自适应 OFDM 系统的时域信道预测研究

针对自适应OFDM系统,提出了一种新型时域信道预测策略。该策略利用递归量化分析方法,量化并估计信道脉冲响应中每个时延抽头的局部可预测性,然后利用每个时延抽头的局部可预测性筛选出信道脉冲响应中重要的时延抽头;最后,利用基于联合回声状态网络的信道预测器实现各个重要时延抽头的信道预测。在仿真部分,利用基于IEEE802.11ah协议的OFDM系统来评估系统的性能。仿真结果表明,通过递归量化分析,发射端可以准确地筛选出信道脉冲响应中重要的时延抽头。除此之外,联合回声状态网络可以产生稀疏的输出权值;而且,由于具有oracle属性,提出的联合回声状态网络在基本的回声状态网络的基础上有91.57%的预测性能提升。     

Effects of high density dispersion fuel loading on the dynamics of a low enriched uranium fueled material test research reactor

The effects of using high density low enriched uranium on the dynamics of a material test research reactor were studied. For this purpose, the low density LEU fuel of an MTR was replaced with high density LEU fuels currently being developed under the RERTR program. Since the alloying elements have different properties affecting the reactor in different ways, fuels U–Mo (9w/o) which contain the same elements in same ratio were selected for analysis. Simulations were carried out to determine the reactor performance under reactivity insertion and loss of flow transients. Nuclear reactor analysis code PARET was employed to carry out these calculations. It is observed that during the fast reactivity insertion transient, the maximum reactor power is achieved and the energy released till the power reaches its maximum increases by 45% and 18.5%, respectively, as uranium density increases from 6.57 gU/cm³ to 8.90 gU/cm³. This results in increased maximum temperatures of fuel, clad and coolant outlet, achieved during the transient, by 27.7 K, 19.7 K and 7.9 K, respectively. The time required to reach the peak power decreases. During the slow reactivity insertion transient, the maximum reactor power achieved increases slightly by 0.3% as uranium density increases from 6.57 gU/cm³ to 8.90 gU/cm³ but the energy generated till the power reaches its maximum decreases by 5.7%. The temperatures of fuel, clad and coolant outlet remain almost the same for all types of fuels. During the loss of flow transients, no appreciable difference in the power and temperature profiles was observed and the graph plots overlapped each other.     

Bitter melon (Momordica charantia): a natural healthy vegetable

Bitter melon provides health benefits against various ailments for improving the quality of life. It is nutrient dense plant-based food containing versatility of bioactive compounds such as alkaloids, polypeptide, vitamins, and minerals. Owing to presence of bioactive compounds, it has the ability to fight against various lifestyle related disorders, e.g. cancer insurgence, diabetes mellitus, abdominal pain, kidney (stone), fever, and scabies. Amongst bioactive moieties, p-insulin is similar to insulin whose subcutaneous injection significantly lower blood glucose levels in diabetic patients. It also contains steroidal saponins called charantin, act alike peptides and certain alkaloids that effectively control sugar level in blood. The therapeutic perspectives have been also highlighted as they are helpful in regulating blood cholesterol thus protecting the body from cardiovascular disorders like atherosclerosis. Whole fruit, seeds and leaves of bitter melon regulates impaired antioxidant status and suppress fat accumulation. Moreover, curative potential of its bioactive components and their utilization in value added food products are also the limelight of article.     

Curing behavior and thermal and mechanical properties enhancement of tetraglycidyl‐4,4′‐diaminodiphenylmethane/4,4′‐diaminodiphenylsulfone using a liquid crystalline epoxy

A thermosetting resin system, based on tetraglycidyl‐4,4′‐diaminodiphenylmethane, has been developed via copolymerization with 4,4′‐diaminodiphenylsulfone in the presence of a newly synthesized liquid crystalline epoxy (LCE). The curing behavior of LCE‐containing resin system was evaluated using curing kinetics method and Fourier transform infrared spectroscopy. The effect of LCE on the thermal and mechanical properties of modified epoxy systems was studied. Thermogravimetric analysis indicated that the modified resin systems displayed a high T_0.05 and char yield at lower concentrations of LCE (≤5 wt%), suggesting an improved thermal stability. As determined using dynamic mechanical analysis and differential scanning calorimetry, the glass transition value increased by 9.7% compared to that of the neat resin when the LCE content was 5 wt%. Meanwhile, the addition of 5 wt% of LCE maximized the toughness with a 175% increase in impact strength. The analysis of fracture surfaces revealed a possible effect of LCE as a toughener and showed no phase separation in the modified resin system, which was also confirmed by dynamic mechanical analysis. © 2016 Society of Chemical Industry     

Recent Developments in Minimal Processing: A Tool to Retain Nutritional Quality of Food

The modernization during the last century resulted in urbanization coupled with modifications in lifestyles and dietary habits. In the same era, industrial developments made it easier to meet the requirements for processed foods. However, consumers are now interested in minimally processed foods owing to increase in their awareness to have fruits and vegetables with superior quality, and natural integrity with fewer additives. The food products deteriorate as a consequence of physiological aging, biochemical changes, high respiration rat,e and high ethylene production. These factors contribute substantially to discoloration, loss of firmness, development of off-flavors, acidification, and microbial spoilage. Simultaneously, food processors are using emerging approaches to process perishable commodities, along with enhanced nutritional and sensorial quality. The present review article is an effort to utilize the modern approaches to minimize the processing and deterioration. The techniques discussed in this paper include chlorination, ozonation, irradiation, photosensitization, edible coating, natural preservative use, high-pressure processing, microwave heating, ohmic heating, and hurdle technology. The consequences of these techniques on shelf-life stability, microbial safety, preservation of organoleptic and nutritional quality, and residue avoidance are the limelight of the paper. Moreover, the discussion has been made on the feasibility and operability of these techniques in modern-day processing.     

Nuclear Data Sheets for A=75

The experimental nuclear data for mass=75 have been reviewed, superceding 1981 evaluation by Ekstrom (81Ek02). Detailed level and decay schemes together with summary (adopted), arguments for Jπ assignments and experimental data of the known nuclei (Cu, Zn, Ga, Ge, As, Se, Br, Kr, Rb) are presented. ⁷⁵Cu has been identified as a β⁻ delayed neutron emission precursor while ⁷⁵Ga has been studied in detail in ⁷⁵Zn β⁻ decay recently. In ⁷⁵Ge, there are quite a few new data available, specially on particle-transfer reactions. Not much new information is known for ⁷⁵As except for a particle transfer reaction and ⁷⁵Se ε decay. The same is true for ⁷⁵Se except for a neutron capture study. New data have been reported for ⁷⁵Kr and ⁷⁵Br.     

Hybrid pyramid-DWT-SVD dual data hiding technique for videos ownership protection

Multimedia Tools and Applications - This paper proposes a hybrid pyramid Discrete-Wavelet-Transform (DWT) Singular-Value-Decomposition (SVD) data hiding scheme for video authentication and...     

A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet

Emergence of MapReduce (MR) framework for scaling data mining and machine learning algorithms provides for Volume, while handling of Variety and Velocity needs to be skilfully crafted in algorithms. So far, scalable clustering algorithms have focused solely on Volume, taking advantage of the MR framework. In this paper we present a MapReduce algorithm—data aware scalable clustering (DASC), which is capable of handling the 3 Vs of big data by virtue of being (i) single scan and distributed to handle Volume, (ii) incremental to cope with Velocity and (iii) versatile in handling numeric and categorical data to accommodate Variety. DASC algorithm incrementally processes infinitely growing data set stored on distributed file system and delivers quality clustering scheme while ensuring recency of patterns. The up-to-date synopsis is preserved by the algorithm for the data seen so far. Each new data increment is processed and merged with the synopsis. Since the synopsis itself may grow very large in size, the algorithm stores it as a file. This makes DASC algorithm truly scalable. Exclusive clusters are obtained on demand by applying connected component analysis (CCA) algorithm over the synopsis. CCA presents subtle roadblock to effective parallelism during clustering. This problem is overcome by accomplishing the task in two stages. In the first stage, hyperclusters are identified based on prevailing data characteristics. The second stage utilizes this knowledge to determine the degree of parallelism, thereby making DASC data aware. Hyperclusters are distributed over the available compute nodes for discovering embedded clusters in parallel. Staged approach for clustering yields dual advantage of improved parallelism and desired complexity in \(\mathcal {MRC}^0\) class. DASC algorithm is empirically compared with incremental Kmeans and Scalable Kmeans++ algorithms. Experimentation on real-world and synthetic data with approximately 1.2 billion data points demonstrates effectiveness of DASC algorithm. Empirical observations of DASC execution are in consonance with the theoretical analysis with respect to stability in resources utilization and execution time.