Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-Length Scaling of Spatially Coupled LDPC Codes Under Window Decoding Over the BEC

We analyze the finite-length performance of spatially coupled low-density parity-check (SC-LDPC) codes under window decoding over the binary erasure channel. In particular, we propose a refinement of the scaling law by Olmos and Urbanke for the frame error rate (FER) of terminated SC-LDPC ensembles under full belief propagation (BP) decoding. The refined scaling law models the decoding process as two independent Ornstein-Uhlenbeck processes, in correspondence to the two decoding waves that propagate toward the center of the coupled chain for terminated SC-LDPC codes. We then extend the proposed scaling law to predict the performance of (terminated) SC-LDPC code ensembles under the more practical sliding window decoding. Finally, we extend this framework to predict the bit error rate (BER) and block error rate (BLER) of SC-LDPC code ensembles. The proposed scaling law yields very accurate predictions of the FER, BLER, and BER for both full BP and window decoding.
MaMIMO CSI-Based Positioning using CNNs: Peeking inside the Black Box

Massive MIMO (MaMIMO) Channel State Information (CSI) based user positioning systems using Convolutional Neural Networks (CNNs) show great potential, reaching a very high accuracy without introducing any overhead in the MaMIMO communication system. In this study, we show that both these systems can position indoor users in both Line-of-Sight and in non-Line-of-Sight conditions with an accuracy of around 20 mm. However, to further develop these positioning systems, more insight in how the CNN infers the position is needed. The used CNNs are a black box and we can only guess how they position the users. Therefore, the second focus of this paper is on opening the black box using several experiments. We explore the current limitations and promises using the open dataset gathered on a real-life 64-antenna MaMIMO testbed. In this way, extra insight in the system is gathered, guiding research on MaMIMO CSI-based positioning systems using CNNs in the right direction.
Long-Short Term Memory Networks For Resource Allocation Forecasting in Wifi Networks

Long Short Term Memory (LSTM) networks have been proven to be a remarkably useful model for time series forecasting. However, there are some elements such as seasonality, holiday effects and regressors which lead to difficulties in the forecasting process. In this paper, we propose a LSTM model with a data preprocessing method called Min-max scaling [7] and apply on predicting the number of views in a public wifi system, based on this number the manager can optimize resource management. The numerical simulation with real data are reported to demonstrate the efficient of our approach.
6-DoF EKF SLAM with global planar features in artificial environments

This paper presents online SLAM for self-localization of mobile robots in artificial environments. This proposed method exploits global planar features as landmarks in extended Kalman filter (EKF). The main purpose of using global planer features is reducing accumulative error of the estimation. Planes are extracted from point cloud of 3-D LiDAR as normals. These normals are projected onto “depth-Gaussian sphere” Those points from each plane are concentrated in one place on the sphere since planes has many normals which are almost same directions. These concentrations of points are deemed as planar features. The state vector of EKF has states of both a robot and landmarks. Prediction steps compute integration of angular velocity from gyroscope and linear velocity from wheel encoders, respectively. Observation steps update the states by associating observed planes and landmark planes. Area of landmark planes are also expanded with every association. Observed planes which are not associated with any landmarks become new landmarks, and the state vector is augmented. Similar landmarks are merged as necessary. To avoid mismatching between observations and landmarks, candidates for the association are selected by some conditions. To evaluate the proposed method, an experiment with an actual robot was performed. It shows the method suppresses accumulative error of self-localization by using the landmarks.
A Broadband Power Divider with 90 Degree Phase Shifter

Power dividers and phase shifters play important roles in microwave systems such as base stations, radars and other mobile devices. With the development of various wireless communication systems, the working bandwidth for many mobile terminal devices will be more and more multi-band or broadband. The RF circuits of power dividers and shifters, therefore, should be designed for multi-band or broadband to meet the development of various wireless communication systems. This will cause some problems for the design of power dividers and shifters.In some broadband circular polarization antenna systems, the application of broadband power dividers and shifters as a feeding network can provide a broadband 90 deg phase to realize a good broadband circular polarization property.Based on the system mentioned above, a broadband power divider-shifter for broadband circular polarization antenna system is designed in this paper. For this divider-shifter design, first a multi-section Wilkinson structure is used for a power divider to realize the broadband property. Next, the transmission line loaded with a T-shape open stub structure is used for a phase shifter to realize the broad band similarly.The working frequency of the proposed system covers from 3 to 5.5 GHz, which proves that the system can supply important structure for our further design of broadband circular polarization antenna.
Metamaterial Inspired High Gain Antenna for Microwave Breast Imaging

This paper presents the design of a microwave breast imaging system using metamaterial (MTM) inspired high gain antenna. The system consists of 9 antenna arrays placing in a hemispherical order. The antenna is a modified version of a traditional Vivaldi antenna, which is further loaded with a single negative metamaterial unit cell to increase the gain and radiation performance. The antenna has an overall bandwidth of 2-10.45 GHz with a maximum gain of 8.2dBi within the bandwidth. With the directional radiation pattern, the antenna exhibits good performance in the imaging setup. The setup is tested in the CST simulation environment with phantom having a tumor inside, placed in the center. After getting the scattered signals from the phantom the data is post-processed and the image is reconstructed. The system can detect the presence of unwanted cells inside the healthy tissue.
Secure Transmission via Lens Antenna Array in Millimeter Wave MISO Systems

Millimeter wave (mmWave) communication is considered as a promising technology for future wireless communication systems. Since a radio-frequency (RF) chain has high cost and high power consumption, it is impractical to implement fully digital beamforming in the mmWave communication systems. In this paper, we propose a secure transmission scheme with limited RF chains by implementing a lens antenna array in a mmWave multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Due to the energy-focusing property of a lens antenna array, the proposed scheme requires a few RF chains for secure communication, not just the number of all antennas. We adopt artificial noise (AN) to degrade the wiretap channel in both cases of available channel state information of the eavesdropper (CSIE) at the transmitter and unavailable that. For the CSIE available case, a simple antennas selection scheme is proposed. We propose an algorithm for selecting antennas and designing analog phase shifters in the CSIE unavailable case. Numerical results demonstrate a remarkable secrecy performance enhancement compared with other hybrid beamforming strategies exploiting a conventional uniform planar array (UPA).
Expression Recognition Method Based on a Lightweight Convolutional Neural Network

Effective emotion recognition algorithms can help machines better understand people and promote the development of human-computer interaction applications. In recent years, many research efforts have used benchmark expression data to train deep neural network models to achieve state-of-art results. These high-accuracy models usually contain hundreds of layers, so they require complex calculations and may not be suitable for real-world scenarios. This paper proposes a lightweight emotion recognition (LER) model to handle the latency problem under natural conditions. The three main contributions of this paper are as follows. 1) The LER model incorporates a densely connected convolution layer and model compression techniques into a framework that eliminates redundancy parameters. 2) Multichannel input is introduced in our work to preprocess the image data, which improves the learning ability of the model. 3) Experiments show that the proposed LER model has better performance on the FER2013 and FERPLUS datasets compared with other lightweight models. Compared with the VGG13 used in previous work, the LER model achieves higher accuracy and reduces the number of parameters by 97 times. Finally, the FERFIN dataset is created, which had fewer noise data and more accurate labels than the FERPLUS dataset.
Prevalence of Chlamydia Trachomatis Infections in Symptomatic Patients in Bulgaria

The aim of the study was to investigate the prevalence of C. trachomatis infection in symptomatic patients and to compare our data with similar studies made in Bulgaria. 822 patients were included in the study with a diagnosis suggestive of Chlamydial infection: urethritis, prostatitis, Reiter syndrome, cervicitis, salpingitis, pelvic inflammatory disease, ectopic pregnancy, infertility, etc. The samples were cell cultured on McCoy and detected by immunofluorescence with anti-lipopolysaccharide monoclonal antibody. The prevalence of C. trachomatis infection in symptomatic patients was about 37% in the investigated 822 urogenital samples (568 women and 254 men). Active infection with C. trachomatis was detected in 39% of the women and in 33% of the men. Our study shows a relatively high prevalence of C. trachomatis infection in symptomatic patients; a lower prevalence of the infection in comparison with other Bulgarian studies, using different methods for detection. The results prove the high sensitivity and specificity of the cell-culture method for the detection of Chlamydial infections and the need for screening of the symptomatic patients and their sexual partners for Chlamydial infection.
A Stacking Ensemble Learning Model for Mental State Recognition towards Implementation of Brain Computer Interface

This paper presents a novel stacking ensemble learning model that aims at improving mental state classification for brain computer interface implementation. The proposed model combines machine learning based methods that use support vector machine, artificial neural network and deep learning with a model selection rule to classify EEG signals into accurate mental states. The proposed ensemble learning model is validated on an EEG dataset in which EEG signals are recorded from four subjects. Three mental tasks are turning mind into Zen condition (Neutral), imagining how to turn a light on (Light) and memorizing scientific paper content (Paper). Experimental results show that this ensemble learning model is robust and effective while comparing with other existing machine learning models and methods for the same purpose.
Fish hook injury of eyelid: An unusual case

Fishing is a common pastime as well as an important source of daily income for people around the world, be it a developed or a developing nation. It can be associated with unusual ocular injuries. Here, we report a case of a young man who presented with a fishhook injury into his right lateral half of upper eyelid piercing through the fornix. To the best of our knowledge, this is the first reported case of fishhook injury from this part of the world and only a few have been published in the literature till date. This case report aims at familiarizing the reader with this uncommon and atypical injury and its better management.
Phase-Only Beampattern Synthesis with Nulling for Linear Antenna Arrays

In this paper, we consider the problem of phase-only beampattern synthesis with nulling for a Linear Antenna Array systems. It involves shaping the radiation pattern using fixed-magnitude excitation signals. Imposing the fixed-magnitude,i.e. phase only, constraint on the excitation signals makes the problem an NP-hard nonconvex. We propose an iterative solution, which is based on ADMM and Proximal frameworks, to tackle the challenging problem. The proposed solution has a significant contribution in providing fast synthesizing method for the radiation pattern that is capable of mitigating interferences coming from other radiators. Finally, we provide simulation results to validate the effectiveness of the solution. Based on the simulation results, our solution performs better than a state-of-the-art solution in terms of null generation and convergence speed.
Machine Learning-Aided Indoor Positioning Based on Unified Fingerprints of Wi-Fi and BLE

This paper deals with an indoor positioning with the aid of machine learning based on the received signal strength indication (RSSI) fingerprints of beacon signals of both Wi-Fi and Bluetooth low energy (BLE). In fingerprint positioning, a site-survey is conducted in advance to build the radio map which can be used to match radio signatures with specific locations. Thus, it can take the impacts of empirical indoor environments into consideration. However, even if the physical positional relationship in the indoor environment is static, the observed RSSI values are dynamically fluctuated according to the probabilistic wireless channels. Unfortunately, it is difficult to analytically capture the stochastic behavior of RSSI in real-environments, and the accuracy of position estimation is degraded due to the model errors. To tackle this challenging problem, machine learning-based logistic regression is applied to fingerprint positioning with the RSSI data set (available as big data). Additionally, by exploiting a unified fingerprint generated from both Wi-Fi and BLE beacon signals, further performance improvement in the estimation accuracy is possible, owing to the transmit diversity effects. The experimental results show the validity of the proposed positioning scheme with the unified Wi-Fi and BLE fingerprint.
Phase Noise, SATCOM and MIL-STD-188-165B

The purpose of this paper is to describe how Phase Noise effects SHF Satellite Communications (SATCOM), and the consequences of the current specifications in MIL-STD-188-165B. This paper leverages the definitions codified in IEEE Std 1139™ − 2008 to show that the impact of Phase Noise on an M-ary phase shift key (PSK) carrier cannot be treated as additive white Gaussian Noise (AWGN) when SATCOM links have realistic energy- per-symbol to noise power spectral density ratio $E_{s}/N_{0}$.
An SISL Sequentially Rotated Feeding Circularly Polarized Stacked Patch Antenna Array

A substrate integrated suspended line (SISL) circularly polarized stacked patch antenna and array is proposed. The stacked patch is introduced to achieve impedance matching and circular polarization. A windmill slot is etched on the driven patch to generate resonant frequencies of 5.05 and 5.3 GHz. Another resonant frequency of 6.5 GHz also exists due to the slot and driven patch. The proposed antenna is based on five dielectric layers, among which two layers are hollowed. On the basis of the measured results of the antenna element, the fractional impedance bandwidth of 28.3% from 4.8 to 6.38 GHz and 3 dB axial ratio (AR) bandwidth from 5.29 to 6.02 GHz are obtained. In addition, the antenna gain is larger than 8 dBic. A sequential rotation feeding network with 90° phase difference between each output port is presented to feed the antenna array with four elements. For the proposed antenna array, the measured results agree well with the simulated results. In the impedance bandwidth from 5.03 to 6.16 GHz and 3 dB AR bandwidth from 5.13 to 6.2 GHz, the antenna peak gain ranges from 12 to 13.8 dBic.
UrbanBox: a Low Cost End-to-End Platform for Smart City Sensing

This paper presents a low cost end-to-end and open-source urban sensor node, called UrbanBox, which is based on the concept of the Internet of Things (IoT). Through the sensing platform made by multiple UrbanBox nodes, users can collect, visualize, and store the real-time data from urban environments. The platform can serve as a tool for developing data-rich smart cities. This paper presents the design, data collection architecture, and the sensors validation process for the implemented prototype.
Three-dimensional Frequency Selective Surface with Quasi-elliptic Bandpass Response

A novel three-dimensional (3-D) frequency selective surface (FSS) exhibiting wide- band and highly selective bandpass response is presented. The proposed FSS consists of a two- dimensional periodic array of vertical, shielded, and suspended striplines. By combining the high- pass property of the periodic array of waveguides with bandstop resonances from the inserted U-shaped striplines, a quasi-elliptic bandpass frequency response over a wide-band from 8.4 GHz to 16.2 GHz is achieved with a 3 dB frequency bandwidth of 63% and a sharp rejection skirt.
Pathway to Intelligent Radio

In this article, we present a pathway to intelligent radio. After briefly reviewing the CR enabling techniques developed in the past couple of decades, we introduce the recent advances on ML in communications, especially those that can be applied to CR straightforwardly. Then we discuss those AI techniques that have been specifically designed to make spectrum sensing and spectrum access intelligent. Finally, we conclude our article with several research challenges in intelligent radio.
An RGB Gait Anonymization Model for Low-Quality Silhouettes

Gait anonymization while maintaining naturalness is used for protecting a person's identity against gait recognition systems when a video of the person walking is uploaded to social media. There has been some research on gait anonymization, but only for high-quality silhouette gaits. We present an RGB gait anonymization model for low-quality silhouette gaits that can generate natural, seamless anonymized gaits for which the original silhouettes cannot be extracted correctly. Our model includes two main networks. The first one, a deep convolutional generative adversarial network, is used to anonymize the original gait by adding to it a random noise vector. By training on high-quality silhouette data, this network can generate a high-quality anonymized silhouette sequence from a low-quality silhouette one. Restricting its input to binary silhouette sequences instead of color gaits forces it to focus on anonymizing the gait rather than changing body color. The second main network, which follows the first one, colorizes the anonymized silhouette sequence generated by the first network by using the color of the original gait. Evaluation in terms of success rate and naturalness demonstrated that our model can anonymize gaits while maintaining naturalness.
Social Network Aware Dynamic Edge Server Placement for Next-Generation Cellular Networks

A large amount of operational bandwidth is consumed due to the redundant data transmissions in a 4th Generation Long-Term Evolution (4G LTE) network and, thus, the operational latency of the network increases when message passing in a social network group is considered. In this paper, we propose a novel edge server placement strategy, Social network Aware Dynamic Edge Server placement (SADES), which uses the information from the overlay social network groups to efficiently identify a few influential base stations to place the edge servers, in an existing 4G LTE network. SADES can be utilized to design efficient ultra-reliable and low latency communication-enabled 5th Generation (5G) network.
Sinhala Sentiment Analysis using Corpus based Sentiment Lexicon

Sentiment analysis has become a much needed natural language processing technique as the usages of sentiment analysis based tools are getting popular. With sufficient amount of lexical resources available, an accurate sentiment analysis can be performed for English. Sinhala is a morphologically rich but a less resourced language. Availability of lexical resources directly effect on the success of the development of natural language processing tools as well as for further researches. Sentiment lexicon is a primary resource required to perform a lexicon based sentiment analysis in any language. We propose a semi-automated framework for constructing a sentiment lexicon for Sinhala Language using a corpus based method. Proposed framework used conjunction as the indicator of polarity for adjectives in a conjoined adjective pair and morphological relationships between adjectives to determine the polarity of adjectives. To the best of our knowledge, such method has not been attempted for sentiment lexicon construction in Sinhala, until this day. The approach was tested for a 3 million POS tagged word corpus and the resultant sentiment lexicon was used to perform a lexicon based sentiment analysis, which produced 65%–69% of accuracy for three different classifiers. Proposed framework demonstrated potential to be more accurate with sufficiently larger text corpuses.
A 28GHz Quadrature Up-conversion Transmitter in 65nm CMOS for 5G mmWave Radio

A 28GHz RF transmitter is designed in 65nm CMOS for 5G millimeter-wave radio applications. The transmitter is based on a direct-conversion quadrature up-conversion architecture for achieving small silicon area and low power dissipation. The image suppression is improved by adopting I/Q LO mismatch calibration at the LO driving buffer of mixer. The power amplifier is designed in a differential type with cross-coupled neutralized capacitors for stability and reverse-isolation improvement. Combined schematic and EM simulation results show that the transmitter gives the power gain of 37 dB, OP1dB of +5.8 dBm, and Psat of +11.6 dBm. The total transmitter consumes 111 mW from a 1.2 V supply and occupies 1.78 mm2.
Application of artificial intelligence in the development of a model of Neural Network for the financial forecast in the Stock Exchange of Lima

The research describes the development of a Neural Network model based on the studies and applications of artificial intelligence whose objective is to create intelligent machines; This model applies time series which allows us to forecast the future price of the shares in the Lima Stock Exchange market. For the development of this model we considered six investment companies whose historical financial data were analyzed and processed to feed the neural network that allowed us to determine the viability, operability and reliability to apply it as a forecasting tool. The results obtained were highly significant considering that the proposed model was accepted due to its high degree of certainty.
A Transfer Learning Approach for Recognizing the Digital Radiator

A radiator consisting of half-wave dipoles that can characterize number 0 to 9 is simulated. The 10 numbers can be identified from their radiation fields without decoding. A deep neural network (DNN) model is trained on a large far-field dataset. This source model can still recognize well based on transfer learning methods even if the target data are obtained under other external conditions. The transfer learning methods of fine-tuning or freezing several layers of the source DNN model are verified, and the results are different on various target data. Some explanations are provided from the perspective of hierarchical structures of the source DNN model. Based on the method of feature matching, the features are extracted from the source model and the target model to verify the effects of transferring knowledge from source model to the target model.