Miracle-dz/UniFAR-FTUs huggingface dataset:
TU‐A‐201B‐03: Dose Reduction and Image Quality Benefits Using Model Based Iterative Reconstruction (MBIR) Technique for Computed Tomography

Purpose: To demonstrate the image‐quality benefits and potential for significant dose reduction with Model‐Based Iterative Reconstruction (MBIR) technique incorporating physical model of computed tomography(CT) systems. Method and Materials: A model based iterative reconstruction (MBIR), a maximum a posteriori (MAP) estimate with edge‐preserving prior, has been developed for x‐ray CTimage reconstruction. It utilizes a more accurate physical model of the imaging chain accounting for system‐optics, noise and non‐idealities in the data, hence improves image quality compared to conventional filtered backprojection (FBP) at significantly reduced dose levels. In this work, a GE multi‐slice CT system was used to acquire a set of multi‐dose data and standard FBP reconstruction. For resolution assessment, a Catphan600® phantom was scanned at three dose levels (40, 20, and 10 mGy with 120kVp spectrum), and images were reconstructed using two methods: FBP with ASiR, and the MBIR. For artifact and image‐quality evaluations, an anthropomorphic CT abdomen phantom (Kyoto Kagaku Co., Ltd) was scanned at four dose levels (120kVp spectrum with 225, 112, 54, and 27 mAs), and a comparative image‐quality study between standard FBP and MBIR in slice and multi‐planar reformat (MPR) modes was made. In addition, few clinical case studies were also used to compare the imaging performance in actual clinical data. Results: From the resolution study, we found that even at 1/4th dose, MBIR images have improved resolution at significantly reduced noise compared to standard state‐of‐the‐art FBP with ASiR. Use of ASIR provides up to 50% dose reduction with equivalent FBP image‐quality. For anthropomorphic phantom, even below 1/8th dose, MBIR images outperformed the corresponding FBP images in both, slice and MPR modes, demonstrating immense potential for dose reduction, yet improved image quality, in clinical CT.Conclusion: Results of the MBIR method demonstrated significant potential for dose reduction and image‐quality improvements in clinical CT.
Statistics of the fractional polarization of compact radio sources in Planck maps

In this work we apply the stacking technique to estimate the average fractional polarisation from 30 to 353 GHz of a primary sample of 1560 compact sources - essentially all radio sources - detected in the 30 GHz Planck all-sky map and listed in the second version of the Planck Catalogue of Compact Sources (PCCS2). We divide our primary sample in two subsamples according to whether the sources lay (679 sources) or not (881 sources) inside the sky region defined by the Planck Galactic mask (fsky ~ 60 per cent) and the area around the Magellanic Clouds. We find that the average fractional polarisation of compact sources is approximately constant (with frequency) in both samples (with a weighted mean over all the channels of 3.08 per cent outside and 3.54 per cent inside the Planck mask). In the sky region outside the adopted mask, we also estimate the {\mu} and {\sigma} parameters for the log-normal distribution of the fractional polarisation, finding a weighted mean value over all the Planck frequency range of 1.0 for {\sigma} and 0.7 for {\mu} (that would imply a weighted mean value for the median fractional polarisation of 1.9 per cent).
Quantum Anomalous Hall Multilayers Grown by Molecular Beam Epitaxy

Quantum anomalous Hall (QAH) effect is a quantum Hall effect that occurs without the need of external magnetic field. A system composed of multiple parallel QAH layers is an effective high Chern number QAH insulator and the key to the applications of the dissipationless chiral edge channels in low energy consumption electronics. Such a QAH multilayer can also be engineered into other exotic topological phases such as a magnetic Weyl semimetal with only one pair of Weyl points. This work reports the first experimental realization of QAH multilayers in the superlattices composed of magnetically doped (Bi,Sb)2Te3 topological insulator and CdSe normal insulator layers grown by molecular beam epitaxy. The obtained multilayer samples show quantized Hall resistance h/Ne2, where h is Planck’s constant, e is the elementary charge and N is the number of the magnetic topological insulator layers, resembling a high Chern number QAH insulator. The QAH multilayers provide an excellent platform to study various topological states of matter.
Reuse Distance based High Performance Multi-level Buffer Cache Algorithm

Multi-level storage system is widely used in network storage, cloud service, virtualization technologies, etc. In general, multi-level storage maintains buffer caches of each level to support the connection efficiently. However, previous multi-level buffer caching algorithms do not consider the workload characteristics of multi-level. A requested block from upper-level buffer cache is from the upper-level buffer cache's miss rather than the application's actual request. This can be characterized by block's reuse distance. In this paper, we observe these workload characteristics in the multi-level buffer cache and propose an efficient buffer cache algorithm based on reuse distance.
Survey of full-reference image quality metrics

This paper aims to give a survey of full-reference image quality metrics, including metrics specifically designed to evaluate image quality, but also metrics for image difference, image fidelity, and more. These metrics have in common that they try to predict the perceived difference between an original image and a modified version of it, this modification can typically be compression, halftoning and blurring. They output one numerical value and/or an image difference map. More than 100 image quality metrics have been reviewed and categorized, and short descriptions and analysis of all metrics and their relationships are given. This should prove valuable to researchers in various fields, to find the most appropriate metric for their application, and to give a better understanding of the state of the art of the field of image quality metrics.
FRAME WORK FOR CONTENT BASED IMAGE RETRIEVAL (Textual Based) SYSTEM

Another method of image processing content based image is said to be possibility of recovery content based information retrieval (CBIR), posing question by image content (QBIC) and (CBIR). This is an application of computer vision meant to explain image retrieval problem. In large databases we have to find the required image by applying some query on the basis of content based shapes, textures colors etc.we find the required data or image. If the ability to estimate or examine the image Content does not exist, in that case search must depend upon metadata like caption or keywords. If the query doesn't match the required contents then it is implemented on some other feature of images to retrieve from the database. This research focus on the Content Based image retrieval with specific domain of Text Based image retrieval (TBIR) system. (Journal of American Science 2010; 6(9):704-707). (ISSN: 1545-1003).
Hall Effect–Mediated Magnetic Flux Transport in Protoplanetary Disks

The global evolution of protoplanetary disks (PPDs) has recently been shown to be largely controlled by the amount of poloidal magnetic flux threading the disk. The amount of magnetic flux must also coevolve with the disk, as a result of magnetic flux transport, a process that is poorly understood. In weakly ionized gas as in PPDs, magnetic flux is largely frozen in the electron fluid, except when resistivity is large. When the disk is largely laminar, we show that the relative drift between the electrons and ions (the Hall drift), and the ions and neutral fluids (ambipolar drift) can play a dominant role on the transport of magnetic flux. Using two-dimensional simulations that incorporate the Hall effect and ambipolar diffusion (AD) with prescribed diffusivities, we show that when large-scale poloidal field is aligned with disk rotation, the Hall effect rapidly drags magnetic flux inward at the midplane region, while it slowly pushes flux outward above/below the midplane. This leads to a highly radially elongated field configuration as a global manifestation of the Hall-shear instability. This field configuration further promotes rapid outward flux transport by AD at the midplane, leading to instability saturation. In quasi-steady state, magnetic flux is transported outward at approximately the same rate at all heights, and the rate is comparable to the Hall-free case. For anti-aligned field polarity, the Hall effect consistently transports magnetic flux outward, leading to a largely vertical field configuration in the midplane region. The field lines in the upper layer first bend radially inward and then outward to launch a disk wind. Overall, the net rate of outward flux transport is about twice as fast as that of the aligned case. In addition, the rate of flux transport increases with increasing disk magnetization. The absolute rate of transport is sensitive to disk microphysics, which remains to be explored in future studies.
Exploiting 3D Medical Equipment Simulations to Support Biomedical Engineering Academic Courses: Design Methodology and Implementation in a Small Scale National Project

Biomedical Imaging is a rapidly evolving area, where new versions of advanced medical equipment/new methodologies based on complex physical phenomena are developed. The cost for the effective training of both academic students/company employees in real training environments is high and in many cases impossible. Most of the times, it is not possible to effectively transfer knowledge due to the limitations of these environments, which has a strong effect both on the theoretical understanding and the practical skills of students. On the other hand, the exploitation of 3D simulations enables for better knowledge acquisition by learners, although not efficiently explored yet. Several studies support that this can be achieved by maximizing the interaction with simulations. To this end, interactive 3D Medical Equipment Simulations have been developed, by exploiting open source software and delivering them through the Web and eLearning Environments. The proposed chapter aims to present the need for such Environments, the development tools, challenges, solutions and possible applications. Exploiting 3D Medical Equipment Simulations to Support Biomedical Engineering Academic Courses: Design Methodology and Implementation in a Small Scale National Project
Comparison of the effect of lecture and video projector teaching methods on students’ attitude, knowledge and practice

Teachers used video projector in the classroom training. The aim of this research was to compare the students’ attitude, knowledge and practice about food health laboratory in conventional lecture and Video projector methods. In this quasi-experimental study; we grouped 40sophomore students of Environmental Health School, Islamic Azad University, and Tehran Medical Sciences Branch in Iran Country into two groups in April 2014. We collected the data by a researcher-made questionnaire including demographic (5) and food health laboratory (29) questions that used four-point Liker scales. We collected and measured the students’ knowledge and practice about food health laboratory in two groups by using pre and post-test. We analyzed the collected data by SPSS 18 software. The mean scores of the lecture and video projector groups were 10.95±5.28 (2-21), and 11.7±5.21 (6-24), respectively; the difference between the pre-test scores of the two groups was statistically significant (p=0.001). Knowledge scores of the two groups was statistically significant (p=0.001). Students’ practice in video projector method was higher than lecture method. The results indicated that the video projector was more efficacious, and more economic in enhancing the students' knowledge rate. This method led to increasing the students’ knowledge, and practice, it might be applied as a surrogate for traditional training such as lecture method in universities of medical sciences in the country.
A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments

Nowadays, Smart Environments (SEs) are pervasively deployed in buildings (e.g., houses, schools, and offices) and outdoor environments with the goal of improving the quality of life of their inhabitants. SEs are usually designed and developed by using well-suited architectures and platforms having the aim of simplifying and making straightforward the SE implementation. Up to now, SEs are mostly reactive and, in some ways, proactive. Current research efforts are devoted to making such environments cognitive, i.e., able to automatically adapt and adhere to the possible changes in users’ needs and behaviors. Anyway, in this field, the development of SEs is still in its infancy. In this direction, the paper proposes a novel Cognitive-enabled, Edge-based Internet of Things (CEIoT) architecture, purposely designed to develop cognitive IoT-based SEs. Such architecture wants to overcome some limitations arising during the usage of common SE platforms and architectures. CEIoT introduces some abstractions ranging from the "in-platform" implementation of decentralized cognitive algorithms to the realization of smart data aggregations.
Centralized Cognitive Radio Based Frequency Allocation for UAVs Communication

Unmanned Aerial Vehicles (UAVs) have known much popularity for dangerous missions for human operators or for applications which do not need human intervention (such as monitoring and surveillance of physical infrastructures and interest areas). They operate in frequency bands (IEEE L-Band, IEEE S-Band, and ISM band) shared with other users. Accordingly, these frequency bands have become overcrowded and UAVs may face the issue of spectrum scarcity. Furthermore, there are particular difficulties associated with aeronautical communication links. Cognitive radio (CR) has emerged as a promising strategy for resolving the problems caused by scarce spectrum. It checks the spectrum availability and allows the adjustment of the transmission parameters. The aim is to opportunistically use spectral bands with minimum interference to applications or other users. In this paper, we present a centralized CR based frequency allocation scheme for UAV-Ground Control Station (GCS) communication in surveillance applications within an urban environment. In the proposed model, the GCS monitors and allocates available WiMAX frequencies using CR and Software Defined Radio (SDR). If no WiMAX frequency is available at a given time, the Wi-Fi will be used. Therefore in the worst case, our approach will have the same performance as when the Wi-Fi is only used for UAV-GCS communication.
Hand-Eye Calibration With a Remote Centre of Motion

In the eye-in-hand robot configuration, hand-eye calibration plays a vital role in completing the link between the robot and camera coordinate systems. Calibration algorithms are mature and provide accurate transformation estimations for an effective camera-robot link but rely on a sufficiently wide range of calibration data to avoid errors and degenerate configurations. This can be difficult in the context of keyhole surgical robots because they are mechanically constrained to move around a remote centre of motion (RCM) which is located at the trocar port. The trocar limits the range of feasible calibration poses that can be obtained and results in ill-conditioned hand-eye constraints. In this letter, we propose a new approach to deal with this problem by incorporating the RCM constraints into the hand-eye formulation. We show that this not only avoids ill-conditioned constraints but is also more accurate than classic hand-eye calibration with a free 6DoF motion, due to solving simpler equations that take advantage of the reduced DoF. We validate our method using simulation to test numerical stability and a physical implementation on an RCM constrained KUKA LBR iiwa 14 R820 equipped with a NanEye stereo camera.
Self-Adaptive Management of SDN Distributed Controllers for Highly Dynamic IoT Networks

The Internet of Things (IoT) is about connecting dynamically billion of devices to the Internet. This large-scale and dynamic topology is very challenging for IoT deployment and management. Software-Defined Networking (SDN) has been applied more and more in recent years as a solution for IoT challenges. The SDN concept of decoupling the control plane from the data plane promotes logically centralized visibility of the entire network and enables the applications to innovate through network programmability. At the same time, there are still some open issues, such as scalability in large IoT environments that include several devices. To face scalability challenges, SDN proposes distributed controllers as a solution to decentralize the control plane while maintaining the logically centralized network view. However, SDN-based architecture, that provides the flexibility and scalability, still lacks the smart or intelligent management to self-adapt to possible dynamic network topology changes. To over-come such issues, we propose a framework that answers automatically the business demands and makes the network self-adaptive. The topology deployment decision is made based on information that the controller gives. So for making sure that our proposed framework gives the best results, we have to study first the topology discovery mechanism in a distributed controller. In this paper, we introduce a self-adaptive management framework of SDN controllers for highly dynamic IoT networks. We evaluate performances of the two most popular distributed SDN controllers (i.e. ONOS and ODL) in a realistic scenario where the network topology changes dynamically. Results show the outperforming of ONOS compared to ODL in discovering the highly dynamic IoT network.
Environmentally Friendly Biogas Production

In this paper there is proposed an environmentally friendly and energy efficient technology of anaerobic digestion of sanitary sewage water sludges with division of all the process into four consecutive phases: hydrolysis, acetogenesis, acidogenesis and methanogenesis. According to the proposition, each phase takes place in a separate isolated capacity. Optimal values of some parameters of the medium are defined for the process intensification at the each phase. These parameter's values allow activating groups of bacteria, acting on the each phase. As a result, it allows to shorten duration of the digestion and to improve environmental characteristics of digested sludges. The new technology also allows increasing biogas yield during the process. In addition it allows to increase methane content in the biogas and to obtain carbon dioxide separately.
IMU-Based Walking Workouts Recognition

To better accurately estimate the calories burnt during popular walking workouts, it is essential to detect the environment under which these workouts are conducted. To our best knowledge, no gait analysis studies have been done so far for such detection. This research addresses this problem by recognizing walking workouts under different environments based on the foot-mounted inertial sensor. Our objective is to recognize ten different workout activities including walking and brisk-walking under flat surface, ascending/descending staircase and upward/downward slope with no stairs. Our algorithm first identifies the extended foot-flat phase, then uses it as a boundary to extract key important features. Decision Tree, Random Forest and K-Nearest Neighbor machine learning algorithms are evaluated to decide which one works the best along with our algorithm.
Average and Instantaneous Throughput Maximization for Millimeter Wave Communications

In this paper, we suggest to maximize the throughput of millimeter Wave (mmwave) Communications. The first approach consists to select the packet length that maximizes the instantaneous throughput. The second approach optimizes packet length in order to maximize the average throughput. Our results are valid for interference limited mmWave communications in the presence of an arbitrary number of relays. Instantaneous throughput maximization allows up to 5 dB gain with respect to fixed packet length as considered in previous papers. Average throughput maximization allows up to 2.5 dB gain with respect to fixed packet length.
DEEP: Design and Evaluation of an Energy-efficient Wireless Sensor Node for Leak Detection in Water Pipes

Wireless sensor network (WSN) has emerged a wide range of applications due to its performance, low cost, flexibility, ease-to-use and its efficiency. One of its most significant applications is the leak detection and localization in water pipelines. Various projects and works are reported in this context. However, improvements still needed in this field especially in term of node design. In fact, WSN is composed of battery powered small nodes that are in charge of collecting and transmitting data wirelessly. These devices are energy constrained and not well evolved in water pipeline application.In this paper, we present sensor node prototypes for water pipeline application. Experimental and comparative studies have been performed for the different node’s components to achieve a final adequate node design, DEEP. Various processing platforms, sensors, amplifiers and communication technologies are theoretically and experimentally studied and evaluated. The comparison and the results presented, in this paper will help node designer especially for water pipeline monitoring. Moreover, they permitted us to design DEEP, an energy-aware sensor node platform by the use of power management techniques.
Filtering and fingerprint matching methods for Wi-Fi radio map based indoor localization

Usually the indoor localization methods are using predictor-corrector methods for the precise location determination. The prediction is based on the analysis of the motion of the user, while the correction is based on a radio map-based fingerprinting. There are some application fields where the precision of the predictor-corrector algorithm is sacrificed for the simplicity of sensors and devices. The recent development of the smartwatches they became ideal devices for monitoring elderly people's health conditions together with their indoor location and activity. Since it is not a navigation like application the expected precision and sample rate of the location determination is lower, however the typical sensors configuration of a smartwatch is more limited compared to a mobile phone. Usually the sensors are required for the motion prediction methods are missing, so the common predictor-corrector methods, filters are not applicable for this type of use case. Different algorithms and filtering methods needs to be developed and applied for this application. This article summarizes our approach and results in developing fingerprint only indoor localization application.
Hybrid Electric Vehicle Traffic Flow Image Restoration using Blind Deconvolution Algorithm

With the characteristics of multi-source, time-varying and fuzzy, traffic decision information needed by hybrid vehicle energy management system has become the major bottleneck to restrict hybrid electric vehicle's energy optimization. The first step to obtain valid traffic information is image processing of traffic flow. In practice, blurring and noise often has an adverse effect on the performance of image processing. Blind deconvolution algorithm was studied in the image restoration of hybrid electric vehicle traffic flow. Simulation results show the validity of proposed algorithm. Keywords-hybrid electric vehicle; energy management; blind deconvolution; image deblurring