Summative Stereoscopic Image Compression using Arithmetic Coding

Image compression targets at plummeting the amount of bits required for image representation for save storage space and speed up the transmission over network. The reduction of size helps to store more images in the disk and take less transfer time in the data network. Stereoscopic image refers to a three dimensional (3D) image that is perceived by the human brain as the transformation of two images that is being sent to the left and right human eyes with distinct phases. However, storing of these images takes twice space than a single image and hence the motivation for this novel approach called Summative Stereoscopic Image Compression using Arithmetic Coding (S2ICAC) where the difference and average of these stereo pair images are calculated, quantized in the case of lossy approach and unquantized in the case of lossless approach, and arithmetic coding is applied. The experimental result analysis indicates that the proposed method achieves high compression ratio and high PSNR value. The proposed method is also compared with JPEG 2000 Position Based Coding Scheme(JPEG 2000 PBCS) and Stereoscopic Image Compression using Huffman Coding (SICHC). From the experimental analysis, it is observed that S2ICAC outperforms JPEG 2000 PBCS as well as SICHC

Malayalam Handwritten Character Recognition Using AlexNet Based Architecture

This research article proposes a new handwritten Malayalam character recognition model based on AlexNet based architecture. The Malayalam language consists of a variety of characters having similar features, thus, differentiating characters is a challenging task. A lot of handcrafted feature extraction methods have been used for the classification of Malayalam characters. Convolutional Neural Networks (CNN) is one of the popular methods used in image and language recognition. AlexNet based CNN is proposed for feature extraction of basic and compound Malayalam characters. Furthermore, Support Vector Machine (SVM) is used for classification of the Malayalam characters. The 44 primary and 36 compound Malayalam characters are recognised with better accuracy and achieved minimal time consumption using this model. A dataset consisting of about 180,000 characters is used for training and testing purposes. This proposed model produces an efficiency of 98% with the dataset. Further, a dataset for Malayalam characters is developed in this research work and shared on Interne

STABILITY-INDICATING REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY METHOD FOR ANALYZING INJECTION DOSAGE FORMULATION CONTAINING MEDROXYPROGESTERONE ACETATE AND ESTRADIOL CYPIONATE

Objective: Stability-indicating reversed-phase high-performance liquid chromatography method with photodiode array detection is described for the assay of medroxyprogesterone acetate (MDA) and estradiol cypionate (ECA) in bulk and injection dosage form. Methods: MDA and ECA were determined on a Cosmicsil (250 mm × 4 mm) C18, 5 μm analytical column using mobile phase of 0.1 M KH2PO4 and acetonitrile (65:35 v/v) supplied isocratically by a flow rate of 1 ml/min. During stress testing, the sample was subjected to stress with 0.1 N HCl, 0.1 N NaOH, 30% hydrogen peroxide, water, and 105°C in oven and sunlight. Method validation was done in accordance with international conference on harmonization. Results: The linear response was obtained over the concentration range from 2.5 to 7.5 μg/ml for ECA and 12.5 to 37.50 μg/ml for MDA. The recoveries of MDA and ECA were 99.31%–99.45% and 99.59%–99.79%, with relative standard deviation ranging from 0.021% to 0.217% and 0.027% to 0.187%, respectively. The limits of detection for MDA and ECA were 0.097 μg/ml and 0.042 μg/ml, respectively. The method was able to selectively quantitate MDA and ECA in the presence of the degradation products and, hence, can be considered as stability-indicating one. Proposed method was applied to the quantification of MDA and ECA in injection dosage form with good precision and accuracy. Conclusion: The method can be employed for routine and quality control analysis of MDA and ECA simultaneously

Securely Training Decision Trees Efficiently

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada et al.) construct an MPC protocol for decision tree training with a communication of $\mathcal{O}(hmN\log N)$, when building a decision tree of height $h$ for a training dataset of $N$ samples, each having $m$ attributes. In this work, we significantly reduce the communication complexity of secure decision tree training. We construct a protocol with communication complexity $\mathcal{O}(mN\log N + hmN + hN\log N)$, thereby achieving an improvement of $\approx \mathsf{min}(h, m, \log N)$ over Hamada et al. At the core of our technique is an improved protocol to regroup sorted private elements further into additional groups (according to a flag vector) while maintaining their relative ordering. We implement our protocol in the MP-SPDZ framework and show that it requires $10\times$ lesser communication and is $9\times$ faster than the state-of-the-art

SARS-CoV-2 Vaccination Associated Transverse Myelitis: A Case Report

While policy makers around the globe have meticulously organised mass immunisation against Coronavirus Disease 2019 (COVID-19), its safety concerns and adverse events that need prompt evaluation are also emerging. Acute Transverse Myelitis (TM) is a rare neurological phenomenon where motor, sensory or autonomic disturbance occurs as a result of spinal cord injury. The aetiology of transverse myelitis is thought to be immune-mediated as a result of infection, parainfectious disorder, autoimmune disease or malignancy. Though a rare disease, acute TM warrants prompt recognition and aggressive therapy for favourable neurological patient outcomes. Hereby, authors presented this case of a 61-year-old male patient who developed symptoms of acute TM, 20 days after receiving an adenovirus vectored ChAdOx1 nCoV-19 vaccine against SARS-CoV-2. The patient was treated with intravenous steroids, supportive care with Foley’s catheterisation and his weakness and bladder control improved over 1 week

Position Based Coding Scheme and Huffman Coding in JPEG2000: An Experimental analysis

Abstract-The paper compares the novel method of position based coding scheme introduced recently by the authors with Huffman coding results. The results show that Position Based Coding Scheme (PBCS) is superior in terms of image compression ratio and PSNR. In PBCS, by identifying the unique elements and by reducing redundancies the coding has been performed. The results of JPEG2000 image compression with Huffman coding and the JPEG2000 based on PBCS are then compared. The results show that the PBCS has better compression ratio with higher PSNR and better image quality. The study, which can be considered as a logical extension of the image transformation matrix, applies statistical tools to achieve the novel coding scheme as a direct extension to wavelet based image compression. The coding scheme can highly economise the bandwidth without compromising on picture quality; invariant to the existing compression standards and lossy as well as lossless compressions which offers possibility for wide ranging applications

Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial

Background Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects. Methods FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762. Findings Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months. Interpretation Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function. Funding UK Stroke Association and NIHR Health Technology Assessment Programme

Performance analysis of preemptive priority retrial queue with immediate Bernoulli feedback under working vacations and vacation interruption

The present investigation deals with performance analysis of single server preemptive priority retrial queue with immediate Bernoulli feedback. There are two types of customers are considered, which are priority customers and ordinary customers. The priority customers do not form any queue and have an exclusive preemptive priority to receive their services over ordinary customers. After completion of regular service for ordinary customer, the customer is allowed to make an immediate feedback with probability r. When the orbit becomes empty at service completion instant for a priority customer or ordinary customer; the server goes for multiple working vacations. By using the supplementary variable technique, we obtained the steady state probability generating functions for the system/orbit. Some important system performance measures, the mean busy period and the mean busy cycle are discussed. Finally, some numerical examples are presented

A Novel Tri-Stage Recognition Scheme for Handwritten Malayalam Character Recognition

AbstractThe paper proposes an efficient and novel approach of Malayalam character recognition which acquires higher recognition rate for entire character set of Malayalam script. The proposed scheme follows a three stage feature extraction. In the first stage we are grouping characters into different classes based on the number of corners, bifurcations, loops and endings. In the second phase we are identifying exact character in the class based on the different feature extraction technique specially defined for each class. In the third stage we are checking the probability of occurrence of the current character in the given position based on defined rules for the formation of words