scholarly journals The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2826
Author(s):  
Othman Isam Younus ◽  
Navid Bani Hassan ◽  
Zabih Ghassemlooy ◽  
Stanislav Zvanovec ◽  
Luis Nero Alves ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Forward Error Correction ◽  
Light Emitting Diode ◽  
Pulse Amplitude ◽  
Eye Diagram ◽  
Artificial Neural

In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol Npps, which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with Npps of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of ~66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

scholarly journals Quality Prediction Modeling of Palm Oil Refining Plant in Malaysia Using Artificial Neural Network Models

2018 ◽  
Vol 7 (3.26) ◽  
pp. 19
Author(s):  
Nurul Sulaiha Sulaiman ◽  
Khairiyah Mohd-Yusof ◽  
Asngari Mohd-Saion
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Oil Refinery ◽  
Oil Refining ◽  
Great Loss ◽  
Refining Plant ◽  
Artificial Neural ◽  
Refined Palm Oil

Malaysia is currently one of the biggest producers and exporters of palm oil and palm oil products. In the growth of palm oil industry in Malaysia, quality of the refined oil is a major concern where off-specification products will be rejected thus causing a great loss in profit. In this paper, predictive modeling of refined palm oil quality in one palm oil refining plant in Malaysia is proposed for online quality monitoring purposes. The color of the crude oil, Free Fatty acid (FFA) content, bleaching earth dosage, citric acid dosage, activated carbon dosage, deodorizer pressure and deodorizer temperature were studied in this paper. The industrial palm oil refinery data were used as input and output to the Artificial Neural Network (ANN) model. Various trials were examined for training all three ANN models using number of nodes in the hidden layer varying from 10 to 25. All three models were trained and tested reasonably well to predict FFA content, red and yellow color quality of the refined palm oil efficiently with small error. Therefore, the models can be further implemented in palm oil refinery plant as online prediction system.  

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