Janusz Gajda

A vehicle classification based on inductive loop detectors

The class of vehicle is one of more important parameters in the process of road traffic measurement. Up to now, strip piezoelectric sensors and video systems have been used. The use of very cheap inductive loop detectors for vehicle classification is also possible. Such vehicle classification systems are based on magnetic profiles recorded from inductive loops. The magnetic profile is sensitive to the loop dimensions. This paper presents a discussion concerning the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes. As characteristics describing the magnetic profile of the vehicle have been used: magnetic profiles in time domain (normalized in amplitude), probability density function and magnetic profiles in vehicle length domain. For real time applications, the conversion of the measured signal into a vector of numerical parameters (a few only) is also proposed. The influence of loop dimensions on a chosen signal parameter was investigated. The case of extremely short loop (10 cm), which allows detection of the number of axles, was also analyzed.

Acoustic Analysis Assessment in Speech Pathology Detection

Abstract Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis) using the a, i and u vowels, spoken at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient

Quantification of Linear and Non-linear Acoustic Analysis Applied to Voice Pathology Detection

Present development of digital registration and methods of recorded voice processing are useful in detection of most pathologies and diseases of a human vocal tract. The recognition of the voice condition requires the creation of a model which is comprised of different acoustic parameters of speech signal. In this study a vector consisting of 31 parameters for analysing the speech signal was created. The speech parameters were extracted from time, frequency and cepstral domains. Using Principal Components Analysis the number of the parameters was reduced to 17. In order to validate the detection of the pathological voice signal, a tenfold cross-validation and confusion matrix were used. The goal and novelty of this work was the analysis of applicability of the parameters selectively used to assess the pathology.

Voice data mining for laryngeal pathology assessment

The aim of this study was to evaluate the usefulness of different methods of speech signal analysis in the detection of voice pathologies. Firstly, an initial vector was created consisting of 28 parameters extracted from time, frequency and cepstral domain describing the human voice signal based on the analysis of sustained vowels /a/, /i/ and /u/ all at high, low and normal pitch. Afterwards we used a linear feature extraction technique (principal component analysis), which enabled a reduction in the number of parameters and choose the most effective acoustic features describing the speech signal. We have also performed non-linear data transformation which was calculated using kernel principal components. The results of the presented methods for normal and pathological cases will be revealed and discussed in this paper. The initial and extracted feature vectors were classified using the k-means clustering and the random forest classifier. We found that reasonably good classification accuracies could be achieved by selecting appropriate features. We obtained accuracies of up to 100% for classification of healthy versus pathology voice using random forest classification for female and male recordings. These results may assist in the feature development of automated detection systems for diagnosis of patients with symptoms of pathological voice.

Thermal Property Analysis of Axle Load Sensors for Weighing Vehicles in Weigh-in-Motion System

Systems which permit the weighing of vehicles in motion are called dynamic Weigh-in-Motion scales. In such systems, axle load sensors are embedded in the pavement. Among the influencing factors that negatively affect weighing accuracy is the pavement temperature. This paper presents a detailed analysis of this phenomenon and describes the properties of polymer, quartz and bending plate load sensors. The studies were conducted in two ways: at roadside Weigh-in-Motion sites and at a laboratory using a climate chamber. For accuracy assessment of roadside systems, the reference vehicle method was used. The pavement temperature influence on the weighing error was experimentally investigated as well as a non-uniform temperature distribution along and across the Weigh-in-Motion site. Tests carried out in the climatic chamber allowed the influence of temperature on the sensor intrinsic error to be determined. The results presented clearly show that all kinds of sensors are temperature sensitive. This is a new finding, as up to now the quartz and bending plate sensors were considered insensitive to this factor.

Analysis of the temperature influences on the metrological properties of polymer piezoelectric load sensors applied in Weigh-in-Motion systems

This paper describes temperature influence on the uncertainty of weighing results in Weigh in Motion systems (WIM). The analysis bases on the long-term investigation of the WIM site, as well as on simulation tests conducted on sensor and conditioning circuit models. The drawn conclusions suggest, that this uncertainty may be significantly reduced if a proper temperature correction algorithm is applied.

Automatic Detection of Parkinson's Disease Based on Modulated Vowels.

In this paper we present a novel approach of automatic detection of phonatory and articulatory impairments caused by Parkinson’s disease (PD). Modulated (varying between low and high pitch) and sustained vowels are considered and analysed. The fundamental frequency of the phonations and its range are computed using the Hilbert-Huang transformation. Additionally, a set with “standard” measures are calculated to model phonatory and articulatory deficits exhibited by Parkinson’s patients. Kernel Principal Component Analysis was also applied in order to reduce the dimensionality of the representation space. The automatic discrimination between speakers with PD and healthy controls (HC) is performed using decision trees. According to the results, modulated vowels are suitable to evaluate phonatory and articulatory deficits observed in PD speech.

Design and accuracy assessment of the multi-sensor weigh-in-motion system

In every country, road networks are one of the largest and highly expensive investment. Equally expensive are the consequences of not ensuring adequate road safety. This creates the need for an efficient and automatic system for the detection of overloaded vehicles [17]. One of the tools designed to measure the weight and individual axle loads of vehicles are the systems weighing vehicles in motion (WIM). Due to their measurement imprecision, WIM systems composed of two lines of sensors are only used as pre-selection systems. At present, the only viable option for reducing measurement uncertainty in dynamic weighing is to increase number of load sensors (Multi-Sensor systems; MS-WIM). The paper presents the results of research on a MS-WIM system equipped with 16 lines of load sensors. The conceptual objectives, system design, results of experimental evaluation of the systems accuracy, and the final conclusions based on a 6-year operating period, during which the system has been used under normal traffic conditions have been presented.

Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms

Knowledge about tumor bed localization and its shape analysis is a crucial factor for preventing irradiation of healthy tissues during supportive radiotherapy and as a result, cancer recurrence. The localization process is especially hard for tumors placed nearby soft tissues, which undergo complex, nonrigid deformations. Among them, breast cancer can be considered as the most representative example. A natural approach to improving tumor bed localization is the use of image registration algorithms. However, this involves two unusual aspects which are not common in typical medical image registration: the real deformation field is discontinuous, and there is no direct correspondence between the cancer and its bed in the source and the target 3D images respectively. The tumor no longer exists during radiotherapy planning. Therefore, a traditional evaluation approach based on known, smooth deformations and target registration error are not directly applicable. In this work, we propose alternative artificial deformations which model the tumor bed creation process. We perform a comprehensive evaluation of the most commonly used deformable registration algorithms: B-Splines free form deformations (B-Splines FFD), different variants of the Demons and TV-L1 optical flow. The evaluation procedure includes quantitative assessment of the dedicated artificial deformations, target registration error calculation, 3D contour propagation and medical experts visual judgment. The results demonstrate that the currently, practically applied image registration (rigid registration and B-Splines FFD) are not able to correctly reconstruct discontinuous deformation fields. We show that the symmetric Demons provide the most accurate soft tissues alignment in terms of the ability to reconstruct the deformation field, target registration error and relative tumor volume change, while B-Splines FFD and TV-L1 optical flow are not an appropriate choice for the breast tumor bed localization problem, even though the visual alignment seems to be better than for the Demons algorithm. However, no algorithm could recover the deformation field with sufficient accuracy in terms of vector length and rotation angle differences.

Eddy-Current Sensors with Asymmetrical Point Spread Function

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 × 2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm.