Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact and Broadband Optical Add-Drop De-Multiplexer With Cascaded Elliptical Micro-Rings on SOI

We demonstrate a four-channel de-multiplexer based on third-order cascaded elliptical micro-rings on silicon-on-insulator. By applying cascaded elliptical micro-rings structure, the fabrication tolerance was released. The spectral responses of four drop-ports over the S, C, and L bands (1450–1630 nm) were measured, which showed an enhanced performance. The four-channel de-multiplexer has box-like transmission spectra with an ultra-wide 3 dB bandwidth of about 2.6 nm, high out-of-band rejection ratio of more than 40 dB, low-channel cross talk of about −38 dB, small footprint of about 0.0012 mm2, and large operational spectral range of more than 180 nm. The de-multiplexer can support high-data-rate signal transmission capacity.
Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios

Partial shading is an unavoidable complication in the field of photovoltaic (PV) generation. Bypass diodes have become a standard feature of solar cell arrays to improve array performance under partial shading scenarios (PSS). However, the current–voltage and power–voltage characteristic data vary with different shading patterns. In this paper, a shading pattern detection algorithm is first proposed to estimate the number of shaded modules in a PV string. The result is then fed to a field-support vector regression (F-SVR) model, in which the measured features are transferred into a style-free high-dimension space prior to data training. The process of style-normalized transformation enables the features to be independent and identically distributed. Both simulations and experiments are conducted to evaluate the F-SVR model's ability to estimate the voltage at maximum power points. The results show that the proposed maximum power point estimation method can evidently reduce prediction errors.
Deep Learning Techniques in Computational Linguistics

Deep Learning can be defined as a subclass of machine learning whose ultimate goal was to produce an efficient technology which could change the style of current technology standards. When compared to some of the existing methods, deep learning (DL) structures have been able to perform dominantly because of its strong learning ability. The ability of DL structures increases with respect to the complicatedness of the problem or the complicated patterns within the datasets. Structure of Deep Learning architectures are also capable of extracting more beneficial features from different datasets. This survey gives an overview about deep learning and its evolution also focusing on different DL architectures, deep learning frameworks and some of the works in Natural Language Processing where deep learning is used. The paper also focuses on some of the Natural Language Processing(NLP) tasks, where deep learning methods are achieving some headway.
Ultra Low Power Sensor for 3-Phase Water-Cut Applications

Precise measurement of the water fraction in oil is of critical importance in the oil industry. Currently, cost-effective, reliable, and compact downhole measurement technologies capable of full-range three-phase measurements are not available. This article presents an ultra low power, low cost, wireless, and noncorrosive resonance sensor based on magnetoelastic technology that has been implemented by an amorphous ribbon and electromagnetic interrogation. The proposed technology enables distinguishing all three gas, water, and oil phases with amplitude changes of 75% and 99% and resonant frequency shifts of 0.46% and 3.88%, respectively, compared to the gas phase. The response of the sensor enables characterizing the water-cut range from 10% to 90%. The addition of a Teflon anticorrosion layer enables sensor deployment for extended periods of time in harsh environments.
Emotion Recognition Using Hybrid Gaussian Mixture Model and Deep Neural Network

This paper aims at recognizing emotions for a text-independent and speaker-independent emotion recognition system based on a novel classifier, which is a hybrid of a cascaded Gaussian mixture model and deep neural network (GMM-DNN). This hybrid classifier has been assessed for emotion recognition on “Emirati speech database (Arabic United Arab Emirates Database)” with six different emotions. The sequential GMM-DNN classifier has been contrasted with support vector machines (SVMs) and multilayer perceptron (MLP) classifiers, and its performance accuracy is indexed at 83.97%, while the other two perform at 80.33% and 69.78% using SVMs and MLP, respectively. These results demonstrate that the hybrid classifier significantly gives higher emotion recognition accuracy than SVMs and MLP classifiers. Our GMM-DNN model yields the results similar to those obtained by human judges in a subjective assessment context. Also, the performance of the classifier has been tested using two distinct emotional databases and in normal and noisy talking conditions. The dominant signal mask provided by the hybrid classifier offers better system performance in the presence of noisy signals.
Research of Industrial Control System Device Firmware Vulnerability Mining Technology Based on Taint Analysis

Aiming at the problem that there is little research on firmware vulnerability mining and the traditional method of vulnerability mining based on fuzzing test is inefficient, this paper proposed a new method of mining vulnerabilities in industrial control system firmware. Based on taint analysis technology, this method can construct test cases specifically for the variables that may trigger vulnerabilities, thus reducing the number of invalid test cases and improving the test efficiency. Experiment result shows that this method can reduce about 23 % of test cases and can effectively improve test efficiency.
A Novel Application Layer DDOS Detecting Method Based on Cluster Method

Application layer DDoS attack challenges web applications seriously. It launches attack by sending a large number of HTTP Get requests to a web server. The anomaly-based method is a promising method, which detects the DDoS attack by comparing the individual surfing behavior with a reference surfing-behavior profile. Yet due to the exist of noisy web logs caused by web-crawling, it is difficult to build robust reference profile for detection. This paper proposes a novel anomaly-based application DDoS detection scheme base on clustering method. Our method could tolerate the web-crawling traces in building reference surfing profile, and can detect different Application layer DDoS attack (e.g., repetitively getting several webpages, randomly getting webpages following hyper-links etc.). The simulation results show that our method can detect application layer DDoS attacks accurately.
Dynamic Network Clustering for Distribution System Resilience Against Typhoon Events

Natural disasters intensified by climate change stimulated the recent efforts to develop power system resilience both in the structural and in the operational sense. This study proffers an operational resilience framework that uses distributed generation (DG) and switches in order to pick up critical loads through dynamically forming DG-powered clusters in a radial distribution system damaged by a typhoon onslaught. As an innovation to consider a typhoon event’s spatiotemporal conditions in cluster formation, we use wind-based line fragility curves to simulate line damages, and introduce a load priority update scheme based on government-mandated flood hazard levels. A mixed-integer optimization problem is also formulated to maximize the priority-weighted load pickup during clustering, without violating power system operation requirements. MATLAB-based implementations use the IEEE 37-node radial distribution test system, modified to include DG units and geographically mapped lines and nodes. Numerical results suggest effectiveness of the proposed scheme in terms of dynamically forming clusters optimized according to load pickup priorities and system operation requirements. Program runtime is also found out to be affected by the system size and the load flow convergence settings.
A Generic Construction of an Identity-based Signature from a Sigma Protocol

Galindo and Garcia introduced a light-wight identity-based signature (IBS) based on Schnorr signature in AFRICACRYPT 2009. Despite the simple construction and the ef-ficiency of Galindo-Garcia IBS, to the best of our knowledge, their followers do not appear except the IBS scheme by Fukumitsu and Hasegawa in CANDAR’17. Although their IBS achieves the tight security, the efficiency is worse than Galindo-Garcia IBS. Their result suggests that Galindo-Garcia IBS utilizes some special properties of Schnorr signature, and we have to deeply observe the structures of both Galindo-Garcia IBS and Schnorr signature to construct their followers. In this paper, we aim to generalize Galindo-Garcia IBS and to search necessary properties of building cryptographic primitives. As a result, we introduce a generic construction of an IBS from a sigma protocol, and find necessary properties to prove the security of the resulting IBS.
End-to-End Navigation with Branch Turning Support Using Convolutional Neural Network

End-to-end navigation refers to methods of generating control signals for mobile autonomous devices directly from external sensors, which is an area gaining attention within the autonomous driving research community. Previous autonomous driving research has mostly been focused on keeping moving vehicles within their lanes, but reliable navigation along other trajectories, including branching off onto other routes, has not yet been achieved. In this study we propose a deep learning system for end-to-end navigation which would allow an autonomous vehicle to turn at intersections. Our system's inputs include camera images and directions to a target, while the outputs are the steering control signals needed to direct the vehicle. We validate the system's performance by conducting experiments involving three different driving scenarios: short, indoor trajectories containing a single branching turn; long, outdoor trajectories containing many branching turns; and long, outdoor trajectories which were not included during training. Our end-to-end navigation system allowed an autonomous robot to successfully follow outdoor trajectories with right and left turns, including those which were not part of the training course.
Preliminary Design and Performance Test of Tendon-Driven Origami-Inspired Soft Peristaltic Robot

Origami-based flexible, compliant and bio-inspired robots are believed to permit a range of medical applications with unpredictable environments. Here in this article, we experimentally demonstrate a novel origami inspired mobile robot structure which reconstructs its shape in the pivotal direction and launches peristaltic motion. To be able to sustain shear stress and counteract buckling, while accommodating the whole Origami robot in some stochastic confined environment, mobile robot sufficiently needed structural rigidity. To meet this requirement, Daler - Rowney canford paper of 150 gsm paper was chosen for carving fold lines. The Yoshimura origami pattern was manifested as the main body of the mobile robot to generate active deformation. In order to prevent buckling of springs during compression and expansion, special supporting structures made up of cardboard having alternate mountain and valley folds and coated in photopolymer resin. Finally, we were able to achieve a peristaltic motion of an average speed of 0.056 ern/sec, while we envision in future to incorporate the same construct for more potential biomedical and industrial application areas.
Wireless Electric Consumption Acquisition Using Image Processing

In this study, the goal is to conduct and develop a system that will transmit electric consumption reading using wireless technology and analyzing the data with the use of image processing. The researcher created a wireless transmission of data acquisition using Raspberry Pi which aimed to make the meter reading more convenient and faster. The process starts by capturing the image of the electric meter using Raspberry Pi cam. The software use is python a powerful multi-paradigm computer programming language, optimized for programmer productivity, code readability and software quality [1]. The picture will be saved in a local folder and then using image processing it will analyze the picture. After analyzing, the result will be sent to the database. The database used in the system is SQL (Structured Query Language the standard programming language to create, update, and retrieve information that is stored in the database [2]. The information stored in the database are the following, data of electric consumption, the time retrieved and account number of the consumer. For the GUI design of the software visual basic studio is use. Programming using this language has also been made easier by using visual tools provided by Visual Studio [3]. Based on the result of the data gathered the study was found out to be effective regarding reading the electric consumption since it produces a rating of above 90% accuracy during functionality test.
Kinematic Analysis of Novel Soft Robotic Arm Based on Virtual Work Principle

In order to improve the motion accuracy of a novel soft robotic arm with extensive degree of freedom, the kinematic model is established. Firstly, a soft robotic arm composed of two single modules is designed. Each single module contains two extension pneumatic silicone actuators (EPSAs). Secondly, the kinematic model of the EPSA is established based on the superelastic material model, the geometric relationship and the virtual work principle. Then, the kinematic model of the single module is established based on the kinematic model of the EPSA and the constant curvature assumption. Next, the kinematic model of the soft robotic arm is established by using the Frenet frame. Finally, the numerical simulation is carried out based on the Obtained kinematic model. In order to verify and modify the above kinematic model, the numerical experiment is carried out based on the finite element method. The results of the two numerical experiments show that the modified kinematic model is almost identical to the finite element model, which indicates the feasibility of this modeling method and the accuracy of the established kinematic model. This study provides the necessary kinematic foundation for the control of a soft robotic arm.
Magnetically Geared Pseudo Direct Drive for Safety Critical Applications

This paper presents an investigation into the electromagnetic design and performance of a fault-tolerant (FT) magnetically geared pseudo-direct-drive (PDD) electrical machine for primary flight control surface electromechanical actuation. It is shown that a large number of combinations of high-speed rotor pole pairs, pole-piece numbers, and stator slot numbers exist, for which a duplex three-phase FT configuration can be realized. Furthermore, in addition to facilitating a lower mass solution, it is also shown that a PDD presents a significantly lower inertia referred to the screw when compared to direct-drive or mechanically geared motor solutions. The findings are validated on a prototype PDD, which has been designed and built to meet the requirements of a primary control surface electromechanical actuator.
Beta Modulation Depth Is Not Linked to Movement Features

Beta power over the sensorimotor areas starts decreasing just before movement execution (event-related desynchronization, ERD) and increases post-movement (event-related synchronization, ERS). In this study, we determined whether the magnitude of beta ERD, ERS and modulation depth are linked to movement characteristics, such as movement length and velocity. Brain activity was recorded with a 256-channels EEG system in 35 healthy subjects performing fast, uncorrected reaching movements to targets located at three distances. We found that the temporal profiles of velocity were bell-shaped and scaled to the appropriate target distance. However, the magnitude of beta ERD, ERS and modulation depth, as well as their timing, did not significantly change and were not related to movement features.
Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia

Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders.
A Bankruptcy Prediction Model Using Random Forest

Bankruptcy Prediction is very important for an organization as well as decision makers such as financiers and investors. Selection of dataset for training the prediction model, the Machine Learning tool used for prediction and various other factors are essential in building an efficient prediction model. The dataset includes financial ratios as attributes that are derived from the financial statements of various companies. The most influencing ratios that are required for predicting bankruptcy are selected on the basis of the Genetic Algorithm which filters out the most important ones from different existing bankruptcy models. These ratios of different companies are fed as an input to train the model being implemented in R. The prediction algorithm used is Random Forest, which will enable us to differentiate between bankrupt and non-bankrupt companies.
Dp2T: Preserving Data Privacy for Top-k Query in Wireless Sensor Networks

With the extensive application of wireless sensor networks in people's daily lives, the preservation of data privacy has become an important and challenging issue. In this paper, a Data Privacy Preserving Top-k query protocol (DP2T) is proposed for state and solve the practical problem of privacy-preserving Top-k query processing in tiered wireless sensor networks. In DP2T, to preserve data privacy, we use a threshold based technique to encode Top-k queries such that management nodes can correctly process query requests over encrypted data without knowing their actual values. The experimental results show the high performance of proposed scheme in terms of privacy preservation and query efficiency.
Extrinsic Calibration of Lidar and Camera with Polygon

Fusion of heterogeneous exteroceptive sensors is the most efficient and effective path to the representation of the environment precisely, as it can compromise various drawbacks of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems is the prerequisite of fusing the multi-sensor information. Researchers have proposed several approaches to estimate the extrinsic parameters. However, these approaches neither rely on human interventions or specifically designed auxiliary object or do not provide the library which makes it hard to test or benchmark. In this paper, we propose a novel extrinsic calibration approach for the extrinsic calibration of a Lidar (Laser Range Finder) and a camera which only based on a polygon board and we offer the relevant tools. In this paper, we firstly track and extract the target polygon from both the image and point-cloud. Then we try to match the polygon between the 2D and 3D feature spaces. With the associated polygon, we are able to get multiple constraints to optimize the extrinsic parameters. At the end, we validate our approach by four configurations, including the simulation, t6/32-beam Lidar and 100-line MEMS-Lidar. The outcome indicates high-precision extrinsic calibration performance.
Proximity Tracker using Received Signal Strength, Particle Filter and Extended Kalman Filter

Received Signal Strength Indicator (RSSI) is a relative variable related to signal strength. The strength of the signal transmitted is used in continuous close-proximity tracking applications. The approximate exact location of a node is estimated using various kinds of filtering techniques. The demand for close-proximity tracking or location and proximity integration increases with the society’s dependency to mobile devices but using such geolocation services are power-hungry and is directly affected by atmospheric conditions. Present tracking services are made relative to the earth’s axis. To address these gaps, this study focuses on integrating Extended Kalman Filter (EKF) and Particle Filter (PF) into a system of Bluetooth-enabled nodes that are capable of relative positioning in three-dimensional space. Three nodes made of Raspberry Pi Zero are tracked by a parent device made of Raspberry Pi 3 Model B. The nodes’ coordinates are displayed in the parent device’s dashboard. Detection of the nodes are done using the library BIueZ. These nodes broadcast themselves to the parent device to determine their range and location using a 750-meter range Bluetooth dongle. The device is tested in both open areas, a memorial park and a beach resort. Using spiral method, the weakest RSSI value measured is -142dB at 776.43 meters and -23dB at 0 meter. RSSI value in an area with obstruction or interference is -168dB at a distance of693.1 meters. The output shows very promising results indicating effectiveness and efficiency in the field of proximity tracking.