Administrative and legal status of the National Agency of Ukraine for Detection, Investiga- tion and Management of Assets Obtained from Corruption and Other Crimes

In the work, using general and special scientific research methods, and the specific combination de-pends on the purpose and objectives of the study. The use of methods of formal logic makes it possible to define, clarify and supplement certain concepts and supplement certain terminological categories and, accordingly, to sys-tematize the conceptual and categorical apparatus. Methods of formal logic and logic of essence, as well as methods of analysis and synthesis, deduction and induction, analogy and induction are also used. The scientific novelty of this article is that it is the result of scientific research on a number of problematic issues of the administrative and legal status of ARMA. The author concludes that the administrative and legal status of the National Agency of Ukraine for Detection, Investigation and Management of Assets Obtained from Corruption and Other Crimes is based on powers in the field of property confiscation management and successful implementation of relevant EU practice in Ukrainian legislation. These powers are not just a typical regulatory state activity, they form fundamen-tally new mechanisms of public-private partnership, which have no analogues in Ukraine so far. The consequence of the implementation of these powers was the creation of a new market for services for the management of seized property, as well as a stock market for seized property, different from the market for confiscated property. In the future, it would be interesting to examine the case law on appealing decisions on the transfer of financial assets to the management of ARMA. As well as expanding the powers of the National Agency of Ukraine for the detection, search and management of assets derived from corruption and other crimes

IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING

The license plate recognition (LPR) is an important system. LPR is helpful in many ranges such as private or public entrance, parking lots, traffic control and theft surveillance. This paper, offers (LPR) consist of four main stages (preprocessing, license plate detection, segmentation, character recognition) the first stage takes a photo by the camera then preprocessing in this image. License plate detection search for matching of license plate in the image to crop the correct plate. Segmentation performed by divide the numbers separately. The last stage is number recognition by using KNN (K- nearest neighbors) is one of the simple algorithms of machine learning used for matching numbers with training data to provide a correct prediction. The system was implemented using python3.5, open-cv library and shows accuracy performance result equal to 90% by using 50 images.

Detection of gamma-ray transients with wild binary segmentation

ABSTRACT In the context of time domain astronomy, we present an offline detection search of gamma-ray transients using a wild binary segmentation analysis called F-WBSB targeting both short and long gamma-ray bursts (GRBs) and covering the soft and hard gamma-ray bands. We use NASA Fermi/GBM archival data as a training and testing data set. This paper describes the analysis applied to the 12 NaI detectors of the Fermi/GBM instrument. This includes background removal, change-point detection that brackets the peaks of gamma-ray flares, the evaluation of significance for each individual GBM detector, and the combination of the results among the detectors. We also explain the calibration of the ∼ 10 parameters present in the method using one week of archival data. Finally, we present our detection performance result for 60 d of a blind search analysis with F-WBSB by comparing to both the onboard and offline GBM search as well as external events found by others surveys such as Swift-BAT. We detect 42/44 onboard GBM events but also other gamma-ray flares at a rate of 1 per hour in the 4–50 keV band. Our results show that F-WBSB is capable of recovering gamma-ray flares, including the detection of soft X-ray long transients. FWBSB offers an independent identification of GRBs in combination with methods for determining spectral and temporal properties of the transient as well as localization. This is particularly useful for increasing the GRB rate and that will help the joint detection with gravitational-wave events.

Ship Target Detection Algorithm Based on Improved Faster R-CNN

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.

A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem

Quality of service multicast routing is an important research topic in networks. Research has sought to obtain a multicast routing tree at the lowest cost that satisfies bandwidth, delay and delay jitter constraints. Due to its non-deterministic polynomial complete problem, many meta-heuristic algorithms have been adopted to solve this kind of problem. The paper presents a new hybrid algorithm, namely ACO&CM, to solve the problem. The primary innovative point is to combine the solution generation process of ant colony optimization (ACO) algorithm with the Cloud model (CM). Moreover, within the framework structure of the ACO, we embed the cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone trail on the edges. Although a high pheromone trail intensity on some edges may trap into local optimum, the pheromone updating strategy based on the CM is used to search for high-quality areas. In order to avoid the possibility of loop formation, we devise a memory detection search (MDS) strategy, and integrate it into the path construction process. Finally, computational results demonstrate that the hybrid algorithm has advantages of an efficient and excellent performance for the solution quality.

Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications

Recently, the number of tunnels is increasing due to urbanization, and fire accidents in tunnels are likewise increasing. In particular, in a long tunnel of more than 1 km it is very difficult to track the exact location of a fire, accident vehicles, and the fire brigade, as well as whether a fire occurred. In this paper, we analyze various types of accidents that may occur in tunnel fires and propose detection, search, and rescue techniques to cope with them. For early detection of accidents, we propose various sensors using Internet of Things (IoT) technology and sensor networks to connect them. These sensors can detect not only a fire but also the position of the vehicle in which the fire is occurring in real time. We also propose a robotic system and operation technique that can be controlled by a fire fighter for more precise search operation. For rescue procedures, localization and tracking technology for fire fighters and robots is proposed. Finally, the efficiency of the proposed system was verified through actual performance tests, including simulations of actual placement and operation in tunnels. Through the construction of the equipment in an actual tunnel 1.9 km long, we show that the proposed system is good enough to cope with fire accidents, in terms of the delivery ratio of the collected data, fire recognition ratio, localization accuracy, and response delay.

A Tracked Robot for Complex Environment Detecting

The complex geographical environment put forward grand challenge to most current detecting robot on the problem of communication control and moving obstacle avoidance.This paper proposed a crawler robot for complex environment detecting.Information acquisition was achieved by Labview programming,the collected sensor information was debugged by fusing and displaying in the Labview interface , in order to realize the stability of the system.The positioning part adopted wireless location technology of ZigBee networks,which can support a large number of network nodes, fast, and reliable security etc.In addition, the upper part of the robot body was equipped with camera and mechanical arm to achieve target capture, carrying something and other tasks. According to the situation of accident, remote control terminal through wireless control function can control the further action of the robot. Through experimental tests, this paper proposes this kind of detection robot had great advantages in detection, search and rescue, it can complete the search under complex and dangerous environment.