Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an AI-driven real time verification of radiotherapy treatments

Monolithic Active Pixel Sensor (MAPS) devices are an effective tool for upstream verification of Intensity Modulated Radio Therapy treatments. It is crucial to measure with high precision the positions of the Multi Leaf Collimators (MLC) used to shape the beam, in order to enhance the quality and safety of treatments. This work describes a two-step procedure applied to the problems of leaf detection and position reconstruction. This procedure represents the building block towards the definition of a real-time verification device capable of detecting and reconstructing the position of the MLC used in the everyday radiotherapy treatments. A Fully Convolutional Neural Network model is used to analyse the high-resolution images produced by Lassena MAPS devices in order to automatically detect the leaves. A Random Forest regression model is then applied to estimate the positions of the detected leaves. The dataset used in the experiments contains 1200 single-leaf images, referring to four leaf positions in the range 1÷25 mm, split in training and test sets with the 80% and 20% of the total samples, respectively. Performance have been evaluated using the Dice loss coefficient for leaf detection, and mean squared error (MSE) as for leaf position estimation. The proposed approach obtained an average dice loss coefficient of 0.85±0.03 on the images in the test set; the MLC positions are estimated with a MSE of 0.04, and a resolution of 68±2µm on the same images.
2D Energy Histograms for Scatter Estimation in an SiPM PET Scanner

PET measurements include a background of scattered radiation which must be estimated as a part of image reconstruction. In principle the estimate can be based on a physics model of the 2D energy histograms, eliminating the need for single-scatter simulation (SSS) and making the scatter estimation independent of the mu map. Past attempts to do this have shown promise but were limited by the dynamic range of energy data, sometimes based on just one bit of energy digitization, and by statistical noise in the measurements. In this work, we use the 6bit energy digitization available in Siemens Biograph Vision SiPM-based scanners, and a denoising technique proposed in 2006 by Popescu et al. We ask if the histograms can be suitably denoised by this approach. List-mode data were acquired based on PET line sources and uniformly filled cylinders. Since scatter sinograms vary smoothly as a function of spatial coordinates, we treated large groups of "A" and "B" crystals as single detectors so that sinograms could be made with millions of coincidences per bin. Energy signals were quantified as 2D A-vs.-B histograms. We used Popescu’s MLEM method to fit the histograms in an expansion with 6 basis functions for A and 6 for B. From this we calculated the scatter fraction in individual sinogram bins. Then we reduced the counts below the levels expected in clinical scans, using a random number generator, and again calculated the scatter fraction. The scatter fraction was essentially constant as the counts per bin were reduced 10,000-fold. De-noising by the MLEM method was successful. The energy-based approach to scatter estimation was stable at low count levels. The method’s accuracy has not yet been investigated. The use of energy-based scatter estimation in PET reconstruction, and the comparison to SSS, are a work in progress.
Task-oriented Deep Network for Ischemic Stroke Segmentation in Unenhanced CT Imaging

In the clinics, unenhanced CT (unCT) are first used for acute ischemic stroke detection, but sometimes it is difficult to delineate acute infarction in the unCT in the first few hours. Currently, deep learning methods can be used for stroke segmentation, but they are based on a large amount of annotation data, especially in unCT imaging. Meanwhile, the Diffusion-weighted magnetic resonance imaging (DWI) can clearly distinguish infarcted brain tissue from healthy region. In this work, we investigate whether external information, i.e., DWI images, can be used to help more clearly delineates acute infraction in the unCT images by a transfer learning network. We detail the network architectures in the transfer learning framework, and describe the training process used to transfer segmentation task in DWI images to unCT images. In particular, intermediate layers in the network trained with DWI images are frozen and then directly are transferred to the network trained with unCT task by parameter fine-tuning. The ISLES2018 dataset is used to validate and evaluate this task-oriented transfer network. Experimental results demonstrate that this task-oriented transfer network can obtain the dice coefficient with 0.793, which is much better than the network that only uses unCT input (dice coefficient: 0.570).
Real-time Molecular Analysis Methods Based on Cloud Computing

Today, quite a lot of attention is paid to the integration of information technology and medicine. E-health network development makes it possible. The principles of e-health networks imply the use of small-sized mobile medical devices - sensors and actuators. As a rule, sensors, and actuators are combined into a network and operate with other network devices: gateways, cloud servers, etc. The article discusses the network scheme, including a portable near-infrared spectrometer for molecular analysis of food products and its interaction with the Internet in the process of data collection and processing.
A 1 × 2 Taper Slot Antenna Array With Flip-Chip Interconnect via Glass-IPD Technology for 60 GHz Radar Sensors

A low cost, broadband and compact antenna in package solution (AiP) is proposed for 60 GHz low-power radar sensor applications. Following a glass integrated passive device (Glass-IPD) manufacturing process, a compact low cost taper slot antenna (TSA) is designed on a substrate with a thickness beyond Yngvesson’s range. To realize high gain and front-to-back ratio, the antenna is optimized by introducing a truncated ground. A <inline-formula> <tex-math notation="LaTeX">$1 \times 2$ </tex-math></inline-formula> antenna array prototype is realized with a dummy transmission line chip in the dimension of <inline-formula> <tex-math notation="LaTeX">$5 \times 7.4$ </tex-math></inline-formula> mm<sup>2</sup>. Including the flip chip interconnect, the measured results demonstrate a 10 dB impedance bandwidth of <inline-formula> <tex-math notation="LaTeX">$47.2 \sim 67$ </tex-math></inline-formula> GHz. The calculated gain is higher than 6.5 dBi in the end-fire direction over the entire 60 GHz band. Based on a physical RLC equivalent circuit, by co-design of the antenna with solder balls, the flip chip interconnect is compensated with a passive network. For the compensation network, its working bandwidth is from 0.1 to 67 GHz, while its insertion loss at 60 GHz is less than 0.2 dB.
A Study on the Composition of Image-Based Ship-type/class Identification System

Ships, coastal posts, and control centers need to verify the information, location, and speed of nearby vessels for various purposes, such as traffic control or maritime warfare. In the field of defense where accurate information on nearby ships is important, radar is used to acquire location information on nearby ships. However, the selection of the opponent's position as well as the high-value units must be more important and prioritized, and image-based warship type/class identification techniques are required in naval warfare. This paper presents a detailed method for system construction that supports detailed identification of warship type/class through structural analysis. Through the development of the proposed system, reliable warship identification can be used for tactical applications in the defense field, thereby contributing to the enhancement of defense capabilities.
Depth Camera based Autonomous Mobile Robot for Indoor Environments

As a consequence of the recent developments in the fields of Artificial Intelligence, Computer Vision and advancements in the processing capabilities of embedded processors, robots are gaining widespread acceptance for applications in indoor environments. The basic requirements of a robot that is meant for interior applications include the ability to localize itself in the environment and navigate through it. While localization using specific markers has its advantages, a more generic solution would involve the use of a 2D map and adequate algorithms to navigate the area using this map. This paper proposes a design for an autonomous robot that can achieve the basic requirements of navigation in a static environment using low-cost 3D cameras like Kinect. The designed robot uses particle filters to localize in a 2D map. Once localized, it uses A* algorithm for global path planning. Once a path is available, feedback from odometry is used by a PID controller to achieve motion control.
Numerical Dispersion Reduction Scheme for Arbitrary Order FDTD Method

In this paper, we present a new approach to reduce the numerical dispersion error of the arbitrary order finite-difference time-domain (FDTD) method in the desired high frequency region. By interpolation of numerical dispersion relation in one-dimensional (1D) and two-dimensional (2D) cases, the numerical disperion error can be significantly reduced for a specific bandwidth especially in the case of high frequency. Two numerical experiments are presented to validate the accuracy of the proposed method.
The Segmentation of Retinal Layer and Fluid in SD-OCT Images Using Mutex Dice Loss Based Fully Convolutional Networks

Spectral domain optical coherence tomography (SD-OCT) is a non-invasive imaging modality for assessing retinal diseases, such as diabetic retinopathy (DR), which is one of the most prevalent diseases responsible for visual impairment and blindness in the world. The main manifestations of DR are retinal deformation and fluid masses, termed diabetic macular edema (DME), which is the primary biomarker for assessing and diagnosing diseases. In the clinic, ophthalmologists can manually segment retinal layers and fluids to get quantitative and diagnostic information, which is the basement of the final diagnosis. However, this manual segmentation is time-consuming and labor-intensive. To facilitate and promote it, researchers have proposed many automated methods, where most of them usually ignore the priorities in ophthalmology and just regard this task as a standard semantic segmentation task. In this study, we consider the priority of the mutex relationship among different layers and introduce it into the dice loss function to build a novel one, named mutex dice loss (MDL). Besides, we propose a novel fully convolutional network based on our proposed depth max pooling (DMP) to segment retinal layers and fluids in SD-OCT images. Experimental results of the proposed method on two public datasets demonstrate promising performance, which also shows the potential to help ophthalmologists in the diagnostic process of DR or other related diseases.
Detecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.
Remote Crack Measurement Using Android Camera with Laser-Positioning Technique

The present study has developed a remote crack-measuring device using an Android camera with the laser-positioning technique. Four laser points projecting on walls along with cracks are photographed and utilized as reference points to process the image ortho-rectification. Then, for the crack recognition, a crack-tracking method based on the edge-detecting algorithm is used and implemented in the system. Finally, a field experiment was conducted to validate the proposed remote crack-measuring system. The preliminary result shows that this system has a great potential for field applications.
Using Federated Learning on Malware Classification

In recent years, everything has been more and more systematic, and it would generate many cyber security issues. One of the most important of these is the malware. Modern malware has switched to a high-growth phase. According to the AV-TEST Institute showed that there are over 350,000 new malicious programs (malware) and potentially unwanted applications (PUA) be registered every day. This threat was presented and discussed in the present paper. In addition, we also considered data privacy by using federated learning. Feature extraction can be performed based on malware. The proposed method achieves very high accuracy (≈0.9167) on the dataset provided by VirusTotal.
A 20W Single Ended Primary Inductor Converter Power Factor Correction Circuit in Critical Conduction Mode with Constant On Time

This paper proposes a Single Ended Primary Inductance Converter Power Factor Correction circuit (SEPIC PFC). The proposed SEPIC PFC is operated in CRitical Conduction Mode (CRM) with Constant On Time (COT). Due to CRM operation, the SEPIC switch is turned on at Zero Current Switching (ZCS), thus, reducing the switching losses. A unity power factor of line current is nearly achieved, resulting in low Total Hrmonics Distortion (THD), over the universal line voltage (90V-260V). The simulation results show that the proposed design does have a smoothed line current and it is suitable for low power applications e.g., Laptops and mobiles chargers.
Real-Time Data Management for IoT in Cloud Environment

Internet of Things (IoT) is changing the physical world. Billions of smart objects across the world generate sensed data about the environmental changes and report them. These sensed data create a lot of traffic in the network, a lot of redundancy, and duplicated records, and increase the storage size in BigData in cloud computing. Some of the sensed data are required to perform a real-time action. In this paper, we propose a framework to provide the decision makers in today's organizations with real-time data that could help them make better decisions. In addition, it could filter and fuse the data with Adaptive Sampling rate using Feedback control mechanism (FCM) in the network layer. The proposed framework will reduce the number of IoT records. Moreover, it avoids a lot of traffic in the network, increases the network life, and minimizes the energy consumption. The proposed approach can help most of Businessmen in their decisions, increases the performance and reduces the cost. It can also be applied to any Business-like ERP, CRM, and SCM. We have implemented and tested it by SQL and CQL. The results of the experiments show that the reduction has been achieved by 82.34 percent compared to the base one. This reduction has improved the energy consumption in the traffic in the network layer, generating an efficient BigData in terms of non-noise, storage capacity and cost.
Software-Defined Edge Computing (SDEC): Principles, Open System Architecture and Challenges

Edge computing is a bridge for realizing the convergence between physical space and cyber space. Large numbers of physical objects produce a huge amount of data that needs to be efficiently processed in the edge side. This situation urgently requires novel ideas and framework in the design and management of edge computing to improve and enhance its performance. In this paper, we propose an approach and principles of Software-Defined Edge Computing (SDEC) from the perspective of cyber-physical mapping, where the ultimate goal is to achieve a highly automatic and autonomous edge computing system. And SDEC can also help realize flexible management and intelligent collaboration among various edge hardware resources and services by way of software. To this end, we design a SDEC-based open edge computing system architecture which decouples upper-level applications from the underlying physical edge resources and builds dynamically reconfigurable smart edge services. This approach can share, reuse and recombine edge resources and services so that overall service capability of the edge side is improved. Finally, we outline several challenges which are worthy of in-depth study and research.
Abstracting Syntactic Privacy Notions via Privacy Games

It is well understood that the huge volumes of data captured in recent years have the potential to underpin significant research developments in many fields. But, to realise these benefits, all relevant parties must be comfortable with how this data is shared. At the heart of this is the notion of privacy — which is recognised as being somewhat difficult to define. Previous authors have shown how privacy notions such as anonymity, unlinkability and pseudonymity might be combined into a single formal framework. We use and extend this work by defining privacy games for individual and group privacy within distributed environments. For each privacy notion, we formulate a game that an adversary has to win in order to break the notion. Via these games, we aim to clarify understanding of, and relationships between, different privacy notions; we also aim to give an unambiguous understanding of adversarial actions. Additionally, we extend previous work via the notion of unobservability.
Natural Language Inference as an Evaluation Measure for Abstractive Summarization

Natural Language Inference (NLI) is the task of determining if a natural language hypothesis can be reasonably inferred from a natural language text. Text Summarization is the task of taking a piece of text and producing a condensed version that retains the salient points of the text in the process. Evaluating the quality of generated summaries is a very ambitious task. Most current methods to evaluate system generated summaries require the presence of human-written summaries for reference, making it an expensive endeavour. This work proposes using NLI as an evaluation measure for system generated summaries. This approach does not need costly reference summaries. The results we obtained show that we can confidently use NLI to determine the correctness of summaries generated by Abstractive Summarizers.
Design of a 28 GHz Power Amplifier in 0.15 μm GaN Technology

This paper presents the design and simulation of two single-stage power amplifiers (PAs) to determine the performance capabilities of WIN Semiconductors 0.15 μm Gallium Nitride (GaN)-on-SiC process. Subsequently a 28 GHz two-stage PA is presented for potential integration into 5G transceiver applications. A 2×100 gate-width device was used to develop a driving stage PA (MMIC1) which produced a simulated 12.8 dB small signal gain at the desired 28 GHz. A 4×100 gate-width device was used to characterize an output stage PA (MMIC2) producing 33 dBm power out, PAE of 41%, and a power gain of 11.2 dB. The subsequently designed two-stage PA, MMIC3, combines MMIC1 and MMIC2 and provides a peak small signal gain of 22.5 dB, peak power output of 33 dBm, peak PAE of 31% and peak power gain of 22.4 dB.
Compact Dual-band Branch-Line Coupler with Short-ended Stubs

A novel compact dual-band branch line coupler with arbitrary power division is proposed. A conventional coupler loaded with half-wavelength short-ended stubs is adopted to realize the dual-band operations. Moreover, the frequency is tunable by changing the characteristic impedance of the transmission line. For practical verification, a branch line coupler ($\varepsilon_{\mathrm{r}}=2.65,\ h=0.5 \text{mm}$) operation at 0.9/1.8GHz is constructed in microstrip technology and tested. The measured results are in good agreement with the simulated ones.
Proposing a Privacy Protection Model in Case of Civilian Drone

Technology-based products are meant for improving the people's quality of life. Since drones or more precisely Unmanned Aerial Vehicle's UAVs were permitted to be used for civilian purposes and applications, many lucrative reasons such as low price, mobility and ease of deployment have captured the attention of commercial organizations. These reasons have motivated the commercial organizations to involve and adopt UAVs in their company's operational body structure which will be positively reflected on the services provided to their clients. UAVs have influential features that can be used to infringe on the privacy of individuals if they are deliberately misused, therefor the commercial organizations willingness and the precious services going to be offered to clients should not be at the cost of privacy. This research will present a privacy detection model to guarantee the success of UAV commercial adoption as well as securing the individual privacy. Proposed model will be implemented in near future to escort the privacy protection of civilian in case of commercial drone.
Terahertz Scattering Computation of Complex Structures Modeled by NURBS Surfaces

In the terahertz (THz) region, the precision and the realism of the CAD models have great influence on EM scattering characteristics. Low-frequency methods are typically limited to problems of small electrical size due to limitations of computation time and system memory. The ray-based high-frequency EM method, shooting and bouncing rays (SBR), are employed to solve THz target scattering. A very common way of representing arbitrary targets is via a surface mesh made of triangular facets. To guarantee the geometrical accuracy, a small size of the mesh elements must be chosen. Ray tracing becomes more time-consuming while in THz band. A special ray-tracing technique that combined geometric optics (GO) with non-uniform rational B-Splines (NURBS) has been implemented. Numerical examples demonstrate the accuracy and efficiency of the approach.
An IoT Node with Organic Pressure Sensor for Structural Health Monitoring System

In this paper, we develop an IoT node with an organic pressure sensor for structural health monitoring (SHM) system. A 100um-thick polyurethane film was sandwiched by top/bottom electrodes to complete the flexible pressure sensor fabrication. IoT sensor nodes using available low-cost components in the market were designed for the SHM system. Sensors collect data about the state of structural infrastructures and send information to a virtual private server through a network gateway. We also built a website on the hosting server for the purpose of SHM including monitoring signals, storing data and other administration tools. Experiment results show that the IoT node with organic pressure sensors has high potential in SHM application. The organic pressure sensor manufacturing steps and the SHM system will be described in detail in this paper.