear Models

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Abstract Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.

A Survey of Middleware Adoption in Nonprofit Sectors: A Sustainable Development Perspective

Nonprofit Organizations (NPOs) are adopting technology to improve their quality of services, scale up, or reduce operation costs. However, due to the heterogeneity of systems they use, NPOs face system-integration challenges when collaborating with other organizations. Middleware is an intermediary software that assists dissimilar systems in working together. This paper explores middleware applications, opportunities, and challenges within the sector. It extensively reviewed the current state of research on middleware usage in the nonprofit sector for all papers published in Scopus and Web of Science (WoS) until 2023. Out of 127 papers returned, only 31 remained after removing duplicates, invalid entries, and out-of-scope publications. Then, we synthesized insights from a thorough survey of these selected papers. In light of the survey results, we observed that NPOs primarily use middleware in a few of the Sustainable Development Goals (SDGs), namely, health (SDG 3), NPO operations (SDG 8 and 9), NPO collaborations (SDG 17), development of sustainable cities (SDG 11), security and disaster management (SDG 16), and education (SDG 4). We also identified several challenges related to using middleware in the nonprofit sector, which include privacy, security, system development and performance, data processing and transfer, and volunteer attrition.

MobileNet-CAL: Tomato pest and disease classification method based on transfer learning and attention mechanism

Based on the MobileNetV3 model, this paper introduces the Coordinate attention (CA) attention mechanism and transfer learning to propose the MobileNet-CAL model to solve the problems of insufficient attention to model channel information, long classification training time, high training cost, and weak generalization ability. The model combines data enhancement, attention mechanism and transfer learning technology to improve the classification effect of the model, and updates the fully connected layer at the same time. Finally, the model is optimized by combining the Auxiliary loss function and the ASGD optimizer to improve the classification accuracy of the model, speed up the collection speed, and enhance the robustness of the model. Finally, the model is evaluated from multiple angles such as Accuracy, F1-score, Precision, Recall, and Specificity in the tomato pest and disease leaf dataset. Its Accuracy reaches 98.59% an accuracy of, which is improved by, and by, 4.47%respectively, compared with the original model. Experiments show that MobileNet-CAL can improve the classification accuracy to a certain extent, verifying that the model proposed 4.05%、3.43%、16.57% in 4.13%this paper is effective in the classification task of tomato pest and disease leaves.

Relay Assisted Device-to-Device Communication for Future Generation Wireless Networks: A Review from Architectural and Challenges Perspective

The rapid evolution of wireless communication technologies, along with the increasing demand for efficient and reliable data transfer, has driven the rethinking of traditional cellular network architectures in the context of next-generation networks. In conventional cellular systems, communication between users typically occurs through base stations. However, to improve efficiency, scalability, and spectral utilization, device-to-device (D2D) communication has been introduced, enabling direct data transmission between users without base station involvement. In this paper, we present a comprehensive review of D2D communication from architectural and challenges perspective for future generation networks. Additionally, the study focuses on relay-assisted D2D (RAD2D) communication, examining the role of machine learning (ML) and artificial intelligence (AI) in optimizing relay selection processes. In light of the existing literature, challenges for implementing RAD2D are discussed from different perspectives such as relay selection, energy efficiency, secure communication, resource allocation, and the management of dynamic network conditions

DLSEA-64: dynamic lightweight symmetric encryption algorithm for secure data transmission in IoT based pervasive sensing applications

ABSTRACT Remote patient monitoring, particularly for the elderly, is gaining popularity due to the Internet of Medical Things (IoMT). However, IoMT devices are highly vulnerable to cyber attacks related to data confidentiality. Hence, lightweight cryptographic algorithms are found to be highly necessary to ensure safe data transfer in such devices. This paper introduces a Dynamic Lightweight Symmetric Encryption Algorithm (DLSEA) in order to challenge issues related to real-time data security and privacy. DLSEA is executed on Arduino Uno (ATmega328P) and Arduino Mega (ATmega2560) microcontrollers by using the Proteus simulation tool that employs a 64-bit temporal secret key and a 64-bit block cipher for encryption of data. Several experiments were conducted using payloads of varying sizes to record execution time, energy consumption, RAM usage, ROM usage and avalanche effect. The experiments demonstrated that for data sizes between 128-512 bytes, DLSEA requires 2.49-3.27 ms on the ATmega2560, which is 47% faster than a similar algorithm known as PRESENT. Additionally, DLSEA is 46% more energy-efficient, with RAM usage decreased by 41.81% and ROM usage decreased by 57.19% compared to PRESENT. Experimental results also show that DLSEA satisfies the plaintext sensitivity test, the key sensitivity test, and the rigorous avalanche criteria test (i.e., ~50%).

Brief Study on Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and pattern recognition, offering state-of-the-art performance in tasks such as image classification, object detection, and segmentation. This study explores the architecture, training strategies, and applications of CNNs, focusing on their ability to automatically learn spatial hierarchies of features through layers of convolutional filters. The research evaluates various CNN architectures, including Lent, Alex Net, and ResNet, highlighting their improvements in accuracy and efficiency. Additionally, the study investigates advanced techniques such as data augmentation, transfer learning, and regularization methods (e. g, dropout and batch normalization), which enhance the model's generalization capabilities. Through empirical experiments, we demonstrate the effectiveness of CNNs in real-world scenarios, including medical image analysis, autonomous driving, and facial recognition. The study concludes with a discussion of the future trends of CNNs, particularly in the context of deep learning optimization, hardware acceleration, and integration with emerging technologies like edge computing and quantum machine learning.