Dynamic Time Slice Calculation for Round Robin Process Scheduling Using NOC

Process scheduling means allocating a certain amount of CPU time to each of the user processes.  One of the popular scheduling algorithms is the “Round Robin” algorithm, which allows each and every process to utilize the CPU for short time duration.  Processes which finish executing during the time slice are removed from the ready queue.  Processes which do not complete execution during the specified time slice are removed from the front of the queue, and placed at the rear end of the queue. This paper presents an improvisation to the traditional round robin scheduling algorithm, by proposing a new method. The new method represents the time slice as a function of the burst time of the waiting process in the ready queue. Fixing the time slice for a process is a crucial factor, because it subsequently influences many performance parameters like turnaround time, waiting time, response time and the frequency of context switches.  Though the time slot is fixed for each process, this paper explores the fine-tuning of the time slice for processes which do not complete in the stipulated time allotted to them

Effect of β/α Strength Ratio on the Stress-Strain Curve of Beta Titanium Alloy by Finite Element Modelling

A systematic study was undertaken to determine the effect of the β/α strength ratio on the stress-strain behavior of near beta titanium alloy by the finite element method where the volume percent of the second phase was constant at 16 vol.%. The β/α strength ratio of the harder β phase to the softer α phase was varied from approximately 4 to 5 where the a phase strength (0.2% YS) was kept constant at 296 MPa. It was found that the flow stress did not vary linearly with the strength ratio. Stress gradients were found in both α and β phases and the nature of the stress gradient was found to depend on α particle shape. In some locations higher stresses were found in near the interface. In β, the stresses were generally higher near the interfaces

Effective segmentation of sclera, iris and pupil in noisy eye images

In today’s sensitive environment, for personal authentication, iris recognition is the most attentive technique among the various biometric technologies. One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil and sclera of a captured eye-image. In our proposed method, initially input image is preprocessed by using bilateral filtering. After the preprocessing of images contour based features such as, brightness, color and texture features are extracted. Then entropy is measured based on the extracted contour based features to effectively distinguishing the data in the images. Finally, the convolution neural network (CNN) is used for the effective sclera, iris and pupil parts segmentations based on the entropy measure. The proposed results are analyzed to demonstrate the better performance of the proposed segmentation method than the existing methods.

Transformer less Series Active Filter for Power Quality Improvement

- To upgrade the power quality in single-stage frameworks with critical loads a transformer less hybrid series dynamic channel is proposed . This venture helps the energy administration and power quality issues identified with electric transportation and spotlights on enhancing electric vehicle load association with the grid. The control technique is intended to counteract current harmonic bends of nonlinear loads to stream into the utility and rectifies the power element of this later. While shielding sensitive loads from voltage disturbance influences, droops, and swells started by the power framework, ridded of the arrangement transformer, the design is invaluable for a mechanical usage. This polyvalent half and half topology permitting the symphonious separation and pay of voltage bends could ingest or infuse the assistant energy to the grid. The aggregate consonant bending is decreased with the adequacy of the fluffy controller. This venture additionally examines on the impact of increases and postponements in the continuous controller dependability. The simulation result  brought out through MATLAB/SIMULINK programming

Photoinduced Electron Spin Resonance Phenomenon in α

The photoinduced phenomenon in α-Cr2O3 nanoscaled spherical particles was investigated in the temperature range of 150 up to 315 K. An X-band electron-spin resonance spectrometry was employed to probe the magnetic behavior in α-Cr2O3 under an IR illumination in the nanosecond regime. The photoinduced effect on both low and high field ESR signals appears above 280 K and is remarkably enhanced just below Néel temperature TN. Such a photoinduced ESR phenomenon disappears in a reproducible way in the paramagnetic insulating state which occurs above TN of crystalline α-Cr2O3. In the antiferromagnetic phase, that is, below TN, the shift of the low field absorption could be attributed to the interaction of the light with specific Cr3+ ions located in strongly distorted sites correlated to strong ligand-field effect

Classification of skin disease using deep learning neural networks with mobilenet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2x lesser computations than the conven-tional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity

Comparative evaluation of protein content in groundnut samples by near infrared reflectance spectroscopy and Skalar colorimetric methods

A lot of research has been done in developing groundnut cultivars with high-quality oil. As a result, methods for routinely determining oil content and quality have been developed and utilized1. However, groundnut is also a source of protein, and obviously, there is a need to develop a rapid, accurate and economic method that can be routinely used for screening a large number of groundnut cultivars for protein content. At the ICRISAT analytical service laboratory, protein (total N) in various crops is routinely determined by colorimetric method using Skalar auto analyser. However, near infrared reflectance spectroscopy (NIRS) also provides an opportunity to determine protein content in groundnut samples; and the method seems attractive as it is low cost, simple and rapid. The NIRS based method provides an automated measurement and has the potential to become a valuable tool for providing analytical support for agricultural research2,3. The objectives of this study were to estimate and compare the relative efficacy of the NIRS method, with that of a conventional colorimetric method, following digestion of ground samples, using Skalar autoanalyser for determining protein in groundnut samples..

Relationship of grain iron and zinc content with grain yield in pearl millet hybrids

Development of pearl millet cultivars with high levels of grain iron (Fe) and zinc (Zn) content can make significant contribution to reducing widespread deficiencies of these micronutrients in populations heavily dependent on staple cereals for their dietary energy and nutritional requirements. It is imperative that breeding of such cultivars must not compromise on grain yield and farmer-preferred traits. Multi-location evaluation of two sets of hybrids with differing genetic composition showed that Fe and Zn contents had highly significant and high positive correlations in both sets of hybrids and in all environments, and they were not correlated with grain weight, implying simultaneous genetic improvement of both micronutrients in large-seeded background is likely to be effective. Both micronutrients had moderate to low negative correlations with grain yield in both sets of hybrids, although not always significant. Such associations might have resulted due to the involvement of inidia germplasm as a common“Source— of high Fe and Zn content in both male and female parents, thereby reducing the genetic diversity between the parental lines for traits associated with heterosis for grain yield. Whether this could also be due to natural negative association between genetic factors for these micronutrients on one hand and grain yield on the other merits further studies through selection experiments using genomic tools as the resolution of this issue has a direct bearing on breeding high-yielding hybrids with high levels of Fe and Zn content in pearl mille

Biochemical Composition and Disease Resistance in Newly Synthesized Amphidiploid and Autotetraploid Peanuts

Genetic diversity in peanut (Arachishypogaea L.) is narrow due to its evolution and domestication processes. Amphidiploids and autotetraploids (newly synthesized tetraploids) were created to broaden its genetic base. Molecular analysis has shown that the newly synthesized tetraploids had broader genetic base; and were genetically divergent when compared to cultivated peanut. Nutritional composition relative to oil, fatty acid composition, O/L ratio, protein, iodine value and presence of plant proteinase inhibitors such as trypsin and chymotrypsin inhibitors were studied in the synthesized tetraploids. Some of the newly synthesized tetraploids had higher amounts of proteinase inhibitors. Evaluation of newly synthesized tetraploids revealed several lines resistant to late leaf spot (LLS) and peanut bud necrosis disease (PBND)