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This paper targets on a generalized vocal mode classifier (speech/singing) that works on audio data from an arbitrary data source. Previous studies on sound classification are commonly based on cross-validation using a single dataset, without considering training-recognition mismatch. In our study, two experimental setups are used: matched training-recognition condition and mismatched training-recognition...
Security concerns increase as the technology for falsification advances and biometrics provides airtight security by identifying an individual based on the physiological and/or behavioral characteristics. Physiological hidden biometrics represented by ECG biomedical signal is highly confidential, sensitive, and hard to steal and replicate, and also hold great promise to provide a more secure biometric...
This paper addresses the problem of decision making when there is no or very vague knowledge about the probability models associated with the hypotheses. Such scenarios occur for example in Internet of Things (IoT), environmental surveillance and data analytics. The probability models are learned from the data by empirical distributions that provide an accurate approximation of the true model. Hence,...
Least angle regression (LARS) by Efron et al. (2004) is a novel method for constructing the piece-wise linear path of Lasso solutions. For several years, it remained also as the de facto method for computing the Lasso solution before more sophisticated optimization algorithms preceded it. LARS method has recently again increased its popularity due to its ability to find the values of the penalty parameters,...
In this paper, we develop a robust generalization of the Gaussian quasi score test (GQST) for composite binary hypothesis testing. The proposed test, called measure-transformed GQST (MT-GQST), is based on a transformation applied to the probability distribution of the data. The considered transform is structured by a non-negative function, called MT-function, that weights the data points. By appropriate...
This paper considers the problem of dynamic support recovery for jointly sparse signals from underdetermined measurements. Unlike Multiple Measurement Vector (MMV) models that assume a fixed support for all time instances, we allow the support to vary temporally following a finite state Markov chain. Instead of using l1 minimization based algorithms, we cast the problem of dynamic support recovery...
In the last few years, composite materials have found an important niche of application in several industries, mainly because of their improved mechanical properties (higher stiffness, strength and resistance to fatigue). In this context, sandwich-composites, a special class of composite materials - are commonly used in the aerospace industry to manufacture lighter components. The increasing use of...
Concrete can be molded to any shape and size, and once hardened it can withstand tremendous amount of compressive loads. This ability of concrete makes it the most widely used material in construction and thus, a need for identification and prediction of its compressive strength. Nondestructive tests have been solely preferred for this purpose and a drop-impact test machine prototype named; Material...
This paper presents the modelling of agarwood oil (AO) significant compounds by different qualities using Scaled Conjugate Gradient (SCG) algorithm. This technique involved of data collection from Gas Chromatography-Mass Spectrometry (GC-MS) for compound extraction. The development of Multilayer perceptron (MLP) is used to discriminate the qualities of AO chemical compounds to the high and low quality...
This paper discusses the development of a low-cost embedded-based electrooculogram (EOG) blink pulse classifier. A signal conditioning circuit from a single quad operational amplifier (Op-Amp) and an Arduino based on the ATmega32u4 AVR 8-bit microcontroller board comprised the major components of the embedded-based classifier. The evaluation of the nearest neighbor algorithm classifier resulted to...
The key interest of machine learning is conventionally training the machine from data that have underlying distribution such as data should have predetermined distribution. Such a constraint on the problem area leads to the technique for development of learning algorithms with notionally verifiable performance accuracy. However, real-world problems are not able to fit smartly into such restricted...
In a vehicle production environment, obtaining information on the condition of the assembled vehicle, allows for increased quality while minimizing rework minutes at the same time. The cost of this information is associated with additional time in production, increasing the overall cost of the vehicle. The focus of this paper is development of a system for contactless measurement and investigation...
Dialect can be defined as a variety of a language that is distinguished from other varieties of the same language by pronunciation, grammar and vocabulary. The process of recognizing such dialects is called Dialect Identification. Kamrupi, although a dialect of the Assamese language, is spoken both in Assam (Kamrup district) and North Bengal. In this paper, we describe a method to identify not just...
This paper describes development of a stand for testing pulse wave sensors of the “Helbe” device at the production stage. The objective of the stand is minimization of large batches of sensors testing time, minimization of human factor and increase the quality of testing. Piezoelectric transducers are used in “Healbe” devices for the heart rate determination. Consequently, the stand should exert a...
This research is motivated through the demand to create routing in indoor environment based on activity recognition approach. A model to discriminate between walking, climbing up stair, and climbing down stair is introduced. Data was collected from a group of participants performing walking up stairs, walking down stairs, and walking on normal path inside the building. 35 features are considered in...
The problem of detecting an anomalous process over multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution parameterized by an unknown parameter (vector). The unknown parameter belongs to one of two disjoint parameter spaces, depending on whether the process is normal or abnormal. The objective...
Identification of musical instruments from the acoustic signal using speech signal processing methods is a challenging problem. Further, whether this identification can be carried out by a single musical note, like humans are able to do, is an interesting research issue that has several potential applications in the music industry. Attempts have been made earlier using the spectral and temporal features...
Noise reduction has always been an important part of any control, acquisition or processing task. In order to increase the usage of some smaller and cheaper, but on the other hand less precise sensor solutions, it is necessary to incorporate some signal processing techniques for noise reduction. Nowadays soft computing techniques such as neural networks are widely used in many signal processing applications...
The testability of equipment has become the key factor affecting equipment availability, and detracts from readiness and mission success. To overcome the current problems associated with the analysis of equipment testability, such as non-comprehensive failure mode coverage, low fault detection rate, and low fault location accuracy, this paper presents a system testability modeling and analysis method...
This paper investigates detection of patterns in brain waves while induced with mental stress. Electroencephalogram (EEG) is the most commonly used brain signal acquisition method as it is simple, economical and portable. An automatic EEG based stress recognition system is designed and implemented in this study with two effective stressors to induce different levels of mental stress. The Stroop colour-word...
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