Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Design of a Planar Crossover for Dual-Frequency Applications

This work demonstrates the design of a planar microstrip dual-frequency crossover. A two section branch-line structure with a symmetrical H-shaped inner transmission line is used to realize the dual-frequency operation in crossover to function at two arbitrarily chosen frequencies. Even-odd mode analysis is employed to derive the closed form design equations. The proposed microstrip crossover is found to be realizable for a large range of frequency ratios from 2.08 to 4.1, which covers most of the practical dual-band applications. The presented theoretical analysis is supported by fabricating a dual-band crossover for WLAN (IEEE 802.11/n) application operating at 2.4 GHz and 5.0 GHz. The measured results indicate more than 20 dB return loss and less than 1 dB insertion loss at the design frequencies. The two channels are well isolated from each other by more than 30 dB. The measured results are found to be well matching with the simulated results.
Ensemble Learning Based on Convolutional Kernel Networks Features for Kinship Verification

Kinship verification based on facial images is one of the popular research topic in the field of face recognition. It's still a challenging problem due to many inevitable factors, such as varying illumination, poses, and expressions. And traditional handcrafted features are usually not robust enough. For the above reasons, in this paper, we extract kernel features by Convolutional Kernel Networks (CKN), which are invariant to particular transformations. After extracting the CKN features, we use feature bagging to classify. It's an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set. Specifically, the CKN features are randomly sampled to train an SVM classifier each time, and then multiple SVM classifiers are combined by majority voting to make prediction. In addition, we collect a large kinship face dataset named LarG-KinFace from Internet search under uncontrolled conditions. The proposed method is evaluated on three datasets KinFaceW-I, KinFaceW-II, and LarG-KinFace. Experimental results demonstrate the efficacy of the proposed method.
LipPass: Lip Reading-based User Authentication on Smartphones Leveraging Acoustic Signals

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.
VCF: Velocity Correlation Filter, Towards Space-Borne Satellite Video Tracking

Tracking a moving target of interests from a space-borne satellite video is really a difficulty, since the target usually occupies only a few pixels in each frame of satellite video. Even it is a long train. Most state-of-the-art tracking algorithms mainly rely on luminance or color features, failing to handle this video tracking problem due to the extremely inadequate quality of targets features. To overcome this difficulty, we propose a velocity correlation filter (VCF), employing velocity feature and inertia mechanism to construct a kernel correlation filter for satellite video targets tracking. The velocity feature has a high discriminative ability to detect moving targets in satellite videos and the inertia mechanism can prevent model drift adaptively. Experimental results on three real satellite video datasets show our proposed approach outperforms state-of-the-art tracking methods with a more than 100 frames per second.
Offloading Dependent Tasks with Communication Delay and Deadline Constraint

We study the scheduling decision for an application consisting of dependent tasks, in a generic cloud computing system comprising a network of heterogeneous local processors and a remote cloud server. We formulate an optimization problem to find the offloading decision that minimizes the overall application execution cost, subject to an application completion deadline. Since this problem is NP-hard, we propose a heuristic algorithm termed Individual Time Allocation with Greedy Scheduling (ITAGS) to obtain an efficient solution. ITAGS first uses a binary-relaxed version of the original problem to allocate a completion deadline to each individual task, and then greedily optimizes the scheduling of each task subject to its time allowance. Through trace-based simulation using real applications, as well as various randomly generated task trees, we study the performance of ITAGS, highlighting the effect of the application deadline, communication delay, number of processors, and number of tasks. We further demonstrate the substantial performance advantage of ITAGS over existing alternatives.
Real-Time Identification of Impaired Gait Phases Using a Single Foot-Mounted Inertial Sensor: Review and Feasibility Study

Contextualization: Identification of gait events is a key step towards enhancing control of robotic devices for rehabilitation and/or assistance of lower limb function. -Gap: Several approaches and techniques that can properly identify gait events and phases have been developed and successfully tested in healthy subjects, but current algorithms had limited success for impaired gait. -Purpose: Here we studied the feasibility of real-time identification of gait phases for impaired subjects using a single inertial sensor on the paretic foot. - Methodology: We carried out a pilot experiment evaluating seven algorithms proposed in the literature for detection of two main events (heel-strike [HS] and toe-off [TO]) for normal and hemiparetic gait. -Results: We obtained a high performance for healthy gait indicating their suitability for real-time implementation for that population. However, we obtained a lower accuracy for all the algorithms during hemiparetic-like gait. -Conclusions: We performed a comprehensive review of the literature and evaluated the current algorithms in gait segmentation using a single inertial sensor on foot or shank for both healthy and paretic gait. Existing algorithms worked as expected in healthy but not paretic gait.
Efficacy of surgery and prophylactic cranial irradiation in stage II and III small cell lung cancer

Background: The role of surgery for stage II and III small cell lung cancer (SCLC) remains controversial. The use of prophylactic cranial irradiation (PCI) for resected SCLC was limited. This study aimed to assess the efficacy of surgery in patients with stage II and III SCLC, and evaluate the value of PCI on resected disease. Methods: A total of 269 consecutive patients with stage II-IIIA SCLC were retrospectively reviewed from January 2010 to December 2015. Of these, 116 patients received surgical resection, and 153 underwent non-surgical treatment. Resected patients were matched 1:1 with non-surgical patients (n=70 in each group). Results: The median follow-up was 30 months. The 1-, 3- and 5-year overall survival (OS) for matched patients were 80.0%, 44.3% and 31.7% in surgical group, and 80.0%, 24.3% and 20.0% in non-surgical group (P=0.009), respectively. Multivariate analysis in matched patients showed that surgery (HR=0.603, 95%CI: 0.404-0.900) and PCI (HR=0.637, 95%CI: 0.427-0.950) were independent prognostic factors for OS. In subgroup analysis, OS benefit related to surgery was marginal for stage II (P=0.09) and IIIA patients (P=0.061), but was significant for selected stage IIIA patients who received adjuvant chemo-radiation and PCI (P=0.01). PCI was associated with improved OS for non-surgical patients (P=0.036), and stage IIIA of surgical patients (P=0.047). Conclusions: These findings suggest a potential OS benefit of surgery in stages II and IIIA patients, particularly in selected stage IIIA patients who received adjuvant chemo-radiation and PCI. The use of PCI for surgical patients with stage IIIA was associated with improved OS.
SAR Image Matching Area Selection Based on Actual Flight Real-Time Image

Matching suitability analysis is a key issue of INS/SAR integrated navigation mode. The existing suitability area selection methods use the simulated real-time image to calculate the matching probability of the scene area and further label it “suitability” or “unsuitability”. If the imaging mode of the simulated image is the same as that of the real image, the suitability area selection model based on the simulated real-time image works well. Otherwise, the model is impractical. In order to address this issue, a novel method is proposed in this paper. The sample dataset is built on the actual flight real-time images, and a hybrid feature selection method based on D-Score and SVM is used to select the suitability features and build the suitability area selection model simultaneously. Experimental results show that the consistency between the prediction results of the model and the ones experts label reaches 81.92%.
Neighbor Consistency Baced Unsupervised Manifold Alignment for Classification of Remote Sensing Image

We perform unsupervised domain adaptation for classification of remote sensing images by exploiting unsupervised manifold alignment approach. Manifold alignment method utilized corresponding points between domains to align data manifolds of source and target domains, where the corresponding points can be constructed by labeled information. Supposing labeled samples are not available in target domain, we proposed neighbor consistency (NC) constraint to select some target points that have reliable predictions. These points and labeled source data are then used to construct corresponding relationships, resulting in unsupervised manifold alignment. The neighbor consistency based unsupervised manifold alignment is denoted as NCUMA in this paper. Both multispectral and hyperspectral remote sensing data have been used to demonstrate the effectiveness of the NCUMA approach.
Auditory-Inspired End-to-End Speech Emotion Recognition Using 3D Convolutional Recurrent Neural Networks Based on Spectral-Temporal Representation

The human auditory system has far superior emotion recognition abilities compared with recent speech emotion recognition systems, so research has focused on designing emotion recognition systems by mimicking the human auditory system. Psychoacoustic and physiological studies indicate that the human auditory system decomposes speech signals into acoustic and modulation frequency components, and further extracts temporal modulation cues. Speech emotional states are perceived from temporal modulation cues using the spectral and temporal receptive field of the neuron. This paper proposes an emotion recognition system in an end-to-end manner using three-dimensional convolutional recurrent neural networks (3D-CRNNs) based on temporal modulation cues. Temporal modulation cues contain four-dimensional spectral-temporal (ST) integration representations directly as the input of 3D-CRNNs. The convolutional layer is used to extract high-level multiscale ST representations, and the recurrent layer is used to extract long-term dependency for emotion recognition. The proposed method was verified on the IEMOCAP database. The results show that our proposed method can exceed the recognition accuracy compared to that of the state-of-the-art systems.
Millimeter-Wave Full-Duplex MIMO Systems with Hybrid Beamforming

In this paper, we investigate a millimeter-wave full-duplex (FD) multiple-input multiple-output system, where an FD base station is capable of concurrently receiving data from a half-duplex (HD) uplink user and transmitting data to an HD downlink user via hybrid beamforming. To effectively mitigate the self-interference and co-channel interference, the sum rate of the uplink and downlink transmissions is maximized by jointly designing hybrid beamforming at the base station and digital beamforming at the two users. The design problem is non-convex due to the multiplication among beamforming matrices, and it is converted to an equivalent weighted sum minimum mean square error (WMMSE) problem for mathematical tractability. An iterative WMMSE hybrid beamforming algorithm is first proposed based on alternative optimization. By adding semi-unitary structures to digital beamforming, a low-complexity semi-unitary hybrid beamforming algorithm is developed without requiring iterations. Computer simulations are used to demonstrate the effectiveness of the proposed systems, as compared with conventional HD systems.
Weighted Graph Embedding-Based Metric Learning for Kinship Verification

Given a group photograph, it is interesting and useful to judge whether the characters in it share specific kinship relation, such as father–daughter, father–son, mother–daughter, or mother–son. Recently, facial image-based kinship verification has attracted wide attention in computer vision. Some metric learning algorithms have been developed for improving kinship verification. However, most of the existing algorithms ignore fusing multiple feature representations and utilizing kernel techniques. In this paper, we develop a novel weighted graph embedding-based metric learning (WGEML) framework for kinship verification. Inspired by the fact that family members usually show high similarity in facial features like eyes, noses, and mouths, despite their diversity, we jointly learn multiple metrics by constructing an intrinsic graph and two penalty graphs to characterize the intraclass compactness and interclass separability for each feature representation, respectively, so that both the consistency and complementarity among multiple features can be fully exploited. Meanwhile, combination weights are determined through a weighted graph embedding framework. Furthermore, we present a kernelized version of WGEML to tackle nonlinear problems. Experimental results demonstrate both the effectiveness and efficiency of our proposed methods.
Integrating Entropy Skeleton Motion Maps and Convolutional Neural Networks for Human Action Recognition

This paper presents an effective method to represent the information of skeleton sequences as images, referred to skeleton motion maps (SMM) and employ convolutional neural networks to recognize the human actions. The proposed approach employs Entropy SMM which captures the temporal evolution of action leading to more effective and discriminative representation. In order to verify the effectiveness of the proposed method, several experiments were conducted on UTD Multimodal Human Action Dataset (UTD-MHAD), Kinect Action Recognition Dataset (KARD), and Multimodal Action Database (MAD) datasets. The Experimental results show the superiority of the proposed method over the existing work.
An Optimal Checkpointing Model with Online OCI Adjustment for Stream Processing Applications

Checkpoint-based fault tolerant method has been widely used to enhance the reliability of Distributed Stream Processing Engines (DSPEs), but a checkpointing process usually introduces considerable overhead. It is a critical issue to choose the Optimal Checkpoint Interval (OCI) that maximizes the processing efficiency. Traditional OCI models consider the recovery time only related to the execution time from the last checkpoint to the moment of the failure. They are not suitable for stream processing jobs because the recovery time is related to the reprocessing workload, which depends on the realtime input data before a failure. A new model is needed to choose the OCI for stream processing applications. Moreover, the input data rate of an stream processing job fluctuates over time. The OCI of an application should also be adjusted dynamically according to the input workload. To solve these problems, we present a novel DSPS Optimal Checkpoint Interval (DOCI) model in this paper. We prove that it maximizes the processing efficiency for a given time period. We propose an approach to dynamically adjust the OCI for an application to accommodate the realtime workload fluctuations. We conduct simulation experiments to verify the effectiveness of DOCI model and the efficiency of the online OCI adjustment algorithm. Experimental results with a real-world dataset show DOCI achieves an improvement on system efficiency by up to 40%, comparing with existing fault-tolerant approaches.
HyperOXN: A Novel Data Center Topology Driven by Machine Learning

Driven by emerging applications such as machine learning, cloud computing, and big data, modern data center network architecture has been evolving to meet the challenging requirements, like scalability, agility, energy efficiency, and high performance. In the meantime, artificial intelligent applications are expediting the convergence of high-performance computing and data centers. To address the challenges noted above, we investigate communication patterns for emerging applications and find that the dynamic and diverse *-cast traffic play a significant impact on the performance of new applications. Inspired by Hypermeshes topology, this paper presents HyperOXN, a novel cost-efficient topology for exascale DCNs. HyperOXN takes advantage of passive optical wavelength division multiplexing technologies. We show that HyperOXN outperforms other topologies in latency, cost, and scalability.
IT R&D Outcomes and Technology Knowledge Flows in Korea

We measured the R&D outcomes of Korea's firms by analyzing the change and the pattern of technology knowledge flows and compared IT R&D outcomes with non-IT R&D outcomes. Using the registered patent data of 2008 and 2009 and their citing patent data till July 2012, we computed technology cycle time (TCT) and various centrality indexes by patent citation analyses (PCA) including social network analysis (SNA). Having done these analyses, we additionally conducted a confirmatory statistical test to compare IT R&D with non-IT R&D in terms of their performances. We found that IT R&D in Korea's firms achieves higher levels of technology development speed, technology spillover and industry absorption, when compared to non-IT segments.
Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation

In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization.
No-Reference Image Sharpness Assessment Using Scale and Directional Models

We propose a new method of no-reference (NR) image sharpness assessment. Our method is based on a multiscale decomposition of non-overlapping blocks, and on analyzing the statistics of Local-Mean Magnitude (LMM) maps. With the detected high-activity blocks, a set of inter-scale and inter-direction sharpness ratios are found. These sharpness ratios are closely correlated with the level of blurring, and is capable of measuring image sharpness effectively from a multiscale and a directional view. They are linearly combined using automatic estimated weighting parameters to induce an overall effective sharpness model. To enhance accuracy, a sharpness factor based on the blur effect on DCT edges and a log-energy sharpness model are also incorporated into the method. Experiments on a large number of public images have shown that our method consistently produces predictions that are highly correlated with human perceptual of image sharpness, and outperforms the current state-of-the-art algorithms.
The Analysis of Coverage and Capacity in mmWave VANET

With the growing shortage of the spectrum resource, the mmWave is becoming a focus in wireless communication area due to its abundant spectral resource. It is widely accepted that the frequency band of mmWave is from 30GHz to 300GHz, which can meet the spectrum demand well. The existing researches on the Vehicular Ad Hoc Network (VANET) mainly focus on the spatial distribution of the vehicles but ignore the communication factors such as blockage effect, channel fading and interference. This paper applies the mmWave to the traditional VANET and uses stochastic geometry method to characterize the coverage probability and area spectral efficiency. As is often the case, we assume that the spatial distribution of the vehicles equipped with directional antenna follows Homogeneous Poisson Point Process. Moreover, we also take the blockage, the channel fading and the interference into consideration. Our antenna gain model is different from the traditional ad hoc network due to our special scenario. Finally, the results of the analytical model are validated by the Monte-Carlo simulation.