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An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0–5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NNinSitu) is evaluated...
Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC-called a Tensor Processing Unit (TPU)-deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second...
This paper proposes an intelligent maximum power point tracking (MPPT) strategy based on the combination of fuzzy logic control (FLC) and artificial neural network (ANN) to improve the power output performances of PV system. Firstly, under various weather conditions, ANN is trained to estimate the optimal MPP voltage (Vmpp) of the PV module. Then, this trained ANN is used as a fundamental stage to...
This work attempts to find the most optimal setting for shallow artificial neural network (ANN) for Bengali digit dataset. Recognition of handwritten Bengali numerals has recently gained much interest among researchers due to significant performance gain found in the recognition of English numerals using artificial neural network. In this work, a new dataset of 70,000 samples were created first by...
Self-management blood glucose (SMBG) and bolus calculations are pivotal components of evidence-based standard of care for young diabetics receiving multiple daily insulin injections. This paper aims at developing a smart bolus estimator that takes into account the amount of insulin on board (IoB), i.e. Insulin remaining in the patient's body, to reduce fear of hypoglycemia and achieve goals of glycemic...
Integration of solar generation into power networks can negatively affect the performance of next generation smart energy grids. Rapidly changing output power of this kind is unpredictable and thus one of the solutions is to predict it by computational intelligence techniques. The stochastic component of solar radiation is highly non-linear in nature because of many factors including time of the year,...
This paper presents a new algorithm for colorizing gray scale natural still images. The algorithm uses artificial neural network (ANN) to predict the low frequency discrete cosine transform (DCT) components of the RGB channels. A set of natural color images are used to train three ANNs. The trained networks estimates the RGB layers of the gray scale image that best match a set of training colored...
Human activity recognition (HAR) is the basis for many real world applications concerning health care, sports and gaming industry. Different methodological perspectives have been proposed to perform HAR. One appealing methodology is to take an advantage of data that are collected from inertial sensors which are embedded in the individual's smartphone. These data contain rich amount of information...
The automotive industry is constantly looking for alternative solutions to reduce manufacturing cost and use renewable materials. Implementing agro-fibres as polymer fillers in thermoplastic matrix will satisfy the automotive criteria without sacrificing the mechanical properties currently set by the conventional fillers such as glass fibre, talc, or mica. This paper proposes the use of wheat straw...
Grid systems have emerged as a means of sharing computational resources and information. Providing services for accessing, sharing and modifying large databases is a crucial task for grid management systems. This paper proposes an artificial neural network (ANN) prediction mechanism that provides an enhancement to data replication solutions within grid systems. Current replication services often exhibit...
Machine vision is still a challenging topic and attracts researchers to carry out researches in this field. Efforts have been placed to design machine vision systems (MVS) that are inspired by human vision system (HVS). Attention is one of the important properties of HVS, with which the human can focus only on part of the scene at a time; regions with more abrupt features attract human attention more...
Signature analysis methods have been proven to deliver good results in the laboratory environment and successfully applied to isolated motors. The influence of fault signal on a non-faulty motor may be interpreted as faulty condition of the healthy motor. Therefore, it is difficult to identify a motor fault within a network and precisely identify the type of fault. This paper presents a supervised...
Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper,...
Signatures are imperative biometric attributes of humans that have long been used for authorization purposes. Most organizations primarily focus on the visual appearance of the signature for verification purposes. Many documents, such as forms, contracts, bank cheques, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures...
This paper presents a novel application of machine learning techniques to the automatic detection of building rooftops in satellite images. The image is first segmented into homogeneous regions using the k-means algorithm. These segments are then treated as candidate rooftop regions which are presented to a novel two-stage classification process, features are extracted from each segment and submitted...
This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods (‘delay-and-add’) with and without additive noise. By contrast to earlier linear methods, the present...
This paper presents a new technique to correct switch failures in a frequency reconfigurable antenna array. The correction procedure requires at the beginning a detection of the failed switch. The detection process relies on integrating sensing lines within the array substrate to monitor the switch failure. The proposed correction technique is based on using Neural Network (NN) to overcome the failed...
The traditional method for detecting the tumor diseases in the human MRI brain images is done manually by physicians. Automatic classification of tumors of MRI images requires high accuracy, since the non-accurate diagnosis and postponing delivery of the precise diagnosis would lead to increase the prevalence of more serious diseases. To avoid that, an automatic classification system is proposed for...
This paper discusses the effort of discriminating plaque psoriasis skin lesions using Artificial Neural Network (ANN) for dermatological early diagnosis based on color representations. For any digital acquired images, colors can be identified numerically, for example, with respect to the unique RGB, HSV and YCbCr pixel indices. Previous work have produced intelligent identification models for selected...
The air conditioning system usually controlled by a simple, proportional integral and derivative (PID) controller with single loop, or by using on-off controller, these controller are widely used because it's simple, but the performance of such a system using these controlling techniques are not accurate, with high power consumption and short compressor life. This paper aims to control air conditioning...
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