Seismic detection and characterization of landslides and other mass movements

Seismic methods used in the study of snow avalanches may be employed to detect and characterize landslides and other mass movements, using standard spectrogram/sonogram analysis. For snow avalanches, the spectrogram for a station that is approached by a sliding mass exhibits a triangular time/frequency signature due to an increase over time in the higher-frequency constituents. Recognition of this characteristic footprint in a spectrogram suggests a useful metric for identifying other mass-movement events such as landslides. The 1 June 2005 slide at Laguna Beach, California is examined using data obtained from the Caltech/USGS Regional Seismic Network. This event exhibits the same general spectrogram features observed in studies of Alpine snow avalanches. We propose that these features are due to the systematic relative increase in high-frequency energy transmitted to a seismometer in the path of a mass slide owing to a reduction of distance from the source signal. This phenomenon is related to the path of the waves whose high frequencies are less attenuated as they traverse shorter source-receiver paths. Entrainment of material in the course of the slide may also contribute to the triangular time/frequency signature as a consequence of the increase in the energy involved in the process; in this case the contribution would be a source effect. By applying this commonly observed characteristic to routine monitoring algorithms, along with custom adjustments for local site effects, we seek to contribute to the improvement in automatic detection and monitoring methods of landslides and other mass movements.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Detection of Raga-characteristic phrases from Hindustani Classical Music Audio

Melodic motifs form essential building blocks in Indian Classical music. The motifs, or key phrases, providestrong cues to the identity of the underlying raga in both Hindustani and Carnatic styles of Indian music. Automatic identification and clustering of similar motifs is relevant in this context. The inherent variations in various instances of a characteristic phrase in a bandish (composition)performance make it challenging to identify similar phrases in a performance. A nyas svara (long note)marks the ending of these phrases. The proposed method does segmentation of phrases through identification ofnyas and computes similarity with the reference characteristic phrase.

An embedded real-time red peach detection system based on an OV7670 camera, ARM Cortex-M4 processor and 3D Look-Up Tables

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second.

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Theta EEG oscillatory activity and auditory change detection

The mismatch negativity is an electrophysiological marker of auditory change detection in the event-related brain potential and has been proposed to reflect an automatic comparison process between an incoming stimulus and the representation of prior items in a sequence. There is evidence for two main functional subcomponents comprising the MMN, generated by temporal and frontal brain areas, respectively. Using data obtained in an MMN paradigm, we performed time-frequency analysis to reveal the changes in oscillatory neural activity in the theta band. The results suggest that the frontal component of the MMN is brought about by an increase in theta power for the deviant trials and, possibly, by an additional contribution of theta phase alignment. By contrast, the temporal component of the MMN, best seen in recordings from mastoid electrodes, is generated by phase resetting of theta rhythm with no concomitant power modulation. Thus, frontal and temporal MMN components do not only differ with regard to their functional significance but also appear to be generated by distinct neurophysiological mechanisms.

Photometric and Halpha observations of LSI+61·303: detection of a 26 day V and JHK band modulation

We present new optical and infrared photometric observations and high resolution H α spectra of the periodic radio star LSI+61◦303. The optical photometric data set covers the time interval 1985-1993 and amounts to about a hundred nights. A period of ∼26 days is found in the V band. The infrared data also present evidence for a similar periodicity, but with higher amplitude of variation ((0.m 2). The spectroscopic observations include 16 intermediate and high dispersion spectra of LSI+61◦303 collected between January 1989 and February 1993. The H α emission line profile and its variations are analyzed. Several emission line parameters -- among them the H α EW and the width of the H α red hump -- change strongly at or close to radio maximum, and may exhibit periodic variability. We also observe a significant change in the peak separation. The H α profile of LSI+61◦303 does not seem peculiar for a Be star. However, several of the observed variations of the H α profile can probably be associated with the presence of the compact, secondary star.

Sistema inalámbrico de control de iluminación

L’objectiu principal del projecte és dissenyar un sistema de control automàtic de il·luminació en un recinte tancat, mitjançant la detecció de presència d’una persona que entra dins del radi d’actuació del sistema. Utilitzant dues plaques amb capacitat de comunicació inalàmbrica es controlaran dos components, un sensor i un actuador. El sensor té la funció de detectar la presència d’una persona per mitjà d’infraroigs, mentre que l’actuador ens proporciona la funcionalitat d’un interruptor amb el qual pogué actuar sobre la llum, encenent-la o apagant-la en funció de l’estat de detecció.