NU-ResNet: Deep Residual Networks for Thai Food Image Recognition

To improve the recognition accuracy of a convolutional neural network, the number of the modules inside the network is normally increased so that the whole network becomes a deeper network. By doing such, it does not always guarantee that the accuracy will be improved. In addition, adding more modules to the network, the required parameter size and processing time are certainly increased. These then result in a significant drawback if such network is utilized in a smartphone in which the computational resources are limited. In this paper, another technique called Identity mapping, which is from the Residual networks, is adopted and added to the network. This technique is applied to the Deep NU-InNet with a depth of 4, 8, and 12 in order to increase the recognition accuracy while the depth is kept constant. Testing this proposed network; that is, NU-ResNet, with THFOOD-50 dataset, which contains various images of 50 Thai famous dishes, the improvement in terms of the recognition accuracy is obtained. With a depth of 4 for NU-ResNet, the achieved Top-1 accuracy and Top-5 accuracy are 83.07% and 97.04%, respectively. The parameter size of the network is only 1.48×106, which is quite small for being used with a smartphone application. Moreover, the average processing time per image is 44.60 ms, which can be practically used in an image recognition application. These results show a promising performance of the proposed network to be used with a Thai food image recognition application in a smartphone

NU-InNet: Thai Food Image Recognition Using Convolutional Neural Networks on Smartphone

Currently, Convolutional Neural Networks (CNN) have been widely used in many applications. Image recognition is one of the applications utilizing CNN. For most of the research in this field, CNN is used mainly to increase the effectiveness of the recognition. However, the processing time and the amount of the parameters (or model size) are not taken into account as the main factors. In this paper, the image recognition for Thai food using a smartphone is studied. The processing time and the model size are reduced so that they can be properly used with smartphones. A new network called NUInNet (Naresuan University Inception Network) that adopts the concept of Inception module used in GoogLeNet is proposed in the paper. It is applied and tested with Thai food database called THFOOD-50, which contains 50 kinds of famousThai food. It is found that NU-InNet can reduce the processing time and the model size by the factors of 2 and 10, respectively, comparing to those obtained from GoogLeNet while maintaining the recognition precision to the same level as GoogLeNet. This significant reduction in the processing time and the model size using the proposed network can certainly satisfy users for Thai-food recognition application in a smartphone

An Improvement of the Arrival Time Estimation of an EV System Using Hybrid Approach with ANN

In this research, an approach for estimating the travelling time used by an electric vehicle and selecting an updating period of such vehicle to a particular location are proposed. The real-time based and historical data based techniques are used with Artificial Neural Network (ANN) as a process for memorizing the offset for estimating the vehicle velocity and updating period in the following round. The route of the vehicle, the time of the day, and the day of the week are taken into account. The proposed approach is analyzed and compared to the conventional approach by testing with the data (time and position of the vehicle) collected from running the vehicle around Naresuan University campus. The data was recorded every 1 second for 3 months using the wireless transmitter installed in the vehicle. From the results, it is found that, using the proposed approach, the bandwidth utilization of the network and the error of the displayed time are improved by 75%. With this significant improvement, if the proposed approach is further developed or utilized, the public vehicle service’s reliability could be increased; thus, less number of private vehicles utilized; resulting in a good environment saving

Analysis of the Multimode Fiber at Low-Frequency Passband Region

Multimode fibers have been used in communication systems for more than 40 years. The characteristics of such fibers have been investigated and it has shown that not just the 3-dB modal bandwidth of multimode fibers, but the high-frequency passbands can also be used to carry the signal; resulting in an increase of the data rate transmitted by a multimode fiber. However, the low-frequency passbands of multimode fibers have not been studied intensively. In this work, the characteristics of low-frequency passbands of multimode fibers are considered. The peak frequency, the amplitude, and the bandwidth of each possible passband are studied. From the simulation results, it is found that these parameters can be estimated. The approximation formulas for these three important parameters are given. Using the results found, the low-frequency passbands of multimode fibers can be utilized; thus, in comparison to the data rate obtained from the 3-dB modal bandwidth, a higher data rate for transmitting a signal over a multimode fiber can be increased at least 3 folds