Journal of ICT Research and Applications

A Subthreshold Biased CMOS Ring Oscillator Model Design in 180-nm Process

2023-03-30T10:46:15+07:00

In this paper, a 180-nm CMOS ring oscillator design, made with halo-implanted transistors and operating in the weak inversion region, is proposed, based on an undergraduate integrated circuit design course methodology for building logic gates and comparing simulated results with reviewed literature data. Halo-implanted channel transistors have a steeper and less distorted voltage characteristic curve compared to uniformly doped channel ones, which makes them a more appropriate option when designing asynchronous digital integrated circuits aimed at low bias and low power. Three gate models were created using weak inversion and pull-up and pull-down networks made with halo-implanted transistors. The results of the study and simulation of the three inverter digital gate topologies showed that the NOT inverter model, as expected, had a higher frequency than the NAND and NOR inverter models. The ring oscillators made with the NOT inverter came up with an 8.25-MHz switching frequency as well as a dynamic power close to 270 nW. A comparison with other ring oscillators from previous studies is also shown.

Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique

2022-10-26T16:17:36+07:00

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques.

Improving Robustness Using MixUp and CutMix Augmentation for Corn Leaf Diseases Classification based on ConvMixer Architecture

2022-10-26T09:44:03+07:00

Corn leaf diseases such as blight spot, gray leaf spot, and common rust still lurk in corn fields. This problem must be solved to help corn farmers. The ConvMixer model, consisting of a patch embedding layer, is a new model with a simple structure. When training a model with ConvMixer, improvisation is an important part that needs to be further explored to achieve better accuracy. By using advanced data augmentation techniques such as MixUp and CutMix, the robustness of ConvMixer model can be well achieved for corn leaf diseases classification. We describe experimental evidence in this article using precision, recall, accuracy score, and F1 score as performance metrics. As a result, it turned out that the training model with the data set without extension on the ConvMixer model achieved an accuracy of 0.9812, but this could still be improved. In fact, when we used the MixUp and CutMix augmentation, the training model results increased significantly to 0.9925 and 0.9932, respectively.

Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset

2023-06-09T09:20:49+07:00

The rate of advancement in the field of artificial intelligence (AI) has drastically increased over the past twenty years or so. From AI models that can classify every object in an image to realistic chatbots, the signs of progress can be found in all fields. This work focused on tackling a relatively new problem in the current scenario-generative capabilities of AI. While the classification and prediction models have matured and entered the mass market across the globe, generation through AI is still in its initial stages. Generative tasks consist of an AI model learning the features of a given input and using these learned values to generate completely new output values that were not originally part of the input dataset. The most common input type given to generative models are images. The most popular architectures for generative models are autoencoders and generative adversarial networks (GANs). Our study aimed to use GANs to generate realistic images from a purely semantic representation of a scene. While our model can be used on any kind of scene, we used the Indian Driving Dataset to train our model. Through this work, we could arrive at answers to the following questions: (1) the scope of GANs in interpreting and understanding textures and variables in complex scenes; (2) the application of such a model in the field of gaming and virtual reality; (3) the possible impact of generating realistic deep fakes on society.

Scene Segmentation for Interframe Forgery Identification

2023-03-13T16:45:28+07:00

A common type of video forgery is inter-frame forgery, which occurs in the temporal domain, such as frame duplication, frame insertion, and frame deletion. Some existing methods are not effective to detect forgeries in static scenes. This work proposes static and dynamic scene segmentation and performs forgery detection for each scene. Scene segmentation is performed for outlier detection based on changes of optical flow. Various similarity checks are performed to find the correlation for each frame. The experimental results showed that the proposed method is effective in identifying forgeries in various scenes, especially static scenes, compared with existing methods.

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

2023-04-26T10:11:37+07:00

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information.

The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs

2023-08-09T17:15:47+07:00

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved.

Smart Card-based Access Control System using Isolated Many-to-Many Authentication Scheme for Electric Vehicle Charging Stations

2023-08-28T14:18:54+07:00

In recent years, the Internet of Things (IoT) trend has been adopted very quickly. The rapid growth of IoT has increased the need for physical access control systems (ACS) for IoT devices, especially for IoT devices containing confidential data or other potential security risks. This research focused on many-to-many ACS, a type of ACS in which many resource-owners and resource-users are involved in the same system. This type of system is advantageous in that the user can conveniently access resources from different resource-owners using the same system. However, such a system may create a situation where parties involved in the system have their data leaked because of the large number of parties involved in the system. Therefore, ‘isolation’ of the parties involved is needed. This research simulated the use of smart cards to access electric vehicle (EV) charging stations that implement an isolated many-to-many authentication scheme. Two ESP8266 MCUs, one RC522 RFID reader, and an LED represented an EV charging station. Each institute used a Raspberry Pi Zero W as the web and database server. This research also used VPN and HTTPS protocols to isolate each institute’s assets. Every component of the system was successfully implemented and tested functionally.