Real Acquisition Research Articles

Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.

Politics of real estate internationalization: contested liberalization in Turkey

ABSTRACT This study explores the slow progress of liberalization of real estate internationalization (REI) in Turkey. Drawing on insights from cultural political economy (CPE), it builds on a qualitative analysis of parliamentary deliberations and argues that real estate acquisition by foreigners in Turkey was highly contentious because of the struggle between competing imaginaries on liberalization, with the neoliberal-transformative imaginaries of the ANAP, the AKP, and their affiliates in the tourism and construction sectors on one side and the national-defensive imaginaries of opposition parties and the bureaucratic elite on the other. By forming a coalition with the Constitutional Court, opposition parties were successful in preventing the adoption of neoliberal-transformative imaginaries for almost thirty years. This policy area could be liberalized only after the 2010 referendum, which allowed the government to restructure the composition of the Court, leading to executive aggrandizement.

Convolution Based Feature Extraction for Edge Computing Access Authentication

In this article, a convolutional neural network (CNN) enhanced radio frequency fingerprinting (RFF) authentication scheme is presented for Internet of things (IoT). RFF is a non-cryptographic authentication technology, identifies devices through the waveforms of the RF transient signals by processing received RF signals on the edge server, which places no cost burden to low-end (low-cost) devices without implementing any encryption algorithm and meet the demands of the real-time access authentication in Internet of things. In the new scheme, the feasibility of extracting features based on one-dimensional (1D) signal convolution is discussed, referring to the method of extracting features from CNN, and combining with the characteristics of signal convolution. A convolution kernel for 1D signals is designed to extract the feature of signals in order to reduce training time and ensure classification accuracy. Therefore, it can improve the accuracy compared with these traditional algorithms, while saving the training time of updating parameters repeatedly as the neural network. The accuracy and training time of thealgorithm are verified in a real signal acquisition system. The results prove that the novel algorithm can effectively improve the classification accuracy in low signal-to-noise ratio (SNR), while keeps the training time in an acceptable range.

Integration of SCADA Services and Power-Hardware-in-the-Loop Technique in Cross-Infrastructure Holistic Tests of Cyber-Physical Energy Systems

Cyber-physical energy system (CPES)—complex juxtaposition of multiple energy domains and communication and automation technologies—requires a sophisticated testing framework to achieve holistic assessment and validation, especially at large scale. In this article, the real supervision, control, and data acquisition (SCADA) system of CPES is integrated with the advanced techniques—real-time simulation (RTS) and power-hardware-in-the-loop (PHIL), in a cross-infrastructure manner to create a realistic validation environment for CPES. On the one hand, the method can be applied to extend the capacity of the infrastructures as well as creating a common resource and expertise pool. On the other hand, this approach combines the realistic data and advanced SCADA services with the flexibility of the RTS platform, which provides the possibility to emulate extreme and faulty scenarios with virtual equipment and topology. The proposed approach is demonstrated via a case study of analyzing the impact of communication on advanced voltage and frequency restoration in an isolated microgrid. The case study is implemented on two remote platforms PREDIS-PRISMES (70 km apart). The validation framework comprises the RTS and PHIL platform coupled with the OPC UA SCADA system in PRISMES, the control algorithm and the communication network simulator located in PREDIS platform.

Implementation of an IoT based Real Time Signal Acquisition System over the Cloud using ThingSpeak Platform for Multi-Zonal Temperature & Humidity Measurement

Implementation of an IoT based Real Time Signal Acquisition System over the Cloud using ThingSpeak Platform for Multi-Zonal Temperature & Humidity Measurement

Shadow detection in SAR images based on greyscale distribution, a saliency model, and geometrical matching

ABSTRACT Shadow information has been widely used in synthetic aperture radar interpretation, but corresponding shadow detection technology has not been given much attention in past studies. In this paper, we propose a hierarchical architecture based on greyscale distribution, a saliency model, and geometrical matching for shadow detection in SAR images. We find that the greyscale distribution of one Gaussian filtered image might contain a ‘distortion’ in its uphill part, which is the effect of shadow existence. Based on this distortion, a global threshold can be determined and then be used to segment candidate shadows. If there is no obvious distortion, a shadow saliency model is proposed as a substitute to extract such candidate areas. Usually, these candidate areas may contain some non-shadow components. According to the geometric relationships between shadow and object, we design a matching strategy to eliminate non-shadow parts from candidate regions. The remained areas are final shadow detection results. Experiments on two real datasets, Moving and Stationary Target Acquisition Recognition and MiniSAR, show that our method performs much better than two other published methods. The results demonstrate the effectiveness and feasibility of our proposed algorithm in practical SAR shadow detection tasks.

부동산에 대한 취득세 세율체계 정비방안 연구

부동산에 대한 취득세 세율체계 정비방안 연구

Machine-vision based handheld embedded system to extract quality parameters of citrus cultivars

This manuscript introduces a handheld machine vision based system design that is capable of standalone operation using touch screen based user interface and also can operate through smartphone based android app. System uses 8.0 Megapixel, 1080p CMOS camera interfaced with quad-core ARM Cortex-A53 processor based computing platform (Raspberry Pi computing platform) for real time image acquisition and processing. Multi-spectral led array has been used to compensate the effect of external illumination and also to increase the accuracy of measurement. System stores acquired images on interfaced 16.0 G.B. external memory card with date and time information. Various segmentation methods have been explored to extract region of interest in acquired images and compared based on the capability of segmentation in real-time. Segmented images have been used to extract different features such as color, shape, size and texture using various image processing algorithms. Extracted features have been fused together and undergone through different statistical and neural network based modelling methods to correlate features dataset generated using handheld system with standard quality parameters of collected citrus samples. Performance of the established correlation models for various quality parameters such as chlorophyll, sugar content, TSS, weight, pH and volume have been evaluated and best performed models for each quality parameter has been used to train the developed handheld machine vision based system. Overall system is battery operated and also enables cloud connectivity using on-board Wi-Fi facility or smartphone based android app. Overall device has dimensions of 12.0 × 6.0 × 4.0 (in cm), weighs 139.07 g and runs with 5-V rechargeable battery.

A scalable online algorithm for passive seismic tomography in underground mines

Understanding and monitoring the seismic responses of rock masses to massive mining are crucial for safety and economic viability of ever larger and deeper underground operations. Seismic monitoring can be used to detect stress variations and hazardous instabilities, but its effectiveness requires accurate estimations of the nonhomogeneous propagation velocity of microseismic waves. While predetermined velocity models are not accurate enough and might bias hypocenter localization, using active-source seismic tomography methods to estimate the velocity field provides limited spatial coverage. Thus, passive seismic tomography using first-arrival traveltimes of mining-induced microseisms (of unknown hypocenters) constitutes a promising tool. However, available methods solving this high-dimensional statistical inverse problem do not scale well with the data set size and cannot easily refine or update estimations with new data. We have developed a novel passive seismic tomography method able to dynamically learn the nonhomogeneous velocity field from a streaming of noisy first-arrival times, online (in real time) or from catalogs. We have developed a new Bayesian approach that avoids linearizing the forward problem and allows for general 3D velocity models. This is combined with the use of the stochastic gradient descent (SGD) method, which underlies much of the recent progress in machine learning and provides increasing accuracy at a cost scaling linearly with the data set size. Moreover, we introduce an adaptive variant of SGD based on raypath density, which significantly improves the speed of the algorithm, and we implement a parallel version of our method enabling its systematic use in real applications. These include the design of optimal sensor locations, the dynamic update of velocity estimates in production conditions, and the real-time determination of hypocenters and their uncertainty. Our method’s reach and effectiveness are illustrated with simulated seismic data on 3D checkerboards, using synthetic and real acquisition geometries, and on a dense 2D velocity grid.

Potential of Surface-to-Tunnel Seismic Tomography to Detect Vertical Faults: Application to the Tournemire Underground Research Laboratory, France

The Tournemire Underground Research Laboratory (Aveyron, France), developed by the French Institute for Radiological Protection and Nuclear Safety, is composed of a tunnel excavated in an argillaceous geological layer belonging to a limestone–argillite–limestone sub-horizontal sedimentary sequence. Sub-vertical fault zones are intercepted by drifts and boreholes excavated from the tunnel in the argillaceous layer at a depth of about 250 m. These fault zones are characterized by velocities lower than those of the embedding rocks. In this study, we assess the capacity of first arrival traveltime tomography to detect and characterize such fault zones in a surface-to-depth transmission configuration with a limited angle aperture. The source at the surface is a sledgehammer and turns out to be very repetitive. The source trigger is transmitted to the acquisition bay via a VHF transmitter. Receivers are located at depth in underground works. First arrivals are clear and traveltime picking is straightforward. Before inverting real data, we conduct synthetic tests with the real acquisition geometry to assess limits and validity of first arrival traveltime tomography to detect a lower velocity fault zone using different initial configurations and inversion constraints during the inversion. We show that introducing a priori information on velocities in the shallow subsurface, limestones and argillaceous layers and constraints on maximum velocities greatly helps improving results and subsequent interpretation. Velocities obtained from the real data display a main lower velocity zone that extends from the galleries in the argillaceous layer right up to the surface in limestones. The location and extension of this lower velocity zone are consistent with fault and fracture zones mapped from geological observations.

Isotropic three-dimensional imaging with lattice light-sheet difference microscopy

Through the interference of several beams with the same phase at the rear pupil, an optical lattice is generated and used as the excitation light field in lattice light-sheet microscopy (LLSM). Although, LLSM offers fast imaging ability through the dithered mode, it suffers from poor axial resolution. On the other hand, the structured illumination microscopy (SIM) mode in LLSM provides a high but anisotropic spatial resolution at the price of a lower imaging speed. In this Letter, we propose a new, to the best of our knowledge, type of optical lattice by defining the composed beam vectors with different phases. The lattice light-sheet difference microscopy (LLSDM) proposed in this work utilizes the new optical lattice along with the conventional one by simply changing the pattern loaded on the spatial light modulator. Through simulations with parameters obtained from the real acquisition procedures and the microscope properties, we examine the feasibility of LLSDM, and obtain nearly isotropic spatial resolution and great alleviation of the sidelobes. These characteristics enable LLSDM to broaden the accessible imaging content of light-sheet microscopes and further facilitate their applications in life sciences.

Developing flexible management strategies in infrastructure: The sequential expansion problem for infrastructure analysis (SEPIA)

Infrastructure systems are usually designed to operate over long time periods. This leads to complex decisions in which the incorporation of risk management strategies is crucial to guarantee a successful operation. Traditional engineering practices focus on finding a fixed set of parameters that fulfill operational objectives throughout the project. This approach is mostly prescriptive and therefore cannot adapt to unexpected changes of external conditions. This work considers an infrastructure network designed to attend a stochastic demand expected to increase over time. The paper presents a model that is capable of integrating different aspects of the decision-making process in a cost-effective manner and incorporates the idea of flexibility. The proposed approach is referred to as Sequential Expansion Problem for Infrastructure Analysis (SEPIA). This approach is based on real performance data acquisition, optimization of decisions given possible future scenarios, and a scheduling strategy that allows operating interconnected components in an infrastructure network. The paper also presents a computational implementation that can effectively find solutions for large decision spaces. Through two examples, it is shown that incorporating flexibility in the decision strategies allows simulating more realistic decision scenarios, as well as improving the long-term performance of the system compared to traditional approaches.

Подходы к картографированию регионального рынка жилья для обеспечения визуализации национального проекта «Жильё и городская среда»

The article shows various ways of mapping the regional residential real estate market, which can be used in the future to visualize the National Project “Housing and Urban Environment”, implemented until 2024. The author demonstrates different types of cartodiagrams. It is shown that the equipment of residential real estate is more convenient to display a petal cartodiagram. Attention is paid to the use of cartodiagram with stroke, the semicircular and mesh cartodiagram. The author gives a significant place in the publication to the method of mapping housing affordability for citizens, as one of the important tasks of the National Project “Housing and Urban Environment” is to create conditions for increasing the level of affordability of residential real estate acquisition. There are 4 availability indicators that can be displayed on the map using special cartograms and the cartogram method. The most successful option, according to the author, is a combination of two techniques for the image of a cartogram on one map. Attention and rather rare means of socio-economic mapping, in particular the pseudoisolines (of isopleth). The author notes that this method is one of the most obvious in mapping the residential real estate market, especially the price situation. The article, by building isoprices, it was clearly shown how to identify the main geographical factors in the pricing of the housing market. Attention is paid to the geoinformation component of the isopleth construction. The algorithm of working with them using the Spatial Analyst module of the ArcView GIS program is shown. According to the author, cartographic research should be completed with the construction of synthetic maps. One example of synthesis in mapping the housing market is a map of its geographical segmentation. Such maps can be supplemented with matrix legends. The author emphasizes the importance of moving away from primitive maps with one or two cartographic methods to complex maps.

Synthetic data augmentation rules for maritime object detection

The performance of deep neural network for object detection depends on the amount of data. In the field of maritime object detection, the diversity of weather, target scale, position and orientation make real data acquisition hard and expensive. Recently, generating synthetic data is a new trend to enrich the training set. However, synthetic data might not improve the detection accuracy. Two problems remain unsolved: 1) what kind of data need to be augmented; 2) how to augment synthetic data. In this paper, we utilise knowledge-based rules to seek effective synthetic samples. Herein, we propose two synthetic data augmentation rules: 1) what to augment depends on the gap between training and expiring data distribution; 2) the robustness and effectiveness of synthetic data depends on the proper proportion and domain randomisation. The experiments show that the average accuracy of boat classification increases 3% with our synthetic data in Pascal VOC test set.

Synthetic data augmentation rules for maritime object detection

The performance of deep neural network for object detection depends on the amount of data. In the field of maritime object detection, the diversity of weather, target scale, position and orientation make real data acquisition hard and expensive. Recently, generating synthetic data is a new trend to enrich the training set. However, synthetic data might not improve the detection accuracy. Two problems remain unsolved: 1) what kind of data need to be augmented; 2) how to augment synthetic data. In this paper, we utilise knowledge-based rules to seek effective synthetic samples. Herein, we propose two synthetic data augmentation rules: 1) what to augment depends on the gap between training and expiring data distribution; 2) the robustness and effectiveness of synthetic data depends on the proper proportion and domain randomisation. The experiments show that the average accuracy of boat classification increases 3% with our synthetic data in Pascal VOC test set.

Design of a Real Time Wireless Data Acquisition System for Control Purposes

Design of a Real Time Wireless Data Acquisition System for Control Purposes

Cross layer packet drop attack detection in MANET using swarm intelligence

The mobile ad hoc networks gained its popularity for its various applications, quick and easy deployment. This network does not require any fixed infrastructure for deployment. Mobile nodes in this network communicate with each other through a wireless communication medium and makes this network highly vulnerable to many attacks. One of the well-known attack is packet drop attack. Whenever the network faces any kind of attack suddenly degrades network performance. In this paper, we have designed a protocol known as cross-layer packet drop attack detection using swarm intelligence (CLPDM-SI). This protocol followed a cluster based collective swarm intelligence detection mechanism to find a malicious node in real data acquisition system which undergoes packet drop attack. Our protocol (CLPDM-SI) is compared with a protocol which does not follow swarm intelligence known as Adapting Cross-Layer Approach for Detecting and Segregating Malicious Nodes (ACLDSM) mechanisms. Our comparison is done based on various QoS parameters delay, throughput, packet delivery ratio and false positive detection in the MAC layer. Our result shows significant improvements after incorporating the swarm intelligence. By inspecting CPU and memory utilization time, the algorithm finds the false positive of suspected malicious nodes.

Cross Sensor Simulation of Tomographic SAR Stacks

This paper presents an algorithm for simulating tomographic synthetic aperture radar (SAR) data based on another stack actually gathered by a real acquisition system. Through the procedure here proposed, the simulated system can be evaluated according to its capability to image complex natural media rather than reference point targets. This feature is particularly important whenever the biophysical properties of the target of interest must be preserved and cannot be easily modeled. The system to be simulated may be different from the original one concerning resolution, off-nadir angles, bandwidth and central frequency. The algorithm here proposed handles these differences by properly taking into account the wavenumbers of the target illuminated by the real survey and requested by the simulated one. The complex images constituting the synthetic stack are associated with the effective vertical interferometric wavenumber peculiar of the geometry to be simulated, regardless of the original data. Furthermore, the three-dimensional resolution cell of the simulated tomographic system is consistent with the simulated geometry concerning size and spatial orientation. These two latter features cannot be guaranteed by simply filtering the original stack. The simulator here proposed has been used to simulate the tomographic stack expected from the forthcoming European Space Agency (ESA) BIOMASS mission. The relationship between baseline distribution and 3D focusing capability was explored; special attention has been paid to the robustness of tomographic power at being a good proxy for the above ground biomass in tropical regions.

Data model development for ‘buying and selling’ transactions as a real estate acquisition method

There are many ways to acquire real estate in Turkey. The most common of them is ‘buying and selling’. The real estate/immovable property ‘buying and selling’ is a legal transaction carried out by rural Land Registry Offices in the General Directorate of Land Registry and Cadastre (GDLRC). The transactions are conducted through Land Registry and Cadastre Information Systems (LRCIS). LRCIS in e-Turkey is going to enable the transactions to be carried out efficiently. Turkish National Geographical Information Systems (TNGIS) studies and many academic studies based on e-Turkey and LRCIS have been developed in harmony with UML diagrams based on the Land Administration Domain Model (LADM) to the ISO standard. Within this context, possible situations that may be encountered in an immovable ‘buying and selling’ have been evaluated and modelled. One of the most important findings is that all immovable ‘buying and selling’ transactions are still not conducted entirely via e-Turkey. The model designed will contribute to the inclusion of elements not managed through e-Turkey. Thus, the trading process may proceed faster and more accurately thanks to the study. Before the process, it is additionally recommended that the application, which is defined as staking out a plot of the land/independent unit from a map/layout plan, and the valuation report showing the market value of the real estate should be made obligatory. Thanks to this obligation, rights of the buyer and seller according to the Confidence Principle and State's Responsibility Principle of the land register can be secured. In addition, the real estate tax values can be collected on the basis of these values. It is recommended to all those involved that they work on the model proposed by the study which guarantees the geometric and legal boundaries and the market value of the real estate with the necessary documents and institutions. In this way interoperability among all institutions can be provided quickly, data duplication can be avoided, correct data access can be ensured, data security can be kept at a high level, and transactions can be completed in a shorter time than the classical method.

Blockchain Inspired RFID-Based Information Architecture for Food Supply Chain

In this paper, we propose a blockchain inspired Internet-of-Things architecture for creating a transparent food supply chain. The architecture uses a proof-of-object-based authentication protocol, which is analogous to the cryptocurrency’s proof-of-work protocol. The complete architecture was realized by integrating a radio frequency identification (RFID)-based sensor at the physical layer and blockchain at the cyber layer. The RFID provides a unique identity of the product and the sensor data, which helps in real time quality monitoring. For this purpose, a small feature size 900-MHz RFID coupled sensor was fabricated and demonstrated for real time sensor data acquisition. The blockchain architecture aids in creating a tamper-proof digital database of the food packages at each instance. A detailed security analysis was performed to investigate the vulnerability of the proposed architecture under different types of cyber attacks.