scholarly journals A Distributed Approach to Speaker Count Problem in an Open-Set Scenario by Clustering Pitch Features

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 157
Author(s):  
Sakshi Pandey ◽  
Amit Banerjee
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Geographical Area ◽  
Outdoor Environment ◽  
Average Error ◽  
Indoor Environments ◽  
Learning Approaches ◽  
Distributed Approach ◽  
Open Set ◽  
Error Count

Counting the number of speakers in an audio sample can lead to innovative applications, such as a real-time ranking system. Researchers have studied advanced machine learning approaches for solving the speaker count problem. However, these solutions are not efficient in real-time environments, as it requires pre-processing of a finite set of data samples. Another approach for solving the problem is via unsupervised learning or by using audio processing techniques. The research in this category is limited and does not consider the large-scale open set environment. In this paper, we propose a distributed clustering approach to address the speaker count problem. The separability of the speaker is computed using statistical pitch parameters. The proposed solution uses multiple microphones available in smartphones in a large geographical area to capture and extract statistical pitch features from the audio samples. These features are shared between the nodes to estimate the number of speakers in the neighborhood. One of the major challenges is to reduce the error count that arises due to the proximity of the users and multiple microphones. We evaluate the algorithm’s performance using real smartphones in a multi-group arrangement by capturing parallel conversations between the users in both indoor and outdoor scenarios. The average error count distance is 1.667 in a multi-group scenario. The average error count distances in indoor environments are 16% which is better than in the outdoor environment.

scholarly journals Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1678 ◽  
Author(s):  
Ahmed H. Salamah ◽  
Mohamed Tamazin ◽  
Maha A. Sharkas ◽  
Mohamed Khedr ◽  
Mohamed Mahmoud
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Indoor Localization ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Indoor Environments ◽  
Learning Approaches ◽  
Positioning Systems ◽  
Learning Techniques ◽  
And Performance

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques.

scholarly journals Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Wei Song ◽  
Seoungjae Cho ◽  
Yulong Xi ◽  
Kyungeun Cho ◽  
Kyhyun Um
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Point Clouds ◽  
Outdoor Environment ◽  
Processing Unit ◽  
Multiple Sensors ◽  
3D Point Clouds ◽  
Video Images

A mobile robot mounted with multiple sensors is used to rapidly collect 3D point clouds and video images so as to allow accurate terrain modeling. In this study, we develop a real-time terrain storage generation and representation system including a nonground point database (PDB), ground mesh database (MDB), and texture database (TDB). A voxel-based flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. We quantize the 3D point clouds into 3D grids of the flag map as a comparative table in order to remove the redundant points. We integrate the large-scale 3D point clouds into a nonground PDB and a node-based terrain mesh using the CPU. Subsequently, we program a graphics processing unit (GPU) to generate the TDB by mapping the triangles in the terrain mesh onto the captured video images. Finally, we produce a nonground voxel map and a ground textured mesh as a terrain reconstruction result. Our proposed methods were tested in an outdoor environment. Our results show that the proposed system was able to rapidly generate terrain storage and provide high resolution terrain representation for mobile mapping services and a graphical user interface between remote operators and mobile robots.

scholarly journals Parking Line Based SLAM Approach Using AVM/LiDAR Sensor Fusion for Rapid and Accurate Loop Closing and Parking Space Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4811 ◽  
Author(s):  
Gyubeom Im ◽  
Minsung Kim ◽  
Jaeheung Park
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Outdoor Environment ◽  
Parking Space ◽  
Parking Space Detection ◽  
Localization Performance ◽  
Line Features ◽  
Loop Closing

Parking is a challenging task for autonomous vehicles and requires a centimeter level precision of distance measurement for safe parking at a destination to avoid collisions with nearby vehicles. In order to avoid collisions with parked vehicles while parking, real-time localization performance should be maintained even when loop closing occurs. This study proposes a simultaneous localization and mapping (SLAM) method, using around view monitor (AVM)/light detection and ranging (LiDAR) sensor fusion, that provides rapid loop closing performance. We extract the parking line features by utilizing the sensor fusion data for sparse feature-based pose graph optimization that boosts the loop closing speed. Hence, the proposed method can perform the loop closing within a few milliseconds to compensate for the accumulative errors even in a large-scale outdoor environment, which is much faster than other LiDAR-based SLAM algorithms. Therefore, it easily satisfies real-time localization performance. Furthermore, thanks to the parking line features, the proposed method can detect a parking space by utilizing the accumulated parking lines in the map. The experiment was performed in three outdoor parking lots to validate the localization performance and parking space detection performance. All of the proposed methods can be operated in real-time in a single-CPU environment.

Real-Time 3-D Mapping for Indoor Environments Using RGB-D Cameras

2012 ◽  
Vol 579 ◽  
pp. 435-444 ◽  
Author(s):  
Liang Chia Chen ◽  
Nguyen Van Thai
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Three Dimensional ◽  
Mapping Method ◽  
Depth Information ◽  
Indoor Environments ◽  
Stereo Camera ◽  
Camera Systems ◽  
Compact Size ◽  
Laser Scanners

For three-dimensional (3-D) mapping, so far, 3-D laser scanners and stereo camera systems are used widely due to their high measurement range and accuracy. For stereo camera systems, establishing corresponding point pairs between two images is one crucial step for reconstructing depth information. However, mapping approaches using laser scanners are still restricted by a serious constraint by accurate image registration and mapping. In recent years, time-of-flight (ToF) cameras have been used for mapping tasks in providing high frame rates while preserving a compact size, but lack in measurement precision and robustness. To address the current technological bottleneck, this article presents a 3-D mapping method which employs an RGB-D camera for 3-D data acquisition and then applies the RGB-D features alignment (RGBD-FA) for data registration. Experimental results show the feasibility and robustness of applying the proposed approach for real-time 3-D mapping for large-scale indoor environments.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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