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DressWild: Feed-Forward Pose-Agnostic Garment Sewing Pattern Generation from In-the-Wild Images
Authors:
Zeng Tao,
Ying Jiang,
Yunuo Chen,
Tianyi Xie,
Huamin Wang,
Yingnian Wu,
Yin Yang,
Abishek Sampath Kumar,
Kenji Tashiro,
Chenfanfu Jiang
Abstract:
Recent advances in garment pattern generation have shown promising progress. However, existing feed-forward methods struggle with diverse poses and viewpoints, while optimization-based approaches are computationally expensive and difficult to scale. This paper focuses on sewing pattern generation for garment modeling and fabrication applications that demand editable, separable, and simulation-read…
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Recent advances in garment pattern generation have shown promising progress. However, existing feed-forward methods struggle with diverse poses and viewpoints, while optimization-based approaches are computationally expensive and difficult to scale. This paper focuses on sewing pattern generation for garment modeling and fabrication applications that demand editable, separable, and simulation-ready garments. We propose DressWild, a novel feed-forward pipeline that reconstructs physics-consistent 2D sewing patterns and the corresponding 3D garments from a single in-the-wild image. Given an input image, our method leverages vision-language models (VLMs) to normalize pose variations at the image level, then extract pose-aware, 3D-informed garment features. These features are fused through a transformer-based encoder and subsequently used to predict sewing pattern parameters, which can be directly applied to physical simulation, texture synthesis, and multi-layer virtual try-on. Extensive experiments demonstrate that our approach robustly recovers diverse sewing patterns and the corresponding 3D garments from in-the-wild images without requiring multi-view inputs or iterative optimization, offering an efficient and scalable solution for realistic garment simulation and animation.
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Submitted 18 February, 2026;
originally announced February 2026.
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SafeTalkCoach: Diversity-Driven Multi-Agent Simulation for Parent-Teen Health Conversations
Authors:
Benyamin Tabarsi,
Wenbo Li,
Tahreem Yasir,
Aryan Santhosh Kumar,
Laura Widman,
Dongkuan Xu,
Tiffany Barnes
Abstract:
The importance of effective parent-child communication about sexual health is widely acknowledged, but real-world data on these conversations is scarce and challenging to collect, due to their private and sensitive nature. Although LLMs have been widely adopted in dialogue generation, they may deviate from best practices and frequently lack realism and diversity. We introduce SafeTalkCoach, a dive…
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The importance of effective parent-child communication about sexual health is widely acknowledged, but real-world data on these conversations is scarce and challenging to collect, due to their private and sensitive nature. Although LLMs have been widely adopted in dialogue generation, they may deviate from best practices and frequently lack realism and diversity. We introduce SafeTalkCoach, a diversity-driven multi-agent dialogue generation framework that simulates parent-child conversations about sexual health, and present an accompanying dataset. SafeTalkCoach integrates crowd-sourced and synthesized scenarios, established sexual health guidelines, evidence-based personas, adaptive control modules, and hierarchical diversification. Through evaluations, we demonstrate that SafeTalkCoach generates diverse conversations while maintaining realism, communication quality, and controllability in practice. Our goal is that the SafeTalkCoach framework and the dataset support both AI research and health communications practices.
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Submitted 13 January, 2026;
originally announced February 2026.
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Integrating OTFS in Airplane-Aided Next-Generation Networking
Authors:
Ashok S Kumar,
Shashank Shekhar,
Gokularam Muthukrishnan,
Muralikrishnan Srinivasan,
Sheetal Kalyani
Abstract:
Next-generation networks explore the opportunistic assistance of airliner/high-altitude platforms (HAPs) in delivering high data rates for terrestrial networks to ensure consistent and reliable communication. When an airliner/HAP moves at very high speeds, its mobility has a substantial impact on ensuring seamless connectivity, stable signal strength, and reliable data transmission. Orthogonal tim…
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Next-generation networks explore the opportunistic assistance of airliner/high-altitude platforms (HAPs) in delivering high data rates for terrestrial networks to ensure consistent and reliable communication. When an airliner/HAP moves at very high speeds, its mobility has a substantial impact on ensuring seamless connectivity, stable signal strength, and reliable data transmission. Orthogonal time frequency space (OTFS) modulation has been shown to provide notable improvement in performance when handling Doppler effects during high-mobility situations. This paper presents an OTFS-based airplane-aided next-generation networking system. In the proposed system, the airliner/HAPs are equipped with a planar antenna array that applies null steering beamforming (NSB) at the transmitter for communication with terrestrial users. A comprehensive performance comparison between OTFS and orthogonal frequency division multiplexing (OFDM) is performed under varying airliner altitude, velocity, array dimension, and Rician factor conditions. The simulation results show that OTFS consistently outperforms OFDM, achieving a lower bit error rate (BER) and more stable performance across different airliner altitudes, velocities, array dimensions, and propagation environments.
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Submitted 21 January, 2026;
originally announced January 2026.
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Physiologically Active Vegetation Reverses Its Cooling Effect in Humid Urban Climates
Authors:
Angana Borah,
Adrija Datta,
Ashish S. Kumar,
Raviraj Dave,
Udit Bhatia
Abstract:
Efforts to green cities for cooling are succeeding unevenly because the same vegetation that cools surfaces can also intensify how hot the air feels. Previous studies have identified humid heat as a growing urban hazard, yet how physiologically active vegetation governs this trade-off between cooling and moisture accumulation remains poorly understood, leaving mitigation policy and design largely…
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Efforts to green cities for cooling are succeeding unevenly because the same vegetation that cools surfaces can also intensify how hot the air feels. Previous studies have identified humid heat as a growing urban hazard, yet how physiologically active vegetation governs this trade-off between cooling and moisture accumulation remains poorly understood, leaving mitigation policy and design largely unguided. Here we quantify how vegetation structure and function influence the Heat Index (HI), a combined measure of temperature and humidity in 138 Indian cities spanning tropical savanna, semi-arid steppe, and humid subtropical climates, and across dense urban cores and semi-urban rings. Using an extreme-aware, one kilometre reconstruction of HI and an interpretable machine-learning framework that integrates SHapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE), we isolate vegetation-climate interactions. Cooling generally strengthens for EVI >= 0.4 and LAI >= 0.05, but joint-high regimes begin to reverse toward warming when EVI >= 0.5, LAI >= 0.2, and fPAR >= 0.5,with an earlier onset for fPAR >= 0.25 in humid, dense cores. In such environments, highly physiologically active vegetation elevates near-surface humidity faster than it removes heat, reversing its cooling effect and amplifying perceived heat stress. These findings establish the climatic limits of vegetation-driven cooling and provide quantitative thresholds for climate-specific greening strategies that promote equitable and heat-resilient cities.
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Submitted 31 October, 2025;
originally announced November 2025.
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SlicerROS2: A Research and Development Module for Image-Guided Robotic Interventions
Authors:
Laura Connolly,
Aravind S. Kumar,
Kapi Ketan Mehta,
Lidia Al-Zogbi,
Peter Kazanzides,
Parvin Mousavi,
Gabor Fichtinger,
Axel Krieger,
Junichi Tokuda,
Russell H. Taylor,
Simon Leonard,
Anton Deguet
Abstract:
Image-guided robotic interventions involve the use of medical imaging in tandem with robotics. SlicerROS2 is a software module that combines 3D Slicer and robot operating system (ROS) in pursuit of a standard integration approach for medical robotics research. The first release of SlicerROS2 demonstrated the feasibility of using the C++ API from 3D Slicer and ROS to load and visualize robots in re…
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Image-guided robotic interventions involve the use of medical imaging in tandem with robotics. SlicerROS2 is a software module that combines 3D Slicer and robot operating system (ROS) in pursuit of a standard integration approach for medical robotics research. The first release of SlicerROS2 demonstrated the feasibility of using the C++ API from 3D Slicer and ROS to load and visualize robots in real time. Since this initial release, we've rewritten and redesigned the module to offer greater modularity, access to low-level features, access to 3D Slicer's Python API, and better data transfer protocols. In this paper, we introduce this new design as well as four applications that leverage the core functionalities of SlicerROS2 in realistic image-guided robotics scenarios.
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Submitted 23 September, 2025;
originally announced September 2025.
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Can LLMs Simulate Personas with Reversed Performance? A Systematic Investigation for Counterfactual Instruction Following in Math Reasoning Context
Authors:
Sai Adith Senthil Kumar,
Hao Yan,
Saipavan Perepa,
Murong Yue,
Ziyu Yao
Abstract:
Large Language Models (LLMs) are now increasingly widely used to simulate personas in virtual environments, leveraging their instruction-following capability. However, we discovered that even state-of-the-art LLMs cannot simulate personas with reversed performance (e.g., student personas with low proficiency in educational settings), which impairs the simulation diversity and limits the practical…
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Large Language Models (LLMs) are now increasingly widely used to simulate personas in virtual environments, leveraging their instruction-following capability. However, we discovered that even state-of-the-art LLMs cannot simulate personas with reversed performance (e.g., student personas with low proficiency in educational settings), which impairs the simulation diversity and limits the practical applications of the simulated environments. In this work, using mathematical reasoning as a representative scenario, we propose the first benchmark dataset for evaluating LLMs on simulating personas with reversed performance, a capability that we dub "counterfactual instruction following". We evaluate both open-weight and closed-source LLMs on this task and find that LLMs, including the OpenAI o1 reasoning model, all struggle to follow counterfactual instructions for simulating reversedly performing personas. Intersectionally simulating both the performance level and the race population of a persona worsens the effect even further. These results highlight the challenges of counterfactual instruction following and the need for further research.
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Submitted 16 March, 2026; v1 submitted 8 April, 2025;
originally announced April 2025.
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Characterizing Collective Efforts in Content Sharing and Quality Control for ADHD-relevant Content on Video-sharing Platforms
Authors:
Hanxiu 'Hazel' Zhu,
Avanthika Senthil Kumar,
Sihang Zhao,
Ru Wang,
Xin Tong,
Yuhang Zhao
Abstract:
Video-sharing platforms (VSPs) have become increasingly important for individuals with ADHD to recognize symptoms, acquire knowledge, and receive support. While videos offer rich information and high engagement, they also present unique challenges, such as information quality and accessibility issues to users with ADHD. However, little work has thoroughly examined the video content quality and acc…
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Video-sharing platforms (VSPs) have become increasingly important for individuals with ADHD to recognize symptoms, acquire knowledge, and receive support. While videos offer rich information and high engagement, they also present unique challenges, such as information quality and accessibility issues to users with ADHD. However, little work has thoroughly examined the video content quality and accessibility issues, the impact, and the control strategies in the ADHD community. We fill this gap by systematically collecting 373 ADHD-relevant videos with comments from YouTube and TikTok and analyzing the data with a mixed method. Our study identified the characteristics of ADHD-relevant videos on VSPs (e.g., creator types, video presentation forms, quality issues) and revealed the collective efforts of creators and viewers in video quality control, such as authority building, collective quality checking, and accessibility improvement. We further derive actionable design implications for VSPs to offer more reliable and ADHD-friendly contents.
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Submitted 17 July, 2025; v1 submitted 22 January, 2025;
originally announced January 2025.
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DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Frugal Resource Utilization
Authors:
Ashok S. Kumar,
Nancy Nayak,
Sheetal Kalyani,
Himal A. Suraweera
Abstract:
In this work, we propose a method for efficient resource utilization of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) elements to ensure fair and high data rates. We introduce a subsurface assignment variable that determines the number of STAR-RIS elements allocated to each user and maximizes the sum of the data rates by jointly optimizing the phase shift…
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In this work, we propose a method for efficient resource utilization of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) elements to ensure fair and high data rates. We introduce a subsurface assignment variable that determines the number of STAR-RIS elements allocated to each user and maximizes the sum of the data rates by jointly optimizing the phase shifts of the STAR-RIS and the subsurface assignment variables using an appropriately tailored deep reinforcement learning (DRL) algorithm. The proposed DRL method is also compared with a Dinkelbach algorithm and the designed hybrid DRL approach. A penalty term is incorporated into the DRL model to enhance resource utilization by intelligently deactivating STAR-RIS elements when not required. The proposed DRL method can achieve fair and high data rates for static and mobile users while ensuring efficient resource utilization through extensive simulations. Using the proposed DRL method, up to 27% and 21% of STAR-RIS elements can be deactivated in static and mobile scenarios, respectively, without affecting performance.
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Submitted 26 July, 2025; v1 submitted 9 July, 2024;
originally announced July 2024.
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Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals
Authors:
Ashok S Kumar,
Sheetal Kalyani
Abstract:
Our objective is to derive the range and velocity of multiple targets from the delay-Doppler domain for radar sensing using orthogonal time frequency space (OTFS) signaling. Noise contamination affects the performance of OTFS signals in real-world environments, making radar sensing challenging. This work introduces a two-stage approach to tackle this issue. In the first stage, we use a generative…
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Our objective is to derive the range and velocity of multiple targets from the delay-Doppler domain for radar sensing using orthogonal time frequency space (OTFS) signaling. Noise contamination affects the performance of OTFS signals in real-world environments, making radar sensing challenging. This work introduces a two-stage approach to tackle this issue. In the first stage, we use a generative adversarial network to denoise the corrupted OTFS samples, significantly improving the data quality. Following this, the denoised signals are passed to a convolutional neural network model to predict the values of the velocities and ranges of multiple targets. The proposed two-stage approach can predict the range and velocity of multiple targets, even in very low signal-to-noise ratio scenarios, with high accuracy and outperforms existing methods.
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Submitted 18 September, 2024; v1 submitted 2 October, 2023;
originally announced October 2023.
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Musical Excellence of Mridangam: an introductory review
Authors:
Arvind Shankar Kumar
Abstract:
This is an introductory review of Musical Excellence of Mridangam by Dr. Umayalpuram K Sivaraman, Dr. T Ramasami and Dr. Naresh, which is a scientific treatise exploring the unique tonal properties of the ancient Indian classical percussive instrument -- the Mridangam. This review aims to bridge the gap between the primary intended audience of Musical Excellence of Mridangam - listeners, artistes…
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This is an introductory review of Musical Excellence of Mridangam by Dr. Umayalpuram K Sivaraman, Dr. T Ramasami and Dr. Naresh, which is a scientific treatise exploring the unique tonal properties of the ancient Indian classical percussive instrument -- the Mridangam. This review aims to bridge the gap between the primary intended audience of Musical Excellence of Mridangam - listeners, artistes and makers -- and the scientific rigour with which the original treatise is written, by first introducing the concepts of musical analysis and then presenting and explaining the discoveries made within this context. The first three chapters of this review introduce the basic scientific concepts used in Musical Excellence of Mridangam and provides background to previous scientific research into this instrument, starting from the seminal work of Dr. CV Raman. This also includes brief discussions of the corresponding chapters in Musical Excellence of Mridangam. The next chapters all serve the purpose of explaining the main scientific results presented in Musical Excellence of Mridangam in each of the corresponding chapters in the treatise, and finally summarizing the relevance of the work.
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Submitted 11 July, 2023;
originally announced July 2023.
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A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution
Authors:
Jun Tao,
Qian Chen,
James W. Snyder Jr.,
Arava Sai Kumar,
Amirhossein Meisami,
Lingzhou Xue
Abstract:
Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of online marketing channels and types of touchpoints bring new challenges to this fun…
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Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of online marketing channels and types of touchpoints bring new challenges to this fundamental problem. We aim to tackle the attribution problem with finer granularity by conducting attribution at the path level. To this end, we develop a novel graphical point process framework to study the direct conversion effects and the full relational structure among numerous types of touchpoints simultaneously. Utilizing the temporal point process of conversion and the graphical structure, we further propose graphical attribution methods to allocate proper path-level conversion credit, called the attribution score, to individual touchpoints or corresponding channels for each customer's path to purchase. Our proposed attribution methods consider the attribution score as the removal effect, and we use the rigorous probabilistic definition to derive two types of removal effects. We examine the performance of our proposed methods in extensive simulation studies and compare their performance with commonly used attribution models. We also demonstrate the performance of the proposed methods in a real-world attribution application.
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Submitted 12 February, 2023;
originally announced February 2023.
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Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings
Authors:
Branislav Stojkovic,
Jonathan Woodbridge,
Zhihan Fang,
Jerry Cai,
Andrey Petrov,
Sathya Iyer,
Daoyu Huang,
Patrick Yau,
Arvind Sastha Kumar,
Hitesh Jawa,
Anamita Guha
Abstract:
The classical machine learning paradigm requires the aggregation of user data in a central location where machine learning practitioners can preprocess data, calculate features, tune models and evaluate performance. The advantage of this approach includes leveraging high performance hardware (such as GPUs) and the ability of machine learning practitioners to do in depth data analysis to improve mo…
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The classical machine learning paradigm requires the aggregation of user data in a central location where machine learning practitioners can preprocess data, calculate features, tune models and evaluate performance. The advantage of this approach includes leveraging high performance hardware (such as GPUs) and the ability of machine learning practitioners to do in depth data analysis to improve model performance. However, these advantages may come at a cost to data privacy. User data is collected, aggregated, and stored on centralized servers for model development. Centralization of data poses risks, including a heightened risk of internal and external security incidents as well as accidental data misuse. Federated learning with differential privacy is designed to avoid the server-side centralization pitfall by bringing the ML learning step to users' devices. Learning is done in a federated manner where each mobile device runs a training loop on a local copy of a model. Updates from on-device models are sent to the server via encrypted communication and through differential privacy to improve the global model. In this paradigm, users' personal data remains on their devices. Surprisingly, model training in this manner comes at a fairly minimal degradation in model performance. However, federated learning comes with many other challenges due to its distributed nature, heterogeneous compute environments and lack of data visibility. This paper explores those challenges and outlines an architectural design solution we are exploring and testing to productionize federated learning at Meta scale.
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Submitted 7 June, 2022; v1 submitted 1 June, 2022;
originally announced June 2022.
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Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System
Authors:
Tobias Schlosser,
Frederik Beuth,
Trixy Meyer,
Arunodhayan Sampath Kumar,
Gabriel Stolze,
Olga Furashova,
Katrin Engelmann,
Danny Kowerko
Abstract:
In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema (DME), as well as the retinal vein occlusion (RVO). However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity…
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In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema (DME), as well as the retinal vein occlusion (RVO). However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. For the disease AMD, we found out a significant deterioration of the visual acuity over time. Within our proposed multistage system, we subsequently classify the VA progression into the three groups of therapy "winners", "stabilizers", and "losers" (WSL classification scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98 %, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modeling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM / no therapy. We achieve a final prediction accuracy of 69 % in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and 50 +- 10.7 % F1-score.
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Submitted 1 October, 2024; v1 submitted 25 April, 2022;
originally announced April 2022.
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Machine Learning for high speed channel optimization
Authors:
Jiayi He,
Aravind Sampath Kumar,
Arun Chada,
Bhyrav Mutnury,
James Drewniak
Abstract:
Design of printed circuit board (PCB) stack-up requires the consideration of characteristic impedance, insertion loss and crosstalk. As there are many parameters in a PCB stack-up design, the optimization of these parameters needs to be efficient and accurate. A less optimal stack-up would lead to expensive PCB material choices in high speed designs. In this paper, an efficient global optimization…
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Design of printed circuit board (PCB) stack-up requires the consideration of characteristic impedance, insertion loss and crosstalk. As there are many parameters in a PCB stack-up design, the optimization of these parameters needs to be efficient and accurate. A less optimal stack-up would lead to expensive PCB material choices in high speed designs. In this paper, an efficient global optimization method using parallel and intelligent Bayesian optimization is proposed for the stripline design.
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Submitted 1 November, 2019;
originally announced November 2019.
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Towards Refactoring of DMARF and GIPSY Case Studies -- A Team 5 SOEN6471-S14 Project Report
Authors:
Pavan Kumar Polu,
Amjad Al Najjar,
Biswajit Banik,
Ajay Sujit Kumar,
Gustavo Pereira,
Prince Japhlet,
Bhanu Prakash R.,
Sabari Krishna Raparla
Abstract:
This paper presents an analysis of the architectural design of two distributed open source systems (OSS) developed in Java: Distributed Modular Audio Recognition Framework (DMARF) and General Intensional Programming System (GIPSY). The research starts with a background study of these frameworks to determine their overall architectures. Afterwards, we identify the actors and stakeholders and draft…
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This paper presents an analysis of the architectural design of two distributed open source systems (OSS) developed in Java: Distributed Modular Audio Recognition Framework (DMARF) and General Intensional Programming System (GIPSY). The research starts with a background study of these frameworks to determine their overall architectures. Afterwards, we identify the actors and stakeholders and draft a domain model for each framework. Next, we evaluated and proposed a fused DMARF over GIPSY Run-time Architecture (DoGRTA) as a domain concept. Later on, the team extracted and studied the actual class diagrams and determined classes of interest. Next, we identified design patterns that were present within the code of each framework. Finally, code smells in the source code were detected using popular tools and a selected number of those identified smells were refactored using established techniques and implemented in the final source code. Tests were written and ran prior and after the refactoring to check for any behavioral changes.
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Submitted 23 December, 2014;
originally announced December 2014.
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An Efficient and Secure Routing Protocol for Mobile Ad-Hoc Networks
Authors:
N. Ch. Sriman Narayana Iyengar,
Syed Mohammad Ansar Sachin kumar,
Piyush Nagar,
Siddharth Sharma,
Akshay Atrey
Abstract:
Efficiency and simplicity of random algorithms have made them a lucrative alternative for solving complex problems in the domain of communication networks. This paper presents a random algorithm for handling the routing problem in Mobile Ad hoc Networks [MANETS].The performance of most existing routing protocols for MANETS degrades in terms of packet delay and congestion caused as the number of mo…
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Efficiency and simplicity of random algorithms have made them a lucrative alternative for solving complex problems in the domain of communication networks. This paper presents a random algorithm for handling the routing problem in Mobile Ad hoc Networks [MANETS].The performance of most existing routing protocols for MANETS degrades in terms of packet delay and congestion caused as the number of mobile nodes increases beyond a certain level or their speed passes a certain level. As the network becomes more and more dynamic, congestion in network increases due to control packets generated by the routing protocols in the process of route discovery and route maintenance. Most of this congestion is due to flooding mechanism used in protocols like AODV and DSDV for the purpose of route discovery and route maintenance or for route discovery as in the case of DSR protocol. This paper introduces the concept of random routing algorithm that neither maintains a routing table nor floods the entire network as done by various known protocols thereby reducing the load on network in terms of number of control packets in a highly dynamic scenario. This paper calculates the expected run time of the designed random algorithm.
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Submitted 11 May, 2010;
originally announced May 2010.