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In this work a machine learning approach is proposed for prediction of volatile substance abuse. Machine learning technique used in this work is artificial neural networks (ANN). Two ANN modules are designed, ANN-D to predict whether a person is using VSA or not and ANN-C to predict the time of use. Input features used are age, gender, country, ethnicity, education, neuroticism, openness to experience,...
This paper proposes a novel hybrid position estimation strategy based on merging two self-sensing techniques according to the operating speed. High-frequency (HF) signal injection algorithm is deployed for zero and low-speeds, while the Machine Learning (ML) method is adopted for medium and high speeds. The proposed position estimator is intended for the fault-tolerant control of an interior permanent-magnet-synchronous...
Training of Artificial Neural Networks (ANN) is an important step to make the network able to accomplish the desired task. This capacity of learning in such networks makes them applied in many applications as modeling and control. However, many of training algorithms have some drawbacks like: too many parameters to be estimated, important calculus time. In this paper, we propose a very simple method...
Ultrasonic NDE uses high frequency acoustic waves to evaluate materials, and often signal processing is required to detect echoes from defects in the presence of microstructure scattering noise. Scattering noise, also known as clutter, interferes with the flaw signal and cannot be completely eliminated by using classical signal processing methods such as band-pass filtering. In this paper, neural...
A distortionless speech extraction in a reverberant environment can be achieved by an application of a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this contribution, we consider the RTF identification challenge in a multi-source scenario. We propose a successive RTF identification (SRI), based on a...
This paper develops new designs for recommender systems inspired by recent advances in graph signal processing. Recommender systems aim to predict unknown ratings by exploiting the information revealed in a subset of user-item observed ratings. Leveraging the notions of graph frequency and graph filters, we demonstrate that a common collaborative filtering method — fc-nearest neighbors — can be modeled...
In this paper, we propose the use of multiple Gaussian kernels for distributed nonlinear regression or system identification tasks by a network of nodes. By employing multiple kernels in the estimation process we increase the degree of freedom and thus, the ability to reconstruct nonlinear functions. For this, we extend the so-called KDiCE algorithm, which allows a distributed regression of nonlinear...
In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN...
Ultrasonic Non-Destructive Testing (NDT) and imaging systems has been widely used for industrial and medical applications. In NDT system, detection and characterization of target signal can be extremely challenging because of the complex echo scattering environment and the system noise. In this paper, an algorithm based on Neural Network (NN) is presented to explore the possible solutions for ultrasonic...
One of the most important aspects of the marketing is to determine what price is to be fixed to sell your products. Pricing is both an art and science that requires an experimental and statistical formula for creating a profile for the brand and the product in the market. There are minimalistic approaches used for pricing the products and to consider what will work for your business. Neural networks...
This work present new parameters based on biometrie handwritten information for the writer identification. The feature extraction is developed by new algorithms based on image processing techniques. The handwritten parameters will be classified by artificial neural network and fusion strategy in order to increase the accuracy. After experiments, and using a dataset composed by 100 writers, this proposal...
This paper addresses experimental damage detection technique on an Aluminium plate (Al) using Lamb waves. Array of four Piezoelectric Wafer (PW) transducers are used to actuate and sense Lamb waves. These transducers are small in size and cost effective. The data received from the experiments at various sensor locations is processed to obtain Root Mean Square Deviation (RMSD) based Damage Index (DI)...
Barrier coverage provides intrusion detection for various national security applications. If the network is randomly deployed, in moderately dense networks, the full end-to-end barrier line might not be provided. To fill the breaks and to assure the intrusion detection, additional nodes have to be introduced. The network should be designed in a way that enables the good (cost/benefit) balance between...
For cardiologists, the detection of cardiac abnormalities is a very delicate and crucial task for the treatment of a patient's condition. This task that requires electronic systems of medical assistance that is more precise, faster and reliable to help cardiologists to analyze and make the right decisions. These medical assistance systems tend to model the human expertise and perception using signal...
This paper aims to combine the practical application of artificial intelligence and modern navigation collision avoidance, to solve the difficult collision problem of ships which going through the complex water area. Based on AHP (The Analytic Hierarchy Process), ANN (Artificial Neural Network) and other methods, this paper proposes the T minutes prediction algorithm. In this algorithm, the parameter...
The Normalized Least-Mean Squares (NLMS) algorithm is a widely used method for linear system identification (e.g., for Acoustic Echo Cancellation (AEC), where the acoustic path between loudspeaker and microphone needs to be estimated). As soon as interferers or background noise are active, step size control becomes a crucial task in order to ensure a fast but stable adaptation. Conventional step size...
The nonnegative least mean square (NNLMS) algorithm has the advantages of simplicity and ease of implementation, but it has a slow convergence rate in sparse nonnegative system identification and its robustness is not strong in an impulsive interference environment. To solve these problems, an lo-norm NNLMS (lo-NNLMS) algorithm is presented by using an lo-norm optimization. Then, an lo-norm nonnegative...
Recent wireless standards prefer orthogonal frequency division multiplexing (OFDM) along with multiple input multiple output (MIMO) to offer high spectral efficiency services for any time anywhere environment. The full advantages of MIMO-OFDM is accessible only when there exist perfect channel information. Improper channel estimation leads to poor quality. In this work, we have developed a multi layered...
Specific algorithm simplifies the critical hardware requirement for smart signal processing applications. HASPA is such supervised ANN learning algorithm that specifically designed for multiple antenna based RADAR signal processing. The algorithm is based on the principle of spatial hearing of the Human Auditory Signal Processing System. In the processor, received signal from multiple sources are...
A general approach to controlling the operation accuracy of memristor-based hardware (MBH) is proposed herein. The following approach is based on application of the neural network algorithms, which make it possible to register the excess of the allowed inaccuracy level of signal processing in MBH. The artificial neural networks of radial basis functions meant to control the level of additive noises...
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