A binary self-organizing map and its FPGA implementation

A binary Self Organizing Map (SOM) has been designed and implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of the binary features

A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement

Low Cost FPGA Implementation of a SPI over High Speed Optical SerDes

Serial Peripheral Interface (SPI) is a commonly used communication protocol that allows serial data transfer between a master and a slave device over a short distance. However, if we require just SPI over long distances currently there is no effective low-cost solution. A SerDes provides a solution to this shortcoming by sending parallel data as a serial transmission and converting it back at the receiver end. However, most of the current SerDes implementations are expensive to implement and cater to very high-speed applications, which is not the case in SPI. In this paper, we present a simple to implement and low cost SerDes solution for sending and receiving multiple SPI and GPIO lines. Our proposed solution makes use of a low cost CLPD / FPGA and is applicable for low data rate applications such as SPI. This paper investigates the simplest solution to the problem, whilst maintaining a reliable single wire / optical link. For testing, we have implemented three novel encoding schemes that all provided good results, each measured by performance against resource usage. One of these encoding schemes has shown a drop-out rate as low as 0.001% over a 24-hour period. Our proposed solution when used in conjunction with an optical fibre medium could potentially allow SPI transmission over several kilometres of distance

5Ghz Chirp Signal Generator for Broadband FMCW Radar Applications

Direct digital synthesis (DDS) is a method of producing an RF analogue waveform which is usually a sine wave. However, there are a limited number of devices capable of producing a high frequency output (of more than 2 GHz). To generate com-plex waveforms, you ideally require a high-end expensive FPGA board with on-board high speed SerDes transceivers coupled with a DAC. The ‘Analog devices’ AD916X series is one of the few devices that can output frequencies over 5 GHz. In this pa-per, we present a low-cost implementation scheme for producing high frequency waveform patterns using Xilinx FPGAs and AD9164, with the minimum of latency. Our proposed solution makes use of the Xilinx 7 series and Ultrascale devices, using the high speed SerDes channels over the FMC connector together with the PCIe bus for fast loading of patterns. With our pro-posed solution it is easy to generate and play back complex waveforms, while maintaining a jitter free and low phase noise output. One of the most important application areas that would benefit from our proposed implementation is the generation of high frequency FMCW radar chirps and simulating target re-sponses especially in the upcoming 77GHz frequency range where the baseband can sweep to 4 GHz

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

Nurse prescribing of medicines in Western European and Anglo-Saxon countries: a systematic review of the literature

<p>Abstract</p> <p>Background</p> <p>A growing number of countries are introducing some form of nurse prescribing. However, international reviews concerning nurse prescribing are scarce and lack a systematic and theoretical approach. The aim of this review was twofold: firstly, to gain insight into the scientific and professional literature describing the extent to and the ways in which nurse prescribing has been realised or is being introduced in Western European and Anglo-Saxon countries; secondly, to identify possible mechanisms underlying the introduction and organisation of nurse prescribing on the basis of Abbott's theory on the division of professional labor.</p> <p>Methods</p> <p>A comprehensive search of six literature databases and seven websites was performed without any limitation as to date of publication, language or country. Additionally, experts in the field of nurse prescribing were consulted. A three stage inclusion process, consisting of initial sifting, more detailed selection and checking full-text publications, was performed independently by pairs of reviewers. Data were synthesized using narrative and tabular methods.</p> <p>Results</p> <p>One hundred and twenty-four publications met the inclusion criteria. So far, seven Western European and Anglo-Saxon countries have implemented nurse prescribing of medicines, viz., Australia, Canada, Ireland, New Zealand, Sweden, the UK and the USA. The Netherlands and Spain are in the process of introducing nurse prescribing. A diversity of external and internal forces has led to the introduction of nurse prescribing internationally. The legal, educational and organizational conditions under which nurses prescribe medicines vary considerably between countries; from situations where nurses prescribe independently to situations in which prescribing by nurses is only allowed under strict conditions and supervision of physicians.</p> <p>Conclusions</p> <p>Differences between countries are reflected in the jurisdictional settlements between the nursing and medical professions concerning prescribing. In some countries, nurses share (full) jurisdiction with the medical profession, whereas in other countries nurses prescribe in a subordinate position. In most countries the jurisdiction over prescribing remains predominantly with the medical profession. There seems to be a mechanism linking the jurisdictional settlements between professions with the forces that led to the introduction of nurse prescribing. Forces focussing on efficiency appear to lead to more extensive prescribing rights.</p

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

FPGA-based CNN for Real-time UAV Tracking and Detection

Neural Networks (NNs) are now being extensively utilised in various Artificial Intelligence platforms specifically in the area of image classification and real-time object tracking. We propose a novel design to address the problem of real time Unmanned Aerial Vehicle (UAV) monitoring and detection using a Zynq Ultrascale FPGA-based Convolutional Neural Network (CNN) implementation. The biggest challenge while implementing real-time algorithms on FPGAs is the limited DSP hardware resources available on FPGA platforms. Our proposed design overcomes the challenge of autonomous real time UAV detection and tracking on a Xilinx's Zynq Ultrascale XCZU9EG System on a Chip (SoC) platform. Our proposed design explores and provides a solution for overcoming the challenge of limited floating-point resources, whilst maintaining real-time performance. The solution consists of two modules: the UAV tracking and the neural network based UAV detection module. The tracking module uses our novel background-differencing algorithm whilst the UAV detection is based on a modified CNN algorithm, designed to give the maximum Field-Programmable Gate Array (FPGA) performance. These two modules are designed to complement each other and are enabled simultaneously to provide an enhanced real-time UAV detection for any given video input. The proposed system has been tested on detecting real-life flying UAVs, achieving an accuracy of 82\%, running at the full frame rate of the input camera for both tracking and Neural Network (NN) detection, achieving similar performance than an equivalent Deep Learning Processor Unit (DPU) Ultrascale FPGA based HD video and tracking implementation, but with lower resource utilisation as shown by our results

FPGA based 77GHz RADAR processing with novel linearisation

There is a renewed interest in low-cost Radio Detection and Ranging (Radar) with the growth in automotive autonomous driving. New System on a Chip (SoC) Field- Programmable Gate Array (FPGA) platforms are now available with onboard Analogue to Digital Converters (ADC)s and Digital to Analogue Converter (DAC)s, making them ideal for Radar processing applications. This is leading to some traditional analogue Radio frequency (RF) electronics being replaced by SoC FPGAs, using a Direct Digital Synthesis (DDS) for frequency ramping. Unfortunately, DDSs also produce unwanted spurious frequencies. In this work we propose an FPGA based Digital Signal Processing / Processor (DSP) processing block for a Frequency Modulated Continuous Wave (FMCW) automotive Radar, implemented on Xilinx’s FPGA, with a digitally generated Chirp, replacing a Voltage controlled oscillator (VCO). For the proposed Radar a novel method of linearisation, for precalibrating the Chirp and FPGA to perform parallel processing of the reflective signal. The proposed design was implemented using MATLAB Simulink Sysgen / Vivado and synthesised on a Zynq FPGA, together with a high-speed ADC and DDS. The design overcomes the challenge of implementing continuous processing on the FPGA platform itself, replacing traditional analogue electronics. The proposed system uses a novel technique of applying dithering to the DDS only when required, greatly reducing the generation of spurious frequencies during transmitting of the linearised chirp. This is the first known 77GHz FPGA to use this technique