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111.
The throughput enhancement of space-time spreading (STS)-based code division multiple access (CDMA) system is investigated in this paper. Variable spreading factor (VSF) is utilized to improve the data throughput of the system in non-frequency selective Rayleigh fading channel. In this contribution, an analytical approach is proposed to compute a new expression for the bit error rate (BER) performance of the STS-based CDMA system against pre-despreading signal-to-noise-ratio (SNR) for different values of spreading factor. The other contribution of the paper is deriving a new analytical expression for the throughput enhancement of the VSF STS-based CDMA system in non-frequency selective Rayleigh fading channel. Simulation and analytical results demonstrate that using VSF method in STS-based CDMA system improves the throughput of the system by keeping the BER performance at a target level. 相似文献
112.
The oscillation of second order neutral difference equations with positive and negative coefficients of the form is investigated. We obtain many new results using the comparison between both first order and second order difference equations. An example is given to show the strength of the obtained results. 相似文献
113.
This paper describes an efficient Web page detection approach based on restricting the similarity computations between two
versions of a given Web page to the nodes with the same HTML tag type. Before performing the similarity computations, the
HTML Web page is transformed into an XML-like structure in which a node corresponds to an open-closed HTML tag. Analytical
expressions and supporting experimental results are used to quantify the improvements that are made when comparing the proposed
approach to the traditional one, which computes the similarities across all nodes of both pages. It is shown that the improvements
are highly dependent on the diversity of tags in the page. That is, the more diverse the page is (i.e., contains mixed content
of text, images, links, etc.), the greater the improvements are, while the more uniform it is, the lesser they are. 相似文献
114.
The production of crude palm oil and palm kernel is a complex problem due to the influence of processing variables and environmental factors. These processing variables influence the amount of crude palm oil and palm kernel losses during processing. Instead of mathematical model, fuzzy logic approach provides a simpler and easier mechanism to describe the relationships between the processing variables and the amount of crude palm oil and palm kernel losses. In this study, four fuzzy expert system models were developed for each palm oil processing station. An approximation of centre of gravity method for defuzzification is also proposed to enable the development of the model using Microsoft®Excel. For comparison purpose, the models were also developed using MATLAB®. Results obtained from the Excel model are found to be very close to the results from MATLAB. 相似文献
115.
Ghulam Muhammad Muhammad Hussain Muneer Al-Hammadi Hatim Aboalsamh Hassan Mathkour Amir Saeed Malik 《Behaviour & Information Technology》2016,35(11):958-967
ABSTRACTThe effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM. 相似文献
116.
Maryam Amiri Hassan Bakhshandeh Amnieh Mahdi Hasanipanah Leyli Mohammad Khanli 《Engineering with Computers》2016,32(4):631-644
Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R 2), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations. 相似文献
117.
Senti‐CS: Building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi‐Square‐based feature weight generation
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Sentiment analysis involves the detection of sentiment content of text using natural language processing. Natural language processing is a very challenging task due to syntactic ambiguities, named entity recognition, use of slangs, jargons, sarcasm, abbreviations and contextual sensitivity. Sentiment analysis can be performed using supervised as well as unsupervised approaches. As the amount of data grows, unsupervised approaches become vital as they cut down on the learning time and the requirements for availability of a labelled dataset. Sentiment lexicons provide an easy application of unsupervised algorithms for text classification. SentiWordNet is a lexical resource widely employed by many researchers for sentiment analysis and polarity classification. However, the reported performance levels need improvement. The proposed research is focused on raising the performance of SentiWordNet3.0 by using it as a labelled corpus to build another sentiment lexicon, named Senti‐CS. The part of speech information, usage based ranks and sentiment scores are used to calculate Chi‐Square‐based feature weight for each unique subjective term/part‐of‐speech pair extracted from SentiWordNet3.0. This weight is then normalized in a range of ?1 to +1 using min–max normalization. Senti‐CS based sentiment analysis framework is presented and applied on a large dataset of 50000 movie reviews. These results are then compared with baseline SentiWordNet, Mutual Information and Information Gain techniques. State of the art comparison is performed for the Cornell movie review dataset. The analyses of results indicate that the proposed approach outperforms state‐of‐the‐art classifiers. 相似文献
118.
Alireza Shamsi Amir Shamloo Negar Mohammadaliha Hassan Hajghassem Jalil Fallah Mehrabadi Masoumeh Bazzaz 《Microsystem Technologies》2016,22(10):2447-2454
Since plasma is rich in many biomarkers used in clinical diagnostic experiments, microscale blood plasma separation is a primitive step in most of microfluidic analytical chips. In this paper, a passive microfluidic device for on-chip blood plasma separation based on Zweifach–Fung effect and plasma skimming was designed and fabricated by hot embossing of microchannels on a PMMA substrate and thermal bonding process. Human blood was diluted in various times and injected into the device. The main novelty of the proposed microfluidic device is the design of diffuser-shaped daughter channels. Our results demonstrated that this design exerted a considerable positive influence on the separation efficiency of the passive separator device, and the separation efficiency of 66.6 % was achieved. The optimum purity efficiency of 70 % was achieved for 1:100 dilution times. 相似文献
119.
Murad Khan Awais Ahmad Shehzad Khalid Syed Hassan Ahmed Sohail Jabbar Jamil Ahmad 《Multimedia Tools and Applications》2017,76(23):24649-24674
Vertical handover gain significant importance due to the enhancements in mobility models by the Fourth Generation (4G) technologies. However, these enhancements are limited to specific scenarios and hence do not provide support for generic mobility. Similarly, various schemes are proposed based on these mobility models but most of them are suffered from the high packet loss, frequent handovers, too early and late handovers, inappropriate network selection, etc. To address these challenges, a generic vertical handover management scheme for heterogeneous wireless networks is proposed in this article. The proposed scheme works in three phases. In the first phase, a handover triggering approach is designed to identify the appropriate place for initiating handover based on the estimated coverage area of a WLAN access point or cellular base station. In the second phase, fuzzy rule based system is designed to eliminate the inappropriate networks before deciding an optimal network for handover. In the third phase, a network selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. The proposed scheme is tested in a mobility scenario with different speeds of a mobile node ranging from very low to very high. The simulation results are compared with the existing decision models used for network selection and handover triggering approaches. The proposed scheme outperforms these schemes in terms of energy consumption, handover delay and time, packet loss, good put, etc. 相似文献
120.
Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences. 相似文献