The petroleum sector has a significant influence on the development of multiphase detection sensor techniques; to separate the crude oil from water, the crude oil tank is used. In this paper, a measuring system using a simple and low cost two parallel plate capacitance sensor is designed and implemented based on a Micro controlled embedded system plus PC to automatically identify the (gas/oil) and (oil/water) dynamic multi-interface in the crude oil tank. The Permittivity differences of two-phase liquids are used to determine the interface of them by measuring the relative changes of the sensor’s capacitance when passes through the liquid’s interface. The experiment results to determine the liquid’s interface is sa

There has been a growing interest in the use of chaotic techniques for enabling secure communication in recent years. This need has been motivated by the emergence of a number of wireless services which require the channel to provide very low bit error rates (BER) along with information security. This paper investigates the feasibility of using chaotic communications over Multiple-Input Multiple-Output (MIMO) channels by combining chaos modulation with a suitable Space Time Block Code (STBC). It is well known that the use of Chaotic Modulation techniques can enhance communication security. However, the performance of systems using Chaos modulation has been observed to be inferior in BER performance as compared to conventional communication

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati

For the most reliable and reproducible results for calibration or general testing purposes of two immiscible liquids, such as water in engine oil, good emulsification is vital. This study explores the impact of emulsion quality on the Fourier transform infrared (FT-IR) spectroscopy calibration standards for measuring water contamination in used or in-service engine oil, in an attempt to strengthen the specific guidelines of ASTM International standards for sample preparation. By using different emulsification techniques and readily available laboratory equipment, this work is an attempt to establish the ideal sample preparation technique for reliability, repeatability, and reproducibility for FT-IR analysis while still considering t

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod