Miracle-dz/UniFAR-FTUs huggingface dataset:
Millimeter Wave High-Isolated MIMO Antenna with Two Opposite Radiation Directions

A novel wideband millimeter wave MIMO antenna is presented in this paper. The propose MIMO antenna is composed of a millimeter wave directional aperture antenna and a millimeter wave magnet-electric (ME) dipole antenna. The impedance bandwidth of the mm-wave aperture antenna for $\vert\mathbf{S}_{11} \vert < -\mathbf{10 \ dB}$ is 16.9% from 37.8GHz to 44.8GHz, and the bandwidth of the ME dipole antenna is 18.8% from 43. $\mathbf{3GHz}$ to 52.3GHz. The distance between the two antenna is less than $0.7\lambda_0(\lambda_0$ is the wavelength in the free space at the center frequency of 45GHz), but the isolation between the two antennas is all greater than 28dB within the band of 37.8GHz to 52.3GHz. The radiation direction of the two antenna is opposite, this is the main reason for the formation of high isolation.
Joint Antenna Selection and User Scheduling for Massive Multiuser MIMO Systems With Low-Resolution ADCs

The use of low-resolution data converters is regarded as a cost-effective solution to reduce the hardware complexity and power consumption of massive multiuser multiple-input and multiple-output (MIMO) systems. In this study, we focus on the problem of joint antenna selection and user scheduling (JASUS) in massive multiuser MIMO uplink systems, in which the base station (BS) is equipped with low-resolution analog-to-digital converters. We aim to simultaneously obtain the optimal BS antenna and user sets that maximize the system sum rate. However, finding the optimal solution to the JASUS problem requires an exhaustive search of all possible combinations of BS antennas and users, and this search incurs a combinatorial complexity that scales exponentially with the number of BS antennas and users. We address this problem by proposing a novel algorithm developed from the cross-entropy optimization (CEO) framework. The simulations produce promising results for the proposed CEO-based JASUS algorithm, which achieves a higher sum rate and a lower symbol error rate compared with other test algorithms.
Road Trail Classification using Color Images for Autonomous Vehicle Navigation

Natural road trail image classification is a challenging problem due to the complexity of the natural road environment. It is useful in many real-world applications such as autonomous vehicle and robot navigation. In recent years, many researchers have explored the use of data obtained from different sensors in solving this problem. In this paper, we use image data captured from standard digital cameras, to address the road trail classification problem. To this end, we develop a database of road trail images and train an artificial neural network (ANN) classifier on features obtained using the bag-of-words (BoW) image feature extraction approach. We show experimentally that the proposed method is effective in classifying road trails.
Multi-authority Attribute-Based Encryption Access Control Scheme in Wireless Body Area Network

How to effectively protect the security of the shared data is a key problem to be solved urgently in wireless body area network (WBAN). Considering of the limitation on resources of sensor nodes and the diversity of potential data requestor in WBAN, we propose a multi-authority ciphertext-policy attribute-based encryption (CP-ABE) access control scheme in cloud storage environment which is provably secure against chosen plaintext attack (CPA) under the standard model. The scheme adopts the idea of hybrid encryption to reduce the computational overhead of data encryption, that is, use symmetric key to encrypt data to ensure efficiency, and use the CP-ABE mechanism to encrypt symmetric key to ensure security. And use of disjunctive normal form structure instead of AND/OR structure of traditional CP-ABE algorithm, effectively improve the system's decryption efficiency. By analyzing the efficiency of encryption and decryption, that shows the scheme is suitable for WBAN.
Efficient Point Cloud Rasterization for Real Time Volumetric Integration in Mixed Reality Applications

Real-time capable simultaneous localization and mapping (SLAM) approaches applying consumer hardware have been extensively researched in recent years. Their 3D reconstruction typically applies voxel volumes stored in regular grid hierarchies, sparse voxel octrees or voxel hash tables. They represent the model implicitly in the form of a truncated signed distance function (TSDF). Data integration is usually achieved by stepping through the reconstruction hierarchy from top to bottom and checking voxel grids against the new input data or by rasterizing input data to find associated voxels. For hierarchical representations, a major challenge remains the efficient determination of relevant portions of the reconstruction to be modified by new input data. We present a novel approach efficiently rasterizing input point clouds into intermediate volumes by the GPU. Our technique performs a simple preprocessing step on the input data to properly account for the TSDF representation, allowing for an accurate and hole-free reconstruction. We show that our approach is well suited for a fast integration of new input data into the hierarchical 3D reconstruction, allowing for real-time performance while only slightly increasing memory consumption.
A New Coil Structure to Reduce Eddy Current Loss of WPT Systems for Underwater Vehicles

Wireless power transfer systems in seawater inevitably suffer some energy loss as a consequence of eddy current loss. Here, we present a coil structure utilizing two transmitter coils placed symmetrically adjacent to each side of the receiver coil. This coil arrangement, termed a 1 × 1 × 1 structure, yields improved power transfer efficiency. The eddy current loss caused by the transmitter coils in a 1 × 1 × 1 system can be reduced to roughly half of that in a 1 × 1 system, with one transmitter coil and one receiver coil. The experimental results show that the power transfer efficiency from the transmitter to the receiver is improved by nearly 10%. After the introduction of equivalent eddy current loss impedance into the circuit, two arrangements of the 1 × 1 × 1 circuit topology are investigated. They consist of a shared or separately compensated capacitance topology. It is found that the shared-compensated capacitance topology is more robust to the change of mutual inductance than the separately compensated capacitance topology. This kind of structure is very useful in underwater vehicle applications.
Geometric symmetry using rotational scanning method for circular form detection

In this paper, we propose a method to recognize a circular form using a geometric symmetry through a rotational scanning process. Circle recognition is a basic and very important technology in computer vision. It has been continuously studied to improve accuracy and speed as machine vision systems are larger and larger. Circle Hough transform (CHT) is the well-known method for circle detection. However, it requires very huge memory and computing devices to handle data in circle detection. Also, it is difficult to recognize circles in real images when the perspective distortion exists. In this paper, we propose an efficient method to find circles in terms of memory and speed by searching centers and radii of circles at once using a rotational scanning method. Experiments were performed to evaluate the proposed method. The proposed method was compared with the gradient-CHT in Open CV, one of the state-of-the-art methods. Experimental results showed that the proposed method is superior to the existing method.
STARLORD: Sliding Window Temporal Accumulate-Retract Learning for Online Reasoning on Datastreams

Nowadays, data sources, such as IoT devices, financial markets, and online services, continuously generate large amounts of data. Such data is usually generated at high frequencies and is typically described by non-stationary distributions. Querying these data sources brings new challenges for machine learning algorithms, which now need to be considered from the perspective of an evolving stream and not a static dataset. Under such scenarios, where data flows continuously, the challenge is how to transform the vast amount of data into information and knowledge, and how to adapt to data changes (i.e. drifts) and accumulate experience over time to support online decision-making. In this paper, we introduce STARLORD, a novel incremental computation method and system acting on data streams and capable of achieving low-latency (millisecond level) and high-throughput (thousands events/second/core) when learning from data streams. Moreover, the approach is able to adapt to data drifts and accumulate experience over time, and to use such knowledge to improve future learning and prediction performance, with resource usage guarantees. This is proven by our preliminary experiments where we built-in the framework in an open source stream engine (i.e. Apache Flink).
Multiple Intermittent Controllers for IoT Home Automation

The central controller in home automation systems is problematic but eliminating it requires the functionality be redistributed. This paper considers the problem of maintaining up-to-date status information for devices with multiple intermittent controllers such as smart phones. In order to suggest a robust protocol to handle this functionality, key insights from existing protocols, MQTT and CoAP are identified. This work then informs the proposal for four novel protocols which are then implemented and tested for comparison of their packet efficiency. It is demonstrated that the combination of devices registration and packet broadcasting makes for the most efficient protocol and should form the basis for further work going forward.
A Proposal of Blockchain-Based Electronic Voting System

The Estonian electronic voting system which is a leading electronic voting system still suffers from universal verifiability issues and may need improvement of its availability. To solve the problems, in this paper we propose a blockchain-based electronic voting system. A blockchain is a distributed database, where the complete data is shared among all participants in the network. A blockchain system by its nature has several advantages that suit an electronic voting system. Its distributed architecture provides high availability to the system because it does not rely on a centralized server. As all participants have complete data, the protocol allows them to verify each block that is appended to the chain. We try to combine the double envelope encryption technique and blockchain technology for our proposed electronic voting system.
Balanced Multi-window Inference

We present a computationally efficient, nonparametric, Bayesian inference algorithm that learns to model and track the state-based behavior of objects solely on the basis of sequential observations. The number of states and the state transition dynamics of the objects may continuously change and evolve over time. Learning occurs across multiple timescales with the steady-state inference error balanced against the ability to rapidly re-learn state-based behavior which may not be stationary or periodic. The performance of the inference algorithm is demonstrated in a tracking application of cognitive radios (CRs) operating in a representative RF environment without any prior information concerning the complex behavior of the CRs.
Microfluidic Device for Analyzing Self-adaption of Cancer Cell During Squeezing in channel

This paper reports an improved method for applying the physical squeezing on cells inside a microfluidic device. It is reported that cancer cells can respond and adapt to extracellular environments in order to cross basement membranes and connective tissues. But, it is not clearly studied that how cancer cells respond and adapt to a confined environment. For this study, we design and develop a one-layer channel to simulate a confined migration environment, and it can apply repetitive and sequential physical squeezing processes to a number of cancer cells inside this microfluidic device. In experiment, every cancer cell suffers a total of 20 physical squeezing processes in channel, and we observed the deformation and adaption using high speed-camera under microscope and the preliminary result indicates that the passing time of cells passing through each row of channel is decreasing.
Phase Modulated MM-Wave Radars in Advanced CMOS Technology

This paper addresses the key elements of MIMO Phase Modulated Continuous Wave radars presenting a $2\times 2$ MIMO radar SoC in 28 nm CMOS technology. The IC, integrated with an antenna module, only requires supply, a crystal oscillator reference and an FPGA with LVDS interface to achieve 7.5 cm range resolution on a 90 by 20 degrees scanning angle.
Markov Decision Process for Emotional Behavior of Socially Assistive Robot

This paper presents a Markov decision process based model for a socially assistive robot. Problem addressed by the model is one to one correspondence of a robot with a human where robot has to convince the human about completing certain tasks. In this regard, emotions of the human and those of the robot are incorporated in the model. Furthermore, emotion transition probabilities and probabilities of robot being able to successfully convince the human are also incorporated. The resulting model however involves large state space. Computational complexity involved in calculation of optimal decision policy from the proposed model is discussed. Consequently, a computational complexity reduction technique is proposed that uses decomposition of the tasks to be performed into sub groups. An online learning framework is also proposed to account for un-modeled parameters in the problem. Behavior of decision making optimal policy obtained from the proposed model has been demonstrated with the help of a simulation based case study.
Estimating the Region of Attraction of Uncertain Systems with Integral Quadratic Constraints

A general framework for Region of Attraction (ROA) analysis is presented. The considered system consists of the feedback interconnection of a plant with polynomial dynamics and a bounded operator. The input/output behavior of the latter is characterized using an Integral Quadratic Constraint (IQC), for which it is assumed an hard factorization holds. This formulation allows to analyze problems involving hard-nonlinearities and uncertainties, adding to the state of practice typically limited to polynomial vector fields. An iterative algorithm based on Sum of Squares optimization is proposed to compute inner estimates of the ROA. The effectiveness of this approach is demonstrated on a numerical example featuring a nonlinear closed-loop system with saturated inputs.
Xiao-Shih: The Educational Intelligent Question Answering Bot on Chinese-Based MOOCs

In this study, the educational intelligent question answering bot named Xiao-Shih has been developed for solving learners' questions as instructors and teaching assistants on MOOCs. Experiments were conducted with Xiao-Shih in a paid course titled "Python for Data Science" on "ShareCourse" which is one of the largest Chinese-based MOOC platform in Taiwan. Over one thousand discussion threads posted in both English and Chinese languages were retrieved to train the bot by natural language processing (NLP) and Random Forest (RF) in machine learning algorithms. This paper presents the implementation details on developing Xiao-Shih. First, we developed an initial version of Xiao-Shih with simply NLP techniques and text similarity approaches. However, Xiao-Shih only obtains 0.413 precision at best with different thresholds of the question similarity. Therefore, features and labels of answering correctness have been collected for the next version of Xiao-Shih. Trained by Random Forest with 70% of the entire dataset, Xiao-Shih obtains 0.833 precision with test dataset. With this educational intelligent question answering bot, learners can solve their problems immediately in seconds rather than wait for humans' response in hours even days. Moreover, Xiao-Shih can also ease instructors' and teaching assistants' burden on answering questions.
Robust Identification of “Sparse Plus Low-rank” Graphical Models: An Optimization Approach

Motivated by graphical models, we consider the “Sparse Plus Low-rank” decomposition of a positive definite concentration matrix- the inverse of the covariance matrix. This is a classical problem for which a rich theory and numerical algorithms have been developed. It appears, however, that the results rapidly degrade when, as it happens in practice, the covariance matrix must be estimated from the observed data and is therefore affected by a certain degree of uncertainty. We discuss this problem and propose an alternative optimization approach that appears to be suitable to deal with robustness issues in the “Sparse Plus Low-rank” decomposition problem. The variational analysis of this optimization problem is carried over and discussed.
An Innovative Hybrid Model for Developing Cross Domain ICT Talent in Digital Economy

Taiwan currently faces a severe talent shortage. According to ManpowerGroup Talent Shortage Survey 2016, 73% of employers in Taiwan experience difficulty in recruiting talents, which is the second highest rate of talent shortage in the world. Human capital is the critical key to well-being and economic growth; therefore, the talent shortage has become an urgent issue for the government to solve. In the age of digital transformation, the ability to use a fast and effective model for developing digital talent is of the utmost importance to Taiwan's future. Hence, this study developed Innovative Hybrid Model for Developing Cross-Domain ICT Talent in Digital Economy to cultivate future talents needed. The program had already produced many of outstanding outcomes and created significantly impacted upon Taiwan's talent development including received the ATD (Association for Talent Development) award of Innovation in Talent Development.
Stochastic Sensitivity Measure-Based Noise Filtering and Oversampling Method for Imbalanced Classification Problems

Class imbalance problems occur in many real-world applications. Oversampling methods are effective to handle class imbalance issues by replicating or generating new minority samples to rebalance the class distribution. However, current methods directly using all minority samples will also use noisy samples to generate new samples which may lead to more severe class overlapping and introduce more noisy samples. In this work, we propose a stochastic sensitivity measure-based noise filtering and oversampling method, i.e. the SSMNFOS, to improve the robustness of oversampling method with respect to noisy samples. Samples yielding high stochastic sensitivities are identified as noises by a neural network ensemble and will not participate in the oversampling method for rebalancing the class distribution. Comprehensive experimental studies are carried out on ten datasets with five different noise levels to analyze the effectiveness of the proposed method. Experimental results show that the SSMNFOS outperforms state-of-the-art methods with 95% statistical significance.
Fuzzy Semantic Agent Based on Ontology Model for Chinese Lyrics Classification

Nowadays, social media is getting more and more popular so that many people choose to absorb the knowledge, share their moods, read news, listen to music, and appreciate the video on the Internet. The popular Chinese songs can be categorized according to their song style, their released decade, their singer, and so on. Currently, the song is always classified as a single category, such as inspiration, love, or family. However, when people listen to a song, they will have a different feeling according to their moods in the moment. This paper adopts the lyrics of the popular Chinese songs on the Internet as the experimental samples. Then, we classify the songs based on the natural language processing, ontology, Word2Vec, and fuzzy inference mechanism. The adopted natural language mechanism contains term comparison and term similarity to compute the different-category weights. Additionally, we also use predefined ontology, knowledge base, and rule base to classify the songs. Moreover, we also adopt the multilayer perceptron neural network with the backpropagation algorithm to train the data under a supervised learning. The learned results are better than the ones of the fuzzy inference mechanism. In the future, this study will enhance ontology, knowledge base, and rule base as well as enlarge the number of experimental samples to improve the performance. Finally, we will combine music appreciation with the robot to make children learn the knowledge more interesting.
Foot Recognition using LBP-kNN for Knee Rehabilitation

The foot recognition can be used in many applications such as gait pattern and knee monitoring exercises; nonetheless, the environment condition and a variety of foot are the main problems of the recognition. In general, the foot recognition system is designed in a closed room or a static background to recognize with bare foot in the same view but it is inconvenience for knee monitoring exercises for a patient. This paper presents a method for the foot recognition using LBP features with kNN classifier. The results are evaluated by accuracy and complexity. These results are also compared with HOG algorithm and SVM classifier. According to the results,the LBP-KNN techniques provides higher accuracy but higher complexity than other methods to recognize the foot from human image.
Two-Stream LSTM for Action Recognition with RGB-D-Based Hand-Crafted Features and Feature Combination

Good action recognition relies on correct interpretation of two critical attributes related to action: the spatial attribute on the detected person's posture, and the temporal attribute on the detected person's body movement. Whereas deep learning has greatly improved image recognition, we have not found a similar progress for action recognition. One of the main reasons is due to the complexity caused by the additional temporal dimension; another, to the fact that there are less annotated training data samples for action recognition than that for image recognition. In this regard, this paper proposes a handcrafted cued LSTM model for human action recognition based on RGB-D data, as a collection of 25 skeleton joints in 3D coordinates, found in NTU-RGB-D, currently the most comprehensive dataset for action recognition. As opposed to the raw data of skeleton joints, handcrafted cues, pre-processed results geared to facilitate focused learning, are proposed as input to the LSTM structure. In particular, pertaining to the spatial cue, the SVIT cue derived by Skeleton View-invariant Transformation is adopted; pertaining to the temporal cue, the Diff cue computed by taking the displacements of all joint across down-sampled raw data is utilized. Based on the train/test protocol, the experiment we conducted on NTU-RGB-D shows that the recognition result based on either of the proposed handcrafted cues is better than that based on the raw data. In addition, by our proposed techniques of feature fusion and/or decision fusion of these two handcrafted cues, the recognition performance is better than that of the state-of-the-art approaches conducting on the same dataset by same train/test protocol.