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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Gunalan, K. Dakshana, M. Sangeetha, Sn Anandan, P. Saveetha, R. Abirami, S. Bannari Amman Institute of Technology Department of Computer Technology Tamilnadu India Sathyabama Institute of Science and Technology Department of Physics Tamilnadu India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350370867

The rapid proliferation of interconnected devices leveraging a diverse array of sensors for internet connectivity, communally known as the Internet of Things (IoT), holds immense potential to reshape both the natural environment and the economic landscape. Despite the minimal individual energy requirements of each sensor, the cumulative power consumption of trillions of such devices is substantial. Consequently, prioritizing the development of long-term, energy-efficient solutions within the IoT framework becomes imperative. This article advocates for the adoption of environmentally conscious IoT technology, elucidating its stance through an exhaustive analysis of energy-saving perspectives and techniques within the IoT domain. The core focus is on fostering the creation of environmentally IoT solutions that are energy-efficient and sustainable. To achieve this overarching objective, attention is directed towards four pivotal components: Machine-to-Machine (M2M) communication, Water-Efficient Telecommunications, Eco-Sustainable and Power-Efficient Wireless Sensing Networks (WSN);RFID (Radio-Frequency Identity) systems with reduced energy consumption, and Power-Efficient Microprocessor Units and Integrated Circuits (IC).Through the incorporation of these foundational principles, this study aims to ensure that the economic and environmental ramifications of IoT remain positive and enduring in the years to come. The ultimate goal is to steer the trajectory of future generations towards the development and adoption of IoT solutions that are not only environmentally conscious but also economically viable. © 2023 IEEE.

关键词： Environmental impact

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Comparing the intensity of variability changes in software product line evolution (Summary)

Comparing the intensity of variability changes in software p...

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2024 Software engineering, SE 2024

作者： Kröher, Christian Gerling, Lea Schmid, Klaus University of Hildesheim Institute of Computer Science Software Systems Engineering Universitätsplatz 1 Hildesheim31141 Germany

ISBN: (纸本)9783885797371

This paper summarizes [KGS23], published in the Journal of systems & Software. Software Product Line (SPL) evolution affects multiple kinds of artifacts like code, build, and variability model. Only part of these artifacts are relevant to variability. We studied the intensity (frequency and amount) in which developers change variability information in our earlier work. Applying this to the Linux kernel revealed that changes to variability information occur infrequently and only affect small parts of the analyzed artifacts. Here, we extend our previous work to a comparative evolution analysis of four SPLs. We provide a detailed analysis of the individual intensities of variability changes by applying our updated approach to each SPL. A comparison of these results confirms our findings of infrequent and small changes to variability information in our previous study. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Change Intensity Evolution analysis Software product line evolution Variability changes

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Equilibria of Data Marketplaces with Privacy-Aware Sellers under Endogenous Privacy Costs

Equilibria of Data Marketplaces with Privacy-Aware Sellers u...

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2025 IEEE Conference on Secure and Trustworthy Machine Learning, SaTML 2025

作者： Sen, Diptangshu Wang, Jingyan Ziani, Juba Industrial & Systems Engineering Georgia Institute of Technology Atlanta United States Computer Science TTIC Chicago United States

ISBN: (纸本)9798331517113

We study a two-sided online data ecosystem comprised of an online platform, users on the platform, and downstream data buyers. The buyers can buy user data on the platform to run a statistic or machine learning task. Potential users decide whether to join by looking at the trade-off between i) their benefit from joining the platform and interacting with other users and ii) the privacy costs they incur from sharing their data. In light of the rapidly changing user privacy attitudes, we introduce a novel modeling element for two-sided data platforms: the privacy costs of users are endogenous and depend on the number of downstream buyers who purchase and access their data. Then, we characterize marketplace equilibria in certain simple settings. In particular, we provide a full characterization in two variants of our model that correspond to different utility functions for the users: i) when each user gets a constant benefit for participating on the platform and ii) when each user's benefit is linearly increasing in the number of other users that participate. In both variants, equilibria in our setting are significantly different from equilibria when privacy costs are exogenous and fixed, the most significant point of difference being that under exogenous privacy costs, the user-side participation rate is completely independent of the platform's price and the buyer-side decisions and thus can never be improved without investing in improving the quality of service offered. This highlights the importance of taking endogeneity in privacy costs into account. Finally, we provide simulations and semi-synthetic experiments to extend our results to more general assumptions;we experiment with different distributions of users' privacy costs and different functional forms of the users' utilities for joining the platform. © 2025 IEEE.

关键词： Data Markets Equilibrium Analysis Game Theory Privacy

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Reducing power consumption in 2 - tier H-CRAN using switch active/sleep of small cell RRHs

Reducing power consumption in 2 - tier H-CRAN using switch a...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Tiwari, Amit Kumar Mishra, Pavan Kumar Pandey, Sudhakar Department of Computer Science and Engineering United Institute of Technology Prayagraj India Department of Information Technology National Institute of Technology Raipur Raipur India

ISBN: (纸本)9781032669663

The shift to 5G networks was motivated by an exponential increase in data traffic requirements and the number of connected devices that adhere to severe quality of service (QoS) criteria. This evolution boosts total energy consumption while indirectly generates polluting carbon footprints. To deal with 5G traffic needs and power consumption issues, a novel 2 tier Heterogeneous Cloud Radio Access Network (H-CRAN) architecture is being developed. This research examines the energy usage in a two-tier 5G HCRAN employing switch active/sleep and Small Cell Remote Radio Heads (HSC-RRHs). We create HSC-RRH switching algorithms to minimise the number of unused HSC-RRH per Macro Cell RRH (HMC-RRH). The simulation results show that our suggested strategies based on switching active/sleep via HSC-RRH can reduce more than 48% of power, which makes them more effective in terms of power savings than existing schemes. © 2024 The Author(s).

关键词： Quality of service

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A preprocessing method combined with an ensemble framework for the multiclass imbalanced data classification

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International Journal of computers and Applications 2022年第12期44卷 1178-1185页

作者： Pavan Kumar, M.R. Jayagopal, Prabhu School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Information Technology & Engineering Vellore Institute of Technology Vellore India

Skewed distributions appear in many real-world classification problems. Skewed distributions, underrepresented classes, and multiple overlapping regions in multiclass imbalanced datasets deteriorate the performance of existing classification algorithms and approaches. In this context, we combine a novel preprocessing procedure to tackle minority classes in multiclass imbalanced problems with an ensemble framework. The preprocessing method oversamples the minority classes based on normalized probability, and then an ensemble called a stacked generalization framework is used to train the model. The motive behind combining the ensemble framework and the preprocessing procedure is to enhance the overall classification performance of the classifier for multiclass imbalanced problems. Experimental results on 20 multiclass imbalanced datasets show that the proposed preprocessing method with the ensemble framework outperforms the representative approaches in 13 datasets for macro average arithmetic (MAvA) and mean F-measure (MFM) metrics. In the case of state-of-the-art techniques, the proposed approach steered 14 datasets for the MAvA metric and 15 datasets for the MFM metric to success. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Classification (of information)

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Performance Analysis of Cooperative VLC with L-PPM Modulation and Diversity Combining Strategies under Perfect and Imperfect CSI for IoT Devices

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IEEE Sensors Journal 2025年第12期25卷 23028-23036页

作者： Dwivedy, Prashant Dixit, Vipul Kumar, Atul PDPM-Indian Institute of Information Technology Design and Manufacturing M.P. Jabalpur482005 India Bennett University School of Computer Science Engineering and Technology Greater Noida India Maulana Azad National Institute of Technology Department of Electronics and Communication Engineering Madhya Pradesh Bhopal462003 India

The rise of digital transformation is driven by the convenience of internet of things (IoT) wireless connectivity, facilitating the development of smarter, more interconnected, and efficient solutions across diverse sectors such as smart homes, cities, industrial automation, healthcare, transportation, agriculture, and environmental monitoring. Visible Light Communication (VLC) emerges as a promising wireless data transmission technique utilizing the visible light spectrum. Integrating VLC with IoT devices (IDs) offers myriad benefits and potential *** a practical scenario, this study presents a novel approach for cooperative VLC (C-VLC) systems that use L-ary pulse position modulation (L-PPM) to establish long-range indoor communication across IDs, even in the presence of noisy channel state information (CSI). Three diversity combining procedures, including equal gain combining (EGC), maximum ratio combining (MRC) and selection-based combining (SBC), are employed and compared in the study. The study examines the bit error probability (BEP) of a C-VLC system using L-PPM modulation for IDs under perfect and imperfect CSI conditions for a M-IDs scenario. The BEP of all three diversity combining strategies is computed using extensive simulations and compared to the performance of direct transmission and on-off keying (OOK) C-VLC systems. These comparisons provide empirical support for the analytical BEP findings. © 2001-2012 IEEE.

关键词： Error statistics

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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OnE: An EEG-based Passive BCI framework for Monitoring Cognitive States During online learning 23

OnE: An EEG-based Passive BCI framework for Monitoring Cogni...

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6th International Conference on Advances in Robotics, AIR 2023

作者： Talukdar, Upasana Paul, Prasun Computer Sc. and Engineering Indian Institute of Technology Guwahati India Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (纸本)9781450399807

Brain fog, characterized by confusion, lack of attention, and mental clarity, is one of the main reasons that lead to the downgrading of performance in the learning process. In recent years, online education has gained quite popularity. Analysis of the cognitive states of the students in real-time during online classes is challenging as immediate and prompt feedback from students in online education is less feasible as compared to that of classroom education. This paper presents an EEG-based Passive Brain-computer Interface (pBCI) framework for identifying the cognitive states of students during online video learning. The framework detects three cognitive states: confusion, attention, and mediation while watching online course videos using EEG data. The paper first presents rigorous experimental analysis to identify the confusion levels, followed by proposing a methodology based on kernel partial least square algorithm to predict the attention and mediation levels. The paper also explores the dominant features associated with confusion levels and the correlation between the three cognitive states. © 2023 ACM.

关键词： Students

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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Control Action Types - Patterns of Applied Control for Self-adaptive systems (Summary)

Control Action Types - Patterns of Applied Control for Self-...

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2024 Software engineering, SE 2024

作者： Kröher, Christian Gerling, Lea Schmid, Klaus University of Hildesheim Institute of Computer Science Software Systems Engineering Universitätsplatz 1 Hildesheim31141 Germany

ISBN: (纸本)9783885797371

This paper summarizes [KGS23], which received the Best Paper award at the 18th Symposium on Software engineering for Adaptive and Self-Managing systems (SEAMS23) in 2023. A Self-adaptive System (SaS) modifies its behavior according to changes in its environment autonomously. Distributed control and central control represent two complementary paradigms to establish this capability. Both of these approaches come with respective advantages and disadvantages. A promising approach to minimize these negative aspects is to combine them to achieve the best of both paradigms. In this paper, we present patterns for integrating distributed and central control, which we call Control Action Types (CATs). Each CAT describes a unique type of interaction between a central controller and a distributed controlled SaS to achieve a desired adaptation. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Decentralized control

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