Recently, the implementation of machine learning algorithms in the analysis of voice disorders has become crucial for providing non-invasive identification of voice disorders using only audio signals. However, some voice pathology detection systems still face challenges such as working with limited acoustic databases, achieving low accuracy, and relying on constant parameters like a single number of hidden nodes. Therefore, this paper presents a method for the identification of voice pathology based on a machine learning algorithm. The voice signals are collected from a voice pathology database called the Malaysian Voice Pathology Database (MVPD). This database is created recently. The features of voices are extracted by using the Mel-Frequency Cepstral Coefficient (MFCC). Furthermore, the proposed method uses the Fast-Learning Network (FLN) algorithm for the classification part. In the proposed method, the FLN algorithm uses a different number of hidden nodes, where it starts with 20 nodes and finishes with 200 nodes. The performance of the proposed method is assessed in terms of many performance metrics. The results show that the proposed FLN algorithm achieves the highest results at the hidden nodes of 110. The FLN algorithm obtains promising results in detecting voice pathology.
Waveform-based localization approaches are considered the most reliable for vehicles in complex heterogeneous environments. However, this paper addresses millimeter-wave (mm-wave) propagation for vehicle to infrastructure (V2I) scenario at 28 GHz and 73 GHz. Particularly, it analyses the key localization elements for waveform-based approach such as received power and angel of arrival. Additionally, this paper investigates the impact of vehicles mobility on the stability these key elements at a certain position. Ray tracing techniques have been used to model the propagation characteristics of the aforementioned bands of mm-wave at a semirealistic V2I communication scenario. The obtained results indicated a proportional correlation between the received power and the elevation angle of arrival at a given location, potentially enhancing localization performance. In the other side, the mobility speed of the vehicle showed a notable impact on the stability of the key localization elements at a certain location.
The increasing need for high data rates in vehicular networks necessitates the development of innovative technologies to improve spectral efficiency (SE) and guarantee the reliability of cellular connections. An innovative approach that uses an integration of Fully Generalized Spatial Modulation (FGSM) with hybrid beamforming (H-FGSM) to improve vehicular communication, exhibiting notable enhancements in SE. This paper explores the utilization of FGSM in conjunction with hybrid beamforming within vehicular communication systems, specifically emphasizing two predominant frameworks: millimeter-wave (mmWave) and IEEE 802.11ad. It employs a complete performance analysis to investigate the effects of H-FGSM on the aforementioned communication frameworks. It is worth mentioning that the mmWave framework has a superior SE of 7bps/Hz in comparison to IEEE 802.11ad, in addition to extended coherence time by 3 times and decreased frequency of adjustments to channel state information (CSI). These characteristics render mmWave very suitable for dynamic vehicular environments that require reliable communication routes. Consequently, the integration of FGSM with hybrid beamforming presents a potentially viable approach for addressing the requirements of advanced intelligent transportation systems in future vehicle networks, which leads to the foundation for future investigations aim at enhancing the efficiency of mmWave communication systems in dynamic vehicular situations.
The rise in high-bandwidth applications like IoT devices, smart appliances, and social networking has driven the development of 5G technology. This research focuses on enhancing the energy efficiency of Time and Wavelength Division Multiplexing Passive Optical Networks (TWDM-PON), which are widely used in PON systems. TWDM-PON is crucial for 5G fronthaul networks, which need to support a thousandfold increase in capacity, ten to a hundred times higher data rates, and reduced latency. To address these needs, the research proposes an algorithm for a flexible TWDM-PON- based mobile fronthaul (MFH) architecture. This system allows Optical Network Units (ONUs) to dynamically allocate bandwidth for efficient 5G fronthaul usage. The proposed network topology facilitates dynamic resource management based on demand, with ONUs positioned near Remote Radio Heads (RRHs) to manage wavelength allocation. Each ONU uses a laser suited to any available wavelength in the transmission link. The study shows that this architecture helps maintain average latency while significantly saving energy. The proposed algorithm optimizes wavelength allocation, achieving a 60% reduction in energy consumption per active wavelength while meeting various latency requirements without compromising 5G fronthaul performance.
The extinction of freshwater fish is mainly due to environmental destruction, which has led to an increase in fish breeding activities in the aquaculture sector. This study presents the implementation of a machine learning approach using the YOLOv5 model to facilitate the fish breeding process by classifying the sex of Mahseer (Kelah) and the gravid broodstock of Tilapia. Traditional methods of sex and broodstock identification are often time-consuming and prone to errors due to a lack of expertise and equipment. A collection of X-ray images of Tilapia fish was gathered and labeled to develop a dataset for training and evaluating the YOLOv5 model. Due to the high cost of obtaining original data for Mahseer, publicly available images from Google were used to build the dataset. The results demonstrated that the YOLOv5 model achieved a precision of 94% and 99.4% for classifying sex and broodstock, respectively, using a maximum dataset of 430 images and 40 images. This finding indicates that YOLOv5 can significantly improve the efficiency and accuracy of broodstock management in aquaculture, contributing to the growth of the aquaculture sector.
