A neural network based fall detector

In this project we present an intelligent fall detector system based on a 3-axis accelerometer and a neural network model that allows recognizing severaI possible motion situations and performing an emergency call only when a fall situation occurs, with low false negatives rate and low false positives rate. The system is based on a two module platform. The first one is a Mobile station (MS) and should be carried always by the person. An accelerometer is implemented in this module and its information is transferred via a radio-frequency channel (RF) to the Base station (BS). The BS is fixed and is connected to a GSM (Global system for Mobile communication) module. A neural network model was built in to the BS and is able to identify falls from other possible motion situations, based on the received information. According to the neural network response the system sends a SMs (short Message service) to a destination number requesting for assistance

Fish fillet authentication by image analysis

The work aims at developing an image analysis procedure able to distinguish high value fillets of Atlantic cod (Gadus morhua) from those of haddock (Melanogrammus aeglefinus). The images of fresh G. morhua (n \ubc 90) and M. aeglefinus (n \ubc 91) fillets were collected by a flatbed scanner and processed at different levels. Both untreated and edge-based segmented (Canny algorithm) regions of interest were submitted to surface texture evaluation by Grey Level Co-occurrence Matrix analysis. Twelve surface texture variables selected by Principal Component Analysis or by SELECT algorithm were then used to develop Linear Discriminant Analysis models. An average correct classification rate ranging from 86.05 to 92.31% was obtained in prediction, irrespective the use of raw or segmented images. These findings pave the way for a simple machine vision system to be implemented along fish market chain, in order to provide stakeholders with a simple, rapid and cost-effective system useful in fighting commercial frauds

Lane background removal in thin-layer chromatography images using continuous wavelet

This paper describes a new methodology to remove the background of the lanesin Thin-Layer Chromatography (TLC) images aiming at improving subsequentband detection. The storage of the biological samples to be analysed by TLC isusually done via plastic containers. Filter paper is an alternative that allowsreduced costs and higher portability, but with consequences in the image analysisstage due to a lane background alteration. In order to overcome this problem, anegative control lane is generated in every chromatographic plate. After preprocessingand lane detection stages a one-dimensional intensity profile is usedfor integrating lane information and the background influence is removed withthe help of the Continuous Wavelet Transform (CWT) decomposition. Theproposed method was tested in 78 lane images, A band detection algorithm wasapplied on lane profiles, and a superior detection rate was achieved for thebackground removed lanes

Machine learning based augmented reality for improved learning application through object detection algorithms

Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-of-the-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience

Peak annotation and data analysis software tools for mass spectrometry imaging

La metabolòmica espacial és la disciplina que estudia les imatges de les distribucions de compostos químics de baix pes (metabòlits) a la superfície dels teixits biològics per revelar interaccions entre molècules. La imatge d'espectrometria de masses (MSI) és actualment la tècnica principal per obtenir informació d'imatges moleculars per a la metabolòmica espacial. MSI és una tecnologia d'imatges moleculars sense marcador que produeix espectres de masses que conserven les estructures espacials de les mostres de teixit. Això s'aconsegueix ionitzant petites porcions d'una mostra (un píxel) en un ràster definit a través de tota la seva superfície, cosa que dona com a resultat una col·lecció d'imatges de distribució de ions (registrades com a relacions massa-càrrega (m/z)) sobre la mostra. Aquesta tesi té com a objectius desenvolupar eines computacionals per a l'anotació de pics de MSI i el disseny de fluxos de treball per a l'anàlisi estadística i multivariant de dades MSI, inclosa la segmentació espacial. El treball realitzat en aquesta tesi es pot separar clarament en dues parts. En primer lloc, el desenvolupament d'una eina d'anotació de pics d'isòtops i adductes adequada per facilitar la identificació de compostos de rang de massa baix. Ara podem trobar fàcilment ions monoisotòpics als nostres conjunts de dades MSI gràcies al paquet de programari rMSIannotation. En segon lloc, el desenvolupament de eines de programari per a l’anàlisi de dades i la segmentació espacial basades en soft clustering per a dades MSI.La metabolómica espacial es la disciplina que estudia las imágenes de las distribuciones de compuestos químicos de bajo peso (metabolitos) en la superficie de los tejidos biológicos para revelar interacciones entre moléculas. Las imágenes de espectrometría de masas (MSI) es actualmente la principal técnica para obtener información de imágenes moleculares para la metabolómica espacial. MSI es una tecnología de imágenes moleculares sin marcador que produce espectros de masas que conservan las estructuras espaciales de las muestras de tejido. Esto se logra ionizando pequeñas porciones de una muestra (un píxel) en un ráster definido a través de toda su superficie, lo que da como resultado una colección de imágenes de distribución de iones (registradas como relaciones masa-carga (m/z)) sobre la muestra. Esta tesis tiene como objetivo desarrollar herramientas computacionales para la anotación de picos en MSI y en el diseño de flujos de trabajo para el análisis estadístico y multivariado de datos MSI, incluida la segmentación espacial. El trabajo realizado en esta tesis se puede separar claramente en dos partes. En primer lugar, el desarrollo de una herramienta de anotación de picos de isótopos y aductos adecuada para facilitar la identificación de compuestos de bajo rango de masa. Ahora podemos encontrar fácilmente iones monoisotópicos en nuestros conjuntos de datos MSI gracias al paquete de software rMSIannotation.Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds (metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging (MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means algorithm)

Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears

In order to diagnose malaria, the test that has traditionally been conducted is the gold standard test. The process mainly entails the preparation of a blood smear on glass slide, staining the blood and examining the blood through the use of a microscope so as to observe parasite genus plasmodium. Although these are several other kinds of diagnostic test solutions that are available and which can be adopted, there are numerous shortcomings which are always observed when microscopic analysis is carried out. Presently, the treatments are hugely conducted based on symptoms and upon the occurrence of false negatives, it might be fatal and may result into the creation of different kinds of implications. There have been a number of deaths which have been associated with malaria and as a result, there is the dire need to ensure that there is early detection of malarial infection among the people. This manuscript mainly provides a review of the current contributions regarding computer aided strategies, as well as microscopic image processing strategies for the detection of malaria. They are discussed based on the contemporary literature

Correction of geometrical distortions in bands of chromatography images

This paper presents a methodology for correcting band distortions in Thin-LayerChromatography (TLC) images. After the segmentation of image lanes, theintensity profile of each lane column is spatially aligned with a reference profileusing a modified version of the Correlation Optimized Warping (COW)algorithm. The proposed band correction methodology was assessed using 105profiles of TLC lanes. A set of features for band characterization was extractedfrom each lane profile, before and after band distortion correction, and was usedas input for three distinct one-class classifiers aiming at band identification. In allcases, the best results of band classification were obtained for the set lanes afterband distortion correction

Immunochromatographic diagnostic test analysis using Google Glass.

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.e., the user) and also stored on a central server along with the RDT image, QR code, and other related information (e.g., demographic data). The same server also provides a dynamic spatiotemporal map and real-time statistics for uploaded RDT results accessible through Internet browsers. We tested this Google Glass-based diagnostic platform using qualitative (i.e., yes/no) human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) tests. For the quantitative RDTs, we measured activated tests at various concentrations ranging from 0 to 200 ng/mL for free and total PSA. This wearable RDT reader platform running on Google Glass combines a hands-free sensing and image capture interface with powerful servers running our custom image processing codes, and it can be quite useful for real-time spatiotemporal tracking of various diseases and personal medical conditions, providing a valuable tool for epidemiology and mobile health

Signal and image processing methods for imaging mass spectrometry data

Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...