Miracle-dz/UniFAR-FTUs huggingface dataset:
Ten Antenna Array Using a Small Footprint Capacitive-Coupled-Shorted Loop Antenna for 3.5 GHz 5G Smartphone Applications

A self-isolated 10-element antenna array operating in the long-term evolution 42 (LTE42) frequency band is proposed for 5G massive MIMO smartphone applications. The proposed antenna elements are placed in a 2D array configuration; they are placed symmetrically along the two long edges of the mobile chassis. The proposed antenna structure is a shorted loop antenna resonating at half-wavelength mode, which is rarely deployed by researchers due to its large size compared to other quarter wavelength antenna structures. It is a printed, shorted, and compact loop antenna of a total footprint area of <inline-formula> <tex-math notation="LaTeX">$6\times6.5$ </tex-math></inline-formula> mm<sup>2</sup> (<inline-formula> <tex-math notation="LaTeX">$\lambda /14.3\times \lambda $ </tex-math></inline-formula>/13.2, where <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is the free space wavelength at 3.5 GHz). A small capacitive coupling flag-shaped strip is used to excite the proposed loop antenna. The compactness is achieved using an inward meandering that forms an internal loop in the element. The position and the dimensions of this loop are used to tune the resonant frequency and matching level at 3.5 GHz. The results (theoretical, simulated, and measured) show that the 3.5 GHz band (3.4-3.6 GHz) is achieved with impedance matching better than −10 dB, and total efficiency higher than 65%. A <inline-formula> <tex-math notation="LaTeX">$10\times10$ </tex-math></inline-formula> MIMO system is formed and it has an excellent MIMO and diversity performance in-terms of the envelope correlation coefficient (below 0.055), and apparently it has the highest channel capacity (about 54.3 bps/Hz) among other MIMO systems of the same order. Simulation results of the specific absorption rate (SAR) demonstrates that the proposed antenna solution satisfied SAR criterion. Thus, the proposed ten-element MIMO antenna represent an excellent candidate for sub-6 GHz 5G smartphone applications.
10.3 A 100MHz-BW 68dB-SNDR Tuning-Free Hybrid-Loop DSM with an Interleaved Bandpass Noise-Shaping SAR Quantizer

Emerging communication and radar applications place enormous demands on ADC performance by requiring wide BW (100MHz) and high DR (70dB). Continuous-time delta-sigma modulators (CT-DSM) [1–2] are a mainstream solution as they deliver high amplifier efficiency, are easy to drive, and provide innate anti-aliasing. However, CTDSMs have some inherent drawbacks: 1) the loop filter is typically based on RC time-constants which are poorly defined and require digital tuning; 2) they do not scale with sampling frequency; and 3) they are very sensitive to DAC ISI and jitter [2]. Discrete-time (DT) DSMs, such as the emerging noise-shaping (NS) SAR architecture, avoid these problems, but cannot provide the essential benefits of CT-DSMs. We propose the new hybrid-loop (HL) DSM architecture, which combines the advantages of both CT and DT DSMs and eliminates the drawbacks. Moreover, a bandpass, time-interleaved noise-shaping (TINS) SAR quantizer [3] further boosts the new architecture’s performance. The prototype HL-DSM provides 68dB SNDR over a 100MHz BW for a quadrature input, without any calibration or tuning, while occupying only 0.09mm2 and consuming 13mW at 1.6GS/s. The resulting 166dB FoMS shows the potential of the HL-DSM as a more robust and practical alternative to CT-DSM.
Robust Finite Time Passivity and Stabilization of Uncertain Switched Nonlinear System

This paper studies the robust finite time passivity, feedback robust finite time passification and global finite time stabilisation of the switched nonlinear systems with structural uncertainties. First, a concept of robust finite time passivity is proposed. Each subsystem is not required to be robust finite time passive. Namely, each active subsystem is finite time passive, even if there exists uncertainties in the switched system. Based on this finite time passivity concept, the finite time stability is obtained for switched nonlinear systems. Second, the robust finite time passivity criterion of switched system is established by the design of a state-dependent switching law. Third, a state-dependent switching law and a set of state feedback controllers are designed to render the closed loop system finite time passive. Finally, the robust finite time passivity property is preserved for the feedback interconnected uncertain switched systems. A numerical example is given to verify the effectiveness of our results.
Opinion Mining: Legitimate vs Spurious Reviews

E-Shops reviews have become a valuable source of opinions, feeling, and experience by existing users which marks the failure and success of products and services. A set of favorable reviews proves that the product or service has vended well and users are pleased with them, and opposing reviews effects adversely. These reviews provide worthwhile information that might be used by prospective users to find sentiments of previously preferred users before making any decision or transaction to buy right product or service from E-shops. Moreover, service providers, vendors or manufacturers also make use of these reviews to discover public opinion, as well as limitations of their products or services. But, the basic fact about underlying online reviews shows something else. These reviews might be spurious, posted or written with hidden purposes, they often involve helpful or favorable opinion in order to boost, promote, and publicize their products and services, or with pessimistic intention to harm opponent prestige and business as well. So, the exploration and investigation of such reviews before opinion mining is significant. In this paper, a methodology is proposed which includes reviews acquisition using the Tag path clustering approach about mobile devices of different make along with metadata from Flipkart, spurious reviews detection based on identical and nearly identical reviews using semantic similarity with review length. Further, an opinion mining process is carried out by using the lexical database (SentiWordNet) approach for the computation of sentimental degree and orientation of both spurious and legitimate reviews
LO Chain (×12) Integrated 190-GHz Low-Power SiGe Receiver With 49-dB Conversion Gain and 171-mW DC Power Consumption

A 190-GHz mixer-based down-conversion receiver realized in a 130-nm SiGe BiCMOS technology featuring high-speed heterojunction bipolar transistors (HBTs) with (<inline-formula> <tex-math notation="LaTeX">$f_{\text {T}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$f_{\text {max}}$ </tex-math></inline-formula>) = (300, 500) GHz is presented. The core part of the receiver consists of a low-noise amplifier (LNA), an active tunable fundamental mixer, an intermediate frequency (IF) buffer amplifier (BA), and an active inverse balun. A wideband, high conversion gain (CG) local oscillator (LO) chain is integrated to make the receiver suitable for applications requiring tunable LO, which contains a multiplier with a multiplication factor of 12 (<inline-formula> <tex-math notation="LaTeX">$\times 12$ </tex-math></inline-formula>) and a driver amplifier. To exploit the best possible performance with ultralow dc power consumption (<inline-formula> <tex-math notation="LaTeX">$P_{\text {dc}}$ </tex-math></inline-formula>), the mixer and LNA transistors operate at forward-biased base–collector junction voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\text {BC}}$ </tex-math></inline-formula>). With a fixed IF frequency at 1 GHz, this receiver exhibits a peak CG of 49 dB with a 14-dB tuning range and a minimum single-sideband noise figure (NF) of 16.5 dB, consuming only 29-mW static dc power for the core part and 171 mW overall, including the LO chain. Within the CG tuning range, this receiver achieves a 6-dB RF bandwidth (BW) from 25 to 32 GHz. Compared with previously reported receivers at similar frequencies, the highest CG and the lowest <inline-formula> <tex-math notation="LaTeX">$P_{\text {dc}}$ </tex-math></inline-formula> with highly competitive performance in terms of BW, CG tuning range, 1-dB output compression point, and NF have been achieved.
Palmprint Classification Detection Algorithm Based on Modified CenterNet

Target detection is the most studied and widely used branch in the field of computer vision technology, mainly focusing on two issues: objection position and category information. CenterNet is one of the excellent performance methods in the one-stage target detection. In this paper, HRNet is used to replace DLA-DCN in the original network structure to detect palmprint delta and other palmprint features. The experimental results show that the average precision of the modified CenterNet in extracting palmprint delta area and other palmprint features is improved with different degree.
Mechanical Comparison of Short Models of Nb3 Sn Low-β Quadrupole for the Hi-Lumi LHC

MQXF is the Nb<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> Sn Low-<inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> quadrupole magnet that the HL-LHC project is planning to install in the LHC interaction regions in 2026 to increase the LHC integrated luminosity. The magnet will be fabricated in two different lengths: 4.2 m for MQXFA, built in the US by the Accelerator Upgrade Project (AUP), and 7.15 m for MQXFB, fabricated by CERN. In order to qualify the magnet design and characterize its performance with different conductors, cable geometries and pre-load configurations, five short model magnets, called MQXFS, were fabricated, assembled and tested. We compare the mechanical behavior of short model magnets using experimental data and new numerical models that take into account the measured coil sizes as a function of position.
26.2 A Doherty-Like Load-Modulated Balanced Power Amplifier Achieving 15.5dBm Average Pout and 20% Average PAE at a Data Rate of 18Gb/s in 28nm CMOS

The continuous growth in demand for high-speed wireless connectivity is one of the main driving forces in the standardization and development of mm-wave systems. 5G new radio (NR) and also satellite communication with recent low-earth-orbit satellite launches are seeing tremendous interest from the industry. More specifically the FR2 band (24.25 to 52.6GHz) is of a particular interest for both applications. To overcome the high path loss at mm-waves, phased-array systems are used to achieve a certain effective isotropic radiated power (EIRP). The use of numerous radiating elements favours compact structures. The necessity for high-speed mixed-signal and digital circuits in multi-Gb/s communication, entails the use of deep-scaled silicon technologies, enabling low cost, compact, and high-yield CMOS front-end solutions.
Radio Resource Allocation and Power Control Scheme in V2V Communications Network

Reasonable resource management scheme is the premise to ensure system reliability and practicability of vehicle-to-vehicle (V2V) communications network. In this paper, spectrum resource allocation and power control scheme is studied. Firstly, the problem of resource block (RB) sharing is considered. Assuming that each V2V link occupies at most one resource block while multiple links can share the same RB. In this work, a factor graph model is proposed corresponding to the RB allocation problem through function mapping. Therefore, the RB allocation problem is equivalent to studying the message update and belief inference problem on the factor graph model. We propose a Belief Propagation based on Real-time Update of Messages (BPRUM) algorithm, in which the message of each node is calculated based on the new messages of other nodes rather than the messages calculated in the last iteration. Secondly, after the RB allocation is completed, a power control scheme under the maximum power limit is proposed by utilizing the Lagrange dual decomposition. And the total throughput of the system is maximized by using the proposed power control scheme. Simulation results show that the resource allocation and power control scheme can achieve a significant increment in spectrum utilization and throughput of the V2V communications network.
Temporal 3D RetinaNet for fish detection

Automatic detection and tracking of fish provides valuable information for marine life science. Deep convolutional networks have been applied with some success but performance is affected by challenging imaging conditions including complex background, variation of light and the low visibility of the underwater environment. Existing works including Fast R-CNN and RetinaNet rely on single frame fish detection and suffer noisy and unreliable detections. In this paper, we propose and examine two 3D deep learning networks using temporal features to improve fish detection performance. The first one called 3D-backbone RetinaNet based 3D ResNet for temporal information is found worse than 2D RetinaNet. The second one called 3D-subnets RetinaNet based on 3D Regression subnet and Classification subnet to extract the temporal information is found better than 2D RetinaNet. To validating the performance of these networks, we also created a new fish data set which will be made publicly available with codes of the proposed networks.
Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

While Doppler radar can be used to measure cardiopulmonary vital signs during sleep, meaningful diagnostic assessments are often subject to knowledge of a subject’s changing sleep posture. The torso Effective Radar Cross Section (ERCS) and displacement magnitude were studied for 20 human subjects in three imitated sleep posture categories using a dual-frequency Doppler radar system in an exploratory examination of the feasibility of using radar to recognize body orientation. Box plot statistical analyses were performed for comparative assessment of ratio variations in ERCS and respiration depth for three different imitated sleep postures. The observed statistical trends and correlations were applied to a physical model to develop posture decision algorithms with initial supine posture data used as a reference. A single-frequency algorithm tracked postures without error for 90% of the subjects using 2.4 GHz data, and 80% using 5.8 GHz data. As accuracy limitations were complementary, a dual-frequency algorithm was developed which recognized postures without error for 100% of the subjects.
Codes Correcting Bounded Length Tandem Duplication

In this paper we extend the problem of correcting a tandem duplication of a fixed number of symbols to the problem of correcting a tandem duplication of a bounded length. This type of error can be represented as a repetition of a symbols block. We propose the upper and the lower bounds on the cardinality based on the sphere-packing bound and constructions of codes correcting one tandem duplication of a bounded length based on Varshamov-Tenengolts code.
4.4 A 1.3TOPS/W @ 32GOPS Fully Integrated 10-Core SoC for IoT End-Nodes with 1.7μW Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode

The Internet-of-Things requires end-nodes with ultra-low-power always-on capability for long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an always-on IoT end-node SoC capable of scaling from a 1.7$\mu$ W fully retentive COGNITIVE sleep mode up to 32.2GOPS (@49.4mW) peak performance on NSAAs, including mobile DNN inference, exploiting 1.6MB of state- retentive SRAM, and 4MB of non-volatile MRAM. To meet the performance and flexibility requirements of NSAAs, the SoC features 10 RISC-V cores: one core for SoC and IO management and a 9-core cluster supporting multi-precision SIMD integer and floating- point computation. Two programmable machine-learning (ML) accelerators boost energy efficiency in sleep and active state, respectively.
Moving object detection for humanoid navigation in cluttered dynamic indoor environments using a confidence tracking approach

Humanoid robot perception is challenging compared to perception in other robotic systems. The sensors in a humanoid are in constant state of motion and their pose estimation is affected by the constant motion of the tens of DOFs (Degrees of Freedom) which in turn affect the estimation of the sensed environmental objects. This is especially problematic in highly cluttered dynamic spaces such as indoor office environments. One of the challenges is identifying the presence of all independent moving/dynamic entities such as people walking around the robot. If available, such information would assist humanoids to build better maps and better plan their motions in unstructured confined dynamic environments. This paper presents a moving object detection pipeline based on relative motion and a novel confidence tracking approach that detects point clusters corresponding to independent moving entities around the robot. The detection does not depend on prior knowledge about the target entity. A ground plane removal tool based on voxel grid covariance is used for separating point clusters of objects within the environment. The proposed method was tested using a Velodyne VLP-16 LiDAR and an Intel-T265 IMU mounted on a gimbal-stabilized humanoid head. The experiments show promising results with a real-time computational time complexity.
DependLoc: A Dependency-based Framework For Bug Localization

As software systems are becoming larger and more complex, debugging poses great challenges to software developers and maintainers. Among various efforts on easing the burden of debugging, bug localization techniques are developed to help locate where a bug occurs in source code files automatically. Information retrieval and deep neural network techniques are often adopted in existing research to achieve bug localization through capturing the textual or semantic similarity between bug reports and source code files. At the same time, some domain- specific eatures in software engineering are also utilized to locate the buggy files. However, the dependency relationship between classes (A depends on $B$ if $A$ references B) is not considered or utilized by existing approaches. In this work, we propose a novel framework DependLoc for bug localization which leverages the dependency relationship among source code files. DependLoc is based on the observation that buggy files may not be highly similar to a bug report but have a dependency relationship with one or more files that are quite similar to the bug report. DependLoc adopts a customized Ant Colony algorithm to quantify the intrinsic dependency relationship (called reference heat) and designs a segment-based encoder to learn this feature. Experimental results on six widely-used benchmark datasets for bug localization show that our approach outperforms the state-of-the-art methods, and Accuracy@10 is improved by 4% on average.
Matrix Analysis on Transmission Zero and Phase Difference in Dual-Band Filtering Power Divider

This paper presents a design approach of dual-band filtering power divider based on coupling matrix. To improve the band-to-band isolation, the generation mechanism of the transmission zero between the two bands has been analyzed. Be guided through the synthesized matrix, the phase difference between the outputs in the two bands can be adjusted to be in-phase or antiphase by altering the location of the feeding probes. Two prototypes using dual-mode dielectric resonator (DR) are designed to show the advantages of the proposed approach.
Motion-Robust Multimodal Heart Rate Estimation Using BCG Fused Remote-PPG With Deep Facial ROI Tracker and Pose Constrained Kalman Filter

The heart rate (HR) signal is so weak in remote photoplethysmography (rPPG) and ballistocardiogram (BCG) that HR estimation is very sensitive to face and body motion disturbance caused by spontaneous head and body movements as well as facial expressions of subjects in conversation. This article proposed a novel multimodal quasi-contactless HR sensor to ensure the robustness and accuracy of HR estimation under extreme facial poses, large-motion disturbances, and multiple faces in a video for computer-aided police interrogation. Specifically, we propose a novel landmark-based approach for a deep facial region of interest (ROI) tracker and face pose constrained Kalman filter to continuously and robustly track target facial ROIs for estimating HR from face and head motion disturbances in rPPG. This motion-disturbed rPPG signal is further fused with a minimally disturbed BCG signal by the face and head movements via a bank of notch filters with a recursive weighting scheme to obtain the dominant HR frequency for final accurate HR estimation. To facilitate reproducible HR estimation research, we synchronously acquire and publicly share a multimodal data set that contains 20 sets of ECG and BCG signals as well as uncompressed, rPPG-dedicated videos from ten subjects in a stable state and large-motion state (MS) without and with large face and body movements in a sitting position. We demonstrate through experimental comparisons that the proposed multimodal HR sensor is more robust and accurate than the state-of-the-art single-modal HR sensor solely with rPPG- or BCG-based methods. The mean absolute error (MAE) of HR estimation is 7.13 BPM lower than the BCG algorithm and 3.12 BPM lower than the model-based plane-orthogonal-to-skin (POS) algorithm in the MS.
Microfluidic Flow in Microchannel Using Different Rotating Modes

This paper explores how to control a microfluidic flow in a microchannel of a platform by centrifugal force. Main purpose is to devise a detection system for the separation of calibrated fluid and non-calibrated fluid. The research presents a microfluidic flow to the reaction chamber after the platform spinning with constant rotating frequency for a period of time. Overflow to the other areas did not happen when the reaction chamber had stomach-shape and the reservoir chamber had rectangular-shape in the platform. In addition, the maximum turbulence kinetic energy happened with the bending angle of the mixing runner to be 55°~60°. All of micro-flow flowed to the reservoir chamber from the reaction chamber when platform rotates with higher speed. This research verified that the optimal design of the platform achieved the mixing flow and controlled the flow of the micro-flow.
SmartPose: An Intelligent Household Appliances Controller Based on Visual Recognition of Human Postures

Smart home can greatly increase the convenience of life, and has become a research hotspot nowadays. The vision-based smart home controlling system is widely used in rooms, such as kitchens, where the needs to pressing switches or touching screens can cause many inconveniences. This paper proposes an intelligent household appliances controller based on visual recognition for human posture, namely SmartPose. This system employs the Back Propagation Neural Network to recognize the key points of the human joints’ key points extracted by a human joints recognition library, namely OpenPose, and then generates posture results. After that, a Raspberry Pi receives the results and controls the home appliances through a relay. The experimental results indicate that this system’s accuracy for recognizing five postures is 99.8%, which can be applied to the control of household appliances.