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Science Autonomy and Planetary Missions: ML and data Science Applied to the ExoMars Mission

Science Autonomy and Planetary Missions: ML and Data Science...

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ieee Aerospace conference

作者： Da Poian, Victoria Lyness, Eric Danell, Ryan Theiling, Bethany Brinckerhoff, William Microtel LLC NASA Goddard Space Flight Ctr 8800 Greenbelt Rd Greenbelt MD 20771 USA Danell Consulting 1313 Addison Court Winterville NC 28590 USA NASA Goddard Space Flight Ctr 8800 Greenbelt Rd Greenbelt MD 20771 USA

ISBN: (纸本)9781665490320

Future planetary science instruments will be capable of producing far more data than can be transmitted back to Earth, potentially leaving valuable scientific data on a planet's surface. Instruments will need to carefully identify the subset of total data to be prioritized for return, as transmission of the full data volume, even after compression, will not be feasible. The concept of science autonomy, where instruments collect measurement data, perform selected science data analyses onboard, and then autonomously act upon those analyses through self-adjustment and tuning of instrument parameters, can be used to identify and produce an optimal and compact data set for return, maximizing the value of each bit returned to Earth. Furthermore, the selection of the next operation(s) to be run following preliminary measurements, without requiring ground-in-the-loop communication, increases mission efficiency and enables successful yet shorter duration missions to hazardous planetary environments. This capability allows missions to prioritize the most compelling or time-critical data, yielding a more efficient and productive scientific investigation overall. In this paper, we present our implementation using different machine learning (ML) techniques (i.e., clustering, classification) for analyzing science data from the Mars Organic Molecule Analyzer (MOMA) instrument onboard the ExoMars rover Rosalind Franklin. MOMA is a dual-source (laser desorption and gas chromatography) mass spectrometer that will search for past or present life on the Martian surface and subsurface through analysis of soil samples. We use data collected from the MOMA flight-like engineering model to develop mass-spectrometry-focused ML techniques. This effort, in preparation for operating on Mars, is aimed at 1) helping the ExoMars science and operations team quickly analyze new data and support them in their decision-making process regarding subsequent operations and, 2) getting a better understandi

关键词： Earth (planet)

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Online-Learning-Enabled Distributionally Robust Motion Control via Uncertainty Propagation and Ambiguity Set compression 63

Online-Learning-Enabled Distributionally Robust Motion Contr...

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63rd ieee conference on Decision and Control, CDC 2024

作者： Wang, Han Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai China

ISBN: (纸本)9798350316339

We develop a safe, efficient, and flexible motion control framework for collision-free navigation of robots in uncertain and dynamic environments. With the gathered real-time data at each control time, the motion distribution of obstacles is estimated from data streams by leveraging the Dirichlet process mixture model. We propose a novel uncertainty propagation method, which theoretically admits the derivation of obstacle position distributions over the entire prediction horizon only based on the current position and motion distribution of obstacles. To enhance the robustness against estimated distribution errors, an ambiguity set utilizing local-moment information of position distributions is constructed. Additionally, we introduce a compression scheme to automatically adjust the complexity (granularity) of the ambiguity set by aggregating distributions that are close in Wasserstein space, thereby flexibly striking a trade-off between control performance and computation time. The distributionally robust collision avoidance constraint based on the compressed ambiguity set is reformulated into separating hyperplanes, which are applied in the model predictive controller to obtain the optimal collision-free trajectory. To resolve the infinite-dimensionality issue inherent in the computation of the separating hyperplanes, we equivalently derive a tractable semi-definite programming formulation exploiting distributionally robust optimization techniques. The efficacy of the proposed framework is demonstrated through simulation. © 2024 ieee.

关键词： Predictive control systems

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Power and Area Optimization Techniques for Reconfigurable Inverse Discrete Cosine Transform FPGA for High Performance Computation Electromagnetics 9

Power and Area Optimization Techniques for Reconfigurable In...

引用

9th ieee International conference on Smart Structures and Systems, ICSSS 2023

作者： Radhika, R. Dinesh, S. Harini, S. Kaviya, S. Hammed, Mallapadi Mohammed Priyadharshini, M. S.A. Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350384208

Multimedia communications like image and video demand a significant amount of bandwidth during data transmissionin uncompressed form. Despite advancements in communication channels and storage capacity, implementation costs frequently place a number of restrictions on channel capacity. compression techniques must be used to reduce the data rate while preserving the arbitrary character of the decoded picture or video signal in order to meet the channel or storage capacity requirements. In order to handle the image or video data, spatial coordinates are converted to the frequency domain. The Inverse Discrete Cosine Transform is the most popular method for inverse transformation in image processing. Using a sum select technique, an IDCT core is developed in this paper to reduce the number of adders. The suggested SSA reduces the use of arithmetic operations by 67.8% and makes better use of the changed images. High performance electromagnetics, extremely accurate image processing, biomedical signal processing, and medical image processing are a few examples of applications for IDCT. © 2023 ieee.

关键词： Image compression

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Fault-tolerant Bayesian Decentralized data Fusion Using Reliability Variables and Mixture Models 27

Fault-tolerant Bayesian Decentralized Data Fusion Using Reli...

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27th International conference on Information Fusion (FUSION)

作者： Shin, Changkyo Dagan, Ofer Ahmed, Nisar Choi, Han-Lim Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon South Korea Univ Colorado Boulder Smead Aerosp Engn Sci Dept Boulder CO USA

ISBN: (纸本)9798350371420;9781737749769

In uncertain and dynamic environments, decentralized data fusion (DDF) techniques have been widely used to estimate the states and the uncertainty levels over large mission spaces in a robust and scalable way. In data fusion frameworks using distributed sensor networks, undetected sensor failures can degrade the quality of fusion results of the entire system. Therefore, DDF methods which are robust to inconsistent data are needed. In this paper, a fault-tolerant Bayesian DDF method using Gaussian mixture models is developed. The probability of agent reliability states, which represent consistency of local estimates that agents share with their neighbors, are modeled as weights of mixture models and estimated together with the target process. The target process and reliability states are updated in a decentralized Bayesian way, exploiting the properties of Gaussian mixture models. To prevent the hypothesis explosion problem of Gaussian mixture models, a mixture compression method considering the physical meaning of mixture weights is utilized. A numerical simulation on a 2D dynamic target tracking problem is presented to verify performance of the suggested algorithm and compared with existing DDF methods. It is shown that the suggested algorithm gives more compact fusion results compared to existing fault-tolerant DDF method.

关键词： State estimation Distributed inference Multirobot systems Distributed sensor network Fault-tolerant Bayesian DDF

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EnD: Enhanced Dedensification for Graph Compressing and Embedding 22

EnD: Enhanced Dedensification for Graph Compressing and Embe...

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22nd ieee International conference on data Mining (ICDM)

作者： Hossain, Tanvir Akbas, Esra Islam, Muhammad Ifte Khairul Georgia State Univ Dept Comp Sci Atalanta GA 30002 USA Oklahoma State Univ Dept Comp Sci Stillwater OK 74075 USA

ISBN: (纸本)9798350346091

Graph representation learning is essential in applying machine learning methods on large-scale networks. Several embedding approaches have shown promising outcomes in recent years. Nonetheless, on massive graphs, it may be time-consuming and space inefficient for direct applications of existing embedding methods. This paper presents a novel graph compression approach based on dedensification called Enhanced Dedensification with degree-based compression (EnD). The principal goal of our system is to assure decent compression of large graphs that eloquently favor their representation learning. For this purpose, we first compress the low-degree nodes and dedensify them to reduce the high-degree nodes' loads. Then, we embed the compressed graph instead of the original graph to decrease the representation learning cost. Our approach is a general meta-strategy that attains time and space efficiency over the original graph by applying the state-of-the-art graph embedding methods: Node2vec, DeepWalk, RiWalk, and xNetMf. Comprehensive experiments on large-scale real-world graphs validate the viability of our method, which shows sound performance on single and multi-label node classification tasks without losing accuracy.

关键词： Graph compression Graph Mining Network Embedding Node Classification GraphML

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Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector

Reg-PTQ: Regression-specialized Post-training Quantization f...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ding, Yifu Feng, Weilun Chen, Chuyan Guo, Jinyang Liu, Xianglong Beihang Univ State Key Lab Complex & Crit Software Environm Beijing Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing Peoples R China Beihang Univ Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Data Space Hefei Peoples R China

ISBN: (纸本)9798350353006

Although deep learning based object detection is of great significance for various applications, it faces challenges when deployed on edge devices due to the computation and energy limitations. Post-training quantization (PTQ) can improve inference efficiency through integer computing. However, they suffer from severe performance degradation when performing full quantization due to overlooking the unique characteristics of regression tasks in object detection. In this paper, we are the first to explore regression-friendly quantization and conduct full quantization on various detectors. We reveal the intrinsic reason behind the difficulty of quantizing regressors with empirical and theoretical justifications, and introduce a novel Regression-specialized Post-Training Quantization (Reg-PTQ) scheme. It includes Filtered Global Loss Integration Calibration to combine the global loss with a two-step filtering mechanism, mitigating the adverse impact of false positive bounding boxes, and Learnable Logarithmic-Affine Quantizer tailored for the non-uniform distributed parameters in regression structures. Extensive experiments on prevalent detectors showcase the effectiveness of the well-designed Reg-PTQ. Notably, our Reg-PTQ achieves 7.6x and 5.4x reduction in computation and storage consumption under INT4 with little performance degradation, which indicates the immense potential of fully quantized detectors in real-world object detection applications.

关键词： detection model compression post-training quantization quantization

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2024 ieee Nordic Circuits and Systems conference, NORCAS 2024 - Proceedings

2024 IEEE Nordic Circuits and Systems Conference, NORCAS 202...

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10th ieee Nordic Circuits and Systems conference, NORCAS 2024

ISBN: (纸本)9798331517663

The proceedings contain 52 papers. The topics discussed include: implementation of the tagged geometric history length access interval predictor;cycle count estimation of VLIW processors using machine learning;towards modularity of the rust RTIC real-time scheduling framework;AMPER-X: adaptive mixed-precision RISC-V core for embedded applications;highly linear on-chip delay generator for a depth profiling CMOS SPAD Raman sensor;multi-stream FFT architectures for a distributed MIMO large intelligent surfaces testbed;an on-chip digital aging sensor circuit utilizing leakage-current based charge accumulation;hardware-accelerated compression core on RISC-V for online-BCG data reduction;and RV-ProViler: evaluating RISC-V ISA for application-specific requirements.

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data-Driven Autonomous Meal Session Detection Using Smartwatches 20

Data-Driven Autonomous Meal Session Detection Using Smartwat...

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20th International conference on Body Sensor Networks

作者： Ariza-Zambrano, W. Camilo Cene, Vinicius H. Goncalves, Dayane Oliveira Caetano, Carlos Penatti, Otavio A. B. Samsung R&D Inst Brazil AI R&D Team Campinas Brazil

ISBN: (纸本)9798331530143

Poor dietary habits are frequently associated with diverse health conditions. Although there are apps designed to monitor eating habits, they require manual annotation, usually leading to user disengagement. This paper presents a datadriven solution using smartwatch sensors to automate meal session detection, addressing limitations of wearables such as a laterality unified model, capturing meal sessions with different durations, model compression to enable embedding, and dynamic inference times to reduce energy consumption. We tested two pipeline variations on a dataset of 91 subjects in a free-living scenario for 12 days, resulting in 93% Precision and 82% Recall, indicating the feasibility of free-living meal session detection. We also developed an application running on Samsung smartwatches to assess the solution feasibility with limited resources. This solution can be used as the building block to track eating-related phenomena such as autonomous dietary guidance and realtime non-invasive glucose monitoring, which remain unsolved problems in literature.

关键词： Meal detection Human activity recognition Wearable devices Inertial signal processing

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Encryption With Private KEY for data Security and Deduplication 10

Encryption With Private KEY for Data Security and Deduplicat...

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10th International conference on Communication and Signal Processing, ICCSP 2024

作者： Debbarma, Shayani Pavithra, Nitturu Prakash, B. Srm Institute of Science and Technology Ktr Dept.of Ctech Chennai India Srm Institute of Science and Technology Faculty Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

A new method of data compression called deduplication helps make better use of storage space and works better when handling duplicate data. Original, unique data copies can be uploaded to storage thanks to deduplication, and subsequent copies are given a linking pointer to the genuine stored copy. data privacy is an important issue in cloud computing that needs to be taken into account. This study reviews and assesses the methods of the most common deduplication algorithms. The resulting survey is tabulated to highlight the precise nature of the deduplication's evolution and varieties. This survey has shown us that deduplication compromises data confidentiality on a number of levels, which opens the door for more unique ideas. © 2024 ieee.

关键词： data privacy

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LEARNABLE NONLINEAR compression FOR ROBUST SPEAKER VERIFICATION 47

LEARNABLE NONLINEAR COMPRESSION FOR ROBUST SPEAKER VERIFICAT...

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47th ieee International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Liu, Xuechen Sahidullah, Md Kinnunen, Tomi Univ Eastern Finland Sch Comp Joensuu Finland Univ Lorraine INRIA CNRS LORIA F-54000 Nancy France

ISBN: (纸本)9781665405409

In this study, we focus on nonlinear compression methods in spectral features for speaker verification based on deep neural network. We consider different kinds of channel-dependent (CD) nonlinear compression methods optimized in a data-driven manner. Our methods are based on power nonlinearities and dynamic range compression (DRC). We also propose multi-regime (MR) design on the nonlinearities, at improving robustness. Results on VoxCeleb1 and VoxMovies data demonstrate improvements brought by proposed compression methods over both the commonly-used logarithm and their static counterparts, especially for ones based on power function. While CD generalization improves performance on VoxCeleb1, MR provides more robustness on VoxMovies, with a maximum relative equal error rate reduction of 21.6%.

关键词： Speaker Verification Nonlinear compression Multi-Regime compression

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