Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Low-Cost Electric-Contact-Assisted Alignment Method for Die Stacking on an Interposer or a Printed Circuit Board

We propose a new simple and low-cost alignment method utilizing electric contact for die stacking on an interposer or a printed circuit board. Without any expensive aligning/bonding equipment assisted by microscopy, we successfully stacked and bonded two silicon dies on a printed circuit board achieving alignment accuracy of <inline-formula> <tex-math notation="LaTeX">$20~\mu \text{m}$ </tex-math></inline-formula> by utilizing only cheap equipment: manual micropositioners, a stage, a stand, a heat gun, a hot plate, and a power supply. In the proposed method, conductive alignment marks are fabricated on the bonding surfaces, and the electric contact between them are used to characterize misalignment. Therefore, the proposed method does not require any expensive complex microscopy equipment for alignment. In addition, alignment can be quickly and simply examined in situ by detecting electrical conduction during bonding. In experiment, alignment accuracy better than <inline-formula> <tex-math notation="LaTeX">$20~\mu \text{m}$ </tex-math></inline-formula> and 100% bonding yield was measured in testing of 1800 bonds connecting two silicon interposer dies which were stacked on a printed circuit board by the proposed method.
A Multi-Scale Parallel Convolutional Neural Network for Automatic Sleep Apnea Detection Using Single-Channel EEG Signals

Sleep apnea is a kind of widespread and serious sleep disorder that disrupts breathing during the sleep of apnea patients. Clinically, sleep apnea events can be monitored and manually scored from whole-night polysomnography by specialists. However, this task tends to be time-consuming and error-prone. In this paper, we propose an automatic sleep apnea detection scheme using single-channel electroencephalography (EEG)signals. The segmented EEG signals with a length of 30-second are firstly filtered by a band-pass filter to denoise. A short time Fourier transform is then used to generate the time-frequency images from corresponding EEG signals. A multi-scale parallel convolutional neural network with mixed depth of layers and mixed sizes of convolutional filters is designed to automatically learn the features from time-frequency representations and make the classification between sleep apnea periods and other periods. Experimental results show the superior performance of the proposed method in terms of sensitivity, specificity, and accuracy, compared to state-of-the-art works. This method provides the possibility to record and analyze the sleep apnea automatically in sleep monitoring.
Hierarchical Feature Fusion With Text Attention For Multi-scale Text Detection

Scene text detection for practical application remains challenging since it is a typical multi-scale object detection task with complex and varying condition. It is noted that the single shot detector (SSD) has shown predominance in object detection among deep learning-based methods but has limited capability in handling multi-scale text detection (MTD) task. In this paper, we strive to improve the performance of MTD task under SSD framework. Specifically, a novel single-shot word-level text detector is proposed. First, to extract the features keeping text-related information for multi-scale text objects, a hierarchical feature fusion module is designed to capture multi-scale inception features and multi-level features with semantic information. Second, to suppress background disturbance in the feature map, a text attention module is developed to learn the location information of the texts existing in the image, which helps to accurately inferring the words, especially when they are of extremely small size. Experimental results on three public word-level texts datasets demonstrate the effectiveness of our proposed method which achieves F-measure 0.89 and 0.79 on the ICADR2013 and ICDAR2015 respectively, and achieve highest accuracy of 0.87 on the SVT.
Utilizing Tweet Content for the Detection of Sentiment-Based Interaction Communities on Twitter

Community detection is one way of extracting insights from voluminous Twitter data. Through this technique, Twitter users can be grouped into different types of communities such as those who interact a lot, or those who have similar sentiments about certain topics. However, most works do not utilize tweet content and simply use directly available information like Twitter follows. Hence, this work explores the incorporation of hashtags and sentiment analysis (also taking into account conversational context) in the input graph for community detection through various schemes. Evaluation was performed by investigating the modularity score, topic similarity/variety, and sentiment homogeneity of the resulting communities. Results suggest that when compared to a baseline graph based on mentions, a scoring approach is more likely to yield a different set of communities compared to the more popular edge-weighting approach. Insights gleaned from the study show the importance of other evaluation methods (depending on the end-goal) aside from usual quantitative metrics of community network structure, and that community detection in conjunction with topic modeling can be a tool for analyzing Twitter discourse.
A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles

In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics. Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T–S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework. By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed $\mathcal {H}_{\infty }$ level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T–S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.
Interference Management in Underlay In-band D2D-Enhanced Cellular Networks : (Invited Paper)

Recently, it has been standardized by the 3rd Generation Partnership Project (3GPP) [1] that device-to-device (D2D) communications should use uplink resources when coexisting with conventional cellular communications. With uplink resource sharing, both cellular and D2D links cause significant co-channel interference. In this paper, we consider a D2D mode selection criterion based on the maximum received signal strength (MRSS) for each user equipment (UE) to control the D2D-to-cellular interference. Specifically, a UE will operate in a cellular mode, if its received signal strength from the strongest base station (BS) is larger than a threshold β; otherwise, it will operate in a D2D mode. Furthermore, in our study, cellular UEs, D2D transmit UEs and D2D receiver UEs constitute the entire UE set, which is a more practical assumption than dropping more UEs for D2D reception only in existing works. The coverage probability and the area spectral efficiency (ASE) are derived for both the cellular network and the D2D one. Through our theoretical and numerical analyses, we quantify the performance gains brought by D2D communications and provide guidelines for selecting the parameters for network operations
Sequence Labeling of Chinese Text Based on Bidirectional Gru-Cnn-Crf Model

Sequence labeling is the basis for many tasks in natural language processing (NLP). It plays an important role in tasks such as word segmentation, named entity recognition (NER), and part-of-speech (POS)tagging. The current mainstream method for sequence labeling is to combine neural network with conditional random field (CRF). The common model is usually a bidirectional RNN-CRF model, which can solve the problem that the labeling task with traditional method cannot be combined well with the context. This paper proposes a Chinese sequence labeling model based on bidirectional GRU-CNN-CRF, which can pay more attention to local features and context relationships, and has better performance in word segmentation and NER. This paper takes the corpus provided by Chinese Wikipedia as the training data set and preprocesses the text by word embedding. The data are then processed through a three-tier architecture of bidirectional Gated Recurrent Unit (GRU), Convolution Neural Network (CNN)and CRF, and finally complete the task of sequence annotation. Compared with the traditional Chinese word segmentation system, this method is more accurate. And it performs better than bidirectional GRU-CRF model on NER issues.
Automatic Image Segmentation With Superpixels and Image-Level Labels

Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing. We propose an automatic image segmentation algorithm based on superpixels and image-level labels. The proposed algorithm consists of three stages. At the stage of superpixel segmentation, we adaptively generate the initial number of superpixels using the minimum spatial distance and the total number of pixels in the image. At the stage of superpixel merging, we define small superpixels and directly merge the most similar superpixel pairs without considering the adjacency, until the number of superpixels equals the number of groupings contained in image-level labels. Furthermore, we add a stage of reclassification of disconnected regions after superpixel merging to enhance the connectivity of segmented regions. On the widely used Microsoft Research Cambridge data set and Berkeley segmentation data set, we demonstrate that our algorithm can produce high-precision image segmentation results compared with the state-of-the-art algorithms.
Design of JESD204B Multi-Channel Data Acquisition and Playback System Based on SoPC

This paper designs a multi-channel data synchronous acquisition and playback system to realize the function of synchronous data acquisition of ADC chips, Field Programmable Gate Array (FPGA)receiving and processing digital data, and DAC playback. Because the high-speed serial protocol JESD204B can easily realize the channel synchronization between the converters and the FPGA, and the serial interface eliminates the redundant associated clock, the system adopts JESD204B as the data interface. Based on the characteristics of good real-time performance, simple structure, flexibility and efficiency of the System of Programmable Chip (SoPC), the system uses SoPC technology to dynamically configure the chips and Intellectual Property (IP)cores through the Microblaze soft core processor. Finally, this paper completed the construction of the hardware platform for real-time signal acquisition and processing playback, and realized the expected functions of the system.
Multi-Objective Optimization-Based Real-Time Control Strategy for Battery/Ultracapacitor Hybrid Energy Management Systems

Battery and ultracapacitor (UC) have complementary advantages, which meet the requirements of energy storage systems (ESSs) for plug-in electric vehicles (PEVs). In this paper, a novel control strategy is proposed to manage the power distribution between the battery and UC for a hybrid energy management system (HEMS) in PEVs. This control strategy aims at realizing less power loss, longer battery lifecycle, as well as UC’s stable terminal voltage and ability of quick charge/discharge. Based on these three optimization targets, we define three sets of loss functions and formulate a multi-objective optimization (MOO) problem. In particular, two different methods, weighted method and no-preference method, are implemented to transform the MOO problem into a uni-objective convex optimization problem. The final problem is solved using the Karush–Kuhn–Tucker conditions. Simulation is conducted to verify the proposed control strategy, while a battery-only ESS and a HEMS utilizing rule-based control strategy are implemented as a comparative study. A scaled-down laboratory prototype is built to validate the theoretical analysis and simulation results. The results indicate that the proposed control strategy brings the benefits of minimized battery current magnitudes and ripples, enhanced system efficiency, stabilized dc-link voltage, and improved dynamic response. Moreover, this strategy exhibits fast computation speed and requires no pre-information of future load demand. Therefore, it can be easily deployed in real-time.
SIMULATING THE IMPACT OF EXTERNAL DEMAND AND CAPACITY CONSTRAINTS IN AEROSPACE SUPPLY CHAINS

By outsourcing major aircraft systems to Tier 1 suppliers, original equipment manufacturers depend heavily on their supply chain to meet the growing demand for aircrafts. However, capacity constraints upstream of the Tier 1 suppliers increase the risk of schedule disruption. Discrete event simulation is commonly applied to analyze capacity constraints in manufacturing systems while analytical models assess financial investment scenarios for capital equipment. This paper demonstrates a combined simulation and analytical modeling approach to simulate the operational and financial implications of capacity constraints in aerospace supply chains. A three-tier supply chain is modeled with the option of investing to remove a capacity constraint in a sub-tier supplier. The results demonstrate how a supplier’s capacity investment decisions and the increasing demand for aircrafts can affect their cash flow and delivery schedule adherence.
Stepwise Magnetization Control Strategy for DC-Magnetized Memory Machine

A memory machine (MM) equipped with hybrid permanent magnets (PMs), i.e., NdFeB and low coercive force PMs, exhibits acceptable torque capability at low speeds and high efficiency at high speeds. Previous literatures have addressed that the constant power speed range (CPSR) of MMs can be further extended by online PM flux control and the requirement of the flux-weakening (FW) inverter current can be reduced as well. Nevertheless, how to coordinate the d-axis vector FW and PM magnetization control over a whole operating range in a reasonable manner remains unreported. Therefore, this paper proposes and implements a stepwise magnetization control strategy on a dc-magnetized MM based on the operating characteristics under various PM magnetization states and speed ranges. The configuration, principle, and mathematical model of the investigated machine are introduced first. Then, the proposed control strategy is established by dividing the operating envelop into several FW regions and an appropriate FW control scheme is utilized at each stage. It demonstrates that the CPSR can be effectively extended by simply applying the demagnetizing current pulses in several steps. This simplifies the control efforts considerably without resorting to continuous PM flux control and frequent actions of the switching devices. The effectiveness of the proposed control strategy is verified by experimental results.
Improved Hybrid Precoding Scheme for mmWave Large-Scale MIMO Systems

Due to the limited spectrum resources, communication utilizing millimeter wave (mmWave) has become a research hotspot, deemed to be an important technology of 5G, in which plenty of antennas can be deployed. However, in mmWave large-scale MIMO systems, it is not feasible to directly use full digital precoding, bringing high energy consumption and high complexity. To overcome this difficulty, hybrid precoding is proposed. In this paper, we put forward a relative weighted-based hybrid precoding scheme on the basis of an energy-efficient precoding architecture only using switches and inverters called SI-based architecture, aimed at adaptively updating the probability distribution of elements in an analog beamformer in the hybrid precoder by minimizing cross entropy. Hence, our proposed scheme can achieve higher achievable sum rate than the conventional method without relative weighted. At last, we provide some simulation results to validate the superiority of our proposed precoding scheme.
Development of a Novel Transverse Flux Tubular Linear Machine With Parallel and Complementary PM Magnetic Circuit for Precision Industrial Processing

The transverse flux permanent-magnet (PM) linear machine has attracted much attention in high-performance direct-drive applications because of its high torque density and efficiency. However, due to its inherent large open-circuit flux leakage, the prominent cogging force makes it difficult to obtain a high accuracy in position and speed control, which restricts its potential for precision industrial processing applications. To overcome this problem, a novel transverse flux linear machine is proposed in this paper. The key is by creatively combining a consequent-pole mover design and a stator-segment-interlacing configuration in such a way that a parallel and complementary transverse magnetic circuit is realized for the moving PMs, which can effectively minimize the open-circuit leakage flux and cogging effect. In this paper, the machine structure, operation principle, and theoretical modeling are introduced, with its electromagnetic performance evaluated by using the finite-element method. A prototype is also built for the experimental verification, and relevant test results agree well with the finite-element predications.
The Characteristic Extraction Method of Fiber Intrusion Signals Based on Band Cutting and Imaging

This thesis put forward a band cutting and imaging to extract characteristic for optical fiber intrusion signals. Firstly, frequency-time distribution of every dimensional intrusion location is obtained by the frequency-time analysis technique of original collected signals. Then, energy band integral goes along the frequency direction. The 2-D images are further stacked to form layers, leading to 3-D space-time-frequency cubic data. Lastly, the corresponding time and space feature spectrum based on the 3-D cube data is as the feature of the intrusion signals. The actual data experiments show that multi-band cutting and imaging is a very effective method for feature extraction and type identification of fiber-optic intrusion signals.
A New Direct Position Determination Algorithm using Multiple Arrays

This paper proposes a new direct position determination (DPD) algorithm using multiple antenna arrays. In this method, the positions of emitters are estimated directly based on the cross-correlation matrix (CCM), which is obtained from the signals received at different arrays. As compared to conventional approaches based on direction of arrival and time difference of arrival, this method does not require beamforming and direction of arrival estimation. Meanwhile, the number of target can be located is not limited by the number of antenna element at each array. In particular, as compared with the existing DPD algorithm, the number of targets is easier to be estimated and the localization accuracy is much higher in the presence of time synchronization error among arrays. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.
Coolabilities API

Disabilities may co-occur with characteristic enhanced strengths (hereby Coolabilities). The Coolabilities API project is centered around an Open Platform available for any individual/business/institutions to seamlessly involve three important goals: 1. to share existing data and code of relevance to coolabilities 2. To create create/share new data/code 3. To build collective intelligence around coolabilities. The core of Coolabilities API is the Coolability-Disability Correlation Database - "CODIC". This paper illustrates the CODIC, algorithms for job matching and developer's platform implemented around it.
A Class-dependent Background Model for Speech Signal Feature Extraction

Universal Background Model (UBM) has been successfully applied to many speech signal classification tasks, such as speaker recognition and microphone recognition. UBM is used to form Gaussian Supervector (GSV) or i-vector, which is a good feature vector representing a piece of speech signal. In this paper, we propose another background model called Class-dependent Background Model (CBM), which makes use of class labels. UBM is completely a generative model, while CBM can be both generative and discriminative. Under some conditions, CBM can consume less time to be constructed than UBM. We also compare the performance of UBM and CBM as the background model to form GSV and i-vector for doing speaker recognition, microphone recognition, and telephone session recognition. Experimental results show that CBM performs very well and can be even better than UBM in most cases.
Opinion Mining and Visualization of Online Users Reviews: A Case Study in Booking.com

The growth of web applications and portals on hotel booking have led to an enormous amount of consumer generated comments and reviews on various hotels and travel services. In this paper, we present a work on the automatic analysis of user reviews on online hotel reviews with the aim to understand public opinions towards facilities and services they receive. We follow an aspect based approach where initially Latent Dirichlet Allocation is utilized to model topic opinions. The aspects specified indicate the important characteristics of the services and the facilities that users address in their reviews. After that, natural language processing approaches are used to analyze textual reviews, specify the dependencies on a sentence level and assist in understanding users opinions. At the final stage of understanding users opinions, several classifiers are trained under different feature sets extracted from the textual reviews and combined in ensemble schemas and their performance is examined on recognizing the polarity of the users' opinions. The results are quite satisfactory and indicate that features such as sentence dependencies assisted the classifiers in achieving accurate performance and that ensemble schemas perform robustly better than individual classifiers.