An analytical solution of Shallow Water system coupled to Exner equation

In this paper, an exact smooth solution for the equations modeling the bedload transport of sediment in Shallow Water is presented. This solution is valid for a large family of sedimentation laws which are widely used in erosion modeling such as the Grass model or those of Meyer-Peter & Muller. One of the main interest of this solution is the derivation of numerical benchmarks to valid the approximation methods

An ultrafast 1 x M all-optical WDM packet-switched router based on the PPM header address

This paper presents an all-optical 1 x M WDM router architecture for packet routing at multiple wavelengths simultaneously, with no wavelength conversion modules. The packet header address adopted is based on the pulse position modulation (PPM) format, thus enabling the use of only a singlebitwise optical AND gate for fast header address correlation. It offers multicast as well as broadcast capabilities. It is shown that a high speed packet routing at 160 Gb/s can be achieved with a low channel crosstalk (CXT) of ~ -27 dB at a channel spacing of greater than 0.4 THz and a demultiplexer bandwidth of 500 GHz

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Visible light emission from reverse-biased silicon nanometer-scale diode-antifuses

Silicon nanometer-scale diodes have been fabricated to emit light in the visible range at low power consumption. Such structures are candidates for emitter elements in Si-based optical interconnect schemes. Spectral measurements of Electroluminescence (EL) on the reverse-biased nanometer-scale diodes brought into breakdown have been carried out over the photon energy range of 1.4-2.8 eV. Previously proposed mechanisms for avalanche emission from conventional silicon p-n junctions are discussed in order to understand the origin of the emission. Also the stability of the diodes has been tested. Results indicate that our nanometer-scale diodes are basically high quality devices. Furthermore due to the nanometer-scale dimensions, very high electrical fields and current densities are possible at low power consumption. This makes these diodes an excellent candidate to be utilized as a light source in Si-based sensors and actuator application

Dynamic priority based reliable real-time communications for infrastructure-less networks

This paper proposes a dynamic priority system at medium access control (MAC) layer to schedule time sensitive and critical communications in infrastructure-less wireless networks. Two schemes, priority enabled MAC (PE-MAC) and optimized PE-MAC are proposed to ensure real-time and reliable data delivery in emergency and feedback systems. These schemes use a dynamic priority mechanism to offer improved network reliability and timely communication for critical nodes. Both schemes offer a notable improvement in comparison to the IEEE 802.15.4e low-latency deterministic networks. To ensure more predictable communication reliability, two reliability centric schemes, quality-ensured scheme (QES) and priority integrated QES, are also proposed. These schemes maintain a pre-specified successful packet delivery rate, hence improving the overall network reliability and guaranteed channel access

Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS