Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization Error Analysis of Indoor Positioning System Based on UWB Measurements

Ultra wide band (UWB) systems use time information instead of the popular received signal strength indication (RSSI). UWB is known for its high position accuracy in localization. RSSI-based localization is easily affected by signal attenuation and has a poor localization accuracy as compared to the time of arrival (TOA) technique. In this paper, different localization algorithms for the UWB system were analytically reviewed. The performance of the localization algorithms is discussed in terms of root mean square and cumulative distribution function of localization errors. The experiment results demonstrate the effectiveness of different localization algorithms for UWB indoor positioning. The fingerprint estimation algorithm shows better performance compared to linearized least square estimation and weighted centroid estimation algorithms. The experimental results show that the linearized least square algorithm has poor performance for UWB indoor localization.
Improving land-cover classification accuracy with a patch-based convolutional neural network: data augmentation and purposive sampling

The unit of classification in land-cover mapping is generally divided into two main categories: pixel and object. When it comes to medium-resolution images, a pixel has generally been used as a unit of classification because the object-based approach is often not as effective due to its coarse resolution. Recently, however, the patch-based approach for land-cover classification has shown higher accuracy levels than the pixel-based approach by exploiting the informative features from neighboring pixels. In this study, the light convolutional neural network (LCNN) was used as a patch-based classification algorithm, and two methods to further improve the classification accuracy for patch-based algorithms were addressed. First, data augmentation by flipping and rotation was applied to LCNN to check if its classification accuracy can increase. Second, the purposive sampling, which considers the heterogeneity of a map, was applied to LCNN. This study shows that the classification accuracy of LCNN can be further improved by data augmentation and purposive sampling and thus confirms that the patch-based approach has a distinct advantage over the pixel-based approach.
Technology Impact Types for Digital Transformation

Digital transformation is based on direct and indirect effects of the application of digital technologies and techniques on organizational and economic conditions on the one hand and new products and services on the other. Its impact can be distinguished in three dimensions: value creation model, value proposition model and customer interaction model. The paper provides a generic model that helps exploring potential cause-effect relationships between the application of digital technologies and their impact on a company along the three dimensions. Based on 75 case studies, the outcome is threefold: (1) a systematic categorization of digital technologies, (2) a set of 10 detailed impact types of digital transformation along with their subgroups, and (3) a coherent model of technologies, causes and impact types along the three dimensions of digital transformation.
Functional Classification of Web Pages with Deep Learning

Automatic processing of websites is of great importance for applications such as search engine that extract information from web pages. Search engines use meta tag values when classifying pages of websites. Meta tag names can change for different languages. For example, for login page, entries such as login, login page or giris, giris sayfası may change from language to language. When the websites are examined, it can be seen that each of the pages created for the same purpose has similar designs. In this study, a deep learning based model was proposed for functional classification of web pages, regardless of language. Transfer learning was used to reduce the cost during the feature extraction process from recorded web page images. Finally, the results of two different experiments are presented for show the effectiveness of the proposed method in the classification of web pages according to their functions.
Design of Phase and Amplitude Controlled Circuits for Active Phased-Array RF Beamforming Networks

In this study, for an 8 co-channel beamforming structure in S-band, the phase and amplitude adjustment block and, first and final stage true time delay (TTD) blocks are defined as sub-blocks by AWR (Applied Wave Research) simulation program were examined separately. The simulation results of the performance of the phase shifter for the phase and amplitude adjustment block, the performance of the digital attenuator, the phase change as per the attenuating steps, the TTD block gain, true time delay and return losses were interpreted. Input and output return loss values for all TTD steps were obtained at levels better than -10 dB.
Hydrocarbon Gettering Effect for Contaminants from Factory Environment

Carbon and hydrocarbon gettering effects for alkali dopant from factory environment and other heavy metal are investigated. The gettering layers are produced by C3H5, C7H7 and C monomer implantation. Potassium is concentrated to the gettering layer region after annealing. The gettering effect for potassium is increasing with increase the hydrocarbon cluster size. However, carbon gettering effects for magnesium and sodium are not dominant. In addition, Molybdenum and tungsten diffuse only a little, so the carbon gettering effects are small. The gettering effect by hydrocarbon cluster dopant for potassium is effective.
IFAL: Issue First Activate Later Certificates for V2X

This paper presents IFAL, a provably secure and privacy conscious scheme for Vehicle-to-Vehicle and Vehicle-to-Infrastructure (V2X) communication. Issue First Activate Later (IFAL) is a practical and secure improvement to the leading European candidate for V2X (ETSI) and one that also merits over the leading US standard. IFAL incorporates a novel cryptographic mechanism that both avoids the need for certificate revocation and which supports vehicles with limited and intermittent connectivity. We introduce a new construction that is equivalent to symmetric key diversification in the public key setting with short, time-delayed activation. We also present a new formalisation of V2X security and privacy which we apply to IFAL to show that it is a provably secure and privacy conscious V2X scheme. IFAL is ETSI compliant and ready for integration into the standard.
Koi Fish Warehousing Based on Mobile Cloud Computing

We propose an application paradigm of mobile cloud computing with big video data for ornamental fish warehousing (OFWare) system dedicated to koi fishes. In fields of aquaculture and agriculture, live creatures are the main products and are difficult to perform warehouse management. Warehousing of high unit-price ornamental fishes such as koi, stingray, and arowana is even more difficult since the warehousing requires identification, in addition to simple counting and classification, of individuals whose shapes and texture patterns are time-variant as they grow. Therefore, rather than using invasive RFID-based systems, we combine mobile cloud computing and big data analytics techniques including image and video collecting and transmission by handheld mobile devices, unsupervised texture pattern classification in fish tank videos, image retrieval based on support vector machine, and statistical analysis for the warehouse management. The proposed system is scalable based on Hadoop framework and a small unit of a single name-node and data-node is able to identify a fish feature among 500,000 koi fishes in 7 seconds.
Distributed Detection of Sparse Stochastic Signals With Quantized Measurements: The Generalized Gaussian Case

In this paper, we consider distributed detection of sparse stochastic signals with quantized measurements. Assume that both the noise and the dominant elements in sparse signals follow the generalized Gaussian (GG) distribution. Since the communication bandwidth in sensor networks is limited, the local sensors send quantized measurements instead of analog data to the fusion center. First, we propose the locally most powerful test (LMPT) detector based on quantized measurements for distributed detection of sparse signals with the GG distribution. Then, the local quantizers are designed by maximizing the efficacy of the proposed quantized LMPT detector. In particular, we obtain the near-optimal closed-form solutions of 1-bit quantizers at the local sensors using the log-concave approximation of efficacy. Multilevel quantizers at the local sensors are numerically obtained using the particle swarm optimization (PSO) algorithm. The asymptotic relative efficiency (ARE) is derived analytically to measure the performance loss of the proposed LMPT detector caused by quantization of local measurements. Simulation results corroborate our theoretical analysis of the proposed quantized LMPT detector.
Longest Prefix Matching with Pruning

Nowadays, an explosive increase of the size of Forwarding Information Base (FIB) in backbone router makes IP lookup a challenging issue. An effective solution is to use Bloom filter. Because of its false positive, to boost longest prefix matching (LPM), sophisticated Bloom filter-based algorithms were proposed, which makes FIB updating extremely difficult. To address this issue, in this paper, we propose a novel IP lookup algorithm which prunes unneeded prefix probes incurred by false positive and supports FIB updating. Experimental results show that our algorithm can save approximately 7 ~ 24% on-chip memory space than the well-known PBF (longest Prefix matching using Bloom Filters) while keeping the same average number of probes of 1.003 per IP lookup. In other words, given a limited on-chip memory budget, our proposed algorithm can handle FIB 7 ~ 24% bigger than PBF. The source code can be found in [1].
Machine Learning-Based Method for Prediction of Virtual Network Function Resource Demands

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are paradigms that help administrators to manage dynamic networks. While SDN allows centralized network control, NFV provides flexible and scalable Virtual Network Functions (VNFs). These paradigms are also enablers for concepts such as Service Function Chaining (SFC) where chains are composed of several VNFs to provide a specific service. However, in order to maximize the benefits from the above-mentioned flexibility, new research questions need to be addressed, e.g., regarding effective management processes for dynamic networks. We proposed a novel learning model based on the flexibility of softwarization and abundant volume of monitoring data in NFV environments to predict VNF resource demands using SFC data. Our model is based on Context and Aspect Embedded Attentive Target Dependent Long Short Term Memory (CAT-LSTM) that consists of Target-Dependent LSTM (TD-LSTM), context embedding, aspect embedding, and attention. We developed this model to obtain high accuracy for the prediction of VNF resources such as the CPU. Our model uses two labeling systems: the qualitative resource state and the quantitative resource usage, both of which are used to evaluate its performance. This assists the administrator in understanding the network conditions, improves prediction performance, and provides practically useful information. Our learning model for predicting VNF resource demands can be utilized to solve essential SFC problems such as auto-scaling and optimal placement, which in turn prevent service interruption and provide high reliability.
Novel Hinge Mechanism for Vacuum Transduction High Performance Capacitive Mems Microphones

In this paper, we present the design, fabrication and first experimental results of a microscale hinge mechanism allowing the mechanical transfer of a force between two atmospheres. When applied to the design of a capacitive MEMS microphone, this mechanism allows the division of the device into two separate parts: a pressure harvesting membrane in air, linked to a capacitive transducer sitting in vacuum. Theoretically, this separation should result in very low acoustical and mechanical noises, thereby increasing overall microphone performance (estimated SNR>75dB(A)), while maintaining a small device footprint (<2mm2).
Design of a Classification Model for a Twitter-Based Streaming Threat Monitor

Receiving timely and relevant security information is crucial to maintaining a high-security level on an IT infrastructure. This information can be extracted from Open Source Intelligence published daily by users, security companies, and hackers. In particular, Twitter has become an information hub for obtaining cutting edge information about many subjects, including cybersecurity. This work discusses the design of a classifier model for a Twitter-based threat monitor for generating a summary of the threat landscape related to a given monitored IT infrastructure. Since the classifier is a crucial element of the processing pipeline that constitutes the threat monitor, its architecture, topology and hyper-parameters must be properly selected to achieve high true positive and true negative classification rates. Our experimental work considered two architectural approaches: a single model for the whole IT infrastructure or an ensemble of models, one for each of several parts of the infrastructure. Within this scope we tested one linear (support vector machine) and one non-linear (multi-layer perceptron) modelling technique. Finally, several model design variables, hyper-parameters and learning parameters were selected by grid-search.
Characteristics of Spoken Discourse Markers and their Application to Speech Synthesis Systems

인간과 인간 사이에서 가장 기본적인 의사소통방법 중 하나인 음성은 기술의 발달과 함께 컴퓨터로 대변되는 기계와 사람 그리고 기계를 매개로 한 사람과 사람의 상호작용에 가장 쉽고 직관적인 인터페이스로 활용되고 있다. 인간과 기계 사이의 음성 인터페이스는 주로 인간의 음성을 인식하는 음성인식 분야와 인간에게 음성정보를 제공하는 음성합성 분야로 나눌 수 있는데 음성합성 분야에서는 합성결과의 자연스러움(naturalness)과 인식성(intelligibility)이 음성합성 시스템의 주요 평가요소로 활용되고 있다. 이러한 자연스러움과 인식성은 합성결과의 정확성(correctness)과는 다르게 주관적인 성격을 지니기 때문에 발화환경이나 발화자의 발화특징 혹은 감정상태 등에 의해 많은 영향을 받게 된다. 이 중 감정상태에 따른 발화문의 음운적 특징에 대한 연구는 국내외에서 활발하게 진행되고 있는데 주로 음의 높낮이, 길이 및 세기의 변화를 기반으로 음운론적인 연구가 이루어지고 있다. 개개인의 주관적인 발화 특징은 발화문의 음운론적인 요소에서뿐만 아니라 발화문장 자체에서도 나타나는데 그 중 문장의 명제 내용에는 직접 관여하지 않으면서 화자의 발화 의도나 심리적 태도를 전달하는 문장의 구성 요소를 담화표지라고 한다[1]. 한국어의 담화표지에 관한 연구는 그 기능과 의미의 측면에서 감탄사나 군말, 간투사, 부사, 불변화사, 화용표지[2] 등으로 다양하게 이루어져 왔는데 그 결과 담화표지의 정의에 대해서는 서로 유사성을 지니고 있지만 그 범위나 인식방법에 대해서는 다양한 의견이 있으며 이러한 문제는 영어권에서도 동일하게 나타나고 있다[3]. 그러나 한국어의 담화표지에 관한 연구는 주로 구어를 문자화한 자료를 기초로 하여왔기 때문에[4] 담화표지의 유형 및 의미 분석이나 문법화 과정에 집중되었고 구어에서 담화표지가 지닌 음성언어의 특징을 파악하기는 어렵다는 문제점을 지니고 있다[2][5][6]. 따라서 본 논문에서는 적절한 형태의 담화표지가 표현된 음성 데이터를 수집하여 담화표지의 음성언어적인 특징을 분석하고, 음성합성 시스템에서 활용하기 위한 담화표지의 음성언어적 표현방식에 대해 논의하고자 한다. 이어지는 2 절에서 담화표지에 대한 선행연구의 흐름 및 특징을 살펴본 뒤 3 절에서 담화표지가 포함된 음성 데이터의 수집 및 음운자질 분석에 대해 논의하고 4 절에서 음성합성 시스템으로의 활용방안을 보인다. 담화표지의 음성언어적 특성과 음성합성 시스템에서의 활용
Constrained User Exposure Matrix Factorization in Recommendation System

Due to the massive items and users limited scope, many existing recommendation systems suffer a more and more serious sparse problem on rating matrix. Most of the recommendation systems employ the content-based method or the collaborative filter model to abbreviate the sparse problem. While in our paper, based on the assumption that users who have rated similar sets of items are likely to have similar preference information, we present a constrained user exposure matrix factorization collaborative filter model and apply it to deal with the matrix sparsity problem, which it makes a better recommendation to users who have very few ratings. In detail, for the sake of capturing the effect of user having rated a particular item, we introduce a constrained matrix to user latent vector space. This effect will have an impact on the prior mean of user latent feature vector. After that, using the user feature vector and item feature vector to estimate the values of unrated items. The experimental results show that the proposed model gets higher prediction precision than the state-of-the-art models in providing recommendation for user having very few ratings.
Smart Transformer Modelling and Hardware in-the-loop Validation

The Smart Transformer (ST) has been proposed to replace the traditional transformer in the smart grid and provide active control to improve the power quality and support the grid stability. The functional ST controls and efficient topologies have been well researched previously. However, most of the works have been focused on the power electronics device level perspective, while system level studies are lacking. The question of whether the ST with active controls can provide system benefits which offset the costs is still under discussion. Before performing the system level studies, an accurate and efficient ST model suitable for such system level studies has to be developed. This paper presents the ST modelling and its order reduction. A comparison with hardware in-the-loop results verifies the accuracy of the proposed models which consist of a full scaled differential-algebraic equation model and simplified algebraic equation model.
Optimization of PID Controller Gain Using Evolutionary Algorithm and Swarm Intelligence

Design of the Proportional-Integral-Derivative (PID) controller for an industrial process represents a challenge due to process complexity and non-linearity. Traditional methods such as Ziegler-Nichols (ZN) for PID controller tuning do not provide an optimal gain; thus, might leave the system with potential instability condition and cause significant losses and damages to the system. This paper investigates the merits of evolutionary and swarm-based optimization algorithms in fine-tuning the parameters of a PID controller. Here, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithm were utilized to optimize the PID controller for a DC motor system. Various fitness functions were provided for the presented algorithms to compute the performance of the controller. A new fitness function was proposed to achieve an outstanding control response for the DC motor system. Results demonstrate the eﬃcacy of the proposed methods in improving closed loop system response.
Effective Self-Interference Cancellation for IBFD (In-Band Full Duplex) Communication System

As the technology has been evolved from 4G to 5G, data traffic growth has been explosively increasing due to the emergence of various applications. In order to satisfy the tremendous required traffic, it is necessary to improve the frequency bandwidth or the signal-to-noise ratio (SNR) in accordance with Shannon's law. However, since there is a limit in the frequency resource, it has become very important to improve the spectral efficiency. In-Band Full Duplex (IBFD) systems can improve spectral efficiency up to 2 times. In this IBFD system, the Self-Interference (SI) occurs inherently, so the desired received signal cannot be demodulated. Self-Interference Cancellation (SIC) must be required to solve this problem. In this paper, a Balanced Feed Network (BFN) based system is designed and evaluated to improve SIC performance at the target frequency 2.45GHz. Simulation results show that the SIC performance is improved by 3 dB compared to the existing system. However, it is also confirmed that the transmission power is decreased by 3 dB.
Minimizing Network Resources Consumed for Link Latency Measurements in SDNs

Accurate link latency information is required for solving many traffic engineering problems, such as congestion avoidance routing and load balancing. Many link latency measurement schemes have been proposed for Software-Defined Networks (SDNs), and many of these require the SDN controllers to inject and remove entries into and from flow tables dynamically. If done frequently, this process can generate a large amount of traffic overhead and consume a lot of processing and computation resources. In this paper, we present a link latency measurement scheme, where entries for latency measurement are already embedded inside flow tables. The main challenge in designing such a scheme lies in how to select measurement paths that can minimize the number of entries added to the expensive TCAM-based flow tables and the amount of traffic generated for latency measurement. We present solutions that can minimize both in this paper.
Building Change Detection Based on Deep Learning and Belief Function

This paper proposes a new approach for building change detection using multi-temporal satellite stereo data. This approach is composed of three main steps. Firstly the building probability map can be derived by a state-of-the-art deep learning approach. In the second step, a decision fusion based fusion model refines and fuses the building changes from satellite stereo imagery and the digital surface models (DSMs). In the last step, the building probability maps are further fused with the building change indicators to generate an improved change detection result. Experiments on the multi-temporal data acquired over 5 years confirms the effectiveness of the proposed approach.
ManiDec: Manifold Constrained Low-Rank and Sparse Decomposition

Low-rank and sparse decomposition based image alignment has recently become an important research topic in the computer vision community. However, the reconstruction process often suffers from the perturbations caused by variations of the input samples. The reason behind is that the consistency of the learned low-rank and sparse structures for similar input samples is not well addressed in the existing literature. In this paper, a novel framework that embeds the manifold constraint into low-rank and sparse decomposition is proposed. Particularly, the proposed approach attempts to solve the original optimization problem directly and force the optimization process to satisfy the structure preservation requirement. Therefore, this novel manifold constrained low-rank and sparse decomposition (ManiDec) can consistently integrate the manifold constraint during the non-convex optimization process, and it can contribute a better solution which is robust to the variance of the input samples. Numerical comparisons between our proposed ManiDec and some state-of-the-art solvers, on several accessible databases, are presented to demonstrate its efficiency and effectiveness. In fact, to the best of our knowledge, this is the first time to integrate the manifold constraint into a non-convex framework, which has demonstrated the superiority of performance.
Zero-Loss Optical Switch Based on Ionic Liquid Microdroplet Ewod Actuatio

This paper reports the first optical shutter based on electrical actuation of microdroplets featuring zero insertion loss in the open state and broad-band operation. These features are achieved by electrowetting-on-dielectric (EWOD) actuation of ionic liquid microdroplets. Due to their negligible vapor pressure, ionic liquids allow the switch to robustly operate in air, unlike previously proposed systems in which the light had to cross several attenuating and refractive layers. Moreover, this solution enables operation in a much wider wavelength range, e.g. in the infrared spectrum where glass has strong absorption. Additionally, the proposed device requires lower voltage to operate (25 V) and features zero static power consumption.