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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Mohsen Sheikh Hassani James Green Rafik Goubran Frank Knoefel Neil Thomas Dept. of Systems & Computer Engineering Carleton University Ottawa Canada Dept. of Systems & Computer Engineering Carleton University Bruyère Research Institute Ottawa Canada Elisabeth-Bruyère Hospital Bruyère Research Institute Ottawa Canada Department of Medicine Elisabeth-Bruyère Hospital Bruyère Research Institute University of Ottawa Ottawa Canada

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Binaural hearing plays a significant role in auditory perception, spatial awareness, and sound source differentiation. Studies have linked cognitive decline, traumatic brain injuries, and neurodegenerative disease with decline in binaural performance, and quantification thereof may provide key information related to early onset of such diseases. Current horizontal binaural tests require multiple external speakers and an anechoic chamber, preventing broad clinical deployment, especially in remote communities. Furthermore, they use design parameters that differ widely. We hereby aim to develop a portable, easy-to-perform binaural hearing performance test, as well as identifying the ideal design parameters used in current literature, including audio prompt type, frequency, duration, and modality (speaker vs. headphone). Results indicate that a voice-form audio, with a sampling frequency of 1 kHz combined with a 4-second duration yields the best outcomes. Moreover, comparisons between speaker modality tests and our proposed headphone modality test using a Head Related Transfer Function (HRTF) reveal a high level of agreement between performances. Notably, the proposed headphone test mitigates sources of bias such as informed guessing due to visual cues, memorization of source locations, and movement of the head during tests. The findings establish the potential of a low-cost, easily accessible binaural performance test applicable across diverse settings. This research contributes insights into the design and implementation of binaural tests, with implications for fields such as audiology and neuroscience.

关键词： Headphones Location awareness Visualization Noise Transfer functions Auditory system Position measurement

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Screening Voltage Disturbance Sources Using Aggregated Data from Power Quality Measurement systems

Screening Voltage Disturbance Sources Using Aggregated Data ...

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Australasian Universities Power engineering Conference, AUPEC

作者： Krzysztof Chmielowiec Grzegorz Wiczyński Dept. of Power Electronics and Energy Control Systems AGH University of Krakow Krakow Poland Inst. of Electrical Engineering and Electronics Poznań University of Technology Poznań Poland

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

In recent years, power quality (PQ) measurement system have become essential for enhancing the performance and reliability of power distribution networks. The number of PQ analyzers installed in these networks has increased significantly. Moreover, PQ measurements are now often integrated into various devices, including balance and industrial energy meters. However, the sheer volume of data generated by these meters makes manual inspection and analysis impractical. This highlights the need for advanced automated tools to manage and interpret these vast datasets. This paper presents an approach that uses aggregated 10-minute PQ measurement data from these meters as a screening method to locate sources of PQ disturbances. By employing both deterministic indicators and machine learning techniques, specifically neural network classification, this approach opens new possibilities for automating the analysis process, enabling network operators to effectively identify and prioritize significant sources of disturbances in distribution networks.

关键词： Meters Performance evaluation Power engineering Voltage measurement Power measurement Power quality Neural networks Manuals Power system reliability Reliability

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Stratification of metastatic melanoma patients based on mutational signatures

Stratification of metastatic melanoma patients based on muta...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Caterina Alfano Lorenzo Farina Manuela Petti PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Genetic instability is one of the hallmarks of cancer, however mutations can occur for different causes and induce different effects. Mutational signatures are characteristic patterns of somatic mutations in cancer genomes, reflecting the underlying mutational processes. A mutational signature can be determined by studying the kind of mutations a patient has acquired during their life: patient stratification based on mutational signatures has become more and more useful in genomic studies, given its possible clinical implications. In this work we focused on Single Base Substitution (SBS) signatures to study a cohort of 115 metastatic melanoma patients. We inferred and identified two mutational signatures characterizing the patients. Based on these signatures we divided patients into two group: the bigger group was characterized by a signature associated with exposure to ultraviolet light, while the smaller group resulted to be mostly composed of patients which did not respond to immunotherapy (anti-PD1) and that presented a low mutational count. More importantly this second group showed a significantly worse survival outcome. The use of mutational signatures is clearly a powerful tool to identify disease sub-types that have a clinical relevance, however we believe that this topic needs further investigation focused on the characterization of patient subtypes with a multi-omics based approach.

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Wind Power Forecast Based on Dimensionality Reduction Using Ridge Regression 5

Wind Power Forecast Based on Dimensionality Reduction Using ...

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5th International Conference on Renewable Energy and Power engineering, REPE 2022

作者： Sun, Shuxin Wu, Fubao Qiao, Ying Lu, Zongxiang Hao, Yuchen Tsinghua University State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Beijing100084 China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Nanjing China Control Ctr State Grid Jiangsu Elect Power Co Ltd Nanjing210017 China

ISBN: (数字)9781665482066

ISBN: (纸本)9781665482066

In order to reduce the randomness and fluctuation of wind power, numerical weather prediction is usually used to forecast power generation. Due to the redundancy of numerical weather prediction factors, dimensionality reduction is carried out to improve the effectiveness of data processing and reduce the complexity of algorithm. In this paper, the multicollinearity diagnosis is carried out to show the existence of multicollinearity among numerical weather prediction variables. The ridge regression method is adopted to screen variables, so as to reduce the dimension of numerical weather prediction data, which are trained later by back propagation neural network or convolutional neural network to fit corresponding power data. In comparison with results using training data without preprocessing, dimensionality reduction can improve predicting effect in most cases and reduce the error caused by improper selection of neural network. © 2022 IEEE.

关键词： Regression analysis

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Lightweight Spiking Neural Network Based Detector for Interweave Cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

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IEEE International Conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

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Dense Residual Networks for Gaze Mapping on Indian Roads

arXiv

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arXiv 2022年

作者： Kapoor, Chaitanya Kumar, Kshitij Vishnoi, Soumya Ramanathan, Sriram Dept. of Electrical and Electronics Engineering Bits Pilani Dept. of Computer Science and Information Systems Bits Pilani

In the recent past, greater accessibility to powerful computational resources has enabled progress in the field of Deep Learning and computer Vision to grow by leaps and bounds. This in consequence has lent progress to the domain of Autonomous Driving and Navigation systems. Most of the present research work has been focused on driving scenarios in the European or American roads. Our paper draws special attention to the Indian driving context. To this effect, we propose a novel architecture, DR-Gaze, which is used to map the driver's gaze onto the road. We compare our results with previous works and state-of-the-art results on the DGAZE dataset. Our code will be made publicly available upon acceptance of our paper. © 2022, CC BY.

关键词： Navigation systems

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Research on General Terminal of Intelligent Measurement and control of Distribution Network Based on Open Architecture

Research on General Terminal of Intelligent Measurement and ...

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2022 IEEE Conference on Telecommunications, Optics and computer Science, TOCS 2022

作者： Xiao, Xiaobing Cai, Yongxiang He, Xiaomeng Li, Yue Ding, Yujie Zhao, Ergang Electric Power Research Institute of Guizhou Power Grid Co. Ltd Guiyang China State Key Lab of Control and Simulation of Power Systems Generation Equipments Dept. of Electrical Engineering Beijing China

ISBN: (纸本)9781665470537

With the continuous advancement of the construction of new power systems, the data information of the power system is increasing exponentially, and the spatiotemporal distribution of the use of smart terminals and electrical equipment is more scattered. Data collection and analysis of traditional power systems will be a huge challenge. In order to meet the actual needs of multi-scenario, multi-service, and changeable measurement and control objects of intelligent distribution network, this paper firstly studies the design of general terminal open architecture for distribution network multi-measurement and control scenarios, proposes a data service architecture based on open architecture, and establishes a self-description-based open architecture design. The unified information model of the Internet of Things protocol, research and development of intelligent measurement, and the controlling of universal terminals meet multiple communication methods, and realize the deployment verification of intelligent measurement. © 2022 IEEE.

关键词： Electric power distribution

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Activity Recognition from Channel State Information for Few-Sampled Scenarios

Activity Recognition from Channel State Information for Few-...

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IEEE Globecom Workshops

作者： Guillermo Diaz Iker Sobron Iñaki Eizmendi Iratxe Landa Manuel Velez Dept. of Communications Engineering University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Computer Languages and Systems University of the Basque Country (UPV/EHU) Bilbao Spain

Human Activity Recognition (HAR) using channel state information (CSI) from wireless systems has been a field in progress for the last few years. In particular, amplitudes of WiFi-based CSI have been widely used for HAR in indoor scenarios. In contrast, CSI phases are less exploited due to higher sensitivity to timing errors in channel estimation. This paper further explores a recent phase processing method to obtain environmental information from CSI and improves classification using a Convolutional Neural Network in few-sampled scenarios. For this purpose, a new dataset has been generated with measurements of three receivers on different days and with different people, for activity recognition and people counting. In addition, we incorporate a transfer learning strategy using fine-tuning along with a data fusion model from different receivers to improve accuracy metrics by training the model and validating it on data taken on different days. Promising results with accuracies higher than 80 % have been obtained with transfer learning between days using 5 % of data for fine-tuning.

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COULD DROPPING A FEW CELLS CHANGE THE TAKEAWAYS FROM DIFFERENTIAL EXPRESSION?

arXiv

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arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

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Online Canonical Correlation Analysis via Rayleigh-Ritz Projections

Online Canonical Correlation Analysis via Rayleigh-Ritz Proj...

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Asilomar Conference on Signals, systems & computers

作者： Vassilis Kalantzis Panagiotis A. Traganitis Charilaos I. Kanatsoulis IBM Research MIT-IBM Watson AI Lab Yorktown Heights NY USA Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Canonical correlation analysis (CCA) is a classical statistical tool that enables processing of multiview data in a plethora signal processing, machine learning and data mining applications, by identifying a common linear subspace from the available data views. Most algorithms tackling the CCA task require all the data per view to be available. Nevertheless, in many cases, data are not available in batch form and may arrive in streaming fashion. This work puts forth a novel, computationally efficient, projection based method for identifying and updating the common subspace on-the-fly, as new data arrive, while retaining its' fidelity. Preliminary numerical tests, on synthetic and real data benchmarks, showcase the potential of the proposed method.

关键词： computers Correlation Machine learning algorithms Signal processing algorithms Machine learning Signal processing Approximation algorithms Performance analysis Computational efficiency Data mining

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