DETECTION OF THE DSSS SIGNAL WITH VARIABLE PN CODES USING THE ANN

- Direct sequence spread spectrum systems appeared and are used to protect the transmitted data DSSS Systems might be one of the solutions for reliable and secured communications. Also, it is one of the approaches used by signals for transmitting bandwidth larger compared to the satisfied frequency related to the original information. The communication systems of SS were vital to suppress interference, complicating the detection and processing of secure communications, the technology of spread spectrum (DSSS) has been initially created for military applications. In a traditional DSSS system, the PN code is the primary key to make the receiver recover the transmitted data. In this paper, by using the MATLAB R2020a is used to simulate the proposed system, it is considered that the transmitter sends data bits and wants to protect the sent data by making each bit send with a PN code consisting of 127 bits randomly without informing the receiver of that. here the artificial neural network (ANN) was used as a tool to find the PN code for each initial value of 7 flip-flops. so, the receiver could detect the transmitted data with BER =0.

Mine MIMO Depth Receiver: An Intelligent Receiving Model Based on Densely Connected Convolutional Networks

Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver.

Occurrence of Aedes cretinus in Cyprus based on information collected by citizen scientists

The current study presents a success story where through a small network of citizen scientists original information regarding the occurrence of Aedes cretinus was received, suggesting the absence of invasive Aedes species on the island of Cyprus. Citizen science records show that Ae. cretinus most likely has an island wide spatial distribution while its seasonal activity is limited to the spring and summer months. Data collected by citizen scientists under ‘The Three Mosquiteers’ citizen science initiative, provided more records than any efforts from the authorities in the last 73 years. The future success and the longevity of this initiative will be strongly dependent on the in-depth engagement and collaboration between local experts in entomology and citizen scientists.

The european foundation for quality management (efqm) model scale: a quantitative instrument

The purpose of the paper is to present a scale to measure EFQM model application in any organization as an instrument. This paper will present a set of original information and tool for the Model users. A quantitative research strategy with SPSS uses to test the validity and reliability of the presented questioners. The EFQM model scale will guide and support future research lines in this field of management. A literature review from previous publications wasconducted. Many papers were selected and aspects related to the purpose. The suggested scale was tested with around 100 NGOs in the Middle East testing the EFQM criteria validity and reliability.The study result presents a clear scale and instrument help to measure the nine criteria presented as a main component for this model, and more furthermore a dozen lines of future research.

Malicious Node Identification in Coded Distributed Storage Systems under Pollution Attacks

In coding-based distributed storage systems (DSSs), a set of storage nodes (SNs) hold coded fragments of a data unit that collectively allow one to recover the original information. It is well known that data modification (a.k.a. pollution attack) is the Achilles’ heel of such coding systems; indeed, intentional modification of a single coded fragment has the potential to prevent the reconstruction of the original information because of error propagation induced by the decoding algorithm. The challenge we take in this work is to devise an algorithm to identify polluted coded fragments within the set encoding a data unit and to characterize its performance. To this end, we provide the following contributions: (i) We devise MIND (Malicious node IdeNtification in DSS), an algorithm that is general with respect to the encoding mechanism chosen for the DSS, it is able to cope with a heterogeneous allocation of coded fragments to SNs, and it is effective in successfully identifying polluted coded fragments in a low-redundancy scenario; (ii) We formally prove both MIND termination and correctness; (iii) We derive an accurate analytical characterization of MIND performance (hit probability and complexity); (iv) We develop a C++ prototype that implements MIND to validate the performance predictions of the analytical model. Finally, to show applicability of our work, we define performance and robustness metrics for an allocation of coded fragments to SNs and we apply the results of the analytical characterization of MIND performance to select coded fragments allocations yielding robustness to collusion as well as the highest probability to identify actual attackers.

Woody and semi-woody species in the collection “Rare and endangered plant species of Siberia” (Novosibirsk)

. The article presents the results of the conservation of woody and semi-woody species under introductionconditions in the collection “Rare and endangered plant species of Siberia” of the Central Siberian Botanical Garden (Novosibirsk). This collection was founded in 1965 years. The analysis of the fund of this collection is carried out. For a longtime (more than 10 years), 22 woody and semi-woody species from 14 families from 8 regions of Siberia are grown in thecollection. Four species are endemic: Sedum populifolium Pallas, Cotoneaster lucidus Schlecht., Sibiraea altaiensis (Laxm.)Schneider and Tilia sibirica Fisher ex Bayer. Most of the species are represented in the collection by small populations. Thisis due to the problems of reproduction of species in the conditions of culture, for many of which methods of vegetative andseed reproduction have not been developed. The traditional method of reproduction used for herbaceous plants – laboratory-greenhouse-soil. This method is often ineffective for woody species due to the long pregenerative period and low biological longevity of seeds. In the introduction, microecological conditions are selected for each species, since they belongto different ecological groups. Among the introduced species there are 13 mesophytes, 8 xerophytes, 1 xeromesophyte. Thearticle presents literary and original information about the methods of reproduction of cultivated species.

Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode

Time series analysis has been an important branch of information processing, and the conversion of time series into complex networks provides a new means to understand and analyze time series. In this work, using Variational Auto-Encode (VAE), we explored the construction of latent networks for univariate time series. We first trained the VAE to obtain the space of latent probability distributions of the time series and then decomposed the multivariate Gaussian distribution into multiple univariate Gaussian distributions. By measuring the distance between univariate Gaussian distributions on a statistical manifold, the latent network construction was finally achieved. The experimental results show that the latent network can effectively retain the original information of the time series and provide a new data structure for the downstream tasks.

A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation

Accurate and efficient image mosaicking is essential for generating wide-range swath images of spaceborne scanning synthetic aperture radar (ScanSAR). However, the existing methods cannot guarantee the accuracy and efficiency of stitching simultaneously, especially when mosaicking multiple large-area images. In this paper, we propose a novel image mosaic method based on homography matrix compensation to solve the mentioned problem. A set of spaceborne ScanSAR images from the Gaofen-3 (GF-3) satellite were selected to test the performance of the new method. First, images are preprocessed by an improved Wallis filter to eliminate intensity inconsistencies. Then, to reduce the enormous computational redundancy of registration, the overlapping areas of adjacent images are coarsely extracted using geolocation technologies. Furthermore, to improve the efficiency of stitching and maintain the original information and resolution of images, we deduce a compensation of homography matrix to implement downsampled images registration and original-size images projection. After stitching, the transitions at the edges of the images were smooth and seamless, the information and resolution of the original images were preserved successfully, and the efficiency of the mosaic was improved by approximately one thousand-fold. The validity, high efficiency and reliability of the method are verified.

A Tool for Energy Consumption Monitoring and Analysis of the Android Terminal

With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.

The Features of Building a Portfolio of Trading Strategies Using the SAS OPTMODEL Procedure

The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.