Investigation of clouds spatial distribution using ground-based lidar

Certain investigations of spatial distribution of clouds by employing ground based lidar are presented. The great changeability is one of the main properties of the clouds in the boundary layer. As a result, their optical properties change rapidly which complicates the modeling of cloud evolution. Suitable equipped lidars are widely used for diagnostics of the clouds via a number of scattering and absorption processes. In particular, lidar can provide information on the distribution of meteorological parameters and of the other important cloud characteristics both in vertical and horizontal directions. Herein the time and spatial distribution of the aerosol backscattering coefficient of S sub t and S sub c type clouds are investigated by means of a correlation analysis of the lidar obtained data provided from several arbitrary directions and heights

A general purpose intelligent surveillance system for mobile devices using deep learning

In this paper the design, implementation, and evaluation of a general purpose smartphone based intelligent surveillance system is presented. It has two main elements; i) a detection module, and ii) a classification module. The detection module is based on the recently introduced approach that combines the well-known background subtraction method with the optical flow and recursively estimated density. The classification module is based on a neural network using Deep Learning methodology. Firstly, the architecture design of the convolutional neural network is presented and analyzed in the context of the four selected architectures (two of them recent successful types) and two custom modifications specifically made for the problem at hand. The results are carefully evaluated, and the best one is selected to be used within the proposed system. In addition, the system is implemented on both a PC (using Linux type OS) and on a smartphone (using Android). In addition to the compatibility with all modern Android-based devices, most GPU powered platforms such as Raspberry Pi, Nvidia Tegra X1 and Jetson run on Linux. The proposed system can easily be installed on any such device benefiting from the advantage of parallelisation for faster execution. The proposed system achieved a performance which surpasses that of a human (classification accuracy of the top 1 class >95.9% for automatic recognition of a detected object into one of the seven selected categories. For the top-2 classes, the accuracy is even higher (99.85%). That means, at least, one of the two top classes suggested by the system is correct. Finally, a number of visual examples are showcased of the system in use in both PC and Android devices

Local modes-based free-shape data partitioning

In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms

Edge flow

In this paper we introduce a new data driven method to novelty detection and object definition in dynamic video streams that indiscriminately detects both static and moving objects in the scene. A sliding window density estimation is introduced in order to reliably detect texture edges. A Sobel filtering process is used to extract gradient of edges. Using this new approach, the detection of object textures1 can be done accurately and in real-time. In this paper we demonstrate the capabilities of the algorithm on video scenarios, and show that object textures in the scene are reliably detected. We are able to show clearly the capability of the algorithm to be robust in occlusion scenarios; working in real-time, and defining clear objects where other techniques attribute such small detections to noise

Evolving clustering, classification and regression with TEDA

In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of large amounts of data. The framework is capable of computationally cheap exact update of data per sample, and can be used for training `from scratch'. All three algorithms are evolving that is they are capable of changing its own structure during the update stage, which allows to follow the changes within the model pattern

Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)

In this paper, a new type of 0-order multi-model classifier, called Autonomous Learning Multiple-Model (ALMMo-0), is proposed. The proposed classifier is non-iterative, feedforward and entirely data-driven. It automatically extracts the data clouds from the data per class and forms 0-order AnYa type fuzzy rule-based (FRB) sub-classifier for each class. The classification of new data is done using the “winner takes all” strategy according to the scores of confidence generated objectively based on the mutual distribution and ensemble properties of the data by the sub-classifiers. Numerical examples based on benchmark datasets demonstrate the high performance and computation-efficiency of the proposed classifier

SwiftSpatial: Spatial Joins on Modern Hardware

Spatial joins are among the most time-consuming queries in spatial data management systems. In this paper, we propose SwiftSpatial, a specialized accelerator architecture tailored for spatial joins. SwiftSpatial contains multiple high-performance join units with innovative hybrid parallelism, several efficient memory management units, and an integrated on-chip join scheduler. We prototype SwiftSpatial on an FPGA and incorporate the R-tree synchronous traversal algorithm as the control flow. Benchmarked against various CPU and GPU-based spatial data processing systems, SwiftSpatial demonstrates a latency reduction of up to 5.36x relative to the best-performing baseline, while requiring 6.16x less power. The remarkable performance and energy efficiency of SwiftSpatial lay a solid foundation for its future integration into spatial data management systems, both in data centers and at the edge

MICE:Multi-layer multi-model images classifier ensemble

In this paper, a new type of fast deep learning (DL) network for handwriting recognition is proposed. In contrast to the existing DL networks the proposed approach has clearly interpretable structure that is entirely data-driven and free from user- or problem-specific assumptions. It is entirely parallelizable and very efficient. First, same fundamental image transformation techniques (rotation and scaling) that are used by other existing DL methods are used to improve the generalization. The commonly used descriptors are then used to extract the global features from the training set and based on them a bank/ensemble of zero order AnYa type fuzzy rule-based (FRB) models is built through the recently introduced Autonomous Learning Multiple Model (ALMMo) method working in parallel. The final decision about the winning class label is made by a committee on the basis of the fuzzy mixture of the trained ALMMo-0 models (where “0” stands for 0 order meaning that the consequent represent a class label, a singleton, not a regression model as in the first order). The training of the proposed MICE system is very efficient and highly parallelizable. It significantly outperforms the best known methods in terms of time and is on par in terms of precision/accuracy. Critically, it offers a high level of interpretability, transparency of the classification model, full repeatability (unlike the methods that use probabilistic elements) of the results. Moreover, it allows an evolving scenario whereby the data is provided in an incremental, online manner and the system structure is being developed in parallel with the classification which opens opportunities for online and real-time applications (on a sample by sample basis). Numerical examples from the well-known handwritten digits recognition problem (MNIST) were used and the results demonstrated the very high repeatable performance after a very short training process which is in addition to the high level of interpretability, transparency

Fast feedforward non-parametric deep learning network with automatic feature extraction

In this paper, a new type of feedforward non-parametric deep learning network with automatic feature extraction is proposed. The proposed network is based on human-understandable local aggregations extracted directly from the images. There is no need for any feature selection and parameter tuning. The proposed network involves nonlinear transformation, segmentation operations to select the most distinctive features from the training images and builds RBF neurons based on them to perform classification with no weights to train. The design of the proposed network is very efficient (computation and time wise) and produces highly accurate classification results. Moreover, the training process is parallelizable, and the time consumption can be further reduced with more processors involved. Numerical examples demonstrate the high performance and very short training process of the proposed network for different applications