This work proposes a novel prototype-based engine fault classification scheme employing the audio signature of engines. In this scheme, Fourier transform and correlation methods have been used. Notably, automated has immense significance in present times, used both audio-based content retrieval indexing multimedia industry. Likewise, it is also becoming increasingly important automobile industries. It observed that real world data are contaminated with substantial noise out fliers. Hence,...

Road safety for automated vehicles requires accurate and early detection of stationary objects in the vehicle's path. Radar can use doppler to effectively identify make these identifications at long range severe weather poor light conditions. In this paper, we propose a radar-based object system that combines signal processing techniques with machine learning technology detect in-path from low level spectra front looking radars. The proposed consists novel image methods extract key features...

Many radar signal processing methodologies are being developed for critical road safety perception tasks. Unfortu-nately, these algorithms often poorly suited to run on embedded hardware accelerators used in automobiles. Conversely, end-to-end machine learning (ML) approaches better exploit the performance gains brought by specialized accelerators. In this paper, we propose a teacher-student knowledge distillation approach low-level We utilize hybrid model stationary object detection as...

The paper presents a novel two-step approach for constructing and training of optimally weighted Euclidean distance based Radial-Basis Function (RBF) neural network. Unlike other RBF learning algorithms, the proposed paradigms use Fuzzy C-means initial clustering optimal factors to train network parameters (i.e. spread parameter mean vector). We also introduce an optimized Distance Measure (DM) calculate activation function. Newton's algorithm is obtaining multiple factor (including DM)....

In this paper, we proposed an hybrid optimal radial-basis function (RBF) neural network for approximation and illumination invariant image segmentation. Unlike other RBF learning algorithms, the paradigm introduces a new way to train models by using factors (OLFs) parameters, i.e. spread parameter, kernel vector weighted distance measure (DM) factor calculate activation function. An efficient second order Newton's algorithm is obtaining multiple OLF's (MOLF) parameters. The weights connected...

In this paper, we propose a novel second order paradigm called optimal input normalization (OIN) to solve the problems of slow convergence and high complexity MLP. By optimizing non-orthogonal transformation matrix units in an equivalent network, OIN absorbs separate learning factor for each synaptic weight as well threshold hidden unit, leading improvement performance MLP training. Moreover, by using whitening negative Jacobian weights, modified version with weights optimization (OIN-HWO)...

In this paper, a second order learning algorithm based on Conjugate Gradient (CG) method for finding Multiple Optimal Learning Factors (MOLFs) of multilayer perceptron neural network is proposed in details. The experimental results several benchmarks show that, compared with One Factor Output Weights (lOLF-OWO) and Levenberg-Marquardt (LM), our CG MOLF optimal output weights which also called MOLFCG-OWO has not only significantly faster convergence rate than that lOLF even super to LM some...

We review the fact that several kinds of neural networks can be trained to approximate other types discriminant functions, thereby throwing some doubt upon utility No Free Lunch theorem. Using a license plate recognition database with 36 classes, we then demonstrate multilayer perceptrons estimate posterior probabilities very poorly when number classes is large. A method for generating desired probability values provided. Then an algorithm developed and demonstrated warping net discriminants...

This paper analyzes a linear discriminant subspace technique from an L-1 point of view. We propose efficient and optimal algorithm that addresses several major issues with prior work based on, not only the LDA but also its L-2 counterpart. includes implementation, effect outliers optimality parameters used. The key idea is to use conjugate gradient optimize cost function find learning factor during update weight vector in subspace. Experimental results on UCI datasets reveal present method...

Abstract: This study analyzes how, in the face of increasing industrialization, LabVIEW and GSM technology are revolutionizing pollution monitoring. Over last ten years, data capabilities have increased by 30%, whereas GSM's remote access has 25%. It illustrates evolution monitoring systems, highlighting how responds to events 40% faster operations can minimize downtime 20%. When combined, they provide a 15% improvement anomaly detection accuracy. shows their environmental impact through...

A system is proposed for recognizing four types of defects present in silicon wafer images. After preprocessing, the applies segmentation algorithms, one per defect type. Approximate posterior probabilities from a multilayer perceptron classifier aid fusing segmentors and making final classification. Numerical results confirm feasibility our approach.

A novel one stage batch training algorithm is proposed in which Newton's method used to find gains on inputs and hidden unit activations. The has far less computational complexity than Leverberg-Marquart because the method's Hessian much smaller that of LM. Numerical results shows converges faster more stable conjugate gradient BFGS.

In order to reduce the computational complexity of kernel machines and improve their performance in multi-label classification, we develop a systematic two step batch approach for constructing training new multiclass machine (MKM). The proposed paradigm prunes kernels, uses Newton's method parameters. each iteration, output weights are found using orthogonal least squares. Algorithm is compared that square support vector machines. Simulation results on many benchmark real life datasets show...

Linear transformation of the inputs alters training performance feed-forward networks that are otherwise equivalent. However, most linear transforms viewed as a pre-processing operation separate from actual training. Starting equivalent networks, it is shown using to multiplying negative gradient matrix with an autocorrelation per iteration. Second order method proposed find maximizes learning in given When diagonal, optimizes input gains. This optimal gain (OIG) approach used improve two...

We propose a multi-step training method for designing generalized linear classifiers. First, an initial multi-class classifier is found through regression. Then validation error minimized by pruning of unnecessary inputs. Simultaneously, desired outputs are improved via similar to the Ho-Kashyap rule. Next, output discriminants scaled be net functions sigmoidal units in classifier. then develop family batch algorithm multi layer perceptron that optimizes its hidden size and number epochs. we...