Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Packaging Structure Using VCSEL Array and Multi-Core Fiber for Co-Packaged Optics

A novel packaging structure for high-speed and high-density optical transceiver module sufficient for co-packaged optics is proposed. The optical transceiver module consists of a bottom-emitting 16-channel VCSEL array, a back-illuminated 16-channel PD array, a driver array, and TIA array. A multi-core fiber is used for each optical interface of optical transmitter and receiver, and a high-density LGA of 0.3 mm pitch is used for the electrical interface. In order to achieve high-speed and high-density CPO transceiver module, a core-less build-up multi-layer substrate manufactured by modified semi-additive process is used as an interposer. Low loss and low crosstalk characteristics up to several tens of GHz of fine pitch wirings enable to accommodate 25-Gbps× 16-channel optical transceiver in a 7.8 × 10 mm interposer.
Improved Clustering Algorithm Based on Hypercube

The hypercube clustering algorithm based on segmented clustering framework has a good effect on trajectory clustering, but when the trajectory points deviate greatly, the direction of the hypercube obtained by the hypercube clustering algorithm will have a large error, which affects the clustering effect. Therefore, an improved clustering algorithm based on hypercube is proposed. The improved algorithm segmented the trajectories by introducing the motion direction, transformed each trajectory into a hypercube sequence, expanded the checking dimension of common sub-trajectories, and gave the intersecting checking and clustering algorithm of common sub-trajectories. In this paper, three real data sets are used to carry out experiments. The results show that the improved algorithm has better clustering effect, and the average clustering number and average clustering accuracy are improved by 65.81 percent and 15.77 percent respectively.
Max-min fair RIS-aided rate-splitting multiple access for multigroup multicast communications

Rate-splitting multiple access (RSMA) can cope with a wide range of propagation conditions in multigroup multicast communications through rate splitting optimization. To breakthrough the group-rate limited bottleneck, reconfigurable intelligent surface (RIS) technique can be introduced to assist wireless communications through enhancing the channel quality. In RIS-aided RSMA multigroup multicasting, how to provide fair and high-quality multiuser service under power and spectrum constraints is essential. In this paper, we propose a max-min fair RIS-aided rate-splitting multiple access (MMF-RIS-RSMA) scheme for multigroup multicast communications, where the rate fairness is obtained by maximizing the minimum group-rate. In doing so, we jointly optimize the beamformers, the rate splitting vector at the transmitter, as well as the phase shifts at RIS. To solve it, we divide the original optimization problem into two subproblems and alternately optimize the variables. The beamforming and rate splitting optimization subproblem is solved by using the successive convex approximation technique. The phase shift optimization subproblem is solved through the penalty function method to achieve a rank-one locally optimal solution. Simulations demonstrate that the proposed MMF-RIS-RSMA scheme can obtain significant performance gain in terms of the minimum group-rate.
System for Identifying Target Objects to Perform Manipulative Operations by Unmanned Underwater Vehicles

The paper proposes a system for identifying target objects to perform manipulative operations by unmanned underwater vehicles (UUV) equipped with multi-link manipulators (MM). This system processes point clouds received from technical vision systems (TVS) mounted on UUV, and provides high-precision identification of the location and shape of target objects relative to the UUV. Then, the specified trajectories of the MM working tool are translated on the determined surface of the target object, taking into account the possible silting, fouling and deformation of this object. The developed system is implemented in the C++ programming language on the NVIDIA Jetson TX2 on-board computer. The results of recognition of underwater objects using a Zed2 stereo camera placed in a sealed box, calibrated in an aquatic environment using the OpenCV library method, are presented. The experimental data obtained were used to simulate the process of manipulation operation performed by the manipulator of the UUV in the software packages CoppeliaSim and Matlab/Simulink. The research results confirmed the operability and high efficiency of the proposed system.
Facial Emotion Recognition using Deep Learning Approach

Human facial emotion recognition pays a variety of applications in society. The basic idea of Facial Emotion Recognition is to map the different facial emotions to a variety of emotional states. Conventional Facial Emotion Recognition consists of two processes: extracting the features and feature selection. Nowadays, in deep learning algorithms, Convolutional Neural Networks are primarily used in Facial Emotion Recognition because of their hidden feature extraction from the images. Usually, the standard Convolutional Neural Network has simple learning algorithms with finite feature extraction layers for extracting information. The drawback of the earlier approach was that they validated only the frontal view of the photos even though the image was obtained from different angles. This research work uses a deep Convolutional Neural Network along with a DenseNet-169 as a backbone network for recognizing facial emotions. The emotion Recognition dataset was used to recognize the emotions with an accuracy of 96%.
Marine Targets Detection for Scanning Radar Images Based on Radar- YOLONet

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would significantly be reduced. In this paper, the range-azimuth information obtained by scanning radar is converted into PPI image, and a Radar-YOLONet is proposed and used for radar image target detection. The model includes CSPDarknet53, SPP, PANet and Power Non-Maximum Suppression layers. The prediction frame coordinates, target category and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. The verification using the constructed X-band navigation radar PPI image dataset shows that compared with the traditional constant false alarm rate detector and the two-stage Faster-RCNN algorithm, the proposed method has significantly improved detection performance under certain false alarm probability conditions. It is more suitable for various environment and target characteristics.
CUEMPATHY: A Counseling Speech Dataset for Psychotherapy Research

Psychotherapy or counseling is typically conducted through spoken conversation between a therapist and a client. Analyzing the speech characteristics of psychotherapeutic interactions can help understand the factors associated with effective psychotherapy. This paper introduces CUEMPATHY, a large-scale speech dataset collected from actual counseling sessions. The dataset consists of 156 counseling sessions involving 39 therapist-client dyads. The process of speech data collection, subjective ratings (one observer and two client ratings), and transcription are described. An automatic speech and text processing system is developed to locate the time stamps of speaker turns in each session. Examining the relationships among the three subjective ratings suggests that observer and client ratings have no significant correlation, while the client-rated measures are significantly correlated. The intensity similarity between the therapist and the client, measured by the averaged absolute difference of speaker-turn-level intensities, is associated with the psychotherapy outcomes. Recent studies on the acoustic and linguistic characteristics of the CUEMPATHY are introduced.
Centralized Multiple Moving Target Detection Using Multistatic Radar System

This study investigates the problem of the multiple moving target detection using multistatic radar system (MSRS). A detector based on generalized likelihood ratio test (GLRT) is presented with unknown target location and velocity. A lack of information about accurate target location will cause an error in registration. Some spatial resolution cells of no targets gather observation data of targets, which leads to extra false alarm, called “ghost target”. Therefore, “ghost target” is discriminated and the interferential amplitude is estimated by maximum likelihood estimation (MLE). Subsequently, the GLRT detector is modified to reduce the false alarm and detect target. Simulation results show that the proposed algorithm can suppress the interference while accurately detecting multiple moving targets.
Multimodal Learning for Improved NFT Price Prediction

NFTs (Non-fungible tokens) refer to digital assets in the form of art, game items and other collectibles that are encoded in smart contracts on blockchain. Starting from 2021, the NFT market has been growing exponentially. However, the overall structures, evolutions and trends of the market have not been sufficiently explored. In this study, we analyze data of 9,045 NFTs which were on sale at OpenSea in April 2021 and predict their price using both visual and non-visual information by a two-stage machine learning approach and an end-to-end deep learning approach. To examine the effectiveness of multimodality, models trained on unimodal and multimodal data are compared. Besides, the effect of different feature fusion techniques on model performance is analyzed. To identify important predictors, the top ten most influential features are also presented. Results show that among two-stage models, the VGG-RF model trained on multimodal data gives the best performance of R squared 69.75% and RMSE 565.49. Among end-to-end models, the multimodal neural network with self and cross-attention achieves the best performance of R squared 69.25 % and RMSE 569.13. Our results also demonstrate that the visual aspect of NFT have an impact on its price, but this impact is weaker than some non-visual information such as total number of bids in history and the collection the NFT comes from. We expect findings of this study facilitate future researches on NFT trading and pricing strategies, and help investors make more advisable investment decisions.
Smart Computational Resource Distribution System with Automatic Classification Interface for CPS

This paper describes a proposal for a classification interface for computational resources distribution in CPS. The classification interface enables to distribute processing to the different computational resources (CPU and FPGA) automatically depending on the type of the processing. As a result, the total processing time is shortened by 50.9% compared to that of the system without the classification interface.
LoRa Network Based Wearable Tracker- A Preliminary Work

In Malaysia, it is reported that many deaths are linked to heart-related complications. Many of these deaths are preventable if the caretaker can be notified of the warning signs leading to the complication. However, appointing a caretaker to monitor a patient around the clock is virtually impossible. The objective of this project is to create a system to notify the caretakers in real-time of the locations of the patients and warn them of possible risks of complications. The proposed system uses the LoRa Network to communicate without needing communication towers or routers as intermediaries. The development of the prototype using the Iterative Waterfall Model is focused on the feasibility of this technology to be equipped in a small tracker device powered by a lightweight lithium battery and to find a way to bridge the connection between the LoRa network and smartphones since currently, there is yet to be a smartphone that comes equipped with such technology. At the end of the project, it is concluded and verified by the testing done that it is indeed feasible to use LoRa as a means of transferring critical information, as it can transmit information for over 100-meters without the need for any intermediary device to facilitate the communications. However, further testing in urban areas and in different weather conditions is needed if such devices are widely used in real-life applications.
Depression Detection Based on Human Simple Kinematic Skeletal Data

Depression is a serious psychiatric disorder that is prevalent worldwide and is usually characterized by persistently depressed mood, impaired mobility, and delayed thinking and cognitive functions. This experiment uses the Kinect V2 device to record simple kinematic skeletal data of body joints of depressed patients and non-depressed patients, directly extracting the presented spatial features and low-level features from the recorded raw Kinect-3D coordinates. Aiming at the symptoms of delayed thinking and cognitive function and impaired mobility in patients with depression, the features of reaction time are obtained from preprocessing, and this prior knowledge is added to the deep learning model to assist the recognition and classification of the model, thereby improving the classification accuracy. The objective of this project is to develop a deep learning model for detecting depression using preprocessed data.
An Automated and Scalable ML Solution for Mapping Invasive Species: the Case of the Australian Tree Fern in Hawaiian Forests

Biodiversity loss and ecosystem degradation are global challenges demanding creative and scalable solutions. Recent increases in data collection coupled with machine learning have the potential to expand landscape monitoring capabilities. We present a computer vision solution to the problem of identifying invasive species. The Australian Tree Fern (Cyathea cooperi) is a fast growing species that is displacing slower growing native plants across the Hawaiian islands. The Nature Conservancy organization has partnered with Amazon Web Services to develop and test an automated tree fern detection and mapping solution based on imagery collected from fixed wing aircraft. We utilize deep learning to identify tree ferns and map their locations. Distinguishing between invasive and native tree ferns in aerial images is challenging for human experts. We explore techniques such as image embeddings and principal component analysis to assist in the classification. Creating quality training datasets is critical for developing ML solutions. We describe how semi-automated labeling tools can expedite this process. These steps are integrated into an automated cloud native inference pipeline that reduces localization time from weeks to minutes. We further investigate issues encountered when the pipeline is utilized on novel images and a decline in performance relative to the training data is observed. We trace the origin of the problem to a subset of images originating from steep mountain slopes and riverbanks which generate blurring and streaking patterns mistakenly labeled as tree ferns. We propose a preprocessing step based on Haralick texture features which detects and flags images different from the training set. Experimental results show that the proposed method performs well and can potentially enhance the model performance by relabeling and retraining the model iteratively.
Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

This study focuses on the protection of soft-biometric at-tributes related to the demographic information of individ-uals that can be extracted from compact representations of face images, called embeddings. We consider a state-of-the-art technology for soft-biometric privacy enhancement, Incremental Variable Elimination (IVE), and propose Multi-IVE, a new method based on IVE to secure multiple soft-biometric attributes simultaneously. Several aspects of this technology are investigated, proposing different approaches to effectively identify and discard multiple soft-biometric at-tributes contained in face embeddings. In particular, we consider a domain transformation using Principle component Analysis (PCA), and apply IVE in the PCA domain. A complete analysis of the proposed Multi-IVE algorithm is carried out studying the embeddings generated by state-of-the-art face feature extractors, predicting soft-biometric attributes contained within them with multiple machine learning classifiers, and providing a cross-database evaluation. The results obtained show the possibility to simultane-ously secure multiple soft-biometric attributes and support the application of embedding domain transformations be-fore addressing the enhancement of soft-biometric privacy.
Model-free adaptive frequency calibration for voltage-controlled oscillators

The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems. However, the high-precision oscillators could be too expensive for civil applications. In this paper, we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator (VCO) equipped with a time to digital converter (TDC), which can significantly improve the frequency accuracy of the VCO thus calibrated. The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration. One advantage of this method is that the working frequency calibration employs the system history of input/output (I/O) data, instead of establishing an accurate VCO voltage-controlled oscillator model. Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities. Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from ±20 ppm to ±10 ppb, which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.
Direct simulation of a turbulent boundary layer up to R(sub)(theta)= 1410

The turbulent boundary layer on a flat plate, with zero pressure gradient, is simulated numerically at four stations between R sub theta = 225 and R sub theta = 1410. The three-dimensional time-dependent Navier-Stokes equations are solved using a spectra method with up to about 10 to the 7th power grid points. Periodic spanwise and stream-wise conditions are applied, and a multiple-scale procedure is applied to approximate the slow streamwise growth of the boundary layer. The flow is studied, primarily, from a statistical point of view. The solutions are compared with experimental results. The scaling of the mean and turbulent quantities with Reynolds number is examined and compared with accepted laws, and the significant deviations are documented. The turbulence at the highest Reynolds number is studied in detail. The spectra are compared with various theoretical models. Reynolds-stress budget data are provided for turbulence-model testing.
Spaceborne SAR Target Recognition Method Based on Edge Computing

Aiming at the problems of the traditional satellite image processing system, such as the extension of the image pro-cessing task and high pressure of broadband data transmission between the satellite and the ground, this paper adopts the idea of edge computing to extend the SAR image processing site from the ground data processing center to the mobile edge processor on the satellite. Taking full account of the limited resources on the satellite, this paper uses channel pruning and knowledge distillation to compress the convolutional classification network. Under the condition of ensuring little accuracy degradation, the all-convolutional networks (A-convnets) is compressed by 14.66 times and the VGG16-LRN is compressed by 109.43 times. ASIC inference chip for edge computing with lower power consumption is used for deployment. Compared with GPU deployment, ASIC chip deployment can obtain more than twice the inference speed with 0.5 times the load power consumption.
Ship Target Recognition in SAR Image with Implicit Complex Information Enhancement

At present, most scholars treat Synthetic Aperture Radar (SAR) images as optical images when deep learning methods are used to study them, and only the amplitude information of SAR images is applied while the complex information is ignored. However, SAR images are essentially complex images, so this paper proposes a SAR image ship target recognition method with implicit complex information enhancement in the input layer, which implicitly provides a complex information representation of the input data by combining the real, imaginary and amplitude three-channel information of SAR images. Experimental validation on the OpenSARShip dataset and experimental comparison using classical neural network methods can reveal that the method in this paper improves the effect of neural network-based ship target recognition to a certain extent.
Spaces of Constant Curvature

This sixth edition illustrates the high degree of interplay between group theory and geometry. The reader will benefit from the very concise treatments of riemannian and pseudo-riemannian manifolds and their curvatures, of the representation theory of finite groups, and of indications of recent progress in discrete subgroups of Lie groups.
Estimating Parking Availability using Decision Tree Regressor

Recent growth in the number of personal vehicles has aggravated the parking issue. Thus smart parking management system is a must in each metropolitan area. We use Decision Tree Regressor (DT) model to forecast the number of available parking places in the upcoming time. We tune the hyper-parameters max_depth of the DT on three datasets, CNR-A, CNR-B, and KLCC. Experiments show that our proposed model can be used for both small and large datasets. Our model outperforms the Random Forest Regressor (RFR), proposed in recent work, in term of MSE, MAE, RMSE, and R2 metrics.
Real sea surface electromagnetic scattering model with multi-physical fields based on satellite data

The characteristics of ocean electromagnetic scattering plays an important role in satellite remote sensing, radar detection and target recognition. The actual marine environment is complex and changeable. The traditional wind wave spectrum sea surface electromagnetic scattering model only considers wind speed and wind direction, which cannot accurately describe the difference of real sea clutter. In this paper, a robust method is proposed to analyze the characteristics of sea surface electromagnetic scattering. Based on the the ocean environment parameters data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), combined with the multi-physical field sea surface geometric model and the two-scale algorithm, the actual sea surface electromagnetic scattering model is built. The simulation results are consistent with the X-band measurements. This method can be used to build the multi-physical field sea surface electromagnetic characteristics database in different sea areas. In order to quickly realize the marine situation awareness, it has an important role in promoting the future development of electromagnetic space integration of the heaven and earth sea.