Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Rank Selection Method in 3GPP 5G NR Systems

A user equipment (UE) estimates channel state information (CSI) which includes the rank indicator (RI), the precoding matrix indicator (PMI), and the channel quality indicator (CQI), and feedbacks the CSI to the base station (BS). Rank Indicator is a number (indicator) that represents how well a Multiple Input Multiple Output (MIMO) system communication works. Therefore, the RI estimation and selection is an important issue for system efficiency. In this paper, we propose an adaptively adj usted RI selection method to accommodate the channel condition. Simulation results demonstrate that the performance of the proposed adaptive method satisfies the performance requirements of the IMT -2020 evaluation. Furthermore, the proposed adaptive method works well in a low-mobility indoor scenario.
High Isolation Compact Four Port MIMO Antenna with Slotted Ground for UWB Applications

This research article has proposed a compact coplanar waveguide fed four-element ultra-wide-band (UWB) multi-input-multi-output (MIMO) antenna accompanying high isolation (S21 < −17dB) and good return loss characteristics (S11 < −10dB). The proposed antenna operates in the entire UWB range (3.1–10.6 GHz) and is also useful for 3.5/5 GHz WLAN and Wi-MAX applications. The proposed antenna comprises four identical radiating elements, a unique asymmetric staircase ground structure, and an L-shaped stub are stacked to achieve good return loss characteristics and also good impedance matching in the entire UWB band. The proposed antenna is incorporated an orthogonal arrangement to achieve the required isolation among the four radiating elements. As a result, the overall size of the intended UWB-MIMO antenna acquired 50 × 50 mm2 dimensions which are printed on the highly available FR-4 substrate (εr = 4.4) with a thickness of 1.6 mm. The performance of the proposed MIMO antenna is thoroughly studied in terms of diversity parameters, where it has been found that ECC < 0.02 with an acceptable channel capacity loss < 0.6 bps/Hz and the other parameters DG, MEG, TARC have been evaluated and presented as well.
Lossless Image Compression Based on Image Decomposition and Progressive Prediction Using Convolutional Neural Networks

This paper presents a lossless image compression method based on the image decomposition and progressive prediction of decomposed images and their coding contexts using convolutional neural networks (CNNs). We first decompose a given input into sub-images by sub-sampling into horizontal and vertical directions. The first sub-image is encoded by an existing non-learning lossless compressor, and the others are encoded progressively using the already encoded ones. While state-of-the-art learning-based encoders proceed with the auto-regressive prediction (pixel-by-pixel raster-scan order prediction) so that the runtime is not practical, our method predicts each sub-image at once and thus executes in practical time. We also design the CNNs to predict pixel values and coding contexts jointly and send the prediction error to the arithmetic encoder for the given coding context. In the case of color input, it is converted to YUV using a reversible transform, and each channel is partitioned in the same way above. Then, all the sub-images are processed progressively from Y to V. Experiments on high-resolution datasets show that the proposed method outperforms all the non-learning codecs and practical-time CNN-based encoders. Compared to learning-based auto-regressive approaches, our method shows comparable results while requiring much less runtime.
Environment Adaptive 3D Pose Estimation Model and Learning Strategy

Recently, 3D pose estimation models using deep learning structures have begun to show outstanding performance. However, the performance is guaranteed only for the general pose included in public databases. In other words, most estimation models sometimes show degraded results when a given video contains uncommon poses from specific situations such as exercise and dance. This problem arises from the limitation of the pose diversity of public databases. We propose a novel estimation model calibration (EM C) framework for environment adaptive 3D pose estimation to solve this problem. This framework aims to calibrate well-trained existing pose estimation models from public databases to suit the environment. To achieve this goal, the framework uses target data to analyze the problems of existing estimation models. Subsequently, the proposed ergonomic model handler generates a calibration dataset by directly correcting the problem caused by the target data. Using the generated calibration dataset, we calibrate the existing pose estimation model. In this paper, we provide various experimental results of pose estimation for verification of the proposed framework. Experimental results demonstrate performance improvements qualitatively and quantitatively in specific poses and show the efficiency of estimation model calibration.
Detection algorithm of pulmonary nodules based on deep learning

Lung cancer has become one of the most harmful cancers to human health as air quality deteriorates worldwide and the number of smokers increases. The early stage of lung cancer is usually characterized by pulmonary nodules. If pulmonary nodules and their cancerization can be detected in time and treated early, it is of great value to improve the survival rate of patients with early lung cancer. In view of the problems of low accuracy, large detection range, low sensitivity and large number of false positives in the current detection model of pulmonary nodules, this paper proposed a detection algorithm of pulmonary nodules based on Faster-RCNN and U-net structure with residual module. The algorithm introduces a Convolutional Block Attention Module (CBAM), which combines channel and space, to realize adaptive feature learning and feature weight, and combines the idea of feature fusion to make full use of deep and shallow information to enhance feature extraction ability. It can improve the sensitivity of pulmonary nodules detection and assist radiologists to diagnose pulmonary nodules effectively.
Fast-Parallel Singular Value Thresholding for Many Small Matrices based on Geometric Feature of Singular Values

This paper proposes a singular value thresholding (SVT) method called fast-parallel SVT for many small matrices that is based on the geometric feature of singular values. By discovering the geometric feature of singular values, SVT is expressed in a completely different format from conventional SVT methods. The new format does not require singular value decomposition, which is the calculation bottleneck with conventional SVT methods, so the amount of floating-point calculation can be reduced. The new format can also be described almost linearly, allowing many matrices to be processed in data in parallel. Through an experiment, the proposed method calculated SVT up to 162.84 times faster with higher calculation accuracy than conventional SVT methods.
An End-To-End Model from Speech to Clean Transcript for Parliamentary Meetings

This paper presents an end-to-end approach for generating readable and clean text directly from speech signal. While conventional automatic speech recognition (ASR) systems are designed to faithfully reproduce utterances word-by-word, we propose a model that emulates the way a human transcriber/editor creates a clean transcript from speech by skipping fillers, substituting colloquial expressions with more formal ones, inserting punctuation, and performing other types of corrections. An evaluation using 700-hour Japanese Parliamentary speech demonstrates the effectiveness of the proposed approach in generating clean texts suitable for human consumption. We also show that forward-backward decoding and multitask learning leveraging approximate faithful transcripts significantly improve the performance of the direct mapping.
Nearby-person Occlusion Data Augmentation for Human Pose Estimation with Non-extra Annotations

Human pose estimation has made significant progress with deep learning techniques, while the estimation of occlusion keypoints is still an unsolved problem. One important reason comes from the insufficiency of the existing benchmark datasets, such as imbalanced body keypoints annotation and lack of occluded training samples. To address this problem, we propose Nearby-person Occlusion Data Augmentation (NODA), a method that provides synthetic nearby-person occlusion images by only utilizing existing annotations. First, we generate rough mask of human bodies with keypoints annotation to build a foreground human body pool. Then, one foreground human body crop is randomly sampled and properly placed over the training human body to synthesis nearby-person occlusion training images. The proposed data augmentation method is easy to implement and deploy to any other methods. Extensive experimental results on MPII benchmark demonstrate the effectiveness of our method with Simple and HRNet as the backbone models. Especially on easily-confusable joints, our method makes significant improvement.
Feature Extraction Suitable for Double JPEG Compression Analysis Based on Statistical Bias Observation of DCT Coefficients

Photographs taken by smartphones and camera devices are generally compressed using JPEG by default when they are saved. If such an image is edited, it is decompressed and processed, and then recompressed through JPEG. Therefore, an edited image must be compressed by JPEG more than once. Using this characteristic, a forensic technique has been studied to detect image tampering by detecting distortions caused by double compression. In our previous study, to analyze the JPEG compression history using a convolutional neural network and (CNN), we observed a histogram calculated from the low-frequency components in $8\times 8$ sized blocks of images having a pixel resolution of $512\times 512$. However, there have been no detailed considerations regarding the range of observed histograms or the selection of DCT coefficients used to extract the features from a given image. In this study, we first examine the range of his-tograms to measure the usefulness of the classification of double JPEG-compressed images, and then examine the classification accuracy by increasing the number of DCT coefficients observed in the low-to mid-frequency components. Our experiment results indicate that [−40,40] is an appropriate range for observing a histogram, and the selection of DCT coefficients strongly depends on the image size because of the difference in the amount of useful statistical information available.
Control Algorithm of Five-Level Asymmetric Stacked Converter Based on Xilinx System Generator

In this work, the development of the control algorithm for multilevel converter using Xilinx system generator has been made. A reduced component count five level converter is proposed and configured using three level flying capacitor converter stacked with a three level diode clamped converter. This converter topology is known as Asymmetric Stacked Multilevel Converter. The proposed converter possesses nine effective states including redundant states to achieve the five levels output voltage. The Phase Disposition Pulse Width Modulation with active voltage balancing algorithm is implemented. This control algorithm takes advantage of these redundant states for generating the output voltage as well as to ensure that the capacitor voltages are balanced. Two platforms are employed for design and simulate this system, the first one was the MATLAB/SIMULINK. Xilinx System Generator is used as a second platform for implementing the control algorithm as a digital module. The simulation results showed that the required functionality of the converter was verified in both platforms.
Nonlinear Differential Coding in DML-based IM/DD Systems Combined with VNLE in High-baudrate Short-reach Optical Transmission

Intensity modulation and direct detection (IM/DD) schemes with pulse amplitude modulation (PAM) is utilized to economically realize short reach transmission systems, such as data center (DC) applications. In the next-generation Ethernet, the transmission rates are expected to reach over 800 Gb/s, a higher baudrate is desirable due to the current demand on DC data traffic. Digital signal processing (DSP) technology is used to increase tolerance against inter-symbol interference (ISI) caused by narrow band components to achieve high speed data rates. The signal-signal beating terms produced by the square-law detection at the receiver side contribute most significantly to the system performance, which are the 2nd-order nonlinear distortions. Volterra nonlinear equalizers (VNLEs) have been proposed for the nonlinear impairments compensation.In this paper, a nonlinear differential coding based spectral-shaping technology and quadratic VNLE are used in directly modulated laser (DML)-based IM/DD systems. The tolerance to bandwidth limitations (BWL) can be increased with nonlinear differential coding PAM (NLDCP), the signal spectrum can also be narrowed. We verified this method in a 10 km system, and the results show that in DML-based IM/DD systems, NLDCP in combination with VNLE used can provide more than 3.8 dB and 1.7 dB BER gain when baud rate is 90 G at O-band and C-band, respectively. The optimal extinction ratio (ER) of the systems with NLDCP is about 0.3 dB and 0.1 dB lager than that in systems without NLDCP at O-band and C-band, respectively. We also investigate the performance dependency on the number of taps in the nonlinear equalizers.
An Overloaded MU-MIMO Signal Detection Method Using Piecewise Continuous Nonconvex Sparse Regularizer

In this paper, we consider the signal detection problem of overloaded massive multi-user multi-input multi-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) and single carrier block transmission with cyclic prefix (SC-CP) systems. For the systems, we employ iterative weighted sum of complex sparse regularizers with group sparsity (IWSCSR-GS) optimization, which is a complex discrete-valued vector reconstruction method that uses discreteness of symbols to estimate unknown vectors, and propose a signal reconstruction method using piecewise continuous nonconvex sparse regularizers, such as smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP), in the optimization problem. Computer simulation results demonstrate that the proposed signal reconstruction method with MCP achieves better symbol error rate (SER) performance than that of not only IWSCSR-GS with l1 norm but also that with lp norm (p=0,1/2,2/3) or l1-l2 difference, which are nonconvex sparse regularizers, and the proposed signal reconstruction method with SCAD achieves the best performance among the methods for large systems with high signal-to-noise ratio (SNR) region.
A multi-source localization method based on clustering and outlier removal

Multiple sound source localization is a hot topic of concern in recent years. In this paper, a multi-source localization method based on weight clustering and outlier removal is proposed to deal with the multiple source localization in the environment with high reverberation time. In this kind of environments, there are always some T-F points consisting of components from multiple sources mixed in the detected spares components. These T-F points, which are called outliers, usually carry the wrong information of localization and could lead to the decline of localization accuracy. To solve this problem, the Point Offset Residual Weight (PORW) and Source Offset Residual Weight (SORW) are introduced to measure the contribution of each T-F point to the localization. The binary clustering is proposed to distinguish and remove the outliers. After that, a statistical histogram of DOA estimation is drawn using the composite weight to weaken the effect of components that interfere with the localization. Finally, the multi-source localization is conducted through peak searching. The objective evaluation of the proposed method is conducted in various simulated environments. The results show that the proposed method achieves a better performance compared with the reference methods in sources localization.
Evaluation on the Readiness Ratio and Mission Reliability of Intelligent Ships Based on the Integrated PHM System Design

Prognostic and health management (PHM) system is adopted in intelligent ships (ISs) to provide predictive maintenance decision support. However, most existing works only focus on monitoring one device with single signal. Furthermore, after PHM-based ISs are designed, quantitative evaluation method for verifying the performance improvement of ISs is limited. Considering these two problems, an integrated framework of multi-device and multi-signal PHM design based on three levels is developed for ISs. And a performance evaluation model in terms of readiness ratio and mission reliability is proposed based on the decision tree. Finally, a real case of a special purpose ship (SPS) is presented to illustrate the proposed evaluation method and demonstrate the effectiveness of the integrated PHM design by comparing to the conventional SPS.
Smart Navigation Detection using Deep-learning for Visually Impaired Person

In order to minimize the problem of unknown paths movements for a VI person, we propose an assistive navigational system to help VI person to navigate in an known or unknown environment. A modified approach is developed in which a visual impaired person or blind person is assisted through a live feed. The model comprises of YOLO(You Only Look Once) based algorithm and a stick to detect objects through IOU (Intersection of Union). The collected pictures of path are annotated and then the model is developed by training of 300 images each of around 25 classes using Yolo v3 network. The path detection is executed through a Novel tracker system that utilizes an offline trained neural network. The accuracy of the proposed model with wearable mask founds to be 81% while with the stick it is found to be 96.14 %.
Research on Recommendation Algorithm Based on E-commerce User Behavior Sequence

With the rapid rise and development of the Internet, e-commerce has become one of the most promising and fastest-growing industries. we propose an architecture of a personalized recommendation system based on offline mining, real-time mining, and deep learning technology. First, through the Flume + Kafka + Spark Streaming system, user behavior data is collected and stored in the business database after data preprocessing First, through the Flume + Kafka + Spark Streaming system, user behavior data is collected and stored in the business database after data preprocessing, which is the work preparing for the next step of data mining. Data mining includes offline mining and real-time mining. Offline mining mainly runs MapReduce on the Hadoop platform, there are also some calculations on the Spark platform, and the calculation results are stored in the business database; The implementation of real-time mining mainly is based on the message subscription of the Kafka cluster, and get real-time consumption statistics through Spark clusters; Deeplearning4j, a deep learning framework, runs on a multi-GPU Spark distributed cluster, which can be optimized the algorithm model online and generate recommendation results to push to the real-time business database and give feedback to the users. ; It can also extract features from these data for training automatically, which is conducive to improving the quality and efficiency of users' shopping decisions, and improve the platform's cross-selling capabilities, shorten the user's shopping path, increase traffic conversion rates.
Online Distributed Fault Detection of Sensor Measurements

In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.
Fast RCS Data Generation Based on InfoGAN

Radar cross section (RCS) is one of the electromagnetic scattering characteristic of targets, and it’s essential in modern military confrontation. This paper introduces a network model based on Information Maximizing GAN (InfoGAN), which is aimed to achieve fast generation of the target’s RCS data in a certain range of parameters. The InfoGAN is trained by RCS data obtained by high frequency asymptotic simulation algorithms. The training process is offline. Then the RCS data of targets with different parameter values is generated by the feed forward network in the online process. The statistical characteristics such as mean value and variance of the generated RCS data are close to the simulation data, which can prove the accuracy and efficiency of such method.
Position and Yaw Angle Control for a Four Rotor Mini Helicopter Based on a Geometric Approach

In this paper, we present a position and yaw angle controller for a four-rotor mini helicopter, which is designed via the backstepping methodology. In order to avoid a singularity problem caused by using of Euler angles, only quaternion is employed to represent the attitude of the airframe. Although the use of quaternion makes it difficult to obtain a desired attitude in a step of the back-stepping procedure, a geometrical approach gives an effective solution for the problem. It is shown in some numerical simulations that the proposed controller can achieve global tracking to demanded position and yaw angle simultaneously.
Continuous biometric authentication for smartphones considering usage environments

With the rapid spread of smartphones, there is a growing need for them to have a user authentication function. However, conventional user authentication methods using PINs, passwords, pattern locks, etc. have a problem in that user authentication is not performed continuously after the first authentication success; therefore, there is a risk that the authenticated smartphone might be used improperly by unauthorized individuals. Given the above background, we propose a new continuous authentication method for smartphones that uses biometric information considering the variation in usage environments and evaluated its effectiveness.