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)

Pulse variational quantum eigensolver on cross-resonance based hardware

State-of-the-art noisy digital quantum computers can only execute short-depth quantum circuits. Variational algorithms are a promising route to unlock the potential of noisy quantum computers since the depth of the corresponding circuits can be kept well below hardware-imposed limits. Typically, the variational parameters correspond to virtual $R_Z$ gate angles, implemented by phase changes of calibrated pulses. By encoding the variational parameters directly as hardware pulse amplitudes and durations we succeed in further shortening the pulse schedule and overall circuit duration. This decreases the impact of qubit decoherence and gate noise. As a demonstration, we apply our pulse-based variational algorithm to the calculation of the ground state of different hydrogen-based molecules (H$_2$, H$_3$ and H$_4$) using IBM cross-resonance-based hardware. We observe a reduction in schedule duration of up to $5\times$ compared to CNOT-based Ans\"atze, while also reducing the measured energy. In particular, we observe a sizable improvement of the minimal energy configuration of H$_3$ compared to a CNOT-based variational form. Finally, we discuss possible future developments including error mitigation schemes and schedule optimizations, which will enable further improvements of our approach paving the way towards the simulation of larger systems on noisy quantum devices

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

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

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

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

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

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

The PRIAMO study: age- and sex-related relationship between prodromal constipation and disease phenotype in early Parkinson's disease.

OBJECTIVES: To explore the impact of sex and age on relationship between prodromal constipation and disease phenotype in Parkinson's disease at early stages. METHODS: A total of 385 Parkinson's disease patients from the PRIAMO study were classified according to the presence of prodromal constipation and followed for 24 months. Multivariable mixed-effect models were applied. All analyses were performed separately for sex (64.1% men) and median age (different by sex: 67 years-old in men and 68 years-old in women). RESULTS: As for sex, prodromal constipation was associated with greater odds of attention/memory complaints and apathy symptoms in women only. As for age, prodromal constipation was associated with lower cognitive and higher apathy scores in older patients only. CONCLUSIONS: Prodromal constipation anticipates lower cognitive performances and more severe apathy since the earliest stages in women and older patients. Sex- and age-related heterogeneity of prodromal markers of Parkinson's disease may impact disease phenotype