Research

29 pages, 4912 KiB

Open AccessArticle

A CSI-Based Multi-Environment Human Activity Recognition Framework

by Baha A. Alsaify, Mahmoud M. Almazari, Rami Alazrai, Sahel Alouneh and Mohammad I. Daoud

Appl. Sci. 2022, 12(2), 930; https://doi.org/10.3390/app12020930 - 17 Jan 2022

Cited by 16 | Viewed by 2817

Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present [...] Read more.

Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present a multi-environment human activity recognition system based on observing the changes in the CSI values of the exchanged wireless packets carried by OFDM subcarriers. In essence, we introduce a five-stage CSI-based human activity recognition approach. First, the acquired CSI values associated with each recorded activity instance are processed to remove the existing noise from the recorded data. A novel segmentation algorithm is then presented to identify and extract the portion of the signal that contains the activity. Next, the extracted activity segment is processed using the procedure proposed in the first stage. After that, the relevant features are extracted, and the important features are selected. Finally, the selected features are used to train a support vector machine (SVM) classifier to identify the different performed activities. To validate the performance of the proposed approach, we collected data in two different environments. In each of the environments, several activities were performed by multiple subjects. The performed experiments showed that our proposed approach achieved an average activity recognition accuracy of 91.27%. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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18 pages, 6963 KiB

Open AccessArticle

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

by Stefan Klir, Reda Fathia, Sebastian Babilon, Simon Benkner and Tran Quoc Khanh

Appl. Sci. 2021, 11(19), 9062; https://doi.org/10.3390/app11199062 - 28 Sep 2021

Cited by 4 | Viewed by 1592

Abstract

Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework [...] Read more.

Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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16 pages, 8210 KiB

Open AccessArticle

A Speech Command Control-Based Recognition System for Dysarthric Patients Based on Deep Learning Technology

by Yu-Yi Lin, Wei-Zhong Zheng, Wei Chung Chu, Ji-Yan Han, Ying-Hsiu Hung, Guan-Min Ho, Chia-Yuan Chang and Ying-Hui Lai

Appl. Sci. 2021, 11(6), 2477; https://doi.org/10.3390/app11062477 - 10 Mar 2021

Cited by 17 | Viewed by 3375

Abstract

Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help dysarthric patients control mobile devices. However, the large [...] Read more.

Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help dysarthric patients control mobile devices. However, the large computation power requirement for the ASR system increases implementation costs. To alleviate this problem, this study proposed a convolution neural network (CNN) with a phonetic posteriorgram (PPG) speech feature system to recognize speech commands, called CNN–PPG; meanwhile, the CNN model with Mel-frequency cepstral coefficient (CNN–MFCC model) and ASR-based systems were used for comparison. The experiment results show that the CNN–PPG system provided 93.49% accuracy, better than the CNN–MFCC (65.67%) and ASR-based systems (89.59%). Additionally, the CNN–PPG used a smaller model size comprising only 54% parameter numbers compared with the ASR-based system; hence, the proposed system could reduce implementation costs for users. These findings suggest that the CNN–PPG system could augment a communication device to help dysarthric patients control the mobile device via speech commands in the future. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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9 pages, 3108 KiB

Open AccessArticle

Personal Atmosphere: Estimation of Air Conditioner Parameters for Personalizing Thermal Comfort

by Tomohiro Mashita, Tetsuya Kanayama and Photchara Ratsamee

Appl. Sci. 2020, 10(22), 8067; https://doi.org/10.3390/app10228067 - 13 Nov 2020

Cited by 3 | Viewed by 1670

Abstract

Air conditioners enable a comfortable environment for people in a variety of scenarios. However, in the case of a room with multiple people, the specific comfort for a particular person is highly dependent on their clothes, metabolism, preference, and so on, and the [...] Read more.

Air conditioners enable a comfortable environment for people in a variety of scenarios. However, in the case of a room with multiple people, the specific comfort for a particular person is highly dependent on their clothes, metabolism, preference, and so on, and the ideal conditions for each person in a room can conflict with each other. An ideal way to resolve these kinds of conflicts is an intelligent air conditioning system that can independently control air temperature and flow at different areas in a room and then produce thermal comfort for multiple users, which we define as the personal preference of air flow and temperature. In this paper, we propose Personal Atmosphere, a machine learning based method to obtain parameters of air conditioners which generate non-uniform distributions of air temperature and flow in a room. In this method, two dimensional air-temperature and -flow distributions in a room are used as input to a machine learning model. These inputs can be considered a summary of each user’s preference. Then the model outputs a parameter set for air conditioners in a given room. We utilized ResNet-50 as the model and generated a data set of air temperature and flow distributions using computational fluid dynamics (CFD) software. We then conducted evaluations with two rooms that have two and four air conditioners under the ceiling. We then confirmed that the estimated parameters of the air conditioners can generate air temperature and flow distributions close to those required in simulation. We also evaluated the performance of a ResNet-50 with fine tuning. This result shows that its learning time is significantly decreased, but performance is also decreased. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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16 pages, 2862 KiB

Open AccessArticle

Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks

by Wei Zhao and Wen-Hsing Kuo

Appl. Sci. 2020, 10(20), 7261; https://doi.org/10.3390/app10207261 - 17 Oct 2020

Cited by 1 | Viewed by 1434

Abstract

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, [...] Read more.

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links’ channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link’s transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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14 pages, 984 KiB

Open AccessArticle

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

by Ye Lin, Dan Chen, Shijia Liang, Zhezhuang Xu, Yang Qiu, Jiahao Zhang and Xinxiang Liu

Appl. Sci. 2020, 10(19), 6816; https://doi.org/10.3390/app10196816 - 29 Sep 2020

Cited by 11 | Viewed by 2543

Abstract

Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly [...] Read more.

Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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21 pages, 6116 KiB

Open AccessArticle

Noise Prediction Using Machine Learning with Measurements Analysis

by Po-Jiun Wen and Chihpin Huang

Appl. Sci. 2020, 10(18), 6619; https://doi.org/10.3390/app10186619 - 22 Sep 2020

Cited by 6 | Viewed by 6649

Abstract

The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at [...] Read more.

The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at the National Synchrotron Radiation Research Center (NSRRC) facility and establish a machine learning model for noise prediction. This study utilized the gradient boosting model (GBM) as the learning model in which past noise measurement records and many other features are integrated as the proposed model makes a prediction. This study analyzed the time duration and frequency of the collected Leq and also investigated the impact of training data selection. The results presented in this paper indicate that the proposed prediction model works well in almost noise sensors and frequencies. Moreover, the model performed especially well in sensor 8 (125 Hz), which was determined to be a serious noise zone in the past noise measurements. The results also show that the root-mean-square-error (RMSE) of the predicted harmful noise was less than 1 dBA and the coefficient of determination (R²) value was greater than 0.7. That is, the working field showed a favorable noise prediction performance using the proposed method. This positive result shows the ability of the proposed approach in noise prediction, thus providing a notification to the laborer to prevent long-term exposure. In addition, the proposed model accurately predicts noise future pollution, which is essential for laborers in high-noise environments. This would keep employees healthy in avoiding noise harmful positions to prevent people from working in that environment. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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15 pages, 1311 KiB

Open AccessArticle

Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications

by Ying-Ren Chien, Kai-Chieh Hsu and Hen-Wai Tsao

Appl. Sci. 2020, 10(17), 5842; https://doi.org/10.3390/app10175842 - 24 Aug 2020

Cited by 7 | Viewed by 2681

Abstract

Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for [...] Read more.

Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for telecare applications. This paper presents a deep convolutional autoencoder to compress the PCG signals. At the encoder side, seven convolutional layers were used to compress the PCG signals, which are collected on the patients in the rural areas, into the feature maps. At the decoder side, the doctors at the remote hospital use the other seven convolutional layers to decompress the feature maps and reconstruct the original PCG signals. To confirm the effectiveness of our method, we used an open accessed dataset on PHYSIONET. The achievable compress ratio (CR) is 32 when the percent root-mean-square difference (PRD) is less than 5%. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing for IOT Applications)

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