GNSS Precise Point Positioning Using Low-Cost GNSS Receivers

There are positioning techniques available such as Real-Time Kinematic (RTK) which allow user to obtain few cm-level positioning, but require infrastructure cost, i.e., setting up local or regional networks of base stations to provide corrections. Precise Point Positioning (PPP) using dual-frequency receivers is a popular standalone technique to process GNSS data by applying precise satellite orbit and clock correction along with other corrections to produce cm to dm-level positioning. At the time of writing, almost all low-cost and ultra-low-cost (few $10s) GNSS units are single-frequency chips. Single-frequency PPP poses challenges in terms of effectively mitigating ionospheric delay and the multipath, as there is no second frequency to remove the ionospheric delay. The quality of measurements also deteriorates drastically from geodetic-grade to ultra-low-cost hardware. Given these challenges, this study attempts to improve the performance of single-frequency PPP using geodetic-grade hardware, and to capture the potential positioning performance of this new generation of low-cost and ultra-low-cost GNSS chips. Raw measurement analysis and post-fit residuals show that measurements from cellphones are more prone to multipath compared to signals from geodetic-grade and low-cost receivers. Horizontal accuracy of a few-centimetres is demonstrated with geodetic-grade hardware. Whereas accuracy of few-decimetres is observed from low-cost and ultra-low-cost GNSS hardware. With multi-constellation processing, improvements in accuracy and reductions in convergence time over initial 60 minutes period, are also demonstrated with three different set of GNSS hardware. Horizontal and vertical rms of 37 cm and 51 cm, respectively, is achieved using a cellphone

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

A New Image Fusion Algorithm Based on Wavelet Transform and Adaptive Neuro Fuzzy Logic Approach

ABSTRACT: Image fusion is a process of integrating multiple images of the same scene into a single output fused image. It minimizes redundancy and reduce uncertainty and extract all the useful information from the source images. The process of image fusion is required for different applications like remote sensing, medical imaging, machine vision and military applications where critical information and quality is required. In this paper the image fusion using the combination of wavelet transform and adaptive neuro fuzzy logic is implemented. The results are compared with the pixel level image fusion in spatial domain with fuzzy and neuro fuzzy logic approach along with the quality evaluation indices for image fusion like entropy, RMSE(Root Mean Square Error), PSNR(Peak Signal to Noise Ratio) and Correlation Coefficient. Experimental results prove that the above algorithm is better than the other fusion techniques. KEYWORDS: fuzzy logic, neuro fuzzy logic, image quality indices, root mean square error, peak signal to noise ratio, correlation coefficient. I.INTRODUCTION Any piece of paper makes sense only when it is able to convey the information across. The clarity of information is important. Image Fusion is a mechanism to improve the quality of information from a set of images. By the process of image fusion, the good information from each of the given images is fused together to form a resultant image whose quality is superior to any of the input images. This is achieved by applying a sequence of operators on the images that would make the good information in each of the image prominent The input images may be from different sensors II. LITERATURE SURVE