Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT Retrofitting Approach for the Food Industry

Industrial food production is one of the biggest businesses that supplies most of the food consumed by the world. Despite being one of the largest industries, the lack of appropriate control and quality due to inefficient food management results in a lot of waste and cost ineffectiveness. With the emergence of Internet of Things (IoT) and Industry 4.0, the food industry can be benefited by adapting to these concepts and enhance the desirable quality of the products. In this paper, an advanced quality check method has been proposed by identifying influencing process parameters and proposes a retrofitting architecture for existing machines by implementing a hardware device capable of collecting vast amount of process data and integrating them with a cloud platform for further analysis.
Wavelet Analysis Based Hidden Markov Model for Large Ship Trajectory Prediction

For the trajectory prediction problem of large ship, it is difficult to establish accurate motion model of moving target. Traditional trajectory prediction algorithm based on Kalman filtering or linear regression have defects of lower precision or long time delay. In this paper, we discuss the trajectory prediction problem based on Hidden Markov Model (HMM). In order to reduce the influence of error accumulation on prediction accuracy, we add wavelet analysis into HMM framework and propose a wavelet-based HMM trajectory prediction algorithm (HMM-WA). Specifically by wavelet transform and single reconstruction, we convert trajectory sequence into a column vector and then use this vector as input of the HMM. The simulation results show that the HMM-WA algorithm can effectively improve the prediction accuracy compared with the classical HMM, linear regression methods and the Kalman filter.
Suspended Tantalum Pentoxide Rib Waveguides for Laser Absorption Spectroscopy

Development of miniature photonic sensors using optical waveguides currently represents an attractive research direction. Owing to more than two decades of research on quantum/interband cascade lasers, laser absorption spectroscopy in the mid-infrared (MIR) domain can be brought to photonic chips. Importantly, the so-called molecular fingerprints, unique molecular absorption spectra, can be addressed in MIR. But waveguides need to adapt for sensing scenarios and one desired feature is a large overlap between the light and the analyte.
Development of a cost-effective actuation unit for three DOF concentric tube robot in minimally invasive surgery

Concentric tube robot (CTR) has shown great potential in minimally invasive surgery with its small-scale construction and unique structure. It is highly suitable for reaching tight and non-linear surgical spaces. Much research has been conducted on the workspace simulation. However, most results are not validated with experimental data. In this paper, we propose a three Degree of Freedom (DOF) CTR with 50cm in length and 3.3 kg in weight, consisting of an actuation unit and a manipulator with two tube assembly. The workspace is developed using the kinematic model simulation, and the results are validated using the laser based experimental data. Experiment results on testing the tip targeting mechanism using visual feedback are also presented. All the results obtained from this study show that the proposed CTR device is a promising tool which can be used in minimally invasive surgical applications.
Systematic Design of Photonic Crystal Cavities with Ultra-Low Modal Volume Considering Different Fabrication Resolutions

Cavities with a large quality factor to modal volume ratio (Q/V-ratio) facilitate strong light-matter interaction via the Purcell effect [1], which is key for a wide range of photonic and optoelectronic applications, e.g., nonlinear optics [2] and cavity quantum electrodynamics [3]. Previously, many studies have been devoted to designing cavities with enhanced Q/V-ratio by changing locations and radii of air holes in conventional Ln or Hn photonic crystal (PhC) cavities. These cavities increased Q-factors by reducing in-plane loss while keeping diffraction-limited V-values (V − 0.3 (λ/n)3) [4]. Recently, bow-tie shaped PhC cavities consisting of two tip-to-tip components were demonstrated to confine light beyond diffraction limit into a deep sub-wavelength V-value [5]. However the low V-value in these cavities is highly dependent on the feature size and shape, e.g. the width of the bow-tie tip connection. Hence, such cavities are extremely sensitive to manufacturing variations and therefore the attainable fabrication resolution (FR) puts a hard limit on the attainable sub-wavelength V-value in bow-tie shaped PhC cavities. A recent study showed that the bow-tie tip connection could be controlled to 12 nm with an error region of ±5 nm [5].
Linear Tapers: Analysis, Design and Applications

Linear tapers are the most common taper which are used for impedance matching networks and also in the design of filters, power dividers, antennas, etc. Surprisingly, they have not been theoretically characterized in the literature. By using theory of small reflections, the characteristics of linear impedance taper (generalized case) and linear width taper (approximate case) have been analyzed in terms of reflection coefficient and results are compared with characteristics of other tapers which validated the same. It has been found that reflection coefficient response for the linear tapers is very close to exponential taper. Moreover, using linear width taper, a novel linearly tapered coupled-line ultra-broadband bandpass filter is designed which possesses a very compact size of $0.34{\lambda _{{g_0}}} \times 0.26{\lambda _{{g_0}}}$ and wide 10 dB passband of 4.5 to 12 GHz.
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

The development of social media has changed the way that travelers visit sightseeing spots. The social Internet of Things (IoT) allows products to automatically generate posts, share content and location information, and help build an online community of users based on their company’s products, so that marketing personnel can also get useful feedback and understand the user’s opinions. In tourism and hospitality industry, to enhance the revisit intention of passengers is an important issue for the purpose of increasing margin. In recent years, related researches had focused on the customers’ revisit behaviors and factors. However, few studies have investigated the related issues that travelers do not want to visit again. Failure to revisit may bring a great damage to the company’s revenue in the future. To avoid this situation, a text mining based approach will be proposed to identify non-revisit factors from online textual reviews in social media. Because it is impossible to determine whether a passenger has intention to revisit, this study proposed a text mining based approach which uses sentiment of text reviews to identify the passenger’s motivations (negative for revisit and non-negative for revisit). Then, feature selection methods, decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machines Recursive Feature Elimination (SVM-RFE) will be utilized to discover the important factors of non-revisit factor set. Back-propagation Neural Networks (BPN) and Support VectorMachines (SVM)will be employed to evaluate the effectiveness of selected feature sets. Finally, experimental results could be provided to travel service providers to improve service quality and effectively avoid non-revisit behaviors in the future. INDEX TERMS Text mining, non-revisit, feature selection, tourism, social media.
Deep Learning Side-Channel Attack Against Hardware Implementations of AES

In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside with the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled sets containing image and correct value pairs to be identified are input to a neural network, and repeatedly learning the set enables the neural network to identify objects with high accuracy. A new side-channel attack method, deep learning side-channel attack (DLSCA), utilizes the high identifying ability of the neural network to try and unveil a secret key of the cryptographic module by being trained with power waveforms and learning the leak model. However, at this stage, attacks on software implementations have been mainly investigated. In contrast, there are few studies about hardware implementations especially such as ASIC circuits. In this paper, we investigate the use of DL-SCA against hardware implementations of AES and demonstrate that it is able to unveil the secret key by applying a new technique named "mixed model dataset based on round-round XORed value. " We also compare the attack performance and characteristics of DL-SCA with conventional analysis methods such as correlation power analysis and conventional template attack.
The null-space-based behavioral control for a swarm of robots tracking a target region in obstacle environments

This paper is concerned with the problem of region-based shape control for a swarm of robots in obstacle environments. The mission is described by several elementary tasks, including region-based shape realization, velocity synchronization, collision avoidance and obstacle avoidance. Then, appropriate control commands are designed separately for each elementary task. On this basis, a null-space-based behavioral control method is applied to the final distributed control law to solve the conflict among the tasks. The simulation results show the effectiveness of the proposed method. All the robots are guaranteed to track a dynamic target region while avoiding the obstacles and the collisions among robots.
Distributed Model Predictive Control for Multiple Unmanned Ground Vehicles Formation with Packet Loss

Combining distributed model predictive control (DMPC) with the terminal invariant set, this paper designs a distributed control scheme for multiple unmanned ground vehicles (UGVs) formation. When unexpected situation such as road construction occurs, UGVs can change formation to cope with emergency. It is an important issue to accurately estimate the lost data and continue to complete the task when data packet loss occurs in the process of UGVs communication. In order to ensure stability of the controller, the terminal invariant set and the local feedback control law are added at the end of predictive time domain, and then solved by using linear matrix inequalities (LMI). Considering the formation task, collision avoidance constraint, control input constraint and terminal constraint, an optimization model is formed. By solving the optimization model, control input and optimal estimation of future path can be confirmed. Then, the updated path is sent to the adjacent UGVs for collision avoidance. Finally, combining the time window, data estimation based on local feedback control law is discussed when the data packet loss occurs.
Vulnerability Detection for Smart Grid Devices via Static Analysis

As a modern power transmission network, smart grid connects abundant terminal devices and plays an important role in our daily life. However, along with its growth are the security threats. Different from the separated environment previously, an adversary nowadays can destroy the power system by attacking its terminal devices. As a result, it’s critical to ensure the security and safety of terminal devices. To achieve it, detecting the pre-existing vulnerabilities in the terminal program and enhancing its security, are of great importance and necessity. In this paper, we introduce Cker, a novel vulnerability detection tool for smart grid devices, which generates an program model based on device sources and sets rules to perform model checking. We utilize the static analysis to extract necessary information and build corresponding program models. By further checking the model with pre-defined vulnerability patterns, we achieve security detection and error reporting. The evaluation results demonstrate that our method can effectively detect vulnerabilities in smart devices with an acceptable accuracy and false positive rate. In addition, as Cker is realized by pure python, it can be easily scaled to other platforms.
Power Allocation for Distributed Compressive Sensing with 1-Bit Quantization over Noisy Channels

Cost-efficient implementation with a low-complexity analog-to-digital converter is necessary for the sensor nodes in the internet of things. In the paper, we study the distributed compressive sensing (DCS) under the constraint of 1-bit quantization at each node. The entire transmission chain, composed of compressive sensing, 1-bit quantization, and joint sourcechannel coding (JSCC), is taken into consideration with joint signal reconstruction at the fusion center. A lower bound on the end-to-end mean square error distortion, which is a function of the measurement rate, distortion of 1-bit quantization, and that of JSCC, is derived under the assumption of the oracle reconstruction. The time-varying channel conditions have a major impact on the distortion of JSCC. Therefore, a suboptimal yet efficient power allocation scheme based on the successive convex approximation method is proposed to minimize the lower bound on the end-to-end distortion. Moreover, a practical coding and joint signal reconstruction scheme is provided to show its consistence with the derived theoretical limits.
Efficient Solid-State Single-Photon Sources Based on Diamond Colour Centers Coupled to Plasmonic Bullseye Resonators

Future applications in applied physical and quantum information science rely on the efficient and deterministic creation of single photons at a high rate. Creating such a single-photon source requires two key components: A quantum light source emitting single photons and a physical structure, which facilitates the efficient collection of the emitted photons. A very promising candidate for a stable optical quantum emitter are defect centers in diamond as they emit single photons even at room temperature. However, as the emission of diamond defect centers behaves similar to a point dipole, standard photoluminescence techniques alone cannot achieve high collection efficiencies. By using nanodiamonds, light losses due to total internal reflection at the diamond surface can be circumvented. Additionally, the small size of the solid-state emitter offers the possibility to incorporate the nanodiamond into suitable photonic or plasmonic structures. Therefore, our interest lies in the coupling of diamond colour centers to suitable plasmonic structures enabling the redirection and, therefore, efficient collection of the emitted light.
Waist Circumference in Children and Adolescents from Different Ethnicities

In their Bulletin 2001; 79 the World Health Organisation (WHO) had published: “The last two decades have witnessed the emergence and consolidation of an economic paradigm which emphasizes domestic deregulation and the removal of barriers to international trade and finance. If properly managed, such an approach can lead to perceptible gains in health status. ” Globalization in the last two decades influenced lifestyle and especially food patterns all over the world (Bauchner H, 2008 and Hu FB, 2008). In a “Nutrition transition” the consumption of dietary fat and/ or high -caloric meals and sweetened drinks has been increased, in developed countries as well as in developing ones (Hawkes C, 2006). At the same time overweight and obesity just as increased and dietrelated chronic diseases like diabetes mellitus II, hypertension or lipid disorders or cardiovascular diseases known from elder adults are observed in children and adolescents. Waist circumference (WC) is a generally accepted measure of central obesity that is a traditional risk factor for cardiovascular disease (CVD). A worldwide standardization of WC is warranted because of considerable differences between different ethnicities. For adults pragmatic ethnic-specific cut-off values for WC were defined between >85 cm and >94 cm for men and between >80 cm and >90 cm for women (Alberti et al., 2005). For children and adolescents from different ethnicities no uniform definition of WC cut-offs exists because of physiological growth and development. The aim of this study is to develop ageand gender-specific reference curves of WC for German children and adolescents, to define cut-off values, to collect percentile curves from other ethnicities, and to compare global findings. Calculation of our cut-off values is based on conventional anthropometric and non-anthropometric cardiovascular risk factors. WC is obligate for the definition of the metabolic syndrome already in youths. Early detection and intervention by lifestyle change are mandatory to prevent adult adiposity and its multiple complications. Thus, precise diagnosis is prerogative for the estimation of the worldwide prevalence of the metabolic syndrome and global intervention.
Self-Clocking Electro-Mechanical Sigma-Delta Modulator Quadrature Error Cancellation for MEMS Gyroscope

This paper presents a novel electromechanical sigma-delta modulator ($\text{EM}-\Sigma\Delta\mathrm{M}$) closed-loop quadrature error cancellation technique for the sense mode of a MEMS vibratory gyroscope. The interface circuit system adopts a quadrature sigma-delta modulator structure, which allows in-phase and quadrature decomposition to efficiently realize noise shaping and quadrature error cancellation. Experimental results demonstrate that the proposed quadrature sigma-delta modulator ($Q-\Sigma\Delta\mathrm{M}$) control loop results in better noise and bias drift performance when compared with an $\text{EM}-\Sigma\Delta\mathrm{M}$ single-loop architecture. Measurement results showed that the proposed quadrature $\text{EM}-\Sigma\Delta\mathrm{M}$ gyroscope achieved −80dB quadrature error cancellation within a bandwidth of 64Hz, and an approximately 5.5-fold improvement in bias instability (decreasing the bias instability from 5°/h to 0.9°/h), compared to the same gyroscope operating in a single-loop $\text{EM}-\Sigma\Delta\mathrm{M}$.
Avaliação da microcirculação: uma nova arma no manejo da sepse?

The progression into multi-organ failure continues to be a common feature of sepsis and is directly related to microcirculatory dysfunction. Based on a PubMed database search using the key words microcirculation and sepsis, twenty-six articles were selected for this review. The relevant references from these articles were also selected and included in this analysis. Orthogonal polarization spectral imaging allows for the bedside assessment of the microcirculation of critically ill patients. Such imaging has established a correlation between microvascular dysfunction and patient outcomes, which allows practitioners to directly assess the effects of therapeutic interventions. However, the causal relationships between microcirculatory dysfunction, adverse outcomes, and the effects of therapies aimed at these microcirculatory changes in sepsis, are not clear.
Integrated Positioning and Obstacle Avoidance for Multi-Quadrotors in an Indoor Office Environment

In this paper, the positioning and obstacle avoidance problem of multi-quadrotors system in complex indoor environment is considered. In order to obtain accurate position of each quadrotor, an integrated positioning method based on the combination of Ultra-Wideband (UWB) and Inertial Navigation System (INS) is applied. In this method, the quadrotor’s acceleration information from the INS is used for the state equation, and the distance information from UWB is used in the measurement equation. Then, the indoor positioning is carried out by the Unscented Kalman filter (UKF) technology. After that, the Artificial Potential Field (APF) method is adopted to realize obstacle avoidance and formation flight of multi-quadrotors and solve the problem of anti-collision between quadrotors. Finally, simulations of two-quadrotors are carried out to demonstrate the effectiveness of the proposed method.
ROS-Based Depth Control for Hybrid-Driven Underwater Vehicle-Manipulator System

This paper presents an algorithmic solution for depth control. Depth control for Underwater Vehicle-Manipulator System (UVMS) is essential to complete a specific underwater manipulation. A large number of algorithms have been designed to tackle the depth control problem. However, because of water currents, algorithm for depth control is required strong robustness. This work describes an adaptive sliding mode control (SMC) method to implement the depth control. The designed SMC method combining the proposed adaptive algorithm can alleviate influence of uncertain parameters of system and external disturbances. The stability is proven by a Lyapunov function. Then, we build a ROS-based underwater simulation environment–UWSim. Real-time depth and velocity of UVMS can be obtained by depth gauge and IMU via ROS nodes. Finally, we conduct a depth control simulation with our proposed hybrid-driven underwater vehicle-manipulator system (HD-UVMS). A comparative simulation is also implemented to verify the effectiveness of the proposed method.
Autonomous Navigation with Improved Hierarchical Neural Network Based on Deep Reinforcement Learning

Compared with traditional navigation strategies in normal environments, the unmanned vehicles in battlefield environments require better navigation strategies. This research formulates the autonomous navigation in battlefield environments as a markov decision process (MDP) and introduces deep deterministic policy gradient (DDPG) to obtain the continuous control signal. Meanwhile, the curriculum learning is employed to increase utilization of samples in this research. Inspired by the biological mechanism, an improved hierarchical neural network is proposed to refine the input information, which plays a better role in coordinating the choice of agent’s behavior. Experimental results show that the models we proposed are able to acquire effective navigation strategies without knowing the whole information of environment. At the same time, it is proved that the hierarchical neural network and the curriculum learning are effective for improving efficiency of learning and generalization capability of models.
EID-based Robust Stabilization for a Class of Nonlinear Uncertain Systems with Disturbances★

This paper concerns robust stabilization for a class of nonlinear systems with uncertainties and disturbaces. The equivalent-input-disturbance (EID) approach is utilized to estimate the influence of nonlinearities and exogenous disturbances on the output of the system. A robust stability condition is derived in terms of linear matrix inequalities. Moreover, two parameters are used to tune the robustness of the system. Simulation results show that it is more effective to stabilize the system using the developed method than using the sliding-mode control (SMC) method.
Implementation of a Wireless Power Transfer System for Prosthetic Hands

In this paper, a wireless power transfer (WPT) system is proposed to supply power continuously to a prosthetic hand. The transmitter circuit (class-E power amplifier) and WPT coils are modeled and implemented along with commercially available receiver circuit and multiple motors. Tissue safety to thermal load is simulated by measuring the tissue specific absorption rate (SAR) using the commercial electromagnetic simulator ANSYS HFSS. The calculated and simulated results of the designed WPT system is validated by measuring the received power where the TX coil mounted on an artificial wrist part is filled with phantom mimicking the properties of human muscle. An algorithm is also proposed to monitor and control the rotation of the prosthetic hand utilizing wireless power.