A study of optimal viewing distance in a parallax barrier 3D display

The optimal viewing distance was proposed as a parameter for designing a parallax barrier 3D display. It can be designed based on simple geometric method and by considering the pitches of image display pixels and parallax barrier, or even including the aperture ratios of the image display pixels and parallax barrier. It can be analyzed by using ray tracing method. By considering the optical refraction index of the cover glass, the angular behavior of the system becomes more realistic; however, the geometric method is difficult to be used. We propose a revised method for estimating the view distance and angular behavior. In this paper, we have demonstrated a designated eye position (DEP) for each view and shown that multiple DEPs make a circular curve around the center of the display. We prove the new concept by comparing the optical ray tracing calculations and optical measurement.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

Optimization of sodium sulfide treatment of rice straw to increase the enzymatic hydrolysis in bioethanol production

The efficiency of sodium sulfide-assisted alkaline pulping for cellulose preparation from Oryza sativa L. rice straw in Vietnam for enzymatic saccharification was investigated. The response surface methodology was used for the determination of optimal technological parameters of alkaline pulping such as active alkali dosage, temperature and time. The optimal technological parameters were established to be active alkali dosage of 7%, treatment temperature of 100 °C and treatment time of 120 min. At these regimes, a maximal sugar yield of 51.8% (over dry rice straw) was obtained. It meant that the saccharification efficiency up to 97.1% could be achieved by using sodium sulfide-assisted alkaline pretreatment method. Addition of sodium sulfide into alkaline pretreatment resulted in higher sugar yield, higher level of depolymerization of lignin and less loss of cellulose. Moreover, liquid hydrolyzate after enzymatic hydrolysis was analyzed by HPLC to determine the compositions of sugar mixture. The fiber morphology in pretreated biomass solid was also revealed by SEM.     

A high quality and small shadow size visual secret sharing scheme based on hybrid strategy for grayscale images

Many secret sharing schemes for digital images have been developed in recent decades. Traditional schemes typically must deal with the problem of computational complexity, and other visual secret sharing schemes come with a higher transmission cost and storage cost; that is, each shadow size is m times as big as the original secret image. The new (2,n) secret sharing scheme for grayscale images proposed in this paper is based a combination of acceptable image quality using block truncation coding (BTC), high compression ratio discrete wavelet transform (DWT) and good subjective performance of the vector quantization (VQ) technique. Experimental results confirm that our proposed scheme not only generates a high quality reconstructed original image but also generates small, random-like grayscale shadows.     

Hybrid wet paper coding mechanism for steganography employing n-indicator and fuzzy edge detector

Data hiding technique can facilitate security and the safe transmission of important information in the digital domain, which generally requires a high embedding payload and good stego image quality. Recently, a steganographic framework known as wet paper coding has been utilized as an effective strategy in image hiding to achieve the requirements of high embedding payload, good quality and robust security. In this paper, besides employing this mechanism as a fundamental stage, we take advantage of two novel techniques, namely, an efficient n-indicator and a fuzzy edge detector. The first is to increase the robustness of the proposed system to guard against being detected or traced by the statistics methods while allowing the receiver without knowledge of secret data positions to retrieve the embedded information. The second is to improve the payload and enhance the quality of stego image. The experimental results show that our proposed scheme outperforms its ability to reduce the conflict among three steganography requirements.     

Visualization of diversity in large multivariate data sets

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.     

Recognition and analysis of cell nuclear phases for high-content screening based on morphological features

Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method.