An instrument for the characterization and calibration of optical sensors

This paper presents the development of a hardware/software system for the characterization of the electronic response of optical (camera) sensors such as matrix and linear color and monochrome Charge Coupled Device (CCD) or Complementary Metal Oxide Semiconductor (CMOS). The electronic response of a sensor is required for inspection purposes. It also allows the design and calibration of the integrating device to achieve the desired performance. The proposed instrument equipment fulfills the most recent European Machine Vision Association (EMVA) 1288 standard ver. 3.1: The spatial non uniformity of the illumination ΔE must be under 3%, and the sensor must achieve an f-number of 8.0 concerning the light source. The following main innovations have achieved this: An Ulbricht sphere providing a uniform light distribution (irradiation) of 99.54%; an innovative illuminator with proper positioning of color Light Emitting Diodes (LEDs) and control electronics; and a flexible C# program to analyze the sensor parameters, namely Quantum Efficiency, Overall System Gain, Temporal Dark Noise, Dark Signal Non Uniformity (DSNU1288), Photo Response Non-Uniformity (PRNU1288), Maximum achievable Signal to Noise Ratio (SNRmax), Absolute sensitivity threshold, Saturation Capacity, Dynamic Range, and Dark Current. This new instrument has allowed a camera manufacturer to design, integrate, and inspect numerous devices and camera models (Necta, Celera, and Aria)

Framework for Automatic PCB Marking Detection and Recognition for Hardware Assurance

A Bill of Materials (BoM) is a list of all components on a printed circuit board (PCB). Since BoMs are useful for hardware assurance, automatic BoM extraction (AutoBoM) is of great interest to the government and electronics industry. To achieve a high-accuracy AutoBoM process, domain knowledge of PCB text and logos must be utilized. In this study, we discuss the challenges associated with automatic PCB marking extraction and propose 1) a plan for collecting salient PCB marking data, and 2) a framework for incorporating this data for automatic PCB assurance. Given the proposed dataset plan and framework, subsequent future work, implications, and open research possibilities are detailed.Comment: 5 pages, 3 figures, Government Microcircuit Applications & Critical Technology Conference (GOMACTech) 202

CAPAS: A context-aware system architecture for physical activities monitoring

Attribute grammars are widely used by compiler-generators since it allows complete specifications of static semantics. They can also be applied to other fields of research, for instance, to human activities recognition. This paper aims to present CAPAS, a Context-aware system Architecture to monitor Physical ActivitieS. One of the components that is present in the architecture is the attribute grammar which is filled after the prediction is made according to the data gathered from the user through the sensors. According to some predefined rules, the physical activity is validated after an analysis on the attribute grammar, if it meets those requirements. Besides that it proposes an attribute grammar itself which should be able to be incorporated in a system in order to validate the performed physical activity.This work has been supported by FCT – Fundação˜ para a Ciência e Tecnologia within the Project Scope: ˆ UID/CEC/00319/2019

Does the Degree of Trunk Bending Predict Patient Disability, Motor Impairment, Falls, and Back Pain in Parkinson's Disease?

Background: Postural abnormalities in Parkinson's disease (PD) form a spectrum of functional trunk misalignment, ranging from a “typical” parkinsonian stooped posture to progressively greater degrees of spine deviation. Objective: To analyze the association between degree of postural abnormalities and disability and to determine cut-off values of trunk bending associated with limitations in activities of daily living (ADLs), motor impairment, falls, and back pain. Methods: The study population was 283 PD patients with ≥5° of forward trunk bending (FTB), lateral trunk bending (LTB) or forward neck bending (FNB). The degrees were calculated using a wall goniometer (WG) and software-based measurements (SBM). Logistic regression models were used to identify the degree of bending associated with moderate/severe limitation in ADLs (Movement Disorders Society Unified PD Rating Scale [MDS-UPDRS] part II ≥17), moderate/severe motor impairment (MDS-UPDRS part III ≥33), history of falls (≥1), and moderate/severe back pain intensity (numeric rating scale ≥4). The optimal cut-off was identified using receiver operating characteristic (ROC) curves. Results: We found significant associations between modified Hoehn & Yahr stage, disease duration, sex, and limitation in ADLs, motor impairment, back pain intensity, and history of falls. Degree of trunk bending was associated only with motor impairment in LTB (odds ratio [OR] 1.12; 95% confidence interval [CI], 1.03–1.22). ROC curves showed that patients with LTB of 10.5° (SBM, AUC 0.626) may have moderate/severe motor impairment. Conclusions: The severity of trunk misalignment does not fully explain limitation in ADLs, motor impairment, falls, and back pain. Multiple factors possibly related to an aggressive PD phenotype may account for disability in PD patients with FTB, LTB, and FNB

Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM

Infestação por ictio em surubim híbrido durante a fase inicial de criação.

bitstream/item/42263/1/COT-165-2011.pd

Validity of the wall goniometer as a screening tool to detect postural abnormalities in Parkinson's disease

none24INTRODUCTION: Software-based measurements of postural abnormalities in Parkinson's disease (PD) are the gold standard but may be time-consuming and not always feasible in clinical practice. Wall goniometer (WG) is an easier, quicker, and inexpensive instrument for screening patients with postural abnormalities, but no studies have investigated its validity so far. The aim of this study was to investigate the validity of the WG to measure postural abnormalities. METHODS: A total of 283 consecutive PD outpatients with ≥5° forward trunk, lateral trunk or forward neck bending (FTB, LTB, FNB, respectively) were recruited from seven centers for movement disorders. Postural abnormalities were measured in lateral and posterior view using a freeware program (gold standard) and the WG. Both angles were expressed in degrees (°). Sensitivity and specificity for the diagnosis of camptocormia, Pisa syndrome, and anterocollis were assessed. RESULTS: WG showed good to excellent agreement (intraclass correlation coefficient from 0.80 to 0.98) compared to the gold standard. Bland-Altman plots showed a mean difference between the methods from -7.4° to 0.4° with limits of agreements from -17.7° to 9.5°. Sensitivity was 100% for the diagnosis of Pisa syndrome, 95.74% for anterocollis, 76.67% for upper camptocormia, and 63.64% for lower camptocormia. Specificity was 59.57% for Pisa syndrome, 71.43% for anterocollis, 89.80% for upper camptocormia, and 100% for lower camptocormia. Overall, the WG underestimated measurements, especially in lower camptocormia with an average of -8.7° (90% of cases). CONCLUSION: WG is a valid tool for screening Pisa syndrome and anterocollis, but approximately 10° more should be added for camptocormia.openTinazzi M.; Gandolfi M.; Artusi C.A.; Lanzafame R.; Zanolin E.; Ceravolo R.; Capecci M.; Andrenelli E.; Ceravolo M.G.; Bonanni L.; Onofrj M.; Telese R.; Bertolotti C.; Polverino P.; Manganotti P.; Mazzucchi S.; Giannoni S.; Vacca L.; Stocchi F.; Casali M.; Zibetti M.; Lopiano L.; Fasano A.; Geroin C.Tinazzi, M.; Gandolfi, M.; Artusi, C. A.; Lanzafame, R.; Zanolin, E.; Ceravolo, R.; Capecci, M.; Andrenelli, E.; Ceravolo, M. G.; Bonanni, L.; Onofrj, M.; Telese, R.; Bertolotti, C.; Polverino, P.; Manganotti, P.; Mazzucchi, S.; Giannoni, S.; Vacca, L.; Stocchi, F.; Casali, M.; Zibetti, M.; Lopiano, L.; Fasano, A.; Geroin, C

Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signals

Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task

Freezing of gait and fall detection in Parkinson’s disease using wearable sensors:a systematic review

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets