Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Low-Profile L-Probe Fed Metasurface Antenna With TM Leaky Wave and TE Surface Wave Resonances

A broadband low-profile L-probe fed metasurface antenna is proposed by well exciting both transverse magnetic (TM) leaky wave and transverse electric (TE) surface wave resonances. The metasurface is composed of an array of rectangular metallic patch cells. An L-shaped probe positioned underneath the finite metasurface is utilized to excite a TM leaky wave resonance and a TE surface wave resonance simultaneously for broadband operation. The dispersion properties of the TM leaky wave and TE surface wave are used to analyze the dual resonance modes. The proposed L-probe fed metasurface antenna achieves a broad −10 dB impedance bandwidth of 34.5% with the peak gain of 10.3 dBi and the front-to-back ratio larger than 18 dB with a low profile of <inline-formula> <tex-math notation="LaTeX">$0.06\lambda _{0}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at the center operating frequency.
A Speech Enhancement Neural Network Architecture with SNR-Progressive Multi-Target Learning for Robust Speech Recognition

We present a pre-processing speech enhancement network architecture for noise-robust speech recognition by learning progressive multiple targets (PMTs). PMTs are represented by a series of progressive ratio masks (PRMs) and progressively enhanced log-power spectra (PELPS) targets at various layers based on different signal-to-noise-ratios (SNRs), attempting to make a tradeoff between reduced background noises and increased speech distortions. As a PMT implementation, long short-term memory (LSTM) is adopted at each network layer to progressively learn intermediate dual targets of both PRM and PELPS. Experiments on the CHiME-4 automatic speech recognition (ASR) task, when compared to unprocessed speech using multi-condition trained LSTM-based acoustic models without retraining, show that PRM-only as the learning target can achieve a relative word error rate (WER) reduction of 6.32% (from 27.68% to 25.93%) averaging over the RealData evaluation set, while conventional ideal ration masks severely degrade the ASR performance. Moreover, the proposed LSTM-based PMT network, with the best configuration, outperforms the PRM-only model, with a relative WER reduction of 13.31% (further down to 22.48 %) averaging over the same test set.
Characterization of a Multiport Coaxial Line Adaptor for Multimodal Waveguides

A technique for the characterization of a class of coaxial-line-to-waveguide adaptors, which is based on the eigenvalues and eigenvectors of their scattering matrix, is presented. The adaptor comprises a number of coaxial-line ports symmetrically distributed on the surface of the waveguide. This allows for the independent excitation and measurement of different propagating modes in a multimodal operation. The latter is required for, e.g., the simultaneous determination of both constitutive parameters in microwave, millimeter-wave, and terahertz material characterization. It is shown that the junction is fully characterized in terms of the eigenvalues of its generalized scattering matrix. The measurement of these eigenvalues through a calibration procedure enables a complete characterization of the adaptor. A four-port coaxial-line adaptor was designed and fabricated. A modified calibration technique together with both measurements and electromagnetic simulations has been used for the determination of its scattering parameters.
Deep Matching Network Based Image Auxiliary Localization System for Unmanned Aerial Vehicle

Localization of the Unmanned Aerial Vehicles (UAVs) have vital importance to provide with navigation information and avoid getting lost. However, the current localization technology mainly depends on GPS or wireless network, which is prone to be interfered by the environment and wireless attack. To solve this issue, we propose a vision based deep matching network for UAV by a image auxiliary localization algorithm. The proposed algorithm integrates vision information and traditional wireless localization information by an information fusion filter to realize more robust localization. The deep matching network realizes localization prediction of image stream by matching the extract features with stored memory segments. The image stream is firstly inputted to a convolutional neural network to extract image features. Then a LSTM is employed to extract localization features from image features. Finally, the matching network will output the localization prediction from the image features and stored memory segments. After experiment, compared to existing method, the mean localization error performance of the proposed approach can mostly improve 49.8%.
Behaviour of Poynting Vector for Guided Modes of Dielectric Optical Waveguides and Its Usefulness in the Design of Evanescently Coupled Optical Devices

In electrical and electronic engineering, the flow of electromagnetic energy is a fundamental physical quantity that is usually estimated by Poynting vector. In this paper we plot and discuss about the spatial and temporal evolution of Poynting Vector for the guided modes of a three layer dielectric optical waveguide. We emphasise that knowledge of the evolution of Poynting vector is very important in order to understand the operation of optical devices such as optical sensors and directional couplers, which are based on the principle of evanescent coupling. We show and report in this paper for the first time, that in the cladding region the decaying factor by which the instantaneous Poynting vector decays exponentially away from the core, is different from that for the time averaged Poynting vector. This decaying factor gives an estimate of the distance up to which the instantaneous Poynting vector is significant outside the core in the cladding region of the waveguide. This is a very useful design parameter for the fabrication of evanescently coupled optical devices. The results are generated for a symmetric slab optical waveguide in which a Si core surrounded by SiO2 cladding (Silicon-on-Insulator, commonly known as SOI) is considered at an operating wavelength of 1.55 µm.
Developing the New Proposal of Intelligence System in Heart and Cardiovascular Diagnosis

The World Health Organization estimates that annually, there are 17.5 million deaths due to cardiovascular-related diseases and the number of patients accumulates. This study aims to apply some AI technologies to the system of cardiovascular diagnosis, to provide a solution for building information channels so that people have an overview of cardiovascular pathology. With this approach, people can have self-diagnosis, classification of cardiovascular pathology and know how to plan the examination and treatment to achieve better outcomes. In this paper, the system is built by using reasoning algorithm combined with supervised learning to carry the diagnosis process through the collection of symptoms. The study was experimented on data collected directly from the medical records at General Hospital of Binh Duong Province, and the experimental results show that the accuracy of diagnosis is over 75% compared to the actual diagnostic results.
An Improved Weighted K-Nearest Neighbors Algorithm for High Accuracy in Indoor Localization

In this paper, we propose an improvement of the traditional Weighted K-Nearest Neighbors (WKNN) that is pervasively used in most WiFi fingerprint-based approaches for indoor localization. Instead of using a fixed value of K, we employ Support Vector Regression to build K-NN model that can determine the appropriate value of K in each specific position. The experimental results show that our proposed method gives higher positioning accuracy compared with other methods.
A Unified and Flexible Eigen-Solver for Rank-Deficient Matrix in MIMO Precoding/Beamforming Applications

Eigenvalue decomposition (EVD) is a widely adopted technique to separate signal, interference, and noise subspaces. The paper presents a unified eigen-solver based on QR decomposition (QRD) to generate eigenpairs associated with the largest eigenvalues or zero eigenvalues, which are required in the MIMO hybrid beamforming systems that need interference suppression. A non-uniformly constrained deflation is proposed, which forces the matrix to deflate in the beginning and efficiently allocates the computation power to the eigenpairs related with the largest eigenvalues. The computation complexity of generating interested eigenpairs is also evaluated for various matrix dimensions. The results demonstrate that the non-uniformly constrained deflation is effective and more computations can be saved if the desired number of eigenpairs is smaller than the rank of the matrix.
Deriving and Updating Optimal Transmission Configurations for Lora Networks

LoRa-based networks exhibit good flexibility in terms of configurable parameters and adjustable modulation properties. Thanks to this, wireless nodes can be tuned to improve their communication behavior. In fact, optimal network-level transmission configurations (<inline-formula> <tex-math notation="LaTeX">$\mathrm {C_{opt}}$ </tex-math></inline-formula>) can be derived in such a way that the global network performance is maximized. To derive this <inline-formula> <tex-math notation="LaTeX">$\mathrm {C_{opt}}$ </tex-math></inline-formula>, one must know the radio-propagation behavior of each node beforehand. Traditionally, this has been pursued by using general, low-precision, propagation models due to the infeasibility (in terms of time and energy) of deriving each individual node propagation behavior. In this work we propose a straightforward bounding technique that reduces up to 73% the energy and time required to obtain the radio-propagation behavior of each individual node in the network, enabling the derivation of network-level optimal transmission configurations. Also, we provide mechanisms to keep this knowledge updated, swiftly reacting to changes in the environment and leading to network performance improvements of 15% when compared to traditional alternatives like LoRaWAN ADR. Furthermore, by means of a testbed we demonstrate that this mechanism can also provide resistance to Denial-of-service attacks. Finally, we incorporate the power consumption into the proposed <inline-formula> <tex-math notation="LaTeX">$\mathrm {C_{opt}}$ </tex-math></inline-formula> formulation and provide a generalizable power-consumption determination methodology. This way we can limit the set of eligible transmission configurations to help extending LoRa network lifetimes more than 40%.
Structural displacement compensation of a gigantic manipulator via deep learning

Structures of robotic systems that handle extremely heavy loads undergo static displacement. The ITER blanket remote handling system, which handles 4-ton objects, has displacements of up to 100 mm at the end effector. We propose a novel method that combines deep learning with a physics-based virtual reality system to compensate for displacement. Our deep learning model was trained by using data obtained from both the virtual reality system and physical measurement data of end effector positions. By using a prototype of the ITER blanket remote handling system, we experimentally show that our method successfully reduces the displacement at the end effector to a maximum error of 5.7 mm and a median error of 1.2 mm. We conclude that our approach provides an effective contribution to ensuring the feasibility and safety of the remote maintenance procedures that are to be performed within the ITER project.
Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network

In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only occurs at the neighbouring robot nodes. Under partially known information, four objectives are simultaneously achieved, i.e, global cooperation and synchronization among manipulators, joint physical limits compliance, neighbor-to-neighbor communication among robots, and optimality of cost function. We develop a velocity observer for each individual manipulator to help them to obtain the desired motion velocity information. Moreover, a negative feedback term is introduced with a higher tracking precision. Minimizing the joint velocity norm as cost function, the considered cooperative kinematic control is built as a quadratic programming (QP) optimization problem integrating with both joint angle and joint speed limitations, and is solved online by constructing a dynamic RNN. Moreover, global convergence of the developed velocity observer, RNN controller and cooperative tracking error are theoretically derived. Finally, under a fixed and variable communication topology, respectively, application in using a group of iiwa R800 redundant manipulators to transport a payload and comparison with the existing method are conducted. Among the simulative results, the robot group synchronously achieves the desired circle and rhodonea trajectory tracking, with higher tracking precision reaching to zero. When joint angles and joint velocities tend to exceed the setting constraints, respectively, they are constrained into the upper and lower bounds owing to the designed RNN controller.
Autonomous Intermuscular Coordination and Leg TrajectoryGeneration of Neurophysiology-based Quasi-quadruped Robot

In this study, we introduce a simple self-controlled quasi-quadruped robot inspired by sensory feedback mechanisms in the hindlimb of decerebrate walking quadrupeds. The main contribution of this paper is to show that a model of the quadruped hindlimb with muscle receptors can coordinate its muscles and generate a leg trajectory without any central pattern generators. The key idea to produce coordinated patterns of limbs and muscles is based on the brainless control approach. A point of the approach is that the actuator module is embedded without any microprocessors nor a controller. Each actuator module adjusts their phases using intrinsic dynamics of them according to the reaction force received from the body-environment dynamics and, as a result, coordinated motor patterns emerge. The robot in this article is driven by artificial pneumatic muscles and controlled by only simple reflex circuits composed of mechanical air valves that switch the airflow to the muscles. Therefore, the robot needs no microprocessors or gait generators, and the only required input for the robot is a constant air-pressure which is supplied from an external air compressor. A walking experiment demonstrates autonomous gait generation, and we compare the time series of joint angles between the robot and an intact cat.
Cognitive Optimization of Multitasking Detection for 2D E-Scan Airborne Surveillance Radar

This paper deals with detection optimization in the case of active electronically scanned array surveillance radar. The major advantage of electronically scan antennas compared to mechanical ones is the ability to interleave different processing. In coastal surveillance, this is a real advance because it is possible to detect maritime and ground target at the same time. A method based on cognition principle is presented here. The first step is to sense the environment and to extract characteristics which are not necessarily the same for sea and ground detection. Then, the proposed method aims to optimize the mode that would be the most suitable for performing a given task in each application ground and sea detection. The last step is to apply this optimization: it can be done by acting on a Software Defined Radar module.
A Data Analytics Maturity Model for Financial Sector Companies

Data analytics allows organizations in the financial sector to gain a competitive advantage through processes aimed at obtaining data, processing them and displaying them as valuable information to understand the behavior of their clients and to be prepared against risks as money laundering, credit fraud, among others. However, organizations cannot easily identify gaps related to personnel, information systems and business processes that hinder the improvement of their data analytics environment. In this context, maturity models evaluate, based on defined criteria, the current state of an organization and identify its maturity level in order to improve based on the findings. In this paper, a maturity model is proposed to identify gaps in analytics environment of financial companies that lead to the reduction of these. This model includes artifacts and evaluation criteria focused on technology, governance, data management, culture and analytics itself, which gives a broader and structured diagnosis process with respect to the analytics environment. The proposed model was tested in three companies of Peruvian financial sector and the results suggest that the specialists obtained a clearer perspective than their initial thoughts on the situation of the analytics environment of their companies.
An Unsupervised Methodology for Musical Style Translation

Deep learning techniques applied in image style transfer have shown great success. In the domain of music, this task can further aid musicians and music-generating AIs in the production of music. In this paper we present an unsupervised methodology for musical style transfer, which do not rely on strictly paired data to train. The model is capable of translating the style of symbolic music from the source domain to the target domain while mostly preserving the content and structure of input data. Output samples are evaluated by genre classifiers and show promising results.
A phase 1 study evaluating the safety and efficacy of DKN-01, an investigational monoclonal antibody (Mab) in patients (pts) with advanced non-small cell lung cancer.

8068 Background: DKN-01 is a high affinity neutralizing humanized Mab targeting extracellular dickkopf-1 (Dkk-1). Dkk-1 inhibits the canonical Wnt/β-catenin signaling pathway and modulates other biological pathways required for cell growth/ differentiation. Prior studies have demonstrated elevated Dkk-1 expression in lung, esophageal and gastric cancer tissue. Preclinical studies have demonstrated that a reduction of free Dkk-1 led to clinical benefit in xenograft models. Methods: A two-part (A and B) phase I, dose-finding study determined safety, maximum tolerated dose and antitumour activity infusing DKN-01 to pts with advanced malignancies with emphasis on pts with non-small cell lung cancer (NSCLC). Other endpoints were progression free survival [PFS], overall response rate [ORR], and overall survival [OS]. Part A of the study tested intravenous (IV) DKN-01 at dose levels between 75 and 600 mg administered weekly or biweekly in a 28 day cycles. For Part B, DKN-01 was administered to refractory NSCLC p.
Threat Management Dashboard for a Blockchain Collaborative Defense

The increasing number of Distributed Denial-of-Service (DDoS) attacks leads to a growing research and development interest in DDoS defense systems. As a response to the increasingly distributed nature of DDoS attacks, many organizations have demonstrated willingness to exchange information concerning threats, incidents, and mitigation strategies. Blockchain is, in this regard, a viable threat sharing platform, where different organizations can interact in a verifiable manner. This paper tackles the security visualization and management issue in a blockchain-based collaborative defense platform, defining an interactive dashboard displaying on-going threat mitigation status and enabling security analysts to react on threats on an individual or group level. The dashboard was implemented and evaluated on real hardware emulating the exchange of threats between three isolated Autonomous Systems (AS).
An Enhanced Offset Min-Sum decoder for 5G LDPC Codes

This paper presents an Enhanced Offset Min-Sum (EOMS) decoder for Low-Density Parity-Check (LDPC) codes used in the 5th generation (5G) mobile communications. It is observed that a significant part of Variable Nodes (VNs) in the 5G LDPC codes are with degree-1 and are very sensitive to be erroneous, leading to the fact that the decoding performance is generally reduced. In the EOMS decoding, the core check nodes (CN) and extension CNs are processed with different update rules. A new CN -update criterion is also proposed by making use of the third minimum value. As a result, the offset factors are adaptively selected and the error probability of degree-1 VNs is significantly reduced. Simulation results show that the proposed EOMS decoder offers a much better error-correction performance than the state-of-the-art benchmarks for several 5G LDPC codes with a negligible complexity overhead.
Automatic Markerless Registration and Tracking of the Bone for Computer-Assisted Orthopaedic Surgery

To achieve a simple and less invasive registration procedure in computer-assisted orthopaedic surgery, we propose an automatic, markerless registration and tracking method based on depth imaging and deep learning. A depth camera is used to continuously capture RGB and depth images of the exposed bone during surgery, and deep neural networks are trained to first localise the surgical target using the RGB image, then segment the target area of the corresponding depth image, from which the surface geometry of the target bone can be extracted. The extracted surface is then compared to a pre-operative model of the same bone for registration. This process can be performed dynamically during the procedure at a rate of 5–6 Hz, without any need for surgeon intervention or invasive optical markers. Ex vivo registration experiments were performed on a cadaveric knee, and accuracy measurements against an optically tracked ground truth resulted in a mean translational error of 2.74 mm and a mean rotational error of 6.66°. Our results are the first to describe a promising new way to achieve automatic markerless registration and tracking in computer-assisted orthopaedic surgery, demonstrating that truly seamless registration and tracking of the limb is within reach. Our method reduces invasiveness by removing the need for percutaneous markers. The surgeon is also exempted from inserting markers and collecting registration points manually, which contributes to a more efficient surgical workflow and shorter procedure time in the operating room.
Learning Competency, Entrepreneurial Orientation Entrepreneurial Innovativeness, and Business Growth: A Study Among Malaysian Chinese Entrepreneurs

It has been widely acknowledged that Malaysian Chinese entrepreneurs significantly contribute in the success of SMEs' businesses. This study examined the effect of entrepreneurial innovativeness on the wholesale and retail SMEs' business growth under the moderating impacts of market turbulence among Chinese entrepreneurs. Additionally, learning competency and entrepreneurial orientation were also assessed entrepreneurial innovativeness' antecedents. In addition, Composition Based View (CBV) as well as Strategic Contingency Theory (SCT) were identified as the underpinning theories for this study. Six hypotheses were developed according to the proposed research model and the data were gathered from target respondents using convenience. A total of 110 retail SMEs' owners from Kuala Lumpur as well as Selangor participated in a face-to-face survey procedure. The data were collected at one point of time across the sample population. The findings revealed the positive and significant impacts of learning competency as well as entrepreneurial orientation on the innovativeness. Likewise, based on the findings, market turbulence as a moderator also influenced significantly on business growth. Furthermore, entrepreneurial innovativeness was found as a mediator between learning competency and business growth as well as between entrepreneurial orientation and business growth.