Advanced open-source tools for detecting and analyzing peaks in cloud radar Doppler spectra

<p>Cloud radar Doppler spectra contain vertically highly resolved valuable information about the hydrometeors present in the cloud. A mixture of different hydrometeor types can lead to several peaks in the Doppler spectrum due to their different fall speeds, giving a hint about the size/ density/ number of the respective particles. Tools to separate and interpret peaks in cloud radar Doppler spectra have been developed in the past, but their application is often limited to certain radar settings, or the code not freely available to other users.</p> <p>We here present the effort of joining two methods, which have been developed and published (Radenz et al., 2019; Kalesse et al., 2019) with the aim to make them insensitive to instrument type and settings, and available on GitHub, and applicable to all cloud radars which are part of the ACTRIS CloudNet network.</p> <p>A supervised machine learning peak detection algorithm (PEAKO, Kalesse et al., 2019) is used to derive the optimal parameters to detect peaks in cloud radar Doppler spectra for each set of instrument settings. In the next step, these parameters are used by peakTree (Radenz et al., 2019), which is a tool for converting multi-peaked (cloud) radar Doppler spectra into a binary tree structure. PeakTree yields the (polarimetric) radar moments of each detected peak and can thus be used to classify the hydrometeor types. This allows us to analyze Doppler spectra of different cloud radars with respect to, e.g. the occurrence of supercooled liquid water or ice needles/columns with high linear depolarisation ratio (LDR).</p>

Binary Tree Based Data Gathering Routing Scheme for Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging area in networking since the era of 21 st century. The major benefits of WSN using sensor nodes make it affordable, scalable, economic and reliable. The limitations of sensor nodes are in terms of fixed and limited power supply, durability, storage and computational facilities which make energy as a vast challenge in deploying sensor nodes in order to prevent them from draining. This paper proposes a novel deployment scheme for connecting the sensor nodes in the form of a 4-sided virtual full binary tree structure. In the proposed scheme, data is expected to reach resource opulence Base Station (BS) via hops as equal to the height of the tree. Also, the stability of the network will increase by an average value of around 82.78% in the range of 49- 98% with existing scheme of the network lifetime with respect to different scenarios. The proposed scheme gives excellent results with a variable number of nodes and changing the size of deployment area of WSN.

A Lightweight Blockchain Scheme for a Secure Smart Dust IoT Environment

Since a smart dust Internet of Things (IoT) system includes a very large number of devices sometimes deployed in hard-access areas, it is very difficult to prevent security attacks and to alleviate bottleneck phenomena. In this paper, we propose a lightweight blockchain scheme that helps device authentication and data security in a secure smart dust IoT environment. To achieve our goals, (1) we propose the structure of the lightweight blockchain and the algorithm of processing the blockchain. In addition, (2) we reorganize the linear block structure of the conventional blockchain into the binary tree structure in such a way that the proposed blockchain is more efficient in a secure smart dust IoT environment. Experiments show that the proposed binary tree-structured lightweight blockchain scheme can greatly reduce the time required for smart dust device authentication, even taking into account the tree transformation overhead. Compared with the conventional linear-structured blockchain scheme, the proposed binary tree-structured lightweight blockchain scheme achieves performance improvement by up to 40% (10% in average) with respect to the authentication time.

A Cleaning Control Knowledge-Based System Based on Complex Problem Solving

The intelligent control of cleaning of rice–wheat combined harvester is a complex problem, which includes the initial setting of cleaning control, judgment of cleaning loss state, cause analysis and selection of corresponding control strategies and many other sub-problems. The knowledge contained in these sub-problems, including knowledge representation methods and reasoning strategies, is different. Therefore, this paper decomposes the complex problem of cleaning control into a sub-problem of hierarchical structure, and constructs a knowledge model of cleaning control based on binary tree structure. In this way, the cleaning control problem can be decomposed into a small set of sub-problems by the judgment of the nodes of the binary tree, until the sub-problems are small enough to be solved directly so as to get the solution of the original problem. It is proved by examples that this method is of great significance to improve the efficiency of knowledge acquisition, management and maintenance of the expert system of rice–wheat combine harvester, and to enhance the knowledge service ability of the expert system of rice–wheat combine harvester. This method can also be used for reference in other fields.

Efficient Searchable Symmetric Encryption Supporting Dynamic Multikeyword Ranked Search

Searchable symmetric encryption that supports dynamic multikeyword ranked search (SSE-DMKRS) has been intensively studied during recent years. Such a scheme allows data users to dynamically update documents and retrieve the most wanted documents efficiently. Previous schemes suffer from high computational costs since the time and space complexities of these schemes are linear with the size of the dictionary generated from the dataset. In this paper, by utilizing a shallow neural network model called “Word2vec” together with a balanced binary tree structure, we propose a highly efficient SSE-DMKRS scheme. The “Word2vec” tool can effectively convert the documents and queries into a group of vectors whose dimensions are much smaller than the size of the dictionary. As a result, we can significantly reduce the related space and time cost. Moreover, with the use of the tree-based index, our scheme can achieve a sublinear search time and support dynamic operations like insertion and deletion. Both theoretical and experimental analyses demonstrate that the efficiency of our scheme surpasses any other schemes of the same kind, so that it has a wide application prospect in the real world.

SISTEM PAKAR DIAGNOSA PENYAKIT SALURAN PERNAFASAN DENGAN METODE FUZZY TSUKAMOTO

Respiratory diseases are one of the most common diseases in Indonesia. Respiratory diseases increase the risk of fatal if not treated immediately. However, it is unfortunate that knowledge about the risk of respiratory disease is still lacking. The search method used in making this expert system is forward chaining with binary tree structure. Namely doing the processing of a set of data, then conducted inference in accordance with the rules applied to find the optimal conclusion. Experts provide rules for determining symptoms and illness. While the calculation and ranking of diseases that may suffer patients using the method fuzzy tsukamoto to provide the results of calculations that are certain based on the parameters. Then the patient's diagnostic process is done by the system. The Diagnostic Expert System for Respiratory Disease has been successfully established and can be used to assist in estimating the illness suffered by the patient as the result of the developed system is not much different from the running system. Based on the comparison of disease diagnosis result in expert system with manual system then the system accuracy level is 90,9%. Based on the website view has the largest percentage of 71.42 in good description, for user friendly / ease of respiratory system experts get the largest percentage of 85.71 in good information, to assist in the process of disease information and treatment get the largest percentage of 57.14 in a good description, to help the diagnosis process becomes easier to get the largest percentage of 71.42 in good information, for this expert system provides information on respiratory disease treatment accurately get the largest percentage of 57.14 in either.

Energy Transfer in a Linear Turbulence Model

Energy transfer is present in many natural and engineering systems which include different scales. It is important to study the energy cascade (which refers to the energy transfer among the different scales) of such systems. A well-known example is turbulent flow in which the kinetic energy of large vortices is transferred to smaller ones. Below a threshold vortex scale the energy is dissipated due to viscous friction. We introduce a mechanistic model of turbulence which consists of masses connected by springs arranged in a binary tree structure. To represent the various scales, the masses are gradually decreased in lower levels. The bottom level of the model contains dampers to provide dissipation. We define the energy spectrum of the model as the fraction of the total energy stored in each level. A simple method is provided to calculate this spectrum in the asymptotic limit, and the spectra of systems having different stiffness distributions are calculated. We find the stiffness distribution for which the energy spectrum has the same scaling exponent (−5/3) as the Kolmogorov spectrum of 3D homogeneous, isotropic turbulence.