Requirements modelling of real-time systems

Real-time systems are characterised by the critical nature of their missions, and the demanding environment with which they interact. Real-time systems are used for dedicated applications. Every application is the subject of special requirements enforced by the customer. Considering the vital role that these systems play, it is imperative that a systematic approach be adopted in modelling their unique requirements. In this thesis I propose such a treatment. Real-time systems are time critical. Temporal requirements are the timing restrictions imposed by the application environment. Previous studies in requirements modelling of real-time systems have focused on adding the notion of time to modelling techniques of traditional systems without regard to the realities of requirements modelling. The information should be presented in the way the user handles it, and not the way which is convenient to the software engineer. I attempt to understand the needs of the users better by modelling the real world as close to the user's perspective as possible, and propose the Real World Model (RWM). RWM is assumed to be developed by users, and requirements engineers. An engineering approach to building the model is provided. A real-time system has a well defined use to its community. A requirements model must rely on the user level activities, and aid the human understanding and communication. In the RWM, a real-time system is viewed as a set of concurrently acting automata, each representing a system entity. This model supports temporal reasoning in easily described ways, for all classes of timing properties. A generalised classification of timing constraints is provided. A requirements modelling language facilitates the description of requirements, and serves as a medium of communication among developers and stakeholders. Jarke et al [Jarke 94] observe that there is a need for a requirements language that manages the relationship between the meta-level domain scheme, and the scenarios that actually instantiate the scheme under development. Here I propose Timed Requirements Language (TRL) to bridge this gulf between the world of stakeholders, and the world of specifiers. TRL has natural looking expressions for formulating the needs. TRL has a number of novel features including the treatment of causality, and the description of static, and dynamic constraints all integrated into one uniform framework. TRL has been used with a number of systems. The generality of the language is validated through its application to specific systems

Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8

This paper proposed the enhancement of Landsat8 imagery through an Un-decimated Dual-Tree Complex Wavelet Transform (UDT-CWT) based denoising method and modified homographic filter for edge preservation. This work has been extended by estimating several vegetation parameters like Normalized Difference of Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MASVI), and Soil & Atmospherically Resistant Vegetation Index (SARVI). Once the estimation of these parameters was done, the effect of noise was verified. Wavelet decomposes the image into frequency subbands and de-noises each subband separately. These subbands help to increase the resolution. The general problem of the homomorphic filter is that it doesn’t enhance the Low-frequency components which also play a key role in estimating Vegetation Indices (VI).So it was modified to enhance the high-frequency components as well as low-frequency details. Monitoring of vegetation parameters using remote sensing is one of the prominent ways in the estimation of crop yield, Land Use Land Cover (LULC), Water resource management, Drought management, etc. The high-resolution image is more preferable than moderate resolution image to retrieve VI. Image denoising and enhancing the spatial resolution helps to retrieve the parameters well and accurate. The proposed algorithm was working on the images of Landsat8

Denoising of Locally Received NOAA images for Remote Sensing Applications

Remote Sensing means capturing images of earth’s surface using satellites. Remote Sensing finds its applications in agriculture sector, climate studies, forest fire detection, pollution monitoring and oceanography etc. In this paper, NOAA images are considered as Remote Sensing images. NOAA images are directly received by using L Band antenna, located at Sri Venkateswara University, Tirupati, Andhra Pradesh state, India. The received NOAA images are denoised using spatial and frequency domain denoising techniques with modified soft thresholding. The proposed thresholding technique preserves the green content of the image even after denoising by which accuracy of outcome can be increased in remote sensing applications. Comparison of the performance is done to prove that the proposed techniques are better than existing methods

Heuristics Techniques for Scheduling Problems with Reducing Waiting Time Variance

In real computational world, scheduling is a decision making process. This is nothing but a systematic schedule through which a large numbers of tasks are assigned to the processors. Due to the resource limitation, creation of such schedule is a real challenge. This creates the interest of developing a qualitative scheduler for the processors. These processors are either single or parallel. One of the criteria for improving the efficiency of scheduler is waiting time variance (WTV). Minimizing the WTV of a task is a NP-hard problem. Achieving the quality of service (QoS) in a single or parallel processor by minimizing the WTV is a problem of task scheduling. To enhance the performance of a single or parallel processor, it is required to develop a stable and none overlap scheduler by minimizing WTV. An automated scheduler\u27s performance is always measured by the attributes of QoS. One of the attributes of QoS is ‘Timeliness’. First, this chapter presents the importance of heuristics with five heuristic-based solutions. Then applies these heuristics on 1‖WTV minimization problem and three heuristics with a unique task distribution mechanism on Qm|prec|WTV minimization problem. The experimental result shows the performance of heuristic in the form of graph for consonant problems

Intracultural Cognizance of Medicinal Plants of Warangal North Forest Division, Northern Telangana, India

Differences in the traditional botanical knowledge of Koya communities inhabiting Eturnagaram Wildlife Sanctuary (Warangal North Forest Division) are investigated. Eighteen villages (16 within the wildlife sanctuary and two outside it) were selected to test the null hypothesis that there exist no cognitive differences among the ethnic inhabitants in their ability to recognize the plants and recall the vernacular names and medicinal uses since they are recipients of the same dry deciduous forest ecosystem services. The Koyas were found to use as medicine 237 species in 66 angiosperm families. Analyses of data gathered from villagers showed that there is significant intracultural diversity in terms of taxonomic groups and growth forms in regard to utilizing the proximate plant resource for their primary healthcare and disease treatment of pets

Efficient multimedia data storage in cloud environment

With the rapid adoption of social media, people are more habituated to utilize the images and video for expressing themselves. Future communication will replace the conventional means of social interaction with the video or images. This, in turn, requires huge data storage and processing power. This paper reports a compression/decompression module for image and video sequences for the cloud computing environment. The reported mechanism acts as a submodule of IaaS layer of the cloud. The compression of the images is achieved using redundancy removal using block matching algorithm. The proposed module had been evaluated with three different video compression algorithms and variable macroblock size. The experimentations has been carried out on a cloud host environment by using VMWarework station platform. Apart from being simple in execution, the proposed module does not incur an additional monetary burden, hardware or manpower to achieve the desired compression of the image data. Experimental analysis has shown a considerable reduction in data storage requirement as well as the processing time.Web of Science39444243

Chiral spin textures creation and dynamics in a rectangular nanostructure

Controlled creation of stable chiral spin textures is required to use them as an energy-efficient information carrier in spintronics. Here we have studied the stable creation of isolated chiral spin texture (skyrmion and antiskyrmion) and its pair through the magnetization reversal of a rectangular nanostructure using spin-polarized currents. An isolated spin texture is created through a negative current pulse. Dynamics of the stable spin texture are explored under external magnetic fields, and the resonant frequencies are calculated. A stable skyrmion pair is created using an asymmetric current pulse, and their interaction is studied using the Thiele equation. The stability of isolated or paired spin texture depends on the DMI strength, spin-polarized current density, and pulse duration. In addition, the stability of the skyrmion pair depends on their initial separation, and a threshold for the separation between skyrmions of 78 nm is observed.Comment: 29 pages, 11 figures, 2 extra figure

Fast disintegrating tablets of flurbiprofen: formulation and characterization

The purpose of development oral fast disintegrating drug delivery is not only to give fast relief but also to overcome difficulty in swallowing tablets and capsules, resulting in non-compliance and ineffective therapy. The aim of present study is to formulate fast disintegrating of flurbiprofen by using superdisintegrants. Flurbiprofen fast disintegrating tablets were prepared by using direct compression method and were characterized for both pre-compression parameters and post-compression parameters to comply with pharmacopoeial limits. From the in vitro drug release studies the optimized formulation showed almost complete drug release (above 99 %) within 15 min. DSC and FTIR studies were carried out to understand the drug-polymer compatibility and revealed that there was no possible interaction between them. Thus developed fast disintegrating tablets may be suitable to give rapid drug delivery and rapid onset of action.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Nicotine, IFN-γ and retinoic acid mediated induction of MUC4 in pancreatic cancer requires E2F1 and STAT-1 transcription factors and utilize different signaling cascades

BACKGROUND: The membrane-bound mucins are thought to play an important biological role in cell–cell and cell–matrix interactions, in cell signaling and in modulating biological properties of cancer cell. MUC4, a transmembrane mucin is overexpressed in pancreatic tumors, while remaining undetectable in the normal pancreas, thus indicating a potential role in pancreatic cancer pathogenesis. The molecular mechanisms involved in the regulation of MUC4 gene are not yet fully understood. Smoking is strongly correlated with pancreatic cancer and in the present study; we elucidate the molecular mechanisms by which nicotine as well as agents like retinoic acid (RA) and interferon-γ (IFN-γ) induce the expression of MUC4 in pancreatic cancer cell lines CD18, CAPAN2, AsPC1 and BxPC3. RESULTS: Chromatin immunoprecipitation assays and real-time PCR showed that transcription factors E2F1 and STAT1 can positively regulate MUC4 expression at the transcriptional level. IFN-γ and RA could collaborate with nicotine in elevating the expression of MUC4, utilizing E2F1 and STAT1 transcription factors. Depletion of STAT1 or E2F1 abrogated the induction of MUC4; nicotine-mediated induction of MUC4 appeared to require α7-nicotinic acetylcholine receptor subunit. Further, Src and ERK family kinases also mediated the induction of MUC4, since inhibiting these signaling molecules prevented the induction of MUC4. MUC4 was also found to be necessary for the nicotine-mediated invasion of pancreatic cancer cells, suggesting that induction of MUC4 by nicotine and other agents might contribute to the genesis and progression of pancreatic cancer. CONCLUSIONS: Our studies show that agents that can promote the growth and invasion of pancreatic cancer cells induce the MUC4 gene through multiple pathways and this induction requires the transcriptional activity of E2F1 and STAT1. Further, the Src as well as ERK signaling pathways appear to be involved in the induction of this gene. It appears that targeting these signaling pathways might inhibit the expression of MUC4 and prevent the proliferation and invasion of pancreatic cancer cells

Statistical Analysis and Deep Learning Associated Modeling for Early stage Detection of Carinoma

The high death rate and overall complexity of the cancer epidemic is a global health crisis. Progress in cancer prediction based on gene expression has increased in light of the speedy advancement using modern high-throughput sequencing methods and a wide range of machine learning techniques, bringing insights into efficient and precise treatment decision-making. Therefore, it is of significant interest to create machine learning systems that accurately identify cancer patients and healthy people. Although several classification systems have been applied to cancer prediction, no single strategy has proven superior. This research shows how to apply deep learning to an optimization method that uses numerous machine learning models. Statistical analysis has helped us choose informative genes, and we've been feeding those to five different categorization models. The results from the five different classifiers are ensembled in the next step using a deep learning technique. The three most common types of adenocarcinoma are those of the lungs, stomach, and breasts. The suggested deep learning-based inter-ensembles model was tested with deep learning-based algorithms on Carcinoma data. The results of the tests show that relative to using only one set of classifiers or the simple consensus algorithm, it improves the precision of cancer prognosis in every analyzed carcinoma dataset. The suggested deep learning-based inter-ensemble approach is demonstrated to be reliable and efficient for cancer diagnosis by entirely using diverse classifiers