Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging.

Movement intention detection from autocorrelation of EEG for BCI

Movement intention detection is important for development of intuitive movement based Brain Computer Interfaces (BCI). Various complex oscillatory processes are involved in producing voluntary movement intention. In this paper, temporal dynamics of electroencephalography (EEG) associated with movement intention and execution were studied using autocorrelation. It was observed that the trend of decay of autocorrelation of EEG changes before and during the voluntary movement. A novel feature for movement intention detection was developed based on relaxation time of autocorrelation obtained by fitting exponential decay curve to the autocorrelation. This new single trial feature was used to classify voluntary finger tapping trials from resting state trials with peak accuracy of 76.7%. The performance of autocorrelation analysis was compared with Motor-Related Cortical Potentials (MRCP).

An automatic correction method for the heel effect in digitized mammography images

The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

Fully electrochemical hyphenated flow system for preconcentration, cleanup, stripping, capillary electrophoresis with stacking and contactless conductivity detection of trace heavy metals

Successful coupling of electrochemical preconcentration (EPC) to capillary electrophoresis (CE) with contactless conductivity detection (C(4)D) is reported for the first time. The EPC-CE interface comprises a dual glassy carbon electrode (GCE) block, a spacer and an upper block with flow inlet and outlet, pseudo-reference electrode and a fitting for the CE silica column, consisting of an orifice perpendicular to the surface of a glassy carbon electrode with a bushing inside to ensure a tight press fit. The end of the capillary in contact with the GCE is slant polished, thus defining a reproducible distance from the electrode surface to the column bore. First results with EPC-CE-C(4)D are very promising, as revealed by enrichment factors of two orders of magnitude for Tl, Cu, Pb and Cd ion peak area signals. Detection limits for 10 min deposition time fall around 20 nmol L(-1) with linear calibration curves over a wide range. Besides preconcentration, easy matrix exchange between accumulation and stripping/injection favors procedures like sample cleanup and optimization of pH, ionic strength and complexing power. This was demonstrated for highly saline samples by using a low conductivity buffer for stripping/injection to improve separation and promote field-enhanced sample stacking during electromigration along the capillary. (C) 2010 Elsevier B.V. All rights reserved.

Improving the Detectability of Sequential Injection Chromatography (SIC): Determination of Triazines by Exploiting Liquid Core Waveguide (LCW) Detection

Coupling a liquid core waveguide cell to a sequential injection chromatograph improved the detection limits for determination of triazine herbicides without compromising peak resolution. Separation of simazine, atrazine, and propazine was achieved in water samples by a 25mm long C18 monolithic column. Detection was made at 238nm using a type II LCW (silica capillary coated with Teflon (R) AF2400) cell with 100cm of optical path length. Detection limits for simazine, atrazine, and propazine were 2.3, 1.9, and 4.5 mu g L-1, respectively. Reduced analysis time and low solvent consumption are other remarkable features of the proposed method.

A simple and sensitive detection of diquat herbicide using a dental amalgam electrode A comparison using the chromatographic technique

This paper describes the use of a dental amalgam electrode (DAE) to evaluate the electrochemical behaviour and to develop an electroanalytical procedure for determination of diquat herbicide in natural water and potato samples. The work was based on the square wave voltammetry responses of diquat, which presented two well-defined and reversible reduction peaks, at -0.56 V (peak 1) and -1.00V (peak 2). The experimental and voltammetric parameters were optimised, and the analytical curves were constructed and compared to similar curves performed by high performance liquid chromatography coupled to ultraviolet-visible spectrophotometric detector (HPLC/UV-vis). The responses were directly proportional to diquat concentration in a large interval of concentration, and the calculated detection limits were very similar, around 10 mu g L(-1) (10 ppb) for voltammetric and chromatographic experiments. These values were lower than the maximum residue limit established for natural water by the Brazilian Environmental Agency. The recovery percentages in pure electrolyte, natural water and potato samples showed values from 70% to 130%, demonstrating that the voltammetric methodology proposed is suitable for determining any contamination by diquat in different samples, minimising the toxic residues due to the use of liquid mercury or the adsorptive process relative to use of other solid surfaces. (C) 2009 Published by Elsevier B.V.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Comparative analysis of the volatile fraction from Annona cherimola Mill. cultivars by solid-phase microextraction and gas chromatography–quadrupole mass spectrometry detection

The analysis of volatile compounds in Funchal, Madeira, Mateus and Perry Vidal cultivars of Annona cherimola Mill. (cherimoya) was carried out by headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–quadrupole mass spectrometry detection (GC–qMSD). HS-SPME technique was optimized in terms of fibre selection, extraction time, extraction temperature and sample amount to reach the best extraction efficiency. The best result was obtained with 2 g of sample, using a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibre for 30 min at 30 °C under constant magnetic stirring (800 rpm). After optimization of the extraction methodology, all the cherimoya samples were analysed with the best conditions that allowed to identify about 60 volatile compounds. The major compounds identified in the four cherimoya cultivars were methyl butanoate, butyl butanoate, 3-methylbutyl butanoate, 3-methylbutyl 3-methylbutanoate and 5-hydroxymethyl-2-furfural. These compounds represent 69.08 ± 5.22%, 56.56 ± 15.36%, 56.69 ± 9.28% and 71.82 ± 1.29% of the total volatiles for Funchal, Madeira, Mateus and Perry Vidal cultivars, respectively. This study showed that each cherimoya cultivars have 40 common compounds, corresponding to different chemical families, namely terpenes, esters, alcohols, fatty acids and carbonyl compounds and using PCA, the volatile composition in terms of average peak areas, provided a suitable tool to differentiate among the cherimoya cultivars.

Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers: a powerful strategy for breast cancer diagnosis

A sensitive assay to identify volatile organic metabolites (VOMs) as biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. Therefore the aim of this study was to establish the urinary metabolomic profile of breast cancer patients and healthy individuals (control group) and to explore the VOMs as potential biomarkers in breast cancer diagnosis at early stage. Solid-phase microextraction (SPME) using CAR/PDMS sorbent combined with gas chromatography–mass spectrometry was applied to obtain metabolomic information patterns of 26 breast cancer patients and 21 healthy individuals (controls). A total of seventy-nine VOMs, belonging to distinct chemical classes, were detected and identified in control and breast cancer groups. Ketones and sulfur compounds were the chemical classes with highest contribution for both groups. Results showed that excretion values of 6 VOMs among the total of 79 detected were found to be statistically different (p < 0.05). A significant increase in the peak area of (−)-4-carene, 3-heptanone, 1,2,4-trimethylbenzene, 2-methoxythiophene and phenol, in VOMs of cancer patients relatively to controls was observed. Statiscally significant lower abundances of dimethyl disulfide were found in cancer patients. Bioanalytical data were submitted to multivariate statistics [principal component analysis (PCA)], in order to visualize clusters of cases and to detect the VOMs that are able to differentiate cancer patients from healthy individuals. Very good discrimination within breast cancer and control groups was achieved. Nevertheless, a deep study using a larger number of patients must be carried out to confirm the results.

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

Detection of antibody to Toxocara vitulorum perieneteric fluid antigens (Pe) in the colostrum and serum of buffalo calves and cows by Western blotting

Toxocara vitulorum, a nematode parasite in the small intestine of cattle and water buffaloes, causes high morbidity and mortality of 1-3 months old buffalo calves. This research evaluated the specific perieneteric antigens (Pe) reactivity of anti-T. vitulorum-Pe antibody (Tv-Pe-Ab) in both immune sera and colostrum from buffalo cows immediately post-partum from buffalo cows. The presence of Tv-Pe-Ab in sera of buffalo newborn calves was also examined at 1 day before and after suckling the colostrum as well as in sera from naturally infected calves at the beginning and peak of the maximum infection and then again during the period of rejection and post-rejection of the parasite. Pe antigens were characterized for Tv-Pe-Ab by SDS-PAGE and Western blot (WB). The SDS-PAGE showed that Pe contained nine protein bands (11, 14, 31, 38, 58, 76, 88,112 and 165 kDa). All Pe bands were recognized by Tv-Pe-Ab in sera and colostrum of buffalo cows. Only the serum antibodies of buffalo calves at 1 day of age after suckling the colostrum and during the beginning of T. vitulorum infection recognized Pe antigen's nine bands. In contrast, serum antibodies from 1-day-old buffalo calves, taken before suckling colostrum, did not react with any protein band. In suckling calves, which reached peak egg output, rejection and post-rejection stages of the infection, serum Tv-Pe-Ab reactivity with lower molecular weight protein bands (11-76 kDa) was lost and only reactivity with the Pe protein bands of higher molecular weight (88, 112 and 165 kDa) remained. (c) 2005 Elsevier B.V. All rights reserved.

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

High sensitivity fiber optic angular displacement sensor and its application for detection of ultrasound

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)