José Chilo

Hardware Implementation of 1D Wavelet Transform on an FPGA for Infrasound Signal Classification

Infrasound is a low frequency acoustic phenomenon that typically ranges from 0.01 to 20 Hz. The data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, i.e., the Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important for real-time signal classification. Performing wavelet decomposition on the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of discrete wavelet transforms (DWT) for real-time infrasound data processing; our approach uses only two FIR filters, a high-pass and a low-pass filter. A compact implementation was realized with pipelining techniques and multiple use of generalized building blocks. The design was described in VHDL and the FPGA implementation and simulation were performed on the QUARTUS II platform.

Automatic design of pulse coupled neurons for image segmentation

Automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive optimization of numerical values. We implement a neuron in a pulse coupled neural network (PCNN) as a recursive function in the ADATE language and then use ADATE to automatically evolve better PCNN neurons for image segmentation. Our technique is generally applicable for automatic improvement of most image processing algorithms and neural computing methods. It may be used either to generally improve a given implementation or to tailor that implementation to a specific problem, which with respect to image segmentation for example can be road following for autonomous vehicles or infrared image segmentation for heat seeking missiles that are to distinguish the heat source of the target from flares.

Different volatile signals emitted by human ovarian carcinoma and healthy tissue

UNLABELLED Many cancers are detected at a late stage resulting in high mortality rates. Thus, it is essential to develop inexpensive and simple methods for early diagnosis. Detection of different malignancies using canine scent, as well as other technical methods, has been reported in peer-reviewed journals, indicating that this may represent a new diagnostic tool for malignancies. AIM This study aims to test the detection of different volatile organic compound signals emitted by ovarian carcinoma and normal tissues. MATERIALS & METHODS A previously tested electronic nose is used in the pilot study to analyze human grade 3 seropapillary ovarian carcinoma samples. The recorded signals were compared with healthy human Fallopian tube specimens. A variety of algorithms were tested and confusion matrices compared. In parallel, an external validation study was performed using the same type and grade of human ovarian carcinomas with healthy myometrium (first part) and postmenopausal ovarium (second part) specimens as controls. Both sample types were obtained from individuals who did not participate in the pilot study. RESULTS Method sensitivity was 100% (15 of 15) in the pilot study. The first part of the validation study demonstrated that 84.8% of cancer tissues (sensitivity: 84.8%) and 88.6% of the control samples (specificity: 88.6%) were correctly classified. In the second part the JRip algorithm correctly classified 75% of cancer tissues (sensitivity: 75%) and 80% of the control ovarian tissues (specificity: 80%). Collating results gives a sensitivity of 84.4%, whereas overall specificity was 86.8%. CONCLUSION Although based on a limited number of samples, our results strongly suggest that specific volatile organic compound signals emitted by ovarian carcinomas may be used for early diagnosis of the disease.

Filtering and extracting features from infrasound data

There are many reasons for using infrasound, i.e. low frequency sound, to monitor various events. Inherent features like its long-distance propagation and the use of simple, ground based equipment in very flexible system are some. The disadvantage is that it is a slow system due to the speed of sound. In this paper we try to show that there are several other advantages if one can extract all the features of the signal. In this way it is hoped that we can get a fingerprint of the event that caused the infrasound. Rayleigh waves and sound from epicentre may be obtained for earthquakes, pressure pulses and electrojets from aurora, core radius and funnel shape from tornados, etc. All these possibilities are suggestive for further R&D of the infrasound detection systems

Electronic Nose Ovarian Carcinoma Diagnosis Based on Machine Learning Algorithms

Ovarian carcinoma is one of the most deadly diseases, especially in the case of late diagnosis. This paper describes the result of a pilot study on an early detection method that could be inexpensive and simple based on data processing and machine learning algorithms in an electronic nose system. Experimental analysis using real ovarian carcinoma samples is presented in this study. The electronic nose used in this pilot test is very much the same as a nose used to detect and identify explosives. However, even if the apparatus used is the same, it is shown that the use of proper algorithms for analysis of the multi-sensor data from the electronic nose yielded surprisingly good results with more than 77% classification rate. These results are suggestive for further extensive experiments and development of the hardware as well as the software.

Real-Time Signal Processing of Infrasound Data Using 1D Wavelet Transform on FPGA Device

Infrasound is a low frequency acoustic phenomenon typically in the frequency range 0.01 to 20 Hz. Data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe,Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important. Using wavelet decomposition as a tool for removing noise from the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of Discrete Wavelet Transform (DWT) for real-time infrasound data processing, in which only two FIR filters, a high-pass and a low-pass filter, are used. With the filter reuse method and techniques such as pipeline, basic operations, by the language VHDL on the platform QUARTUS II,FPGA simulation and implementation are fulfilled. This implementation takes advantage from the low sampling rate used by the infrasound monitoring system that is only 18 Hz.

Comparison of three feature extraction techniques to distinguish between different infrasound signals

Comparison of three feature extraction techniques to distinguish between different infrasound signals

A Low Cost Digital Data Acquisition System for Infrasonic Records

This paper describes a new digital data acquisition system that can be used to record signals from infrasound events. The system includes a QF4A512 programmable signal converter from Quickfilter Technologies and a MSP430 microcontroller from Texas Instruments. The signal output of the infrasound sensors is converted to digital via a 16-bits analog to digital converter (ADC). To prevent errors in the conversion process, Anti-Aliasing Filters are employed prior to the ADC. Digital filtering is performed after the ADC using a digital signal processor, which is implemented on the QF4A512.

Discrimination of Nuclear Explosions Sites by Seismic Signals using Intrinsic Mode Functions and Multi-Modal Data Space

Signal processing and feature extraction are investigated using the Empirical Mode Decomposition (EMD). It is believed that this approach is well suited for non-linear and non-stationary data. With EMD any complicated set of data can be decomposed into a finite, and usually small number, of functions called Intrinsic Mode Functions (IMFs). A new discriminating system is presented here that is capable of discriminating between different seismic signals from nuclear testing sites based on the IMFs and the multi-modal data space. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space. This space is capable of extracting similarities among several signals through a combination of multiple metrics. This is a new way of associating data. After illustrating the technique with an investigation of an audio data example (piano), we examine the characteristics of seismic signals from nuclear testing (explosions). The results presented in this paper indicate that a relatively simple discriminating system can successfully cluster and classify seismic events.

A flexible electronic nose for odor discrimination using different methods of classification

Ovarian cancer is one of the leading causes of death from cancer in women. The lifetime risk is around 1.5%, which makes it the second most common gynecologic malignancy (the first one being breast cancer). To have a definitive diagnose, a surgical procedure is generally required and suspicious areas (samples) will be removed and sent for microscopic and other analysis. This paper describes the result of a pilot study in which an electronic nose is used to “smell” the aforementioned samples, analyze the multi-sensor signals and have a close to real-time answer on the detection of cancer. Besides being fast, the detection method is inexpensive and simple. Experimental analysis using real ovarian carcinoma samples shows that the use of proper algorithms for analysis of the multi-sensor data from the electronic nose yielded surprisingly good results with more than 77% classification rate. The electronic nose used in this pilot study was originally developed to be used as a “bomb dog” and can distinguish between e.g. TNT, Dynamex, Prillit. However, it was constructed to be a flexible multi-sensor device and the individual (16) sensors can easily be replaced/exchanged. This is suggestive for further investigations to obtain even better results with new, specific sensors. In another pilot experiment, headspace of an ovarian carcinoma sample and a control sample were analyzed using gas chromatography-mass spectrometry. Significant differences in chemical composition and compound levels were recorded, which would explain the different response obtained with the electronic nose.