Performance evaluation of active canopy sensor towards a wireless variable-rate fertilizer application system in Paddy Production

The performance of a crop canopy sensor measuring crop response to nitrogen variation of a local paddy variety, Siraj 297, was evaluated. Data for various crop parameters was taken before every fertilizer treatment application. The results show that the sensor was not able to distinguish the crop response to different fertilizer treatments in the early stages of crop growth. In the panicle initiation and booting stages (50 DAT and 70 DAT), the sensor showed better performance in the red edge and NIR spectral bands. A linear response was also observed for NDVI and NDRE indices. The results from this work will be used to develop a suitable mathematical model for a variable rate fertilizer application system

Evaluation of radar reflectivity-rainfall rate, Z-R relationships during a stratiform event in the tropics

Abstract—A number of factors can certainly affect the accuracy of Z-R relationships; including poor hardware calibration. The inaccuracy might also be due to the differences between the ground-level precipitation and the precipitation aloft since a radar does not scan all the way down except at close range. Several Z-R relationships had been proposed in the attempt to achieve better accuracy for rainfall estimates by radar system in the tropical climate. Nonetheless, the most accurate Z-R relationship for Malaysia weather radar is yet to be investigated and identified. This paper presents the analyses of previously proposed Z-R relationships for Malaysia weather using new radar data and ground rainfall rate

Classification of precipitation types detected in Malaysia

The occurrences of precipitation, also commonly known as rain, in the form of “convective” and “stratiform” have been identified to exist worldwide. In this study, radar return echo or known as reflectivity have been exploited in the process of classifying the type of rain endured. The Malaysian meteorology radar data is used in this investigation. It is possible to discriminate the types of rain experienced in such tropical environment by observing the vertical characteristics of the rain structure. Heavy rain in tropical region profoundly affect microwave and milimetre wave signals, causing interference on transmission and signal fading. Required fade margin for wireless system largely depends on the type of rain. Information relating to the two most prevalent types of rain are critical for the system engineers and researchers in their endeavour to improve the reliability of communication links. This paper highlights the quantification of percentage occurrences over 1 year period of 2009

Characterization And Classification Of Bioactive Compound In Natural Products By FTIR And Multivariate Data Analysis

Bioactive compounds are one of the natural products used especially for medicinal, pharmaceutical and food application. Increasing research performed on the extraction, isolation and identification of bioactive compounds, however non to date has explored on the identification of flavonoids classes. Therefore, this study was focused on the development of algorithm for rapid identification of flavonoids classes which are flavanone, flavone and flavonol. Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was performed. The results showed that the flavonoids classes were identified according to spectral region assigned using the PCA algorithm based on the FTIR spectrum of the samples. The study concluded that FTIR coupled with PCA analysis can be used as a molecular fingerprint for rapid identification of flavonoids classes. The comparative studies of other flavonoids classes are still under investigation using the same method

Diabetic retinopathy grading using ResNet convolutional neural network

Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classification. In the first stage, the image has been pre-processed using different transformations to standardize the images and to enhance the images quality. It has been proven that Gaussian filtering is quite effective in this context to enhance the images contrast. In the second and third stage, the convolution neural network (CNN), one of the best neural network architecture for image analysis applications, has been used. The concept of transfer learning and fine tuning have been advocated in this paper and applied for ResNet18 using the publicly available Kaggle dataset. The problem of DR diagnosis has been handled as a multi-class classification problem where there are five levels of the disease severity (– No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR). The final model has achieved accuracy of 70 %, recall of 50% and specificity of 88% outperforming other models built from scratch with less training time and proving the efficiency of transfer learning in this context. The training process has considered the problem of imbalanced dataset using two different ways and it has been discovered that using imbalanced dataset sampler is a very efficient solution. The final model developed in this research could be used as the main unit for a computer aided system to be hosted online for DR detection and diagnosis

Diabetic retinopathy grading using ResNet convolutional neural network

Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classification. In the first stage, the image has been pre-processed using different transformations to standardize the images and to enhance the images quality. It has been proven that Gaussian filtering is quite effective in this context to enhance the images contrast. In the second and third stage, the convolution neural network (CNN), one of the best neural network architecture for image analysis applications, has been used. The concept of transfer learning and fine tuning have been advocated in this paper and applied for ResNet18 using the publicly available Kaggle dataset. The problem of DR diagnosis has been handled as a multi-class classification problem where there are five levels of the disease severity (– No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR). The final model has achieved accuracy of 70 %, recall of 50% and specificity of 88% outperforming other models built from scratch with less training time and proving the efficiency of transfer learning in this context. The training process has considered the problem of imbalanced dataset using two different ways and it has been discovered that using imbalanced dataset sampler is a very efficient solution. The final model developed in this research could be used as the main unit for a computer aided system to be hosted online for DR detection and diagnosis

Algorithm for rapid identification of flavonoids classes

Bioactive compounds are one of the natural products used especially for medicinal, pharmaceutical and food application. Increasing research performed on the extraction, isolation and identification of bioactive compounds, however non to date has explored on the identification of flavonoids classes. Therefore, this study was focused on the development of algorithm for rapid identification of flavonoids classes which are flavanone, flavone and flavonol and also their derivatives. Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was utilized. The results exhibited that few significant wavenumber range provides the identification and characterization of the flavonoids classes based on PCA algorithm. The study concluded that FTIR coupled with PCA analysis can be used as a molecular fingerprint for rapid identification of flavonoids

A survey of component carrier selection algorithms for carrier aggregation in long term evolution-advanced

Given that the demand for real-time multimedia contents that require significantly high data rate are getting of high popularity, a new mobile cellular technology known as Long term Evolution-Advanced (LTE-A) was standardized. The LTE-A is envisaged to support high peak data rate by aggregating more than one contiguous or non-contiguous Component Carriers (CCs) of the same or different frequency bandwidths. This paper provides a survey on the case where the LTE-A is working in backward compatible mode as well as when the system contains only LTE-A users. Note that the backward compatible mode indicates that the LTE-A contains a mixture of the legacy Long Term Evolution Release 8 (LTE) users that support packets (re)transmission on a single CC and the LTE-A users that are capable of utilizes more than one CCs for packets (re)transmission. It can be concluded from the study that the CC selection algorithms for newly-arrived LTE users can benefit from the channel diversity and the load status whereas the carrier aggregation that does not allocate all of the available CCs to the newly arrived LTE-A users shown to be more efficient

RF Energy Harvesting Wireless Networks: Challenges And Opportunities

Energy harvesting wireless networks is one of the most researched topics in this decade, both in industry and academia, as it can offer self-sustaining sensor networks. With RF energy harvesting (RF-EH) embedded, the sensors can operate for extended periods by harvesting energy from the environment or by receiving it as an Energy signal from a hybrid base station (HBS). Thus, providing sustainable solutions for managing massive numbers of sensor nodes. However, the biggest hurdle of RF energy is the low energy density due to spreading loss. This paper investigates the RF-EH node hardware and design essentials, performance matrices of RF-EH. Power management in energy harvesting nodes is discussed. Furthermore, an information criticality algorithm is proposed for critical and hazardous use cases. Finally, some of the RF-EH applications and the opportunities of 5G technologies for the RF-EH are introduced

A study on channel and delay-based scheduling algorithms for live video streaming in the fifth generation long term evolution-advanced

This paper will investigate the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in the legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay, and maximizing network throughput are shown via simulations. As the M-LWDF having a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-