Hardware/software co-design of fractal features based fall detection system

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%

Development of thermally formed glass optics for astronomical hard x-ray telescopes

The next major observational advance in hard X-ray/soft gamma-ray astrophysics will come with the implementation of telescopes capable of focusing 10-200 keV radiation. Focusing allows high signal-to-noise imaging and spectroscopic observations of many sources in this band for the first time. The recent development of depth-graded multilayer coatings has made the design of telescopes for this bandpass practical, however the ability to manufacture inexpensive substrates with appropriate surface quality and figure to achieve sub-arcminute performance has remained an elusive goal. In this paper, we report on new, thermally-formed glass micro-sheet optics capable of meeting the requirements of the next-generation of astronomical hard X-ray telescopes

Encapsulation of a zinc phthalocyanine derivative in self-assembled peptide nanofibers

Cataloged from PDF version of article.In this article, we demonstrate encapsulation of octakis(hexylthio) zinc phthalocyanine molecules by non-covalent supramolecular organization within self-assembled peptide nanofibers. Peptide nanofibers containing octakis(hexylthio) zinc phthalocyanine molecules were obtained via a straight-forward one-step self-assembly process under aqueous conditions. Nanofiber formation results in the encapsulation and organization of the phthalocyanine molecules, promoting ultrafast intermolecular energy transfer. The morphological, mechanical, spectroscopic and non-linear optical properties of phthalocyanine containing peptide nanofibers were characterized by TEM, SEM, oscillatory rheology, UV-Vis, fluorescence, ultrafast pump-probe and circular dichroism spectroscopy techniques. The ultrafast pump-probe experiments of octakis(hexylthio) zinc phthalocyanine molecules indicated pH controlled non-linear optical characteristics of the encapsulated molecules within self-assembled peptide nanofibers. This method can provide a versatile approach for bottom-up fabrication of supramolecular organic electronic devices. © 2012 The Royal Society of Chemistry

Orthogonally bifunctionalised polyacrylamide nanoparticles: a support for the assembly of multifunctional nanodevices

Polyacrylamide nanoparticles bearing two orthogonal reactive functionalities were prepared by reverse microemulsion polymerisation. Water-soluble photosensitisers and peptide or carbohydrate moieties were sequentially attached to the new nanospecies by orthogonal conjugations based on copper- catalysed azide-alkyne cycloaddition and isothiocyanate chemistry

HRNN4F: Hybrid deep random neural network for multi-channel fall activity detection

Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron

X-ray scatter measurements from thermally slumped thin glass substrates for the HEFT hard x-ray telescopes

We have performed x-ray specular reflectivity and scattering measurements of thermally slumped glass substrates on x-ray diffractometers utilizing a rotating anode x-ray source at the Danish Space Research Institute (DSRI) and synchrotron radiation at the European Synchrotron Radiation Facility (ESRF) optics Bending Magnet beamline. In addition, we tested depth graded W/Si multilayer-coated slumped glass using x-ray specular reflectivity measurements at 8.048 keV and 28 keV and energy-dispersive measurements in the 20-50 keV rang at a double-axis diffractometer at the Orsted Laboratory, University of Copenhagen. The thermally slumped glass substrates will be used to fabricate the hard x-ray grazing incidence optics for the High-Energy Focusing Telescope. We compared the measurements to the SODART- mirrors from the SRG telescope mission program. The surface scatter measurement of the thermally slumped glass substrates yields Half Power Diameters (HPD's) of single- bounce mirrors of full-illuminated lengths of ~ 40 arcseconds for typical substrates and as low as ~ 10 arcseconds for the best substrates, whereas the SODART mirrors yields HPD's of ~ 80 arcseconds with very little variation. Both free-standing glass substrates and prototype mounted and multilayer-coated optics were tested. The result demonstrate that the surface scatter contribution, plus any contribution from the mounting procedure, to the Half Power Diameter from a telescope using the slumped glass optics will be in the subarcminute range.In addition we measured low surface microroughness, yielding high reflectivity, from the glass substrates, as well as from the depth graded W/Si multilayer-coated glass glass (interfacial width 4.2 Å)

Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model