Demand feeding system using an infrared light sensor for brown-marbled grouper juveniles, Epinephelus fuscoguttatus

In general, demand feeding devices are equipped with a mechanical trigger switch. Such a switch is not suitable for juvenile fish with a small body size, because the body weight is insufficient to trigger the feeder. An infrared light sensor that does not require the fish to push a feeder switch is more suitable for small fish. The brown-marbled grouper Epinephelus fuscoguttatus is an important fish species in Southeast Asia. The purpose of this study was to compare the growth rates (GRs) of brown-marbled grouper juveniles reared using customised demand feeding devices with an infrared light sensor (the infrared light demand feeder (IRDF) group) and automatic feeding devices (the automatic feeder (AF) group). The results indicated that GRs of standard lengths and body weights showed no significant differences using one-way analysis of variance; however, the standard length of the IRDF group showed a tendency of a higher GR than the AF group. Although the feed conversion ratio (FCR) also showed no significant difference, the FCR of the IRDF group was more efficient, indicating that the IRDF group yielded a more desirable FCR. These results indicate that IRDF can be used in the culture of brown-marbled grouper juveniles. In view of the working schedule of the fish farm staff, IRDF are superior to other feeding devices, because they are less labour-intensive than usual tasks. We conclude that IRDF is a useful feeding system for aquaculture

Iris recognition using self-organizing neural network

Among biometric systems for user verification, iris recognition systems represent a relatively new technology. Our system consists of two main parts: a localizing iris and iris pattern recognition. The raw image is captured using a digital camera. The iris is then extracted from the background after enhancement and noise elimination. Due to noise and the high degree of freedom in the iris pattern, only parts of the iris structure are selected for recognition. The selected iris structure is then reconstructed into a rectangle format. Using a trained self-organizing map neural network, iris patterns are recognized. The overall accuracy of our network is found to be about 83%

Tumor detection using IRIS pattern.

Cancer was the top 10 killer in the world. WHO reported that in years 2000, death cause by cancer patient is 6.2 million people worldwide (WHO, 2003). The earlier cancer patient know he was infected by cancer would give him higher chances to cure it. It is event better if we can predict and prevent it rather than curing it. With iridology, we could analyze cell and body activities (Lindlahr, 1919). When cells growth abnormally, iris will show some sign and changes that iridologist could tell it tumor stated to grow (Lindlahr, 1919). Thus this could prevent the tumor from grow or grow into second stage that is cancer

Investigation of error performance in OFDM with network coding techniques in multiple relay networks

The aim of this paper proposes an orthogonal frequency-division multiplexing (OFDM) with network coding to improve the error performance of the system when the messages are transmitted from user to receiver. Two-way relay (TWR) networks are applied to reduce the transmission time slots. The exclusive-OR (XOR) coding is used for network coding in which source nodes exchange their information via TWR nodes. The XOR coded bits provides redundancy to achieve the transmit diversity gain which improves the error performance of the TWR network. OFDM is exploited for TWR to obtain the frequency selective fading nature of wireless channels. The different modulation schemes such as Quadrature Phase Shift Keying (QPSK), 16-Quadrature Amplitude Modulation (QAM) and 64-QAM with OFDM system are simulated and QPSK is selected as it gives the lowest bit error rate (BER). The multiple relaying schemes with different numbers of the information packets are also considered in this paper. Simulation results show that multiple relay schemes provide faster transmission time and better error rate performance. Moreover, different kinds of channel coding schemes such as Convolutional, Reed-Solomon (RS) and turbo codes are applied in OFDM system with network coding to compare and evaluate the BER performance of the proposed system. From the simulation results, network coded OFDM scheme with turbo codes give better BER performance for given Signal-to-Noise Ratio (SNR) in relaying scheme with different numbers of information packets compared to those of convolutional and RS codes. It shows that, the error rate performance and transmission time is reduced 10 percent than the conventional scheme at even at low SNR value

Fingerprint identification and recognition using backpropagation neural network

Biometrics is a technology which identifies a person based on his physiology or behavioral characteristics. Fingerprint identification and recognition is a biometrics method that has been widely used in various applications because of its reliability and accuracy in the process of recognizing and verifying a person's identity. The main purpose of this paper is to develop a fingerprint identification and recognition system. The system consists of three main parts, image acquisition, processing and identification and recognition. Fingerprint images are acquired and stored in the database in the image acquisition stage. These images are then enhanced in the image processing stage by performing gray level enhancement, spatial filtering, image sharpening, edge detection, segmentation, and thinning processes. After the image has been processed, it is fed into the backpropagation neural network as input in order to train the network. After training, the neural network is ready to perform the identification and recognition operations (matching process). A neural network has been successfully developed to identify and recognize the core part of fingerprint images

Determination of visual axis of tiger grouper juveniles Epinephelus fuscoguttatus, to develop a demand feeding system

In a demand feeding system, the fish turn on the switch of the feeder to get feed. In order to develop a demand feeding system with an infrared light sensor to which juvenile tiger grouper, Epinephelus fuscoguttatus visually respond, the visual acuity and visual axis of the juveniles were determined to obtain fundamental understanding of their vision. Three farmed juveniles were anaesthetized with MS222 and fixed in Bouin’s solution. The left retinae of each juvenile were cut into nine regions (Figure 1). The specimens were embedded in paraffin, cut into 6 μm thick tangential sections, and stained with haematoxylin-eosin. The density of cone cells (0.01mm2) in each region was counted in the stained sections. Visual acuity was calculated using cone cell densities and lens diameter. The highest cone density of each juvenile was 359 (bottom, B), 394 (temporal, T) and 380 cells/0.01mm2 (temporal, T) respectively. The estimated minimum separable angles of the highest density regions were 4.312×10-3, 3.661×10-3, 3.592×10-3 radian in each juvenile respectively. The estimated visual acuities were 0.068, 0.080 and 0.081 in those regions. These results showed that the visual axis of tiger grouper juveniles was in forward

Mobile machine vision for railway surveillance system using deep learning algorithm

Trains have been a popular transportation in our daily life. However, there is no proper surveillance system for obstacle detection at the railway, leading to the happen of unwanted accidents. In order to overcome this issue, machine vision embedded with deep learning algorithm can be implemented. Obstacle detection can be achieved through vision-based object detection, where the object classification model computes the images similarity to its respective classes, classifying its potential as an obstacle. In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). The detection model used in this paper is Single-Shot multibox Detection (SSD) MobileNet detection model. This model can be implemented with Raspberry Pi to simulate the object detection algorithm virtually. During simulation, the object recognition algorithm is able to detect and classify various objects into its respective classes. By applying past research approaches, the developed object detection model is able to analyze image as well as real-time video feed to identify multiple objects. Any object that has been detected at the Region of Interest (ROI) can be characterized as an obstacle

Optimization of crop disease classification using convolution neural network

This paper presents the deep learning model by Convolution Neural Network (CNN) in training the crop disease classifier via image classification. A camera will be equipped and applied in artificial intelligent drone to operate as a crop monitoring system used for agriculture. Agriculture productivity is a key component of country economy. Crop diseases can lead to a drop in the quality and quantity of agricultural products. Famers are facing problems to detect the crop diseases accurately in huge region of crops. Therefore, CNN based method for crop disease detection is proposed. Dataset contains of 16,257 color images which has a total of categories have been fed into the model, out of which 10 categories are of diseased crop leaves. The CNN model contains 7 convolution layers with the number of filters 32, 64, two layers with 128 filters, three layers with 256 filters and filter size $3\times 3$ is the proposed approach to perform crop disease classification, with the best testing accuracy of 99.02%. The crops are classified correctly using the suggested CNN design. The suggested CNN design is validated and evaluated which achieves accuracy of 99.02%, 0.98% error, 99% recall, 99% precision and 0.99 score of F-measure. In this paper, achievement of the proposed CNN model is reaching a promising result and simulated successfully in classifying the crop disease