Miracle-dz/UniFAR-FTUs huggingface dataset:
Systemic Air Embolization During Percutaneous Transthoracic Needle Biopsy: Imaging Findings, Management Strategies, and Review of the Literature

Percutaneous transthoracic needle biopsy is an indispensable tool that allows the minimally invasive characterization of pulmonary nodules, masses, and inflammatory disease. Systemic air embolism is a very rare complication resulting from percutaneous transthoracic needle biopsy and can be catastrophic. Knowledge of this entity may minimize morbidity and mortality after percutaneous transthoracic needle biopsy. In contrast to the majority of published reports, we report an asymptomatic patient with systemic air embolism after percutaneous transthoracic needle biopsy and emphasize the significance of this rare complication, as hospital code teams may be unfamiliar with this complication. We also review imaging findings and management options for this rare entity.
Impact of mobility and deployment in confined spaces on low power and lossy network. (Impact de la mobilité et du déploiement dans des espaces confinés sur un réseau à faible consommation et à perte)

Wireless Sensor Networks (WSNs) technology is one of the building blocks ofthe Internet of Things (IoT). Due to their features of easy deployment and flexibility,they are used in many application domains. Low-Power and Lossy Networks(LLNs) are a special type of WSNs in which nodes are largely resources constrained.For LLNs, convergecast is one of the basic traffic modes, where all traffic in the networkis destined to a predefined destination called the sink. While considering theIoT application domains, convergecast is not the only traffic mode in the network.The sink needs to send commands to certain sensors to perform actions. In this application,anycast is another basic traffic mode. In anycast, the traffic from the sinkis destined to any member of a group of potential receivers in the network.Traditionally LLNs are formed by static sensor nodes and rarely change positions.Due to the strict resource constraints in computation, energy and memory ofLLNs, most routing protocols only support static network. However, mobility hasbecome an important requirement for many emerging applications. In these applications,certain nodes are free to move and organize themselves into a connectednetwork. The topology would continuously change due to the movement of nodesand radio links instability. This is a hard task for most routing protocols of LLNs toadapt rapidly to the movement and to reconstruct topology in a timely manner.The goal of this thesis is to propose an efficient mobility support for routingprotocols in LLNs. We focus on convergecast and anycast, which are the most usedtraffic modes in LLNs, in mobile network scenarios.We propose an enhancement mechanism, named RL (RSSI and Level), to supportrouting protocols in convergecast LLNs in mobility. This mechanism helps routingprotocol make faster decisions for detecting mobility and updating next-hop neighborsbut suffers from high overhead. We propose a dynamic control message managementto enhance the overhead performance of RL and implement it on top ofRouting Protocol for Low-power and Lossy network (RPL) and we named it RRD(RSSI, Rank and Dynamic). After taking into account hysteresis of the coveragezone of the transmission range of nodes, we optimized RRD. This enhanced versionis called RRD+. Based on RRD+, we proposed MRRD+ (Multiple, RSSI, Rankand Dynamic) to support multiple sinks in convergecast LLNs in mobility. ADUP(Adaptive Downward/Upward Protocol) is a routing solution that supports bothconvergecast and anycast in LLNs concurrently.We evaluated the performance of our contributions in both simulation usingCooja simulator and experiment (only for ADUP) on TelosB motes. The resultsobtained in both simulation and experiment confirm the efficiency of our routingprotocols.
On the Possibility of Parallel Programs Synthesis from Algorithm Graph Specification

To solve the actual problem of automated parallel programming, the UPL language is proposed for specifying the M-graph of the algorithm and a roadmap for generating programs on its basis for various parallel platforms in automatic mode is presented. For this purpose, in addition, the user must specify the distribution functions that map calculations to the platform resources. The algorithm for generating a loop nest by the M-graph of the algorithm and the combined distribution function is described, and its operation is demonstrated by a simple example.
Adaptive Refinement and Decimation under Free-Form Deformation

The Free-Form Deformation (FFD) methods are a family of intuitive, efficient and versatile free-form modelling techniques, which rely on an ambient warp of the space cocooning an embedded object. However, the distortion caused by FFD significantly degrades the approximation quality of polygon-mesh objects. We present a curvature-based adaptive refinement and decimation algorithm which solves this problem in the context of repeated interactive FFD and improves on previous work [22, 13, 211 in three respects: it addresses sampling issues, incorporates decimation as well as refinement, and reduces computational complexity.
EEG-based human emotion recognition using k-NN machine learning

Mental health issue is growing rapidly in these recent years. Teenagers and young adult aged of 16–30 years old are the most common victims. Mental health is a really serious issue concerning emotional health. One of the causes on emotional health issues is a lack of self-awareness, which is the key cornerstone on maintaining emotional state. In this study, EEG Neurostyle of 24 channels was used to obtain brain electrical signals. The mental emotions of the subjects were obtained from their reactions due to a set of audio-visual stimuli of approximately 5 minutes, the subject consists of 6 subjects aged 18–22 years old. The expressions of the subjects were recorded EEG signals separately to ensure their emotion according to the source (i.e. sad clips resulting sad emotion). The signals were processed using DFT and PSD to extract their features. The features were used to classify the emotions into 4 classes: happy, sad, scared, and disgust. In this study, the k-NN as classifier was used and obtained training and testing accuracy for all the features were greater than 52 % and 30 % for 4 classes.Mental health issue is growing rapidly in these recent years. Teenagers and young adult aged of 16–30 years old are the most common victims. Mental health is a really serious issue concerning emotional health. One of the causes on emotional health issues is a lack of self-awareness, which is the key cornerstone on maintaining emotional state. In this study, EEG Neurostyle of 24 channels was used to obtain brain electrical signals. The mental emotions of the subjects were obtained from their reactions due to a set of audio-visual stimuli of approximately 5 minutes, the subject consists of 6 subjects aged 18–22 years old. The expressions of the subjects were recorded EEG signals separately to ensure their emotion according to the source (i.e. sad clips resulting sad emotion). The signals were processed using DFT and PSD to extract their features. The features were used to classify the emotions into 4 classes: happy, sad, scared, and disgust. In this study, the k-NN as classifier was used and obtained traini.
Hybrid Cooperative Positioning for Vehicular Networks

This paper proposes a hybrid cooperative positioning (CP) algorithm suitable for vehicular network applications which can fuse the measurements from global navigation satellites, ground stations, signals of opportunity, inter-node ranging from neighbouring vehicles and onboard inertial navigation systems (INS). By applying the framework of generalized approximate message passing (GAMP), the complex CP problem is transformed into an iterative yet lower computational load process. In each iteration, the time recurrence of navigation states and initialization of GAMP computation are conducted based on Kalman filter. The proposed algorithm guarantees the overall positioning performance of multiple vehicles in a hybrid navigation scenario, and improves the robustness and accuracy of CP navigation systems. Simulation results show that the proposed algorithm has better estimation accuracy than traditional CP algorithms, and has 20 times less computational load than the best existing algorithm with equivalent accuracy.
Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces

Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service (S2aaS) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications.
A 22.3-nW, 4.55 cm2 Temperature-Robust Wake-Up Receiver Achieving a Sensitivity of −69.5 dBm at 9 GHz

This article presents a miniaturized wake-up receiver (WuRX) that is temperature-compensated yet still consumes only 22.3 nW (7.3 nW excluding the temperature-compensation blocks) while operating at 9 GHz ( ${X}$ -band). By moving the carrier frequency to 9 GHz and designing a high impedance, passive envelope detector (ED), the transformer size is reduced to 0.02 cm2 while still achieving 13.5 dB of passive RF voltage gain. To further reduce the area, a global common-mode feedback (CMFB) technique is utilized across the ED and baseband (BB) amplifier that eliminates the need for off-chip ac-coupling components. Multiple temperature-compensation techniques are proposed to maintain constant bandwidth of the signal path and constant clock frequency. The WuRX was implemented in 65- (RF) and 180-nm (BB) CMOS and achieves −69.5- and −64-dBm sensitivity with and without an antenna, respectively. Importantly, the sensitivity is demonstrated to vary by only 3 dB from −10 to 40 °C. This article demonstrates state-of-the-art performance for WuRXs operating at >1 GHz while achieving the smallest area and best temperature insensitivity.
Anonymization and De-Anonymization of Mobility Trajectories: Dissecting the Gaps Between Theory and Practice

Human mobility trajectories are increasingly collected by ISPs to assist academic research and commercial applications. Meanwhile, there is a growing concern that individual trajectories can be de-anonymized when the data is shared, using information from external sources (e.g., online social networks). To understand this risk, prior works either estimate the theoretical privacy bound or simulate de-anonymization attacks on synthetically created datasets. However, it is not clear how well the theoretical estimations are preserved in practice. In this article, we collected a large-scale ground-truth trajectory dataset from 2,161,500 users of a cellular network, and two matched external trajectory datasets from a large social network (56,683 users) and a check-in/review service (45,790 users) on the same user population. The two sets of large ground-truth data provide a rare opportunity to extensively evaluate a variety of de-anonymization algorithms (nine in total). We find that their performance in the real-world dataset is far from the theoretical bound. Further analysis shows that most algorithms have under-estimated the impact of spatio-temporal mismatches between the data from different sources, and the high sparsity of user generated data also contributes to the under-performance. Based on these insights, we propose four new algorithms that are specially designed to tolerate spatial or temporal mismatches (or both) and model location contexts and time contexts. Extensive evaluations show that our algorithms achieve more than 17 percent performance gain over the best existing algorithms, confirming our insights. Further, we propose two new location-privacy preserving mechanisms utilizing the spatio-temporal mismatches to better protect users’ privacy against the de-anonymization attack. Evaluation results show that our proposed mechanisms can reduce the performance of de-anonymization attacks by over 8.0 percent, demonstrating the effectiveness of our insights.
A Synchronization Algorithm for Burst-Mode MR-OFDM System of 802.15.4g

This paper presents a refined coarse frame synchronization algorithm for long repeated-sequence based preamble STF (short training field) in burst mode MR-OFDM system of 802.15. 4g which is of low decision delay and resistant to timing uncertainty due to metric plateau. The precision of coarse timing and frequency offset estimation is improved. A fine symbol timing and frequency offset estimation algorithm based on LTF (long training field) is also proposed which provides an overall solution for the synchronization of burst mode MR-OFDM system. Simulation results are given to provide reference for implementation.
Sources and Possible Causes of Errors and Misconceptions in Operations of Integers

This research investigated students’ sources and causes of errors and misconception in solving routine problems involving addition, subtraction, multiplication, and division of integers. This qualitative study involved observation of eight Year 7 classes and interviews with the respective classroom teachers. Sixteen Year 7 students who exhibit errors when solving the problems given in the Error Identification Integer Test (EIIT) were also interviewed to probe their thinking. The different types of errors were categorized according to how they were construed. The sources of errors were found to stem from carelessness, poor basic knowledge such as the inability to multiply and divide even the whole numbers, inability to assimilate concepts of integers since they are used to the schema of whole numbers, and rule mix-up which is also the result of surface understanding. Teachers were questioned about common errors and possible reasons for these errors made by their students. The main cause of errors and misconceptions is superficial understanding, which was most probably due to teachers rushing to complete the extensive syllabus, and consequently, students resorted to memorizing rules because of surface understanding. Teaching episodes were found to lack multiple-representation, creativity, as well as cooperative learning and active learning.
Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network

Accurate battery aging prediction is essential for ensuring efficient, reliable, and safe operation of battery systems in electric vehicle application. This article presents a novel battery aging assessment method based on the incremental capacity analysis (ICA) and radial basis function neural network (RBFNN) model. The RBFNN model is used to depict the relationship between battery aging level and its influencing factors based on real-world operation datasets of electric city transit buses. The ICA method together with the Gaussian window (GW) filter method is used to derive the peak values of IC curves which are utilized to represent battery aging levels, and the support vector regression (SVR) method is used in several scenarios for data preprocessing. The considered influencing factors include accumulated mileage of vehicles and initial charging state-of-charge (SOC), average charging temperature, average charging current, and average operating temperature of battery systems. The datasets collected from real-world electric city buses are used for RBFNN model training, validation, and test. The results show that an average prediction error of 4.00% is reached, and the derived model has a confidential interval of 92% with the prediction accuracy of 90%. This work provides insights for battery aging prediction based on massive real-time operation data.
Investigation on positioning control strategy and switching optimization of an equal coded digital valve system

This article concerns high accuracy positioning control with switching optimization for an equal coded digital valve system. Typically, pulse number modulation control cannot realize micro-positioning due to the characteristics of step-wise flow variation, therefore, a new position controller consisting of a model-based pulse number modulation and a differential pulse width modulation strategy is proposed to control the position of a hydraulic cylinder at high and low velocity cases, respectively. In addition, in order to solve several problems caused by the pulse number modulation and differential pulse width modulation, such as increased number of switchings and large difference among number of switchings of valves, a switching optimization consisting of a switching cost function, a circular buffer and a circular switching method is proposed. An adaptive weight of the switching cost function is proposed for the first time to reduce the total number of switchings under different pressure differences and its design criterion is presented. A circular buffer and a new circular switching method are used to improve the degree of equal distribution of switchings when the pulse number modulation and differential pulse width modulation are used, respectively. Comparative experimental results indicated that the average and the minimum positioning error for the proposed controller are only 10 and 1 μm, respectively. The number of switchings and the degree of equal distribution of switchings are significantly optimized. Moreover, the pressure fluctuations caused by the proposed controller remain acceptable.
Thermal-Kinect Fusion Scanning System for Bodyshape Inpainting and Estimation under Clothing

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Analytics of stock market prices based on machine learning algorithms

This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.
An Improved Geometric Approach for Palette‐based Image Decomposition and Recoloring

Palette‐based image decomposition has attracted increasing attention in recent years. A specific class of approaches have been proposed basing on the RGB‐space geometry, which manage to construct convex hulls whose vertices act as palette colors. However, such palettes do not guarantee to have the representative colors which actually appear in the image, thus making it less intuitive and less predictable when editing palette colors to perform recoloring. Hence, we proposed an improved geometric approach to address this issue. We use a polyhedron, but not necessarily a convex hull, in the RGB space to represent the color palette. We then formulate the task of palette extraction as an optimization problem which could be solved in a few seconds. Our palette has a higher degree of representativeness and maintains a relatively similar level of accuracy compared with previous methods. For layer decomposition, we compute layer opacities via simple mean value coordinates, which could achieve instant feedbacks without precomputations. We have demonstrated our method for image recoloring on a variety of examples. In comparison with state‐of‐the‐art works, our approach is generally more intuitive and efficient with fewer artifacts.
Research and Practice of Personalized Learning Recommendation Based on Dynamic Community Discovery

In view of the problems existing in the current online learning recommendation system, community discovery algorithms in complex networks are introduced to study the structure and evolution of learning communities, and learners' habits, learning behaviors, learning environments, learning tools and other factors are considered to discover and dynamically update the community; at the same times, it can provide community recommendations, community recommendation, knowledge resource recommendation, dynamic quality information sharing, to provide more intelligent services for learners, so as to promote the common development of users and communities.
Estimation and Minimization of the Cramer-Rao lower bound for radio direction-finding on the azimuth and elevation of planar antenna arrays

In this paper an approach of obtaining optimal planar antenna arrays consisting of omnidirectional sensors is proposed. The novelty of the proposed approach is to apply an exact expression of the Cramer-Rao lower bound for an arbitrary planar antenna array consisting of a number of omnidirectional elements which has been presented in the further chapters of the paper. The obtained formula describes the influence of antenna elements locations on the direction-of-arrival estimation accuracy. It has been shown that the direction-of-arrival accuracy via planar antenna arrays is determined as the sum of squares of differences between all omnidirectional elements coordinates along x- and y-axis. Thus knowing an expected area or sector of signal source it is very easy to calculate optimal arrangement of antenna elements in order to reduce direction-finding errors, because obtained by that way positions gives the best match according to the maximum likelihood criterion. It is worth nothing that such antenna arrays are useful in the way that they allow estimating the coordinates of radio emission sources in the three-dimensional coordinate space, i.e. in azimuth and elevation. In order to confirm the proposed methodology optimal antenna arrays constructed after minimization of the new formulas are researched. It is found out that the new shapes of antenna arrays based on the analytical expressions have better direction-of-arrival accuracy in comparison with the circular ones.