A Carrierless Amplitude Phase (CAP) Modulation Format: Perspective and Prospect in Optical Transmission System

The explosive demand of broadband services nowadays requires data communication systems to have intensive capacity which subsequently increases the need for higher data rate as well. Although implementation of multiple wavelengths channels can be used (e.g. 4 × 25.8 Gb/s for 100 Gb/s connection) for such desired system, it usually leads to cost increment issue which is caused by employment of multiple optical components. Therefore, implementation of advanced modulation format using a single wavelength channel has become a preference to increase spectral efficiency by increasing the data rate for a given transmission system bandwidth. Conventional advanced modulation format however, involves a degree of complexity and costly transmission system. Hence, carrierless amplitude phase (CAP) modulation format has emerged as a promising advanced modulation format candidate due to spectral efficiency improvement ability with reduction of optical transceiver complexity and cost. The intriguing properties of CAP modulation format are reviewed as an attractive prospect in optical transmission system applications

Comparative study on the accelerated thermal aging behavior between palm and rapeseed natural ester oils

The suitability of natural ester oils as an insulating medium in power transformers is discussed in this paper. Owing to environmental concerns, natural ester oils have great potential as mineral oil substitutes in power transformers. In this paper, the aging behaviors of palm and rapeseed natural ester oils were compared with that for mineral oil. The performance of these natural ester oils was assessed based on their properties (moisture content, acidity, and relative content of dissolved decay products) after accelerated thermal aging. The results showed that the palm oil has better performance compared to the rapeseed oil after accelerated thermal aging for 1500 h because of its lower acidity. This was further supported by the presence of sludge in the rapeseed oil after 1500 h of aging

Deep learning generative adversarial network model for automated detection of diabetic retinopathy

Diabetic retinopathy (DR) is a leading disease that cause impaired vision with a consequence of permanent blindness if it is undiagnosed and untreated at the early stages. Alas, DR often has no early warning sign and may cause no symptoms. Particularly, recent statistics recorded that about 382 million individuals globally, with the number predicted to rise to 592 million by 2030 are suffers from DR. Due to the obvious large number of DR patients and limited medical resources in particular areas, patients with DR may not be treated in time, therefore missing out the best treatment options and eventually leading to irreversible vision loss. Unfortunately, a manual diagnosis to examine DR is tedious, time consuming, and error-prone, besides the consequences of manual interpretation which is highly dependent on the medical expert experiences to identify the presence of small features and significance of DR. This manual method opens to the inconsistency of the diagnosis. Thus, Automated Diabetic Retinopathy Detection aims to reduce the burden on ophthalmologists and mitigate diagnostic inconsistencies between manual readers by classifying DR stages using previous DR images with stages labels using Deep Learning. Generative Adversarial Network (GAN) is one of the major improvement of deep learning with potential to enhance the performance of automated detection significance of DR. Two different experiments were conducted and compared resulting in the best result with GAN evaluated by Frechet Inception Distance (FID), precision and recall

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises