Miracle-dz/UniFAR-FTUs huggingface dataset:
A Design Method of Microstrip Series-fed Array Antenna

In this paper, a new design method of microstrip series-fed array antenna for vehicle radar technology is proposed. The energy transmission and radiation between antenna elements are expressed and deduced using S-parameters in this method. A constant-amplitude and in-phase microstrip series-fed array antenna is designed through the analysis of this method. The results show that the bandwidth of the antenna ranges from 23.78 GHz to 24.27 GHz for| S11| < −10 dB and the gain is 12.35 dBi at the center frequency.
A Soft Robotic Hand Based on Bellows Actuators for Dishwashing Automation

Automation in the food services industry is not as developed as in the automotive and electrical industries. This may be because (1) the tasks involve high-speed operations in unstructured environments and (2) the lack of effective robotic end-effectors. In this letter, we focus on the automation of dishwashing operations and propose a robotic hand capable of withdrawing and grasping a dish plate from a pile, even when there is water or oil left on the plate in a post-meal scenario. The robotic hand consists of withdrawing and grasping mechanisms. Each mechanism is actuated by two bellows actuators. Soft pads with special groove patterns were fabricated and used to provide sufficient friction when they come into contact with the plate. A theoretical analysis of the withdrawing and grasping operations was performed, and an analytical model of the bellows actuator was established by considering both the pressure thrust force and the elastic force. Bellows actuators were designed based on the analysis and fabricated using both 3D printing and casting methods. Characterization of bellows actuator was performed to validate the analytical model, and agreement was found on the thrust force, but discrepancy occurred in the elastic force due to the complex structure and deformation mode of bellows. Finally, handling experiments on different plates were conducted and results demonstrated that the proposed robotic hand can successfully withdraw and grasp a plate from a pile.
DAKOTA: Sensor and Touch Screen-Based Continuous Authentication on a Mobile Banking Application

Authenticating a user in the right way is essential to IT systems, where the risks are becoming more and more complex. Especially in the mobile world, banking applications are among the most delicate systems requiring strict rules and regulations. Existing approaches often require point-of-entry authentication accompanied by a one-time password as a second-factor authentication. However, this requires active participation of the user and there is continuous authentication during a session. In this paper, we investigate whether it is possible to continuously authenticate users via behavioral biometrics with a certain performance on a mobile banking application. A currently used mobile banking application in Turkey is chosen as the case, and we developed a continuous authentication scheme, named DAKOTA, on top of this application. The DAKOTA system records data from the touch screen and the motion sensors on the phone to monitor and model the user’s behavioral patterns. Forty-five participants completed the predefined banking transactions. This data is used to train seven different classification algorithms. The results reveal that binary-SVM with RBF kernel reaches the lowest error scores, 3.5% equal error rate (EER). Using the end-to-end DAKOTA system, we investigate the performance in real-time, both in terms of authentication accuracy and resource usage. We show that it does not bring extra overhead in terms of power and memory usage compared to the original banking application and we can achieve a 90% true positive recognition rate, on average.
Scaling Up the IFDS Algorithm with Efficient Disk-Assisted Computing

The IFDS algorithm can be memory-intensive, requiring a memory budget of more than 100 GB of RAM for some applications. The large memory requirements significantly restrict the deployment of IFDS-based tools in practise. To improve this, we propose a disk-assisted solution that drastically reduces the memory requirements of traditional IFDS solvers. Our solution saves memory by 1) recomputing instead of memorizing intermediate analysis data, and 2) swapping in-memory data to disk when memory usages reach a threshold. We implement sophisticated scheduling schemes to swap data between memory and disks efficiently. We have developed a new taint analysis tool, DiskDroid, based on our disk-assisted IFDS solver. Compared to FlowDroid, a state-of-the-art IFDS-based taint analysis tool, for a set of 19 apps which take from 10 to 128 GB of RAM by FlowDroid, DiskDroid can analyze them with less than 10GB of RAM at a slight performance improvement of 8.6%. In addition, for 21 apps requiring more than 128GB of RAM by FlowDroid, DiskDroid can analyze each app in 3 hours, under the same memory budget of 10GB. This makes the tool deployable to normal desktop environments. We make the tool publicly available at https://github.com/HaofLi/DiskDroid.
LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing

Real-time 6D pose estimation is a key component for autonomous indoor navigation of Unmanned Aerial Vehicles (UAVs). This letter presents a low-latency LiDAR SLAM framework based on LiDAR scan slicing and concurrent matching, called LoLa-SLAM. Our framework uses sliced point cloud data from a rotating LiDAR in a concurrent multi-threaded matching pipeline for 6D pose estimation with high update rate and low latency. The LiDAR is actuated using a 2D Lissajous spinning pattern to overcome the sensor's limited FoV. We propose a two-dimensional roughness model to extract the feature points for fine matching and registration of the point cloud. In addition, the pose estimator engages a temporal motion predictor that assists in finding the feature correspondences in the map for the fast convergence of the non-linear optimizer. Subsequently, an Extended Kalman Filter (EKF) is adopted for final pose fusion. The framework is evaluated in multiple experiments by comparing the accuracy, latency, and the update rate of the pose estimation for the trajectories flown in an indoor environment. We quantify the superior quality of the generated volumetric map in comparison to the state-of-the-art frameworks. We further examine the localization precision using ground truth pose information recorded by a total station unit.
Image Super Resolution in Real World Using Variational Auto Encoder

Most of the existing super-resolution methods trained only by simulated datasets are difficult to achieve good performance in real-world scenes. Besides, it is difficult to obtain well-aligned real-world image pairs between high-resolution and low-resolution spaces for training. To tackle this problem, we proposed a novel super-resolution framework based on variational auto encoder. In particular, we firstly utilized a variational auto encoder to map the degraded low-resolution images and the real-world low-resolution images to the same latent space. Meanwhile, the high-quality images were mapped to another latent space by another variational auto encoder. An additional convolutional neural network was used to learn the mapping between the two latent spaces. After that, the information in the mapped latent space was decoded and the high-resolution images were reconstructed by the decoder. We have compared the performance of our proposed method and those of state-of-the-art methods including SRGAN. , ESRGAN. , and CycleGAN algorithms. The experimental results demonstrate that the proposed method outperforms the above methods in the super-resolved task in real world.
Lightweight Audio Source Localization for Swarm Robots

The ability to localize a sound source is essential for safe integration of robots in our society. However, its high computational demand makes it challenging to equip small swarm robots with such capability. Therefore, this paper investigates potential means for developing a lightweight sound source localization method. It introduces the Cutting The Plane (CTP) algorithm which leverages recent advancements in embedded microphone technology to reduce the computation complexity of audio source localization. We analyzed its performance and compared it to a software-based approach using simulation. Finally, we performed tests on real hardware to show the feasibility of using our system for relative localization between swarm robots.
Improved Preparation of 2,4-Thiazolidinedione

The widely utilized industrial product 2,4-thiazolidinedione (3) has been chosen as the starting material for our ongoing research on rosiglitazone,1 which is a selective PPAR-γ receptor inhibitor for the treatment of type-II diabetes.2 Based on the literature3 and our own experience, we developed an efficient one-step method to obtain 3 using conc. hydrochloric acid.4 This improved synthesis seemed to be much better than the previous methods,3 all of which shared the disadvantage that the strongly acidic waste from the reaction and solvents from the recrystallization process have to be disposed of without harm to the environment. In order to alleviate these problems, we decreased the amount of hydrochloric acid and our results show that the yield of 3 is still acceptable while the concentration of HCl has been decreased by more than one half (Table 1). This result encouraged us to do further research,5 which resulted in a green and inexpensive method to prepare 3. Herein, we report this economic and environmentally friendly synthesis of 3, starting from chloroacetic acid (1) and thiourea (2) using water as the solvent for the preparation as well as the recrystallization process.
Socially Assistive Robotics combined with Artificial Intelligence for ADHD

This paper presents a patient-centered interaction project of Pepper humanoid robotic therapy applications for children with attention deficit. This new therapeutic methodology was created to support and make therapeutic work more attractive. Pepper comes with a tablet and two identical cameras. The tablet is used to make the patients interact with the application, while the cameras are used to capture their emotions in real-time to understand the degree of attention and any difficulties they may have. The interaction with the tablet takes place through some exercises in the form of games. The exercises performed by the patients are analyzed and combined with the data acquired by the cameras. The combination of these data is elaborated to propose adequate levels of therapeutic activity. This process leads to the digitization of the patients' therapeutic path so that any improvement (or worsening) is monitored and makes Pepper a reliable and predictable technological intermediary for the child. The work was developed in collaboration with a diagnostic and therapeutic center, where it is being tested. By interacting with a humanoid robot, children show greater involvement, which can be explained, according to psychologists, by the fact that a robot is emotionally less rich than humans and the patients feel less fearful.
A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.
Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms

Concerted efforts continue to be made in the search for superior lithium-ion conducting solids to replace the highly reactive liquid electrolytes typically used in rechargeable batteries. LISICON-type materials have been studied extensively over the last few decades, providing abundant experimental data, but to date no overall design principle for achieving high conductivity has been forthcoming. In this communication we present results of systematic sets of fi rst-principles calculations based on the cluster expansion method, as well as fi rst-principles molecular dynamics (FPMD) simulations carried out to calculate Li-ion conductivities at high temperature, for a diverse range of compositions. A machine-learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of fi rst-principles calculations and focused experiments can greatly accelerate the materials design process by enabling a wide compositional and structural phase space to be examined effi ciently. Lithium-conducting oxides in the system LiO 1/2 A O m /2 B O n /2 (where m and n denote the formal valences of cations A and B , respectively), known as LISICONs and corresponding to general formula Li 8 − c A a B b O 4 (where c = ma + n b ), [ 1 ] have been intensively studied since the 1970s. The original LISICON composition, Li 3.5 Zn 0.25 GeO 4 , was reported to exhibit an ionic conductivity of over 10 − 1 S cm − 1 at 673 K, [ 2 ] and stimulated a fl urry of new research. Although the conducting properties of many different LISICONs have since been reported by various groups, there are still many compositions that have yet to be
Terahertz Metamaterial Intelligent Identification by Private Preserving Deep Learning

Metamaterials are artificial materials, designed with special structures, and can exhibit controllable electromagnetic properties. To address the looming threat of stealthy monitoring caused by metamaterials, the detection and identification of them is the next research frontier but have not yet been explored. In this research, a crypto-oriented convolutional neural network is designed to identify metamaterial in mixtures, which is an accurate and secure paradigm of AI-based terahertz smart sensing.
Galvanic Impulse Wireless Communication for Biomedical Implants

Wireless intra-body communication is a promising approach for providing efficient and secure connectivity for medical implants. The low power consumption of the electronics and the conductivity of the biological tissues facilitate the system implementation, which makes the technique more power-efficient than the traditional radio frequency wireless systems. The galvanic intra-body communication uses the electrical current for signal transmission in the conductive medium of the biological tissues. In this paper, we propose an ultra-low-power communication approach by implementing a galvanic impulse method for communication between an implant and an on-body device. The communication system is designed, manufactured with off-the-shelf electronic components, and measured in the phantom and in-vivo animal experiment. The implant power consumption is $45~\mu \text{W}$ for the data rate of 64 kbps with a bit error percentage below 0.5% for the implant depth of 14 cm. The design supports long-lasting battery-powered implant sensory and communication system.
Deep learning for Antisocial Behaviour Analysis on Social Media

Social Media platforms have become an imperative source of information related to urban functions and social behaviour. Analysis of social media data can provide meaningful insights into the personality and behaviour traits of its users. Behaviour studies have been typically conducted using questionnaire and survey data. Since most of our activities have moved online, our behaviour on social media has become a proxy for our real-world behaviour. In this paper, we collected data from Swarm and Twitter, two widely used social media platforms, to detect user’s antisocial behaviour with a high degree of accuracy. The behaviour was then categorized into different classes of antisocial behaviour using cutting edge deep learning technology. Four different deep learning models and two different word embeddings were experimented with to achieve a final model accuracy of 99%. Visually enhanced interpretation of the classification process and the word clouds for some of the most commonly used antisocial terms are presented, along with model training and error analysis.
Guido: Augmented Reality for Indoor Navigation Using Commodity Hardware

Indoor positioning is one of the difficult problems in current navigation systems. There is an increasing demand for detecting the locations of objects and humans inside closed environments in various fields including surveillance, robotics, and entertainment. Recent works focus on indoor navigation systems using different technologies including Wireless Local Area Network (WLAN), Radio Frequency Identification (RFID), Inertial Measurement Unit (IMU), and Simultaneous Localization and Mapping (SLAM). Research reveals using these technologies alone is inefficient in terms of accuracy and cost. To address this issue, we propose a marker-based Augmented Reality (AR) indoor navigation system with integrated SLAM and IMU. We use Unity’s AR Foundation Framework for highly accurate results with minimum hardware requirements.
Designing Interactive Systems for Seniors: An Educational Experience

This paper describes the results of a three years educational design experience targeted at collecting seniors' needs and wishes and to identify some possible answers through the proposal of a set of conceptual projects. The interaction design experience involved more than 150 undergraduate students who interviewed about 60 seniors aged 70 or more and then worked on their proposals. This paper reports the most interesting conceptual projects, organized by topic and complemented by observations about the challenges faced by the young designers, with the final goal of sharing them for inspiring further design and development.
A Multi-Modal Approach to Patient Activity Monitoring

This paper presents our patient activity monitoring system (PAMS), which combines a wrist-worn device and a real-time location system (RTLS) with active tags. Four main data streams were used: location coordinates, 3D accelerometer, heart rate, and electrodermal activity (EDA) data. Using non-overlapping, normalized power windows, data are fed into random forest and KNN classifiers. These classifiers divide data into specific ambulation classes including time of inactivity, posture, and wheelchair monitoring data. The classification performance (F1 score) of our machine learning techniques ranges from 87.6% to 99.2% which is quite promising. These metrics provide clinicians with valuable insight into the activity levels of patients and patients’ progress towards achieving their ambulation goals.
A Deep Reinforcement Learning Approach for Type 2 Diabetes Mellitus Treatment

Type 2 diabetes mellitus (T2DM) is a chronic disease that requires continuous treatments. T2DM treatments aim to achieve not only short-term but, more importantly, long-term control of the patient’s glucose to a normal level. We believe Reinforcement Learning (RL) can be an effective approach to learn and further recommend the ideal sequence of treatments that optimize the patient’s long-term outcome. In this paper, we implement RL with the deep Q network. Our RL model learns from a T2DM patient registry dataset that consists of the clinical data of newly diagnosed T2DM patients over a twelve-month period including four follow-ups since their first visit. The RL model is trained to recommend the number of oral antidiabetic drugs and the number of insulins. Our experiments have shown that our RL model can improve the long-term hemoglobin A1c goal achieving rate by up to 15%. This confirms that the model learns good medication patterns that favor the long-term glucose control.
Construction of Basic Haptic Systems for Feeling the Intermolecular Force in Molecular Models

本研究は，可視化される分子情報と力覚デバイスに提示される力により，分子間力を体感できる新しい分子模型システム教材の基盤構築と基本要件の確立を目的としたものである．本稿では，本システムのコンセプトと方法について述べ，次いで，開発した基本システムの構成と実行例を示す．さらに，本システムの最初の実施結果に基づき，力覚デバイスや可視化に求められる基本要件について考察した結果を報告する
Amorphous Y-Ba-Cu-O Thin Films: Noise Studies and Pyroelectric Properties for Uncooled Infrared and Terahertz Detection Applications

Oxygen depleted semiconducting Y-Ba-Cu-O exhibits an amorphous phase ($a$-YBCO), obtained at low deposition temperature (150 °C), which can be applied to fast uncooled near-infrared or THz detection. In the first part, we investigate room temperature noise performances of planar and trilayer detectors fabricated on silicon substrates. The best noise equivalent power (NEP) and detectivity $D^{\ast}$ are at the state of the art: e.g., $NEP=2.0\ \text{pW}/\text{Hz}^{1/2}$ and $D^{\ast}=6.6\times 10^{9}\ \text{cm}\cdot\text{Hz}^{1/2}/\mathrm{W}$ for planar structures. In the second part, we examine the pyroelectric response of $a$-YBCO to extract the pyroelectric coefficient $p=600\ \mu\mathrm{C}\cdot \mathrm{m}^{-2}\cdot \mathrm{K}^{-1}$ at 300 K, as first estimate.
Moving Vehicle Wheel Parameter Extraction via Micro-Doppler Feature Based on Matching Pursuit

Vehicle recognition using radar echo is critical in many applications. However, the discrimination of different kinds of vehicles is still a difficult problem because of their similar structures. For moving vehicles, besides the conventional features, the micro-Doppler (m-D) features induced by the rotation of vehicle's wheels are also useful in vehicle recognition. Most of current researches related to the m-D effect still analyze only a few scattering points and they are unable to extract the m-D features from the radar echo of the whole scene. Given this problem, in this paper, a moving vehicle wheel parameter extraction method via micro-Doppler feature based on matching pursuit is proposed, which can suppress the irrelevant signal components and extract the m-D features of wheels from the echo signal of the area illuminated by radar. Then the positions and the radiuses of wheels can be extracted for different kinds of vehicles discrimination. The simulations are given to validate the effectiveness of the algorithm and the results show the potential of the proposed method in moving vehicles recognition.
A Prototype Conversational Agent for Dietary Supplements

According to National Institute of Standards and Technology (DSQAP), approximately 8 out of every 10 people in the United States take dietary supplements, including vitamins and mineral supplements, representing an annual expenditure of more than $40 billion. Opened domain question answering (QA) systems or search engines might not be able to resolve complex specialized queries with enough relevance and coherence in one conversation turn. Therefore, we developed a prototype conversational agent(CA) system catered to resolve user queries regarding dietary supplements. This pilot study demonstrates the feasibility of using MindMeld and iDISK domain knowledge base to develop an CA system responding 77% questions in a good way.