NTU Journal of Engineering and Technology

Air pollution is one of the main causes of health problem in urban areas. Vehicles are the major sources of the air pollution in urban cities. To prevent the air pollution situation from increasing, we have developed an advanced outdoor air quality measurement system using a ESP8266 microcontroller and senores. It can be placed at crowded automobile locations to monitor pollutants emitted from vehicles. Ozone gas (O3) and nitrogen dioxide (NO2) was measured in this study. The system provides continuous monitoring of vehicle air pollution with a high time accuracy.

OFDM-IM is a spatial modulation technique applied to OFDM's block subcarriers, resulting in a modified version of OFDM. Compared to traditional OFDM, OFDM-IM boasts several benefits, such as enhanced bit-error rate (BER) performance and power efficiency. When working with OFDM-IM, the specification of subcarrier-activation-patterns (SAPs) is done by utilizing either look-up tables (LUTs) or a combinatorial approach to conform with data. However, the data-to-SAP mapping methods of both suffer from increased complexity as the number of subcarriers expands. The paper proposes a direct data-to-SAP mapping that eliminates the need for any type of look-up tables. Through the presented analysis and computer simulation results, it is demonstrated that the OFDM-IM system utilizing the proposed mapping scheme outperforms equivalent traditional OFDM-IM systems in both complexity and bit error rate (BER) performance.

The spreadability of large and diverse computer graphics applications, highly powerful and diversified programmable hardware platforms, rapid advances in their programing techniques have all permitted design different hardware accelerators for many applications. Whereas a rapid increase in graphics performance accelerators has made them a compelling platform for computational demands tasks in a wide variety of application domains. Therefore, graphics accelerators have attracted us because of their important applications in our daily life. This paper tries to achieve those goals through a regular review of similar studies within this field of research. The process started by intensely surfing the famous specialized digital libraries and, as a result, 40 related papers were collected and examined upon several important technical subjects: technical motivations that underlie the chosen graphics algorithm, software developments that have led to the interest in this graphics algorithm, hardware platform used, programming tool, and the design intended for 2D or 3D graphics. We believe that the software techniques presented in this paper will be helpful for researchers who plan to develop new graphics accelerators algorithms. Also, it gives the required introductions in developing hardware graphics accelerators and help select materials, techniques, and tools.

Human life has always relied heavily on communication. Communication through the Internet has advanced significantly in recent years, and activities such as live video streaming are now commonplace. Streaming protocols, which are always growing and maturing, are utilized to guarantee video streaming is quick and smooth. Many streaming protocols are already available, and many people find it difficult to choose one that best meets their needs. This paper presents a review of studies of several available streaming protocols, as well as their main characteristics along with their performance, based on several criteria to demonstrate the validity of choosing one of them.

Biometrics based personal verification for mobile phone devices are currently well-known. In this study, a verification approach is suggested depending on fingerphoto pictures. Couple of Deep Fingerphotos Learning (CDFL) approach is proposed, where two Deep Learning (DL) networks are involved. A fingerphoto picture of the index finger is verified using the first DL network. To recognize a fingerphoto pictureof a middle finger, another DL network is used. Then, the outputs of the two networks are integrated. Fingerphoto pictures from the IIITD smartphone fingerphoto dataset are used in this work. The results yield that the accuracy of the first DL network is reported as 76.95% and the accuracy of the second DL network is reported as 86.33%. Whereas, the overall accuracy of the proposed CDFL method after integrating both DL networks is benchmarked as 96.48%.

Breast cancer in general is a common and a deadly disease. Early detection can significantly reduce the chances of death. Using automated feature extraction and classification algorithms, physician's experience in diagnosing and detecting breast cancer can be aided. This paper focuses on various statistical and machine learning(ML) studies of mammography datasets for enhancing the accuracy of breast cancer diagnosis and classification basedon various variables. The Naïve Bayes,the K-nearest neighbors (KNN),the Support Vector Machine (SVM),the Random Forest (RF),the Logistic Regression(LR), Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, arethe most widely used technologies in this field. This study provides an overview of the existing Computer-aided diagnosis/detection (CAD) systems based on artificial intelligence(AI) classification techniques and many types of medical image modalities. Potential research initiatives to build more efficient and accurate CAD systems have been investigated.