scholarly journals Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4283
Author(s):  
Md.-Habibur Rahman ◽  
Md. Shahjalal ◽  
Moh. Khalid Hasan ◽  
Md.-Osman Ali ◽  
Yeong-Min Jang
Keyword(s):  
Light Emitting Diode ◽  
Characteristic Curve ◽  
Analysis Data ◽  
Image Sensor ◽  
Data Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
High Data ◽  
Receiver System ◽  
Optical Camera Communication

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

scholarly journals Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Anitha Ganesan ◽  
Anbarasu Balasubramanian
Keyword(s):  
State Of The Art ◽  
Characteristic Curve ◽  
Scene Recognition ◽  
Color Histogram ◽  
Support Vector ◽  
Svm Classifier ◽  
Scene Classification ◽  
Visual Descriptors ◽  
Outdoor Scenes ◽  
Institute Of Technology

AbstractIn the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)—95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs—2.08% and 4.92%, respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.

scholarly journals Performance Analysis and Improvement of Optical Camera Communication

2018 ◽  
Vol 8 (12) ◽  
pp. 2527 ◽  
Author(s):  
Moh. Khalid Hasan ◽  
Mostafa Zaman Chowdhury ◽  
Md. Shahjalal ◽  
Van Thang Nguyen ◽  
Yeong Min Jang
Keyword(s):  
User Satisfaction ◽  
High Performance ◽  
Focal Length ◽  
Sampling Rate ◽  
Image Sensor ◽  
Edge Length ◽  
Data Rate ◽  
Principal Factors ◽  
Communication Distance ◽  
Optical Camera Communication

Optical camera communication (OCC) is a technology in which a camera image sensor is employed to receive data bits sent from a light source. OCC has attracted a lot of research interest in the area of mobile optical wireless communication due to the popularity of smartphones with embedded cameras. Moreover, OCC offers high-performance characteristics, including an excellent signal-to-interference-plus-noise ratio (SINR), high security, low interference, and high stability with respect to varying communication distances. Despite these advantages, OCC suffers from several limitations, the primary of which is the low data rate. In this paper, we provide a comprehensive analysis of the parameters that influence the OCC performance. These parameters include the camera sampling rate, the exposure time, the focal length, the pixel edge length, the transmitter configurations, and the optical flickering rate. In particular, the focus is on enhancing the data rate, SINR, and communication distance, which are the principal factors determining the quality of service experienced by a user. The paper also provides a short survey of modulation schemes used in OCC on the basis of the achieved data rate, communication distance, and possible application scenarios. A theoretical analysis of user satisfaction using OCC is also rendered. Furthermore, we present the simulation results demonstrating OCC performance with respect to variations in the parameters mentioned above, which include the outage probability analysis for OCC.

scholarly journals Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4802
Author(s):  
Roger Resmini ◽  
Lincoln Silva ◽  
Adriel S. Araujo ◽  
Petrucio Medeiros ◽  
Débora Muchaluat-Saade ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Operating Characteristic ◽  
Cancer Survival ◽  
Characteristic Curve ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnose Breast Cancer ◽  
Causes Of Mortality

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

scholarly journals Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6057
Author(s):  
Taha Essalih ◽  
Mohammad Ali Khalighi ◽  
Steve Hranilovic ◽  
Hassan Akhouayri
Keyword(s):  
Energy Efficiency ◽  
Orthogonal Frequency Division Multiplexing ◽  
Dynamic Range ◽  
Light Emitting Diode ◽  
Electrical Power ◽  
High Sensitivity ◽  
Data Rate ◽  
Optical Wireless ◽  
High Data ◽  
Communication Links

Underwater optical wireless systems have dual requirements of high data rates and long ranges in harsh scattering and attenuation conditions. In this paper, we investigate the advantages and limitations of optical orthogonal frequency-division multiplexing (O-OFDM) signaling when a silicon photo-multiplier (SiPM) is used at the receiver in order to ensure high sensitivity. Considering a light-emitting diode (LED) transmitter and taking into account the limited dynamic range imposed by the transmitter and the SiPM receiver, we study the performance of three popular O-OFDM schemes, i.e., DC-biased, asymmetrically-clipped, and layered asymmetrically-clipped O-OFDM (DCO-, ACO-, and LACO-OFDM, respectively). We consider a constraint on transmit electrical power PTxe and take into account the required DC bias for the three considered schemes in practice, showing the undeniable advantage of ACO- and LACO-OFDM in terms of energy efficiency. For instance, for the considered SiPM and LED components, a spectral efficiency of ∼1 bps/Hz with a data rate of 20 Mbps, a link range of 70 m, and a target bit-error-rate (BER) of 10−3, ACO and LACO allow a reduction of about 10 and 6 mW, respectively, in the required PTxe, compared to DCO-OFDM. Meanwhile, we show that when relaxing the PTxe constraint, DCO-OFDM offers the largest operational link range within which a target BER can be achieved. For instance, for a target BER of 10−3 and a data rate of 20 Mbps, and considering PTxe of 185, 80, and 50 mW for DCO-, LACO-, and ACO-OFDM, respectively, the corresponding intervals of operational link range are about 81, 74.3, and 73.8 m. Lastly, we show that LACO-OFDM makes a good compromise between energy efficiency and operational range flexibility, although requiring a higher computational complexity and imposing a longer latency at the receiver.

scholarly journals A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G

Optics ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 70-86
Author(s):  
James Dzisi Gadze ◽  
Reynah Akwafo ◽  
Kwame Agyeman-Prempeh Agyekum ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
System Performance ◽  
Direct Detection ◽  
High Capacity ◽  
Digital Signal ◽  
Data Rate ◽  
Radio Over Fiber ◽  
Data Traffic ◽  
Modulation Formats ◽  
High Data ◽  
Receiver System

Due to the unprecedented growth in mobile data traffic, emerging mobile access networks such as fifth-generation (5G) would require huge bandwidth and a mobile fronthaul architecture as an essential solution in providing a high capacity for support in the future. To increase capacity, utilizing millimeter waves (mm-waves) in an analog radio over fiber (RoF) fronthaul link is the major advancement and solution in achieving higher bandwidth and high data rate to cater for 5G mobile communication. In this paper, we demonstrate the feasibility of transmission and reception of a 100 Gbits/s data rate link at 28 GHz. The performance of three modulation formats (16-PSK, 16-QAM and 64-QAM) have been compared for an optical fiber length from 5 km up to 35 km for two detection systems; coherent and direct detection. Also, in this paper, the transmission impairments inherent to transmission systems are realized through the implementation of a digital signal processing (DSP) compensation scheme in the receiver system to enhance system performance. Quality factor (QF) and bit error rate (BER) are used as metrics to evaluate the system performance. The proposed system model is designed and simulated using Optisystem 16.

Interference measurements for high data rate TDMA transmission through ACTS

1998 ◽  
Author(s):  
Robert Kerczewski ◽  
Duc Ngo ◽  
Diepchi Tran ◽  
Quang Tran ◽  
Brian Kachmar
Keyword(s):  
Data Rate ◽  
High Data Rate ◽  
High Data ◽  
Interference Measurements

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

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