Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomy QoS-aware service provisioning in IoT Techniques

Cloud computing has arisen as a correlative response to deal with the troubles stood up to in figuring. While dispensed computing lets in us to greater deal extra with time/delay-delicate IoT functions (e.g., wise frameworks and ill-disposed climate), there is a scope of practical difficulties. For instance, the asset-compelled nature of mist hubs and heterogeneity of IoT occupations entangle endeavors to proficiently timetable errands. In this manner, to greater clean out time/delay-delicate unique IoE demands, the creators contribute by using imparting a sharp layer between IoE devices and haze hubs to contain a speedy and versatile learning-based mission planning strategy. We cautiously think about the exhibition of the proposed procedure the utilization of reproduction, as pleasantly as its accuracy the use of formal confirmation. The difference discoveries are promising, each in expressions of solidarity utilization and Quality of Service (QoS).
CRISPR Cas9 genome editing approach for gRNA of the genes associated with Schizophrenia: A computational approach

It is of critical need to increase the number of restorative and curative approaches against Schizophrenia. In our work, focus has been made on identifying the best guide RNA (gRNA) for one such approach namely Crispr Cas9 system. These gRNAs directly target the major nine genes responsible for Schizophrenia in Crispr Cas9 technique. To accurately identify said gRNAs for our study, various computational tools and servers working on different algorithms have been used along with off and on target checks. Crispr is an emerging and commendatory gene editing technology and gRNAs play a key role in the technique, hence it becomes possible for us to better understand the cause and the pathophysiology behind the disease on a genetic level through the use of gRNAs. Further validation of the results through laboratory protocols and clinical criteria presents a novel approach in the prevention and treatment of the disease.
MR FLUID OF LIQUID GALLIUM DISPERSING MAGNETIC PARTICLES

The heat convection, which can be provided from a magneto-rheological (MR) fluid, is a useful process in converting heat from high to low temperature and vice versa. In this work, the property and preparation of liquid gallium-based MR fluid is presented. The prepared MR fluid disperses micron-sized iron or nicked metal powders in liquid gallium (Ga). In this experiment, supercooling of MR fluid was investigated in order to obtain the condition under which the liquid state can be kept below the melting point. Moreover, the viscosity and elasticity of the prepared MR liquid were measured as a function of magnetic flux density. Finally, it was demonstrated that liquid Ga is a useful base for preparing a new type MR fluid.
Soc Design of Intelligent Recognition Based on RISC-V

The RISC-V open source instruction set architecture has received extensive attention in the field of embedded low power consumption due to its advantages such as simplicity and low power consumption. However, due to the low computational power provided by RISC-V, it is difficult to deploy convolutional neural networks with high algorithm complexity. If deep learning cannot be applied to RISC-V, it will limit the promotion of RISC-V in the fields of automatic driving, face recognition, target tracking, natural language processing, etc. In this design, a CNN accelerator is designed, which is connected to the co-processor interface of RISC-V, and a set of user-defined instruction sets is designed to call the CNN accelerator to solve the problem that RISC-V cannot deploy neural networks. Through testing, when the soc system power consumption is only 0.4w, the co-CNN accelerator can accelerate the CNN more than 1000 times, meeting the requirements of low-power embedded field.
Design and Analysis of 29 GHz Millimeter-waves Phased Array Antenna with Reduced Mutual Coupling

In this study, a multiple-input multiple-output (MIMO) antenna for wide scanning operating at 29 GHz is designed. Each element of the MIMO antenna is situated at a distance of λ/2. The proposed high isolation and wide scanning antenna combine to achieve the desired phased-array antenna. To minimize the mutual coupling between the radiators, slits are introduced in the radiating part. The MIMO antenna maintains a compact structure of 43.5 × 5.3 mm2, Roger’s 4350B laminated with a thickness of 0.8 mm was employed as a substrate. The proposed array has a very low mutual coupling of (S21< -24 dB), and realize gain of 9.3 dBi at a scanning angle of 70°, The antenna performance was studied in term of the S-parameter, beam scanning performances, and surface current distribution.
Dual-Path Multi-Level Feature Fusion Network for Semantic Segmentation of Remote Sensing Image

Different objects with similar spectral features are common in remote sensing images, such as trees and low-vegetation, building and roads. It is important to segment them well for urban planning, traffic navigation, and so on. However, the existing multi-level feature fusion methods ignore the relationship among all features of each level, making these objects hard to distinguish. In this paper, we propose a Dualpath Multi-level Feature Fusion Network (DMFFN) to make good use of the features of backbone. This network includes two paths to fuse the features and model the dependences between them. After getting the features from two paths, we utilize a cross-attention module to decoder them for better segmentation. Experimental results over two datasets show that DMFFN outperforms state-of-the-art methods.
The More The Merrier: Reconstruction of Twitter Firehose

Twitter is a rich microblogging platform with live user generated content that is valued by research. The Twitter API gives access to tweets posted by users. Unfortunately, full data access is only available for high-paying customers. We propose a system that can access otherwise unobtainable data. First, our system enables retrieving more live tweets than the free API returns. Second, it provides means to archive tweets that are older than a week which Twitter’s API cannot do. We accomplish this by an informed prediction of tweet IDs generated based on the the Snowflakes service. Using our system with only 10 tokens, we are able to collect more than 35 million tweets/week, that were not part of the free tweet stream provided by Twitter. Our system enables research and deeper analysis of Twitter trends or historic events by providing large amounts of Twitter data.
Six-degree-of-freedom Robot Trajectory Planning Based on MATLAB

With the development of science and technology in various countries, industrial robots have become a key research object in various countries. The study of trajectory and dynamics of industrial robots can help to improve robot efficiency. In this paper, the structure and DH parameters of KUKA six-degree-of-freedom industrial robot arm are analyzed, five spatial trajectory points are planned, and the corresponding joint angles are solved by using inverse kinematic numerical solution method. The trajectory planning of the industrial robot joint space is carried out by using the fifth polynomial interpolation method to the kinematic analysis and the inverse kinetic analysis are completed by using the Newton Euler method. This study provides a basis for the trajectory planning and dynamics Analysis of such robots.
Secure E-Voting System using Deep Learning Techniques

Democracy is vital in many emerging economies because it places power in control of the general public, who choose government via the electoral procedure. Elections rely heavily on polling places and people. Numerous changes have happened in electoral processes since the year of the republic till now. Subsequently, Ballot sheets were implemented as a voting medium, but this was later replaced by electronic voting machines, in which voters must select from a list of buttons, each symbolizing a symbol. This paper proposes techniques for ensuring voter identification and privacy through fingerprint scanners and other biometric identification techniques-based machine learning algorithms. As a result, we offered a new approach to speed up the voting process and eliminate similar issues. For face identification, we employed Convolutional Neural Network (CNN) architectures such as Alex Net, Visual Geometry Group (VGG-16), and Random Forest Algorithms in this study. The accuracy provided by Alex Net, VGG-16, and Random Forest (R.F.) Algorithms was 94.16%, 98.45%, and 92%, respectively. VGG-16 architecture outperformed all other algorithms with a 98.45% accuracy. We utilized an ATmega328-based Arduino for finger detection.
Ship Remote Driving and Monitoring Platform Architecture Construction Research

Ship autonomous navigation is an advanced stage of ship intelligent navigation. Domestic and foreign institutions have made many efforts in the technical progress of intelligent navigation development. Before the fully autonomous navigation technology is mature, autonomous ships need to rely on remote driving and monitoring platforms to monitor and control ships. In order to achieve the progress of the ship intelligent navigation, proposed the architecture construction ideas of remote driving and monitoring platforms. The platform construction ideas are provided from the perspective of platform positioning, platform functions, overall architecture, system design and information interaction. The construction of the platform is helpful to provide development direction and experimental verification platform for domestic and foreign scientific research scholars and engineers.
2-Way Localization of RFID Tags

Identification and localization of hospital equipment (op instruments) or blood bags improves cost-efficiency, safety and provides easy, direct access when needed. The system presented here combines different types of localization in addition to identification with RFID. It is based on passive UHF RFID tags that provide information about distance and angle by means of phase estimation (FD-PDOA and SD-PDOA). This 2-way localization is enhanced by an estimation of the motion within the room via TD-PDOA. This paper presents the discrete hardware setup, the measurement concept and a demonstrator. It shows a maximum measurement tolerance of 0.5 m in distance and 6° in angle estimation, as well as an accuracy of up to 89 % in distance difference under calibrated conditions.
Kidney Disease Detection and Identification Using Artificial Intelligence

Kidney Disease is the 3rd most substantial cause as mentioned and has the highest rate of morbidity and mortality rated all over the world. Chronic Kidney Disease is detected at a very later stage therefore making it one of the most harmful diseases. According to the Electronic Medical Records it is very hard for the detection of Chronic Kidney Disease. Severe medical treatment like hemodialysis is required for the procedures conducted for the curing of Renal Disease. Chronic Kidney disease has affected nearly about 10% of the present day population which itself claims how contagious this disease is. Blood Pressure, Heart Ailment, Glucose Level, Sodium, Potassium are all the causes for the identification of CKD.
A modified lead-free piezoelectric BZT–xBCT system with higher TC

Very recently, a high-performance Pb-free piezoelectric system Ba(Zr0.2Ti0.8)O3–x(Ba0.7Ca0.3)TiO3 (abbreviated as BZT–xBCT) has been reported, showing a very high d33 of 620 pC N−1 at x = 50. However, the Curie temperature TC (for x = 50) is relatively low (93 °C), thus restricting its applications. We report a method to increase the TC of the lead-free system BZT–xBCT, that is, increasing the TC of both tetragonal and rhombohedral terminals. The modified system Ba(Zr0.15Ti0.85)O3–x(Ba0.8Ca0.2)TiO3 showed a higher TC of 114 °C at x = 53. We also found that both d33 and ε along the morphotropic phase boundary (MPB) decreased with a deviation from the cubic–tetragonal–rhombohedral triple point. This result is explained by considering an increase in polarization rotation barrier with a deviation in composition from the triple point. Our results implied that to achieve a high d33 with a high TC, a nearly vertical MPB is required. Our work may give a new insight into how to design ‘ideal’ Pb-free piezoelectrics with both high piezoelectricity and high TC.
Hand Bone X-rays Segmentation and Congregation for Age Assessment using Deep Learning

Bone Age Assessment (BAA) is one of the greatest clinical practices to diagnose growth potential and genetic disorders of a child. Bone X-ray shows the human inside body using ionizing radiation, helping doctors in diagnosing diseases or providing treatment. In this work, we utilize the scans of hand bone X-ray, which is a type of medical images in pediatric hand radiographs for BAA. We propose a 3-step scheme for this task, including segmentation, regression and congregation. Considering 3 main components of one hand including phalanges, metacarpals and carpals as Regions Of Interest (ROI), we segment the images and create 6 new datasets. Thanks to the precisely segmentation of ROIs, our datasets facilitate better BAA scheme by ignoring unnecessary parts, e.g., background of the images. Our proposed regression method with congregation achieves the best mean absolute error of 5.69. Furthermore, extensive evaluations are performed for different ROIs, demonstrating the impact of each part of hand bone images in BAA.
Radial Distributions of the HCN/CO Ratio in Nearby CO Bright Galaxies

Simultaneous observations of HCN and CO lines were made along the major axes of six nearby CO bright galaxies with the Nobeyama 45-m telescope. The beam-averaged HCN/CO ratios are 0.01 to 0.1. The distributions of the HCN/CO ratio along the major axes (and the minor axis for M 51) show different patterns from one galaxy to another. By adding data obtained from the outer disk, however, we found gradual decreases in the HCN and CO intensities and the HCN/CO ratio with increasing radius in M 51 from the nuclear region to 3 kpc, similar to our previous result for NGC 253. The HCN/CO ratio is very sensitive to the observing beam size on a linear scale. The ratio, which reflects the fraction of dense molecular gas, correlates with the star-formation activity derived from the far-infrared luminosity.
On radio signatures to mitigate the MAC addresses randomization for Wi-Fi analytics in real-world scenarios

MAC randomization occurs when non-connected Wi-Fi devices continuously change their MAC address to confuse analytics systems. It is a real challenge for building services based on presence, location, and tracking data since the information is useless due to the produced distortion. This contribution aims to mitigate this problem by constructing unique, stable, and reliable identifiers for these non-connected Wi-Fi devices. We propose a new system that builds identifiers based on the Probe Request frames. In addition, we present what is (to the best of our knowledge) the most exhaustive field study on the accuracy of this type of system with real users and real devices in operation. In this study, we compare the hand count performed in a campus building with the count done automatically by our MAC de-randomization system, obtaining an accuracy between 87 and 95%.
THE SUZAKU VIEW OF THE SWIFT/BAT ACTIVE GALACTIC NUCLEI. II. TIME VARIABILITY AND SPECTRA OF FIVE “HIDDEN” ACTIVE GALACTIC NUCLEI

The fraction of Compton-thick sources is one of the main uncertainties left in understanding the active galactic nucleus (AGN) population. The Swift Burst Alert Telescope (BAT) all-sky survey for the first time gives us an unbiased sample of AGNs for all but the most heavily absorbed sources NH > 1025 cm−2). Still, the BAT spectra (14–195 keV) are time averaged over months of observations and therefore hard to compare with softer spectra from the Swift XRT or other missions. This makes it difficult to distinguish between Compton-thin and Compton-thick models. With Suzaku, we have obtained simultaneous hard (>15 keV) and soft (0.3–10 keV) X-ray spectra for five Compton-thick candidate sources. We report on the spectra and a comparison with the BAT and earlier XMM observations. Based on both flux variability and spectral shape, we conclude that these hidden sources are not Compton thick. We also report on a possible correlation between excess variance and Swift BAT luminosity from the 16 day binned light curves, which holds true for a sample of both absorbed (four sources), unabsorbed (eight sources), and Compton-thick (Circinus) AGNs, but is weak in the 64 day binned BAT light curves.
Continuous nonlinear dimensionality reduction by kernel Eigenmaps

We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-based mixture of affine maps from the ambient space to the target space. Unlike most spectral NLDR methods, the central eigenproblem can be made relatively small, and the result is a continuous mapping defined over the entire space, not just the datapoints. A demonstration is made to visualizing the distribution of word usages (as a proxy to word meanings) in a sample of the machine learning literature.
Efficient Nonlinear Multi-Parameter Decoupled Estimation of PMSM Drives Based on Multi-State Voltage and Torque Measurements

Accurate and fast parameter estimation is essential for efficient operation and control of permanent magnet synchronous machine (PMSM). In this paper, a novel multi-parameter decoupling estimation method based on multi-state measurement is proposed to analyze and estimate the important parameters including winding resistance, permanent magnetic flux linkage, and $d-q$ axis inductances. An improved PMSM model containing inverter distorted voltage and nonlinear inductance considering magnetic saturation is established at first, then the model is decoupled to reduce the cross influences among the parameters and improve the computation efficiency through model dimension reduction according to the proposed decoupling method, and finally each parameter is estimated from multi-state voltage and torque measurement with improved efficiency and accuracy. This decoupling method separates the resistance and the inductance based on electromagnetic torque and reluctance torque, which is the crucial part of the estimation method proposed in this paper. Experiment on a PMSM driving system and comparison with existing methods are conducted to show that the proposed estimation method can improve the accuracy of estimation and calculation efficiency.
Computational study of detonation wave propagation in narrow channels

A numerical study of the propagation of regular detonation waves is conducted in the context of narrow channels undergoing strong wall confinement. To deal with shock waves, chemical reactions, heat and viscous stresses, a high-order Navier-Stokes solver based on Weighted Essentially Non-Oscillatory (WENO) scheme, coupled with the Strang splitting method, is used in the framework of multi-species reacting mixtures. Results show that the wall dissipative effects decrease the speed of the detonation wave compared to the Chapman-Jouguet (CJ) detonation velocity. In addition, the multidimensional results reveal that the development of the thermo-diffusive boundary layers behind the leading shock wave induces an expansion flow, which then determines the contour of the sonic envelope. From the Master Equation and the generalized CJ condition, which are derived and compared to the results of the current simulations, the main energy withdrawals are found to be related to the streamline divergence as well as to th.
Secrecy Performance of Energy Harvesting Based D2D Communications in Spectrum-Sharing Networks

This work considers a secure cognitive device-to-device (D2D) communications network, where the D2D source (S) transmits the secret information to the D2D destination (D) through energy-constrained D2D relay (R) using the spectrum of the primary base station (BS) under the malicious attempt of D2D eavesdropper (E). We propose secure time switching-based relaying (TSR) in a D2D wireless-powered cognitive radio network. D2D relay adapts the amount of energy harvested from the S using TSR protocol while assuming some information leakage to E. The exact and asymptotic closed-from expressions of secrecy outage probability of the proposed system are derived when the channel state information from R to the cellular user link is outdated. Furthermore, we derive novel exact and asymptotic expressions for the power outage probability.
The Anglet: An E/H-plane Bent, 90-Degree Twisted, TE101/TM110-Mode Singlet Building Block

In this work, a subclass of the singlet building block is defined. Termed the anglet, a waveguide cavity-based structure is demonstrated that combines a bend, a 90-degree polarization rotation, and produces one pole and one transmission zero. The anglet is defined due to its unique ability to be viewed as either a TE101 -mode singlet or a TM110-mode singlet based on the users coordinate point of view together with the requirements for the bend direction and polarization rotation. Two types of anglets are demonstrated by simulation and a third-order filter is demonstrated for the incorporation of an anglet in a practical design. Emphasis on its unique capabilities are highlighted due to its nature as a fundamental-mode resonator with evanescent bypass coupling. Furthermore, an accurate approximation for the source-load coupling is described and a prototype filter is fabricated and measured in order to validate the concept.
A High-Linear Rail-to-Rail Class-AB PGA with Transconductance Compensation Based on Common-Mode Detection

A low total harmonic distortion (THD) programmable gain amplifier (PGA) with dual operational amplifiers (op-amps) for electrocardiogram (ECG) signal acquisition applications is presented. The proposed op-amp comprises the rail-to-rail input stage and class-AB output stage. To solve the transcon-ductance (gm) variation caused by the supply voltage changes in rail-to-rail amplifiers with the traditional DC level-shifting technique, a common-mode-detection-based transconductance linearization scheme is proposed in this paper, associated with a 7-steps resistor-feedback digital gain control from 1 V/V to 12 V/V, which can realize less than 2.7% transconductance variations. The proposed rail-to-rail class-AB PGA is implemented in 0.18-µm CMOS technology with 2.7-V∼5-V power supply voltage. Spice simulation results show that the THD is -110 dB with an input signal of 5 mVpp and the input-referred noise spectrum density is $\mu {\mathbf{V}}/\sqrt {Hz}$ at a 10-Hz frequency point, after adopting an 8-kHz chopping. The total DC current including the bias circuit is 30µA at 5 V.
Temporal Shape Transfer Network for 3D Human Motion

This paper presents a learning-based approach to perform human shape transfer between an arbitrary 3D identity mesh and a temporal motion sequence of 3D meshes. Recent approaches tackle the human shape and pose transfer on a per-frame basis and do not yet consider the valuable information about the motion dynamics, e.g., body or clothing dynamics, inherently present in motion sequences. Recent datasets provide such sequences of 3D meshes, and this work investigates how to leverage the associated intrinsic temporal features in order to improve learning-based approaches on human shape transfer. These features are expected to help preserve temporal motion and identity consistency over motion sequences. To this aim, we introduce a new network architecture that takes as input successive 3D mesh frames in a motion sequence and which decoder is conditioned on the target shape identity. Training losses are designed to enforce temporal consistency between poses as well as shape preservation over the input frames. Experiments demonstrate substantially qualitative and quantitative improvements in using temporal features compared to optimization-based and recent learning-based methods.