Study of cucumber mosaic virus gene expression in Capsicum annuum

Cucumber mosaic virus (CMV) is a plant pathogenic virus in the genus Cucumovirus, family Bromoviridae. It has the potential and reputation of having the widest host range of any known plant virus including monocotyledons and dicotyledons, herbaceous plants, shrubs and trees. CMV is one of the major diseases in Capsicum annum (chilli). Chilli plant samples exhibiting virus-like disease symptoms were collected from Taman Pertanian Indera Mahkota (location 1) and Greenhouse 12 of Horticulture Research Centre, Serdang (location 2). Viral disease was detected based on symptoms like mosaic-mottling, yellow ringspots and cholorotic that appeared on the leaves. The isolation of total RNA was done by using Vivantis GF-1 total RNA extraction kit. RT-PCR technique was used to detect the presence of virus disease symptoms gene in chillies. Identification of causal agents was based on cDNA amplified product size, using virus-specific oligonucleotides. Actin was used as the internal PCR control. The product size of the DNA fragment was 315 bp. From RT-PCR, the expression of CMV can be detected in chilli plants that exhibited the virus-like disease symptoms. This research revealed that some of the chilli plants at the Taman Pertanian Indera Mahkota and Greenhouse 12 of Horticulture Research Centre have been affected by this viral disease

Characteristics of fulgurite-like structures under HV conditions: effects on electrical earthing systems

Fulgurites are natural tubes of glass formed by the fusion of silica sand or rock from a lightning strike. The fulgurites have been produced artificially in HV conditions in the past. Recent studies have found that fulguritic structures can be formed in some other materials, such as bentonite and cement, as well. These fulgurites can change the overall physical and electrical properties of the original materials. Thus, formation of fulgurites can modify the performance of electrical earthing systems, both ordinary and those improvised with backfill materials. This study investigates the fulgurite formation under alternating, direct and impulse current application. Bentonite and sand were tested under high voltage conditions. The type of fulgurites and their effects on electrical earthing systems were studied by analyzing the resistivity and permittivity of original materials and fulgurites. It has been found that fulgurites formation has a severe effect on the earth resistance of grounding systems

Incremental learning of deep neural network for robust vehicle classification

Existing single-lane free flow (SLFF) tolling systems either heavily rely on contact-based treadle sensor to detect the number of vehicle wheels or manual operator to classify vehicles. While the former is susceptible to high maintenance cost due to wear and tear, the latter is prone to human error. This paper proposes a vision-based solution to SLFF vehicle classification by adapting a state-of-the-art object detection model as a backbone of the proposed framework and an incremental training scheme to train our VehicleDetNet in a continual manner to cater the challenging problem of continuous growing dataset in real-world environment. It involved four experiment set-ups where the first stage involved CUTe datasets. VehicleDetNet is utilized for the framework of vehicle detection, and it presents an anchorless network which enable the elimination of the bounding boxes of candidates’ anchors. The classification of vehicles is performed by detecting the vehicle’s location and inferring the vehicle’s class. We augment the model with a wheel detector and enumerator to add more robustness, showing improved performance. The proposed method was evaluated on live dataset collected from the Gombak toll plaza at Kuala Lumpur-Karak Expressway. The results show that within two months of observation, the mean accuracy increases from 87.3 % to 99.07 %, which shows the efficacy of our proposed method

Machine learning technique for phishing website detection

The Internet has emerged as an indispensable tool in both our personal and professional life in our modern day. As a direct consequence of this, the number of customers who make their purchases over the Internet is quickly increasing. Internet users may be vulnerable to a wide variety of web threats because of this fact. These threats may result in monetary loss, fraudulent use of credit cards, loss of personal data, potential damage to a brand's reputation, and customer mistrust in e-commerce and online banking. Phishing is a sort of cyber threat that may be defined as the practice of imitating a genuine website for the purpose of stealing sensitive information such as usernames, passwords, and credit card numbers. This research focuses on strategies for detecting phishing attacks. This study apply a machine learning approach to detect a phishing attack. As a result, this study able to detect phishing with accuracy 94%

Software optimization of vision-based around view monitoring system on embedded platform

Image processing algorithm requires high computational power. Optimizing the algorithm to be run on an embedded platform is very critical as the platform provides limited computational resources. This research focused on optimizing and implementing a vision-based Around View Monitoring (AVM) system running on two embedded boards of Cortex-A7 quad and Cortex-A15 quad-core, and desktop platform of Intel i7 core. This paper presented a study on several techniques of software optimization that is removing code redundancy and multi-threading. The two methods improve the total processing time of the AVM system by 45% on ARM Cortex-A15 and 47% on ARM Cortex-A7

Deep learning-based water segmentation for autonomous surface vessel

Visual-based obstacle detection from an autonomous surface vessel (ASV) is a complex task due to high variance of scene properties such as different illumination and presence of reflections. One approach in implementing the task is through extracting waterlines to enable inferring of vessel orientation and obstacles presence. Classical computer vision algorithms for detection holds limitation in robustness and scalability. With recent breakthroughs in deep neural network architectures, vision-based object detection is seen to obtain high performance. In this work, the deep learning models based on Convolutional Neural Network (CNN) to implement binary semantic segmentation is studied. This architecture identifies each pixel to water and non-water classes. In purpose of benchmarking models, Fully Convolutional Network (FCN), SegNet and U-Net are trained on a publicly available dataset, IntCatch Vision Data Set (ICVDS), to evaluate the performance. From the experiments carried out, quantitative results show effectiveness of the models with accuracy all above 95.55% and lowest average speed of 11 frames per second. To improve, pre-trained networks (VGG 16, Resnet-50 and MobileNet) are used as a backbone, obtaining an improved accuracy above 98.14% with lowest inferring speed of 10 frame per second. Using the developed ASV, new dataset of 143 images called Malaysia ASV Dataset (MASVD) is collected, labelled and made publicly available. The trained models are tested with the newly collected dataset obtaining accuracy of 75%. The high accuracy performance shows potential for the models to be employed for collision avoidance algorithm in ASV navigation

A Simplified Top-Oil Temperature Model for Transformers Based on the Pathway of Energy Transfer Concept and the Thermal-Electrical Analogy

This paper presents an alternative approach to determine the simplified top-oil temperature (TOT) based on the pathway of energy transfer and thermal-electrical analogy concepts. The main contribution of this study is the redefinition of the nonlinear thermal resistance based on these concepts. An alternative approximation of convection coefficient, h, based on heat transfer theory was proposed which eliminated the requirement of viscosity. In addition, the lumped capacitance method was applied to the thermal-electrical analogy to derive the TOT thermal equivalent equation in differential form. The TOT thermal model was evaluated based on the measured TOT of seven transformers with either oil natural air natural (ONAN) or oil natural air forced (ONAF) cooling modes obtained from temperature rise tests. In addition, the performance of the TOT thermal model was tested on step-loading of a transformer with an ONAF cooling mode obtained from previous studies. A comparison between the TOT thermal model and the existing TOT Thermal-Electrical, Exponential (IEC 60076-7), and Clause 7 (IEEE C57.91-1995) models was also carried out. It was found that the measured TOT of seven transformers are well represented by the TOT thermal model where the highest maximum and root mean square (RMS) errors are 6.66 °C and 2.76 °C, respectively. Based on the maximum and RMS errors, the TOT thermal model performs better than Exponential and Clause 7 models and it is comparable with the Thermal-Electrical 1 (TE1) and Thermal-Electrical 2 (TE2) models. The same pattern is found for the TOT thermal model under step-loading where the maximum and RMS errors are 5.77 °C and 2.02 °C

Integration of Stereo Vision and MOOS-IvP for Enhanced Obstacle Detection and Navigation in Unmanned Surface Vehicles

This paper addresses the development of a stereo vision-based obstacle avoidance system using MOOS-IvP for small and medium-sized Unmanned Surface Vehicles (USVs). Existing methods predominantly rely on optical sensors such as LiDAR and cameras to discern maritime obstacles within the short- to mid-range distances. Nonetheless, conventional cameras encounter challenges in water conditions that curtail their effectiveness in localizing obstacles and planning paths. Furthermore, LiDAR has limitations regarding angular resolution and identifying objectness due to data sparsity. To overcome these limitations, our proposed system leverages a stereo camera equipped with enhanced angular resolution to augment situational awareness. The system employs recursive estimation techniques to ascertain the position and dimensions of proximate obstacles, transmitting this information to the onboard control unit, where MOOS-IvP behaviour-based software produces navigation decisions. Through the real-time fusion of data obtained from the stereo vision system and navigational data, the system is able to achieve Enhance Situational Awareness (ESA) and facilitate well-informed navigation decisions. Developing a state-of-the-art maritime object detection technique, the system adeptly identifies obstacles and swiftly responds via a vision integration protocol. During field tests, our system proves the efficacy of the proposed ESA approach. This paper also presents a comprehensive analysis and discussion of the results derived from deploying the proposed system on the Suraya Surveyor USV platform across numerous scenarios featuring diverse obstacles. The results from these various scenarios demonstrate the system’s accurate obstacle detection capabilities under challenging conditions and highlight its significant potential for safe USV operations

Stretchable and Bendable Polydimethylsiloxane- Silver Composite Antenna on PDMS/Air Gap Substrate for 5G Wearable Applications

An engineered composite conductor is essential for developing a wearable antenna that is not only flexible but also stretchable. This paper presents the use of polydimethylsiloxane (PDMS) as the substrate and custom polydimethylsiloxane-silver conductive paste for wearable applications. The antenna is designed with an air gap PDMS substrate between the patch and sawtooth partial ground at 3.5 GHz to enhance the bandwidth and gain. Furthermore, the proposed antenna is flexible and can be bent as well as stretched up to 20%, making it suitable for use on the human body. This study investigates the antenna’s performance under bending and stretching to mimic the human body’s structure and movements. Additionally, the specific absorption rate (SAR) of the wearable antenna was analyzed for safety purposes