Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Estimation in One-Bit Massive MIMO Systems: Angular Versus Unstructured Models

Millimeter wave (mmWave) massive MIMO cellular systems will be characterized by increased bandwidths and the ability to handle a large number of users. To cope with the power consumption problem due to an increased number of receive antennas, the idea of equipping one-bit ADCs at the base station has been proposed. The goal of this paper is to establish performance bounds on the channel estimation of one-bit mmWave massive MIMO receivers for different types of channel models. The Cramér-Rao bound (CRB) is a lower bound on the performance of unbiased estimators and sets a benchmark for the design of channel estimators. We consider both a structured channel model for a single user where the channel is composed of a superposition of multipaths characterized by path delays and directions-of-arrival (DOAs), and an unstructured channel model where the channel is a generic FIR filter. The Fisher information matrix (FIM) for these channel models are derived in closed form. The CRB is also extended to a dictionary-based channel model, where the path delays and DOAs are selected from small perturbations on a discrete grid, and a sparsity constraint applies to the vector of path loss components. We also derive the Bayesian CRB when the array response is imperfectly known and is affected by perturbations in the sensor pattern or position. The CRBs are evaluated numerically and the effects of various system parameters on the CRB are studied. The dependencies between channel parameters and the effect of array perturbations are also investigated.
Extra-curricular Encounters and Needs in the Learning Environment:: The Case of Students with Disabilities at University of Venda

This paper investigated the encounters and needs of students with disability with regard to their extracurricular activities at the University of Venda. A descriptive design was employed in which questionnaires were administered to 67 students with disabilities (aged 19 to 44 years) at the University of Venda. Data was analyzed using descriptive statistics. Among the findings, 27(40.5%) of the participants indicated that there was a need for equipment, support and funds for the promotion of disabled sporting activities and recreation. It is, therefore, recommended that the Department of Higher Education and Training must support all educational institutions to promote extra-curricular activities including indoor and outdoor games that will cater for all categories of disabilities.
A Fully Integrated 384-Element, 16-Tile, $W$ -Band Phased Array With Self-Alignment and Self-Test

This paper describes the design and implementation of a scalable <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band phased-array system, with built-in self-alignment and self-test, based on an RFIC transceiver chipset manufactured in the TowerJazz 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SiGe BiCMOS technology with <inline-formula> <tex-math notation="LaTeX">$f_{T}/f_{\text {MAX}}$ </tex-math></inline-formula> of 240/270 GHz. The RFIC integrates 24 phase-shifter elements (16TX/8RX or 8TX/16RX) as well as direct up- and down-converters, phase-locked loop with prime-ratio frequency multiplier, analog baseband, beam lookup memory, and diagnostic circuits for performance monitoring. Two organic printed circuit board (PCB) interposers with integrated antenna sub-arrays are designed and co-assembled with the RFIC chipsets to produce a scalable phased-array tile. Tiles are phase-aligned to one another through a daisy-chained local oscillator (LO) synchronization signal. Statistical analysis of the effects of LO misalignment between tiles on beam patterns is presented. Sixteen tiles are combined onto a carrier PCB to create a 384-element (256TX/128RX) phased-array system. A maximum saturated effective isotropic radiated power (EIRP) of 60 dBm (1 kW) is measured at boresight for the 256 transmit elements. Wireless links operating at 90.7 GHz using a 16-QAM constellation at a reduced EIRP of 52 dBm produced data rates beyond 10 Gb/s for an equivalent link distance in excess of 250 m.
Toward a Personalized Learning Path through a Services-Oriented Approach

In most existing E-learning systems, activities' content and order are presented in a static manner without taking into consideration the learners characteristics, profiles or competencies. The challenge is to adapt and regulate learning processes according to the learner profile by applying learning models that use new information technologies. There are several adaptation approaches of E-learning environments, such as, adaptive hypermedia system, semantic web, etc. In our proposed system, we adopted a Competency Based Approach to offer each learner an individualized learning path for the acquisition of the competence targeted on the basis of the collaborative filtering. Concerning the technological aspect, the system is implemented as a web services while adhering to a service-oriented architecture. This allows interoperability with heterogeneous learning systems
A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering

With the advantages of convenient access and free parking, urban dockless shared bikes are favored by the public. However, the irregular flow of dockless shared bikes poses a challenge for the research of flow pattern. In this paper, the flow characteristics of dockless shared bikes are expounded through the analysis of the time series location data of ofo and mobike shared bikes in Beijing. Based on the analysis, a model called DestiFlow is proposed to describe the spatio-temporal flow of urban dockless shared bikes based on points of interest (POIs) clustering. The results show that the DestiFlow model can find the aggregation areas of dockless shared bikes and describe the structural characteristics of the flow network. Our model can not only predict the demand for dockless shared bikes, but also help to grasp the mobility characteristics of citizens and improve the urban traffic management system.
An Innovative GIS Method for Evaluating the Visibility of the Road Using the ArcMap-Tools

Road visibility is critical to motorists in making decisions such as stopping, slow downing, turning, entering a traffic stream from a driveway, or merging into traffic. Adequate visibility allows motorists the time they need to avoid vehicle crashes and conflicts and will help keep roadways operating safely and smoothly. Insufficient visibility is a significant factor in roadway crashes and near collisions. This paper utilizes the ArcMap-GIS viewshed tools, and the location analysis techniques to present an evaluation of the existing visibility on portions of the interstate highway I70 in the State of Missouri compared to AASHTO requirements. The evaluation of the existing visibility is conducted on the I70 segments at Boone, Callaway, and Cooper counties. This method is a useful tool for understanding location-based risks of limited visibility on the I70 or similar highways. The GIS techniques used show that the ArcMap-GIS tools can be used effectively in determining the road visibility and locating the hazardous locations without the need for field visits. Many highways were built years before tools like geographic information systems (GIS) and other computer aided designs were available. Therefore, this method can be considered as a relevant aid for assessing geometric consistency of the I70 interstate highway and similar roads, because it identifies different segments of the road layout that offer considerably different sight distances.
A generic space definition framework to support seamless indoor/outdoor navigation systems

Contemporary public buildings are becoming conglomerates of open, semi‐open and closed spaces, with indoor, outdoor and underground sections. For humans and robots to navigate seamlessly through such environments, new flexible approaches need to be developed. Navigation systems generally rely on a network (nodes and edges) as an abstraction of underlying space availability. However, indoor and outdoor networks have different origins. While indoor systems rely on indoor space subdivision approaches, current outdoor systems utilize road‐based network approaches. Linking such networks via particular nodes is possible but restrictive. Many spaces in the built environment are not strictly indoor or outdoor spaces and are thus often omitted from navigation networks, further limiting navigation options. To overcome these shortcomings, we introduce a new space definition framework in which the entire built environment is categorized into indoor, outdoor, semi‐indoor and semi‐outdoor spaces. We provide strict definitions for the four space categories. Our framework allows the same navigation network extraction approaches to be used and therefore enables seamless indoor/outdoor path computation for single or combinations of locomotion modes. The notions of semi‐indoor and semi‐outdoor spaces offer new options for further tailoring of the navigation path with respect to environmental factors, which we demonstrate with two use cases.
Media Elektronik : 2580-0760 Penggunaan Bahasa Indonesia sebagai Pivot Language pada Mesin Penerjemah Madura-Sunda dengan Metode Transfer dan Triangulation

This paper is an attempt to focus on investigating the pivot (bridge) language technique, where the pivot language used to improve Statistical Machine Translation (SMT) quality. In this case, Indonesian is used as a pivot language, where each available corpus can be used to support the Madurese-Sundanese language pair. Experiments that have been carried out using the parallel corpus of the Indonesian-Madurese and Indonesian-Sundanese languages are equal to 5K and 6K sentences respectively, while the monolingual corpus used Malay, Sundanese and Indonesian each at 10K, 10K and 100K sentences. This study compares the results of applying the Triangulation and Transfer methods using Indonesian as a pivot language. The results of the research proved that the Triangulation method has better acceleration when compared to the Transfer method. From the results of the experiments conducted, the Triangulation method increased the average Indonesian pivotbased SMT testing by 6.18% for Madura-Sundanese SMT and 7.27% for Madurese-Sundanese SMT.
Uplink Performance Enhancement Through Adaptive Multi-Association and Decoupling in UHF-mmWave Hybrid Networks

The rapid increase of wireless applications and services coupled with the arrival of 5G and Internet of Things will not only exacerbate demand for further capacity at the downlink (DL) but also crucially at the uplink (UL). One of the most potential enablers to simultaneously optimize both links is the DL/UL decoupling (DUDe) technique which does so by exploiting the possibility of associating each user to a different base station (BS) in each link direction. Moreover, the increasing desire to incorporate millimeter-wave (mmWave) communications in future networks further enriches the possibilities to achieve higher capacities. To this end and in contrast to existing works which use dual association based on minimum path-loss (Min-PL), this paper investigates the merits of adopting capacity-based multi-association in ultra-high frequency (UHF) and mmWave hybrid networks, where mobile users may simultaneously connect to multiple UHF small cells (SCells), millimeter-wave SCells and/or UHF macro cells (MCells). It will be shown that, our joint association and resource management approach can provide higher data rates and energy efficiency than many benchmark techniques. To achieve an insight into the performance of the proposed design, the results present a comprehensive comparison based on single and multi-connectivity as well as coupled and decoupled association for a variety of important metrics.
Multi-label Classification and Sentiment Analysis on Textual Records

In this thesis we have present effective approaches for two classic Nature Language Processing tasks: Multi-label Text Classification(MLTC) and Sentiment Analysis(SA) based on two datasets. For MLTC, a robust deep learning approach based on convolution neural network(CNN) has been introduced. We have done this on almost one million records with a related label list consists of 20 labels. We have divided our data set into three parts, training set, validation set and test set. Our CNN based model achieved great result measured in F1 score. For SA, data set was more informative and well-structured compared with MLTC. A traditional word embedding method, Word2Vec was used for generating word vector of each text records. Following that, we employed several classic deep learning models such as Bi-LSTM, RCNN, Attention mechanism and CNN to extract sentiment features. In the next step, a classification frame was designed to graded. At last, the start-of-art language model, BERT which use transfer learning method was employed. In conclusion, we compared performance of RNN-based model, CNN-based model and pre-trained language model on classification task and discuss their applicability.
AI Trainning Platform and AI Implementation with DQN for Realtime Strategy Game

Research on video game AI has been done for a long time. In recent years, due to the rapid development of neural networks, the game AI which was considered difficult to build has appeared. After these AI beat humans in turn based games with complete information, such as GO, the next research direction has been focused on simultaneous games with incomplete information. The RTS game is one of the simultaneous-move games with incomplete information; therefore, in this paper we report the design of a platform for AI learning in the RTS game whose mechanism has been simplified. In addition, we implemented an AI for this type of game, and carried out some experiments.
Parameter Prediction of Stretch-Blow Molding Process of PET Using Neural Networks

This study presents a parameter selection strategy developed for the Stretch-Blow Molding (SBM) process to minimize the weight of preforms used. The method is based on a predictive model developed using Neural Networks. The temperature distribution model of the preform was predicted using a 3-layer NN model with supervised backpropagation learning. In addition, the model was used to predict the uniform air pressure applied inside the preform, taking into account the relationship between the internal air pressure and the volume of the preform. Parameters were validated using in situ tests and measurements performed on several weights and lengths of a 0.330 Liter Polyethylene Terephthalate (PET) bottles. Tests showed that the model adequately predicts both the blowing kinematics, mainly zone temperatures and blowing and stretching pressures along the walls of the bottle while maintaining the bottle strength and top load requirements. In the second step, the model was combined to automatically compute the lowest preform weight that can be used for a particular 330 ml bottle design providing a uniform wall thickness distribution.
On-chip testing of embedded silicon transducers

System-on-chip (SoC) technologies are evolving towards the integration of highly heterogeneous devices, including hardware of a different nature, such as digital, analog and mixed-signal, together with software components. Embedding transducers, as predicted by technology roadmaps, is yet another step in this continuous search for higher levels of integration and miniaturisation. Embedded transducers fabricated with silicon/CMOS compatible technologies may have more limitations than transducers fabricated with fully dedicated technologies. However, they offer industry the possibility of providing low cost applications for very large market niches, while still keeping acceptable transducer sensitivity. This is the case, for example, for accelerometers, micromirrors display devices or CMOS imagers. Embedded transducers are analog components. But given the fact that they work with signals other than electrical, the test of these embedded parts poses new challenges. Test technology for SoC devices is rapidly maturing but many difficulties still remain, in particular for addressing the test of analog and mixed-signal parts. In this paper, we present our work in the field of MEMS (microelectromechanical systems) on-chip testing with a brief overview of the state-of-the-art.
Measuring Programming Competence by Assessing Chunk Structures in a Code Transcription Task

In a simple transcription task in which sections of Java program code are copied by freehand writing, it is demonstrated that chunk related temporal signals are sufficiently robust to permit the measurement of programming competence. An experiment with 24 participants revealed that the number of views of the stimulus per trial and the duration of writing per stimulus view are both strongly correlated with independent measures of Java competence.
Computer-Aided Cancer Diagnosis and Grading via Sparse Directional Image Representations

Prostate cancer and breast cancer are the second cause of death among cancers in males and females, respectively. If not diagnosed, prostate and breast cancers can spread and metastasize to other organs and bones and make it impossible for treatment. Hence, early diagnosis of cancer is vital for patient survival. Histopathological evaluation of the tissue is used for cancer diagnosis. The tissue is taken during biopsies and stained using hematoxylin and eosin (H&E) stain. Then a pathologist looks for abnormal changes in the tissue to diagnose and grade the cancer. This process can be time-consuming and subjective. A reliable and repetitive automatic cancer diagnosis method can greatly reduce the time while producing more reliable results. The scope of this dissertation is developing computer vision and machine learning algorithms for automatic cancer diagnosis and grading methods with accuracy acceptable by the expert pathologists. Automatic image classification relies on feature representation methods. In this dissertation we developed methods utilizing sparse directional multiscale transforms– specifically shearlet transform–for medical image analysis. We particularly designed theses computer visions-based algorithms and methods to work with H&E images and MRI images. Traditional signal processing methods (e.g. Fourier transform, wavelet transform, etc.) are not suitable for detecting carcinoma cells due to their lack of directional sensitivity. However, shearlet transform has inherent directional
Fuzzy Gain Scheduling PID Control for Position of the AR.Drone

This paper describes the design and implementation of fuzzy gain scheduling PID control for position of the AR.Drone. This control scheme uses 3 PID controllers as the main controller of the AR.Drone, in this case to control pitch, roll and throttle. The process of tuning parameters for each PID is done automatically by scheduling determined by Takagi-Sugeno-Kang (TSK) fuzzy logic model. This paper uses five function sets of PID parameters that will be evaluated by fuzzy logic in order to tune PID controllers. Error position (x,y,z), as inputs of controller, enters the PID Signal block yielding the ouputs in term of error, integral error and differential error. These signal become the inputs of the fuzzy scheduler to yield outputs pitch, roll and throttle to the AR.drone. The control scheme is implemented on the AR.Drone to make it fly to forming a square in the room. The experimental results show that the control scheme can follow the desired points, and process scheduling PID parameters can be shown.
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.
A secure multifactor remote user authentication scheme for Internet of Multimedia Things environment

To attain ubiquitous connectivity of everything, Internet of Things (IoT) systems must include “multimedia things. ” Internet of Multimedia Things (IoMT) is a heterogeneous network of smart multimedia things connected together and with other physical devices to the Internet so as to achieve globally available multimedia services and applications. Due to the ever increasing amount of multimedia data in IoT environments, securing these systems becomes crucial. This is because these systems are easily susceptible to attacks when information or any service is accessed by the users. In this paper, we propose a secure three‐factor remote user authentication scheme for IoMT systems using ECC. The formal security proof performed using ROR model and BAN logic confirms that an attacker will not be able to extract sensitive user information. Through informal security analysis, we justify the resistance of the scheme against several security attacks. The performance comparison shows that the scheme is efficient in terms of computational cost, security features, and attack resistance. Furthermore, simulation of the scheme using AVISPA and Proverif proves that the scheme is secure against all active and passive attacks.
A multi-objective optimization approach for simultaneously lightweighting and maximizing functional performance of vehicle body structure

This study presents a hybrid approach to integrate the comprehensive sensitivity analysis method, support vector machine technology, modified non-dominated sorting genetic algorithm-II method and the technique for order preference by similarity to ideal solution, which have been applied to multi-objective lightweight optimization of the B-pillar structure of an automobile. First, numerical models of the static–dynamic stiffness and the crashworthiness performance of automobile are established and validated by experimental testing. Then, the comprehensive sensitivity analysis method is used to define the final optimization variables. Experimental design and support vector machine based surrogate model techniques are introduced to establish the approximate model; subsequently, the modified non-dominated sorting genetic algorithm-II algorithm is applied to the multi-objective lightweight optimization design of the B-pillar structure, and the non-dominated solution set is determined. The principal component analysis method is applied to determine the weight of each objective. Finally, the technique for order preference by similarity to ideal solution method is used to rank Pareto front from best to worst to obtain the optimal solution; furthermore, a comparison between the original model and optimized design denotes that the mass of the B-pillar being reduced by 22.55% under the other impacting indicators is well guaranteed. Therefore, the proposed hybrid approach provided promising prospects in the lightweight and crashworthiness optimization application of the B-pillar.