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Optimizing Monocular Depth Estimation through Bi-Level Nested Architecture Integration

Optimizing Monocular Depth Estimation through Bi-Level Neste...

引用

international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Faiaz Hasanuzzaman Rhythm Tareque Bashar Ovi Nomaiya Bashree Md. Raisul Islam Ratul Hussain Nyeem Md Abdul Wahed Deptartment of EECE Military Inst. of Sci. and Tech Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Monocular depth estimation (MDE) has evolved into a vital research focus in visual computing and autonomous robotic systems. While recent deep learning approaches have advanced the field, existing methods often struggle to jointly incorporate global context and fine-grained local details, and may be hampered by complex, computationally expensive network architectures. To address these challenges, we employ the U 2 Net framework, which incorporates Residual U-Blocks designed to adapt the area of reception and capture multi-scale contextual data. By nesting U-shaped structures, U 2 Net effectively increases feature extraction depth while maintaining computational efficiency, thus offering a more balanced and scalable solution for MDE. Our proposed model, developed using the NYUDepth V2 dataset, exceeds contemporary methods in critical assessment measures, including RMSE, RMSE(log), and Absolute Relative Error (Abs Rel). These improved results highlight the robustness and precision of proposed model or a range of practical applications in robotics, augmented reality, and other domains requiring real-time scene geometry understanding.

关键词： Deep learning Adaptation models Visualization Depth measurement Computational modeling Reliability engineering Robustness Real-time systems Computational efficiency Robots

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VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

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Disruptive Technologies (ICDT), international conference on

作者： Payal Chhabra Tanmay Arora Vibhore Jain Neeraj Gandhi Swapnil Bhatnagar Nitin Arora Department of (CSE-AIML) KIET group of institutions Ghaziabad India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

There are approximately 21 million visually im-paired individuals who depend on other people for their basic work. This research adds to the current understanding of assistive technology by introducing a method for choosing and optimizing the best object detection models suited for accessi-bility environments. This research enhances the Visicompanion functions and assists visually impaired individuals in doing crucial daily tasks independently by selecting a suitable model. This research compares the analysis of two models i.e. Y oLov8 [1] and YOLOV9 [2] and identifies which algorithm is best for the application. YOLOv8 is known for its rapid inference speed which is beneficial for real-time systems whereas YOLOv9 introduces advanced detection capability that improves accuracy but may increase problems for processing time and computational resource requirements. This research uses evaluation metrics like precision, recall, size of model, and resource utilization across different hardware configurations. The results showed thatYOLOv9 is slightly better than YOLOv8 with mAP reaching 95% and precision 96.8%.

关键词： measurement computer vision Computational modeling Object detection Assistive technologies Real-time systems Inference algorithms Hardware Resource management Disruptive technologies

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A Study on Intelligibility, Naturalness and Fluency of Text-to-Speech systems

A Study on Intelligibility, Naturalness and Fluency of Text-...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Priyadharshini S Rumana Nachiyar M Usharani K Computer Science and Business System Ramco Institute of Technology India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Text-to-Speech (TTS) technology advancements have resulted in the developing of sophisticated models like Tacotron and WaveNet. This research evaluates these models based on clarity, naturalness, and flow. Tacotron employs a comprehensive architecture that converts text into mel-spectrograms, excelling in prosody and fluency. WaveNet, a probabilistic model, directly generates raw audio waveforms, delivering exceptional sound quality. The LJSpeech dataset assessed performance through metrics such as Word Error Rate (WER) and Mean Opinion Score (MOS). Tacotron achieved a WER of 6.2%, indicating strong fluency with some alignment challenges. WaveNet outperformed Tacotron with a WER of 4.7% and a MOS of 4.6, demonstrating greater naturalness but some minor fluency issues. Tacotron is ideal for expressive applications, while WaveNet excels in realism. Future research could explore hybrid models to combine these strengths and address current limitations..

关键词： measurement Error analysis Computational modeling computer architecture Probabilistic logic Text to speech Informatics

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Intelligent Road Condition Monitoring and Analysis for Safer Highways

Intelligent Road Condition Monitoring and Analysis for Safer...

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Innovation in Computing and Engineering (ICE), international conference on

作者： M. Mohamed Iqbal Nandhana S Vijay V Sreehari Veeramachaneni Information Technology Sri Sivasubramaniya Nadar College Of Engineering Chennai Tamilnadu

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

Road damages are one of the greatest threats to the safety of road users as they contribute to mechanical breakdowns and eventual accidents. This project addresses the problem by presenting a possible solution of how to develop an efficient road damage detection system using the YOLOv5 object detection algorithm. The system makes use of the images from the road damage datasets to create a YOLOv5 model that is made to detect potholes, joint cracks, and alligator cracks and outlines where the potholes are with bounding boxes. Furthermore, GPS coordinates of the detected damage are logged and stored in cloud database and this can be used to provide road management authorities information on hazardous road conditions for action. The system also investigates pothole depth prediction through MiDaS, an emerging method in the area of Depth Estimation. The proposed system attains an average of 78.1% detection accuracy and a mAP@50 of 76.6% on average. These results validate the system’s real-time pothole and crack detection and classification performance while setting the foundation for enhanced detection sensitivity, depth prediction, and navigation integration for real-time pothole warning to drivers. By integrating geo-tagging based on IoT, the effectiveness of performing anticipatory repair and maintenance of road infrastructure is highly increased, therefore road safety is improved.

关键词： YOLO Technological innovation Solid modeling Sensitivity Depth measurement Volume measurement Road safety Real-time systems Maintenance Vehicles

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mBERT: A Query Refinement Model for Marathi Word Sense Disambiguation

mBERT: A Query Refinement Model for Marathi Word Sense Disam...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Vivek A. Manwar A. B. Manwar Department of Computer Science Sant Gadge Baba Amravati University Amravati Maharashtra India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Word Sense Disambiguation involves determining the appropriate meaning of a word within a given context. Natural language processing continues to face challenges related to ambiguity. Reducing these ambiguities for applications such as machine translation, data extraction, information retrieval, query response systems. In this paper, the proposed mBERT model for word disambiguation in Marathi query refinement show good performance with accuracy of 92.5%. This accuracy shows the model's proficiency in correctly interpret the intended meanings of ambiguous words within Marathi queries. With a precision of 91.2%, the model effectively minimizes false positives, ensuring that the refined queries remain relevant and meaningful and recall score is 90.8%, shows its capability to retrieve the most relevant information from the query context. The model's balanced performance is further highlighted by a high F1 score of 91.0%, which reflects a harmonious blend of precision and recall.

关键词： measurement Sentiment analysis Accuracy Information retrieval Solids Robustness Machine translation Data mining Faces Context modeling

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Multiphysics modeling of a Novel MEMS Accelerometer Based on Electromagnetic Induction

Multiphysics Modeling of a Novel MEMS Accelerometer Based on...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-systems EuroSimE

作者： Ozberk Ozturk Fares Tounsi Laurent Francis Denis Flandre Murat Kaya Yapici Faculty of Engineering and Natural Sciences Sabanci University Istanbul Türkiye ICTEAM Institute UCLouvain Louvain-la-Neuve Belgium Department of Electrical and Computer Engineering University of Washington Seattle USA

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

This work reports on the development of a micromachined monolithic single-axis electromagnetic accelerometer based on the relative motion between the windings of a concentric planar transformer, whose internal inductor is suspended. The mechanical and electromagnetic modelling of the sensor is presented, as well as the implementation of a virtual read-out circuit. Electromagnetic simulations were used to extract the electrical performance parameters in the form of a lumped-element model. The results demonstrate that rotation between the two windings leads to an approximately linear variation in the mutual inductance. Furthermore, measurement circuit simulations revealed that the sensor could achieve an in-plane acceleration sensitivity of 32.9 mV/g for a matched network when excited with 3V pp , 100 MHz excitation voltage.

关键词： Accelerometers Micromechanical devices Analytical models Inductance Sensitivity Voltage measurement Windings Electromagnetic induction Transformers Integrated circuit modeling

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Network Intrusion Detection by Integrating BI-LSTM for Enhanced Security

Network Intrusion Detection by Integrating BI-LSTM for Enhan...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Lakshmi Priya P. Kandibanda Lohith Joel M. Johnson Jyotsna C. Aiswariya Milan K. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The rising interconnectivity of digital systems has brought about the challenges of network security as high risks of data theft and unauthorized access through intrusions in networks. This paper details the development of a Network Intrusion Detection System that uses deep learning techniques and is designed to detect normal as well as anomalous activities within the network, such as zero-Day vulnerabilities, as discussed has proven that this model was capable of obtaining excellent accuracy, up to 99.25%, with precision and recall metric being well achieved, owed to advanced techniques, in this case, hyperparameter tuning. This will mean the model's capability to clearly differentiate legitimate and malicious traffic. In addition, explainable AI techniques such as SHAP and LIME deliver insights into the contributions or importance of various features about a model's decision-making.

关键词： measurement Deep learning Explainable AI Digital systems Computational modeling Neural networks Network intrusion detection Network security Intelligent systems Tuning

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Anomaly Detection on Gas Pipeline Operational Data Using Classic Seasonal Decomposition and Level Shift Anomaly Detection Architecture

Anomaly Detection on Gas Pipeline Operational Data Using Cla...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), international conference on

作者： Akmal Muzakki Bakir Aditya Firman Ihsan Widi Astuti School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Gas pipeline infrastructure is critical to energy distribution, but its operational existence is often threatened by anomalies such as leaks, malfunctions, and system disruptions. Early detection of these anomalies is critical to prevent safety hazards, operational inefficiencies, and economic losses. This paper proposes an anomaly detection framework using Classic Seasonal Decomposition and Level Shift Anomaly Detection methods for frequency selection and labeling processes, as well as LSTM and VAE-GAN algorithms for anomaly detection. The dataset consists of time-series sensor measurements, including pressure, temperature, energy flow rate, and gas composition. Classic Seasonal Decomposition isolates trend, seasonal, and residual components, enhancing periodic anomaly detection. Meanwhile, Level Shift Anomaly Detection identifies abrupt changes in data levels that signal critical events. LSTM leverages temporal dependencies to detect anomalies with high accuracy in data with strong sequential patterns, while VAE-GAN is effective in modeling complex data distributions and capturing anomalies in datasets with less obvious patterns. Experimental results demonstrate the effectiveness of the proposed approach, with model performance evaluated using F1-Score for each LSTM and VAE-GAN model. The proposed framework shows great potential for peering into real-time monitoring systems, improving operational efficiency, supporting data-driven decision making, and significantly reducing the risk of system failure.

关键词： Temperature measurement Computational modeling Pipelines Decision making computer architecture Market research Data models Real-time systems Anomaly detection Long short term memory

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GRU in Anomaly Detection for IoT: A Comparative Study

GRU in Anomaly Detection for IoT: A Comparative Study

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： V Sivakumar Aenugu Harshith Reddy Allam Nehal Varma School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

In this paper, we present a comparative analysis of Gated Recurrent Unit (GRU) models against other deep learning architectures, particularly focusing on anomaly detection and failure prediction within IoT-based industrial systems. GRU’s simplified structure, which accelerates training and reduces memory usage, makes it well-suited for real-time applications in IoT, outperforming its Long Short-Term Memory (LSTM) counterparts in many cases. However, its limitations in handling long-term dependencies, noisy data, and larger datasets can impede performance in more complex scenarios. Several studies have explored hybrid models that combine GRU with architectures like Transformers and CNNs to improve accuracy, especially in environments with irregular and noisy data. This paper discusses the computational efficiency of GRU and its application in edge computing while addressing its weaknesses through hybrid approaches. By leveraging insights from recent literature and projects, we evaluate GRU’s effectiveness in IoT applications and propose a hybrid GRU-Transformer model to enhance anomaly detection capabilities.

关键词： Training Computational modeling Predictive models Transformers Real-time systems Noise measurement Anomaly detection Long short term memory Industrial Internet of Things Predictive maintenance

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AI-Enhanced Cyber Security Proactive Threat Detection and Response systems

AI-Enhanced Cyber Security Proactive Threat Detection and Re...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Siva Koteswara Rao Katta Department of Software Engineering Sony Interactive Entertainment Austin Department of Mechanical Engineering KIETW India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The introduction of AI-Enhanced Cyber Threat Detection, which aids in improving organizational security through sophisticated algorithms, has caused a paradigm change in cybersecurity. The proactive posture of AI-driven detection systems that autonomously sift through massive data sets to identify patterns suggestive of cyber intrusions and attacks is explored in this research study. These tools relieve human analysts of routine tasks like threat identification and incident response, freeing them to focus on more strategic matters like making decisions and developing mitigation measures. Artificial intelligence (AI) based detection also makes monitoring network activities easier in real-time, increasing operational efficiency and decreasing dwell time. Another approach to accomplishing this in cybersecurity is combining different technologies, including threat intelligence platforms, with AI or SOAR systems. Further research and improved collaboration are needed to strengthen AI-enabled cyber threat identification in the face of obstacles like adversarial attacks and ethical concerns. Given the prevalence of modern e-crimes, this paper stresses the need for joint defence strategies and proactive cybersecurity measures to safeguard digital assets.

关键词： Explainable AI Organizations Telecommunication traffic Threat assessment Real-time systems Time factors Artificial intelligence computer security Stress Stress measurement

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