Fast Digital Convolutions using Bit-Shifts

An exact, one-to-one transform is presented that not only allows digital circular convolutions, but is free from multiplications and quantisation errors for transform lengths of arbitrary powers of two. The transform is analogous to the Discrete Fourier Transform, with the canonical harmonics replaced by a set of cyclic integers computed using only bit-shifts and additions modulo a prime number. The prime number may be selected to occupy contemporary word sizes or to be very large for cryptographic or data hiding applications. The transform is an extension of the Rader Transforms via Carmichael's Theorem. These properties allow for exact convolutions that are impervious to numerical overflow and to utilise Fast Fourier Transform algorithms.Comment: 4 pages, 2 figures, submitted to IEEE Signal Processing Letter

Fast Mojette Transform for Discrete Tomography

A new algorithm for reconstructing a two dimensional object from a set of one dimensional projected views is presented that is both computationally exact and experimentally practical. The algorithm has a computational complexity of O(n log2 n) with n = N^2 for an NxN image, is robust in the presence of noise and produces no artefacts in the reconstruction process, as is the case with conventional tomographic methods. The reconstruction process is approximation free because the object is assumed to be discrete and utilizes fully discrete Radon transforms. Noise in the projection data can be suppressed further by introducing redundancy in the reconstruction. The number of projections required for exact reconstruction and the response to noise can be controlled without comprising the digital nature of the algorithm. The digital projections are those of the Mojette Transform, a form of discrete linogram. A simple analytical mapping is developed that compacts these projections exactly into symmetric periodic slices within the Discrete Fourier Transform. A new digital angle set is constructed that allows the periodic slices to completely fill all of the objects Discrete Fourier space. Techniques are proposed to acquire these digital projections experimentally to enable fast and robust two dimensional reconstructions.Comment: 22 pages, 13 figures, Submitted to Elsevier Signal Processin

Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis

Mild Traumatic Brain Injury (mTBI) is a significant public health challenge due to its high prevalence and potential for long-term health effects. Despite Computed Tomography (CT) being the standard diagnostic tool for mTBI, it often yields normal results in mTBI patients despite symptomatic evidence. This fact underscores the complexity of accurate diagnosis. In this study, we introduce an interpretable 3D Multi-Modal Residual Convolutional Neural Network (MRCNN) for mTBI diagnostic model enhanced with Occlusion Sensitivity Maps (OSM). Our MRCNN model exhibits promising performance in mTBI diagnosis, demonstrating an average accuracy of 82.4%, sensitivity of 82.6%, and specificity of 81.6%, as validated by a five-fold cross-validation process. Notably, in comparison to the CT-based Residual Convolutional Neural Network (RCNN) model, the MRCNN shows an improvement of 4.4% in specificity and 9.0% in accuracy. We show that the OSM offers superior data-driven insights into CT images compared to the Grad-CAM approach. These results highlight the efficacy of the proposed multi-modal model in enhancing the diagnostic precision of mTBI.Comment: Accepted by the Australasian Joint Conference on Artificial Intelligence 2023 (AJCAI 2023). 12 pages and 5 Figure

A Novel Tractor Operated Grass Seed Harvester Developed in India

The demand of green and dry fodder in India is estimated to increase to 1170 and 650 m tonne whereas availability is expected to be at 411.3 and 488 m tonne in 2025, respectively, depicting deficit of about 64.9% green fodder and 24.9% dry fodder (Vision 2030, ICAR - IGFRI, Jhansi, 2011). In forages, availability of quality seed is only 25-30% in cultivated fodder and less than 10 % in range grasses and legumes (Vision 2050, IGFRI). Prices paid for grass seeds of native species vary from Rs.5,000 to 6,500 per kg for clean, un-haired seeds due to excessive use of manual labour in seed collection and removing hairy portion. In order to increase the capacity of collection of grass seeds from standing crop, A tractor operated grass seed harvester was developed under a collaborative research project of Indian Council of Agricultural Research two Institutes viz. Indian Grassland and Fodder Research Institute and Central Institute of Agricultural Engineering, keeping in view the requirements of common grasses used as feed material in Indian context. This grass seed harvester was made using nylon brushes arranged in specific fashion on a rotating cylinder and a winding reel in front of rotating cylinder to collect grass seed from the grasses standing in the fields, where tractor can operate. The specific features of this machine were variable speed of rotating cylinder brush, helical arrangement of brushes on the cylinder to carry the detached seed in to the seed box, variable height of operation and front mounting of the machine on tractor. This machine was tested for seed collection in Pennisetum pedicellatum (Dinanath grass), Cenchrus cilliaris (Anjan grass ) and Megathyrsus maximum (Guinea grass). Seed collection capacity of the machine was 4.24 to 7.12 kg/h in Dinanath grass during 2nd operation, 2.10 to 3.56 kg/h in Anjan grass and 1.61 to 3.56 kg/h in Guinea grass at the full maturity of the grass seeds in two passes of the machine in to and fro direction. The field capacity of seed collection operation ranged from 0.21 to 0.47 ha/h for the grasses in which it was operated

In-vitro release study of hydrophobic drug using electrospun cross-linked gelatin nanofibers

Delivering hydrophobic drug within hydrophilic polymer matrix as carrier is usually a challenge. Here we report the synthesis of gelatin nanofibers by electrospinning, followed by testing them as a potential carrier for oral drug delivery system for a model hydrophobic drug, piperine. Electrospun gelatin nanofibers were crosslinked by exposing to saturated glutaraldehyde (GTA) vapor, to improve their water resistive properties. An exposure of only 6 min was not only adequate to control the early degradation with intact fiber morphology, but also significantly marginalized any adverse effects associated with the use of GTA. Scanning electron microscopy imaging, Fourier transform infrared spectroscopy and thermogravimetric analysis were done to study nanofiber morphology, stability of drug and effect of crosslinking. The pH of release medium was also varied as per the gastrointestinal tract for in-vitro drug release study. Results illustrate good compatibility of hydrophobic drug in gelatin nanofibers with promising controlled drug release patterns by varying crosslinking time and pH of release medium

AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application