Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Characterization for Vehicle-to-Infrastructure Communications in Millimeter-Wave Band

In recent years, the intelligent transport system (ITS) has been developed rapidly because of global urbanization and industrialization, which is considered as the key enabling technology to improve road safety, traffic efficiency, and driving experience. To achieve these goals, vehicles need to be equipped with a large number of sensors to enable the generation and exchange of high-rate data streams. Recently, millimeter-wave (mmWave) technology has been introduced as a means of meeting such a high data rate requirement. In this paper, a comprehensive study on the channel characteristics for vehicle-to-infrastructure (V2I) link in mmWave band (22.1-23.1 GHz) for various road environments and deployment configurations is conducted. The self-developed ray-tracing (RT) simulator is employed with the calibrated electromagnetic (EM) parameters. The three-dimensional (3D) environment models are reconstructed from the OpenStreetMap (OSM). In the simulations, not only the vehicle user equipment (UE) moves, but also the other vehicles such as cars, delivery vans, and buses move around the vehicle UE. Moreover, the impacts of the receiver (Rx) multiple antennas and beam switching technologies at the vehicle UE are evaluated as well. The channel parameters of the V2I link in mmWave band, including received power, Rician $K$ -factor, root-mean-square delay spread, and angular spreads are explored in the target scenarios under different simulation deployments. This work aims to help the researchers understand the channel characteristics of the V2I links in mmWave band and support the link-level and system-level design for future vehicular communications.
Resource Allocation for Two-Way Active Channels with Unequal Subchannel Noise Powers

In traditional passive parallel channel models, the subchannel gains are dictated by the propagation medium. The so-called active channel (AC) modeling however allows for the subchannel gains to be adjusted, thereby providing additional degrees of freedom for optimal design, in addition to the degrees of freedom offered by adjusting the source transmit powers over different subchannels. We herein study the problem of sum-rate maximization for a two-way AC, with unequal noise powers over the subchannels which enable communication between two transceivers. This sum-rate maximization problem is solved subject to two constraints on the transceivers total transmit powers and one constraint on the total AC's power. We use the Karush-Kuhn-Tucker (KKT) conditions to propose a computationally efficient solution to this problem. More specifically, we use the KKT conditions to restrict the feasible set to a set of possible solutions, and then, search through these solutions to obtain the optimal solution to the sum-rate maximization problem. Doing so, we show that only a subset of the subchannels may participate in the information exchange between the two transceivers. Through numerical examples, we show that a two-way AC can significantly outperform an equivalent two-way passive channel under equal total noise power.
Characterization of Line-of-Sight Link Availability in Indoor Visible Light Communication Networks Based on the Behavior of Human Users

We characterize the line-of-sight (LOS) link availability in indoor visible light communication (VLC) networks based on the behavior of human users. The VLC link availability is impacted by humans in three distinct ways: (1) Users turn the lights on or off in each room. (2) Users may carry mobile devices. (3) Users constitute mobile obstacles that block or shadow the channel between the transmitter and the receiver. First, we develop a mathematical framework for VLC link availability and a probabilistic model of the VLC network with respect to human behavior in indoor environments. Second, we design a realistic multi-user, indoor VLC system simulation with static and mobile VLC devices that are expected to be found in smart home environments. We present the following four sets of results: (1) We report statistics on the blockage durations of VLC links and categorize the links with respect to these statistics. (2) We demonstrate the performance of Selection Diversity versus Maximal Ratio Combining for mobile VLC devices carried by humans in a smart home setting. (3) We show that optimal LED resource allocation policies for multiple users are impacted by the VLC link blockage events caused by humans. (4) We demonstrate that the link blockage events in different rooms become dependent due to humans who transition between rooms. Based on our results, we provide new directions for the design of network architectures for indoor VLC systems.
Efficient Algorithms for Robust Factored Radar STAP

We deal with robust adaptive beamforming (RAB) for factored radar space-time adaptive processing (STAP) as a worst-case signal-to-interference-plus-noise ratio (SINR) maximization, because of mismatches in the desired signal and the interference-plus-noise covariance. To this end, we first reformulate the (beamformer unstructured) RAB problem as a second-order cone program (SOCP), and forcing the factored structure in the STAP weight vector, we come up with a new problem solved via an alternating optimization method. Second, we transform the worst-case SINR maximization into a biquadratic matrix inequality (BQMI) problem. We show that there is a closed-form solution for the BQMI problem, which implies that the robust optimal choice for the factored STAP processor is the Kronecker product of the spatial and temporal steering vectors. This is the most important finding of this paper suggesting that in the presence of steering and covariance mismatches (following the considered model), it is convenient to avoid adaptivity and to just use the non-adaptive beamformer.
Quality of Service Enhancement in Wireless LAN: A Systematic Literature Review

In early, IEEE introduced 802.11 standards for Wireless Local Area Networks (WLANs). Due to the rapid increase in the human demand of wireless networks worldwide. It required efficiency for the current generation, IEEE introduced the 802.11e standard for quality of service in 2005. This standard based on the EDCA mechanism for different priority traffic to provide QoS in WLANs. Nowadays EDCA is still one of the most important techniques of channel access for providing quality of service in the latest standards of IEEE for WLANs (802.11a, 802.11ax). EDCA based on four different traffic priorities (Voice, Video, Best effort, Background). The main contribution of this research is to systematically review researches on QoS enhancement of wireless LAN. This study Identifying, categorizing, and synthesizing the appropriate researches based on Kitchenham's systematic review methodology. This review identifies the methods which are used for improving the QoS of WLAN. We also focused on the issues as contention window adjustment and channel sensing which causes a lot of collision in EDCA. There are also fairness issues between low and high priority traffic which cause starvation. Preliminary results have been reported by answering 2 selected research Questions. 30 studies were selected out of the initial set of 55 research articles. Studies answering selected research questions have been included in this report.
Design of Cardiac Status Indicator and R-R Interval Adjustment Circuits

Despite great advancements in medical field, millions of people suffer from cardiac arrhythmia. The solutions for arrhythmia rely largely on early diagnosis and recognition, and therefore in this paper, we have designed a Cardiac Status Indicator and R-R interval (RRI) adjustment circuit to continuously indicate the status of ECG and generate anti-wave to normalize the abnormal waveform for next-generation cardiac pacemakers. The design accurately detects RRI's and for RRI lesser than 0.6s, an anti-wave is generated to suppress extra heartbeats against irregular heartbeats and on the other hand, for RRI greater than 1s, an anti-wave with extra beats is generated to normalize the heartbeat. The simulation is implemented in 0. 18µm CMOS process and the circuits operate in the range of 0.9V to 1.8V.
Incremental Few-Shot Object Detection

Existing object detection methods typically rely on the availability of abundant labelled training samples per class and offline model training in a batch mode. These requirements substantially limit their scalability to open-ended accommodation of novel classes with limited labelled training data, both in terms of model accuracy and training efficiency during deployment. We present the first study aiming to go beyond these limitations by considering the Incremental Few-Shot Detection (iFSD) problem setting, where new classes must be registered incrementally (without revisiting base classes) and with few examples. To this end we propose OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples. This is achieved by an elegant adaptation of the efficient CentreNet detector to the few-shot learning scenario, and meta-learning a class-wise code generator model for registering novel classes. ONCE fully respects the incremental learning paradigm, with novel class registration requiring only a single forward pass of few-shot training samples, and no access to base classes – thus making it suitable for deployment on embedded devices, etc. Extensive experiments conducted on both the standard object detection (COCO, PASCAL VOC) and fashion landmark detection (DeepFashion2) tasks show the feasibility of iFSD for the first time, opening an interesting and very important line of research.
Agreement on the Venture’s Reality Presented in Business Plans Purifying a Multi-Dimensional Measurement Instrument to Improve Validity and Reliability

The business plan is one of the most frequently available artifacts to innovation intermediaries of technology based ventures’ presentations in their early stages [1] [4]. Agreement on the evaluations of venturing projects based on the business plans highly depends on the individual perspective of the readers [5], [6]. One reason is that little empirical proof exists for descriptions in business plans that suggest survival of early-stage technology ventures [7] [9]. We identified descriptions of transaction relations [10] [13] as an anchor of the snapshot model business plan to business reality [13]. In the early-stage, surviving ventures are building transaction relations to human resources, financial resources, and suppliers on the input side, and customers on the output side of the business towards a stronger ego-centric value network [10] [13]. We conceptualized a multidimensional measurement instrument that evaluates the maturity of egocentric value networks based on the transaction relations of different strength categories that are described in business plans of early-stage technology ventures [13]. In this paper, the research design and the instrument are purified to achieve high agreement in the evaluation of a business plan [14] [16]. As a result, we present an overall research design that can reach acceptable quality. The paper so contributes to the literature on business analysis in the early-stage of technology-based ventures and the research technique of content analysis.
Evaluation Technique of 3D Point Clouds for Autonomous Vehicles Using the Convergence of Matching Between the Points

In this paper, we propose a new map evaluation technique for autonomous vehicles using a 3D point cloud. Localization in autonomous driving is an important technology. Attention is focused on accurate 3D mapping and point cloud data, because this map data is needed to estimate vehicle position. However, the constructed 3D point group may have errors due to the measurement. Localization has also been known to fail in places where the terrain has few distinct features. Our technique focuses on localization process to evaluate the map. The goal of our proposal is to calculate the probability of success or failure of localization. This evaluation method uses convergence by matching. Evaluation tests showed that the places where the localization is possible, and the place where the error remains on the map can be clearly separated. In future, the range of the input 3D point cloud is made into the range applicable to Localization, and we evaluate the validity of the proposed method by increasing the set.
Stacked Denoising Autoencoder Enhanced Polar Codes Over Rayleigh Fading Channels

Polar codes, with low encoding/decoding complexity and capacity-achieving potential, have drawn much attention recently. It is very critical to study the impact of fading on polar codes implementation into wireless communications. Existing research works on polar codes over the fading channel use simplified fading channel models while further reducing the frame error rate is highly needed. In this letter, using the structure of polar codes we propose the deep learning based scheme for polar codes over Rayleigh fading channels. Simulation results verify that our proposed scheme can significantly decrease the frame error rate for polar codes over Rayleigh fading channels.
Robust Localization with Architectural Floor Plans and Depth Camera

Autonomous navigation is of great importance for service robots. Such robots need various technologies, especially localization, and mapping. In this paper, we focus on the localization problem. Commonly, to solve the localization problem, the creation of a map is needed. However, creating the map takes considerable time and costs. Therefore, one solution for indoor environment is to use architectural floor plans since buildings have own floor plans. If a robot can use them for localization, it enables users to cut the time and costs for creating the map from scratch. However, the floor plans sometimes do not match with a real building. Besides, sensor measurement sometimes contains objects such as a pedestrian, which are not contained in the floor plans. In this paper, we propose a localization algorithm with architectural floor plans that is robust to such inconsistencies. The algorithm estimates a robot coordinate by matching the floor plan with the point clouds obtained from depth images. Outliers derived from the inconsistencies in the point clouds are filtered with plane information from the depth images with the Generalized ICP framework. We tested our algorithm with floor plans in a real building and in a simulator as a case study. The results show that our algorithm can localize a robot with average more than twice accuracy compared to AMCL and be used for real-time applications.
Elimination of Ocular Artifacts From Single Channel EEG Signals Using FBSE-EWT Based Rhythms

Electroencephalogram (EEG) is a diagnostic test, and it measures the entire brain’s electrical activity. The EEG signals have been used in many applications such as the diagnosis of neurological abnormalities, the brain-computer interface (BCI), the detection of sleep-related pathologies, etc. The EEG signal is contaminated with ocular artifact during the acquisition, and the filtering of this artifact is indeed required for efficient processing of this signal. In this work, we have proposed a method for the removal of ocular artifacts from the EEG signal. The Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT) is used for the extraction of EEG rhythms namely, <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm, <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> rhythm, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> rhythm, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> rhythm and <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> rhythm sub-signals from the ocular artifact contaminated EEG signal. The enhanced local polynomial (LP) approximation based total variation (TV) (LPATV) filtering is applied over the contaminated <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm to obtain both LP and TV components. The filtered <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm sub-signal is obtained based on the subtraction of both LP and TV components from the contaminated <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm sub-signal. The filtered EEG signal is evaluated by combining the filtered <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm with <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> rhythm, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> rhythm, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> rhythm, and <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> rhythm sub-signals. The energy ratio of the <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> rhythm and the mean absolute error (MAE) in the power spectral density (PSD) values for all other rhythms are used as the performance metrics for the evaluation of the proposed method. The experimental results reveal that the proposed method has a better performance with a minimum average MAE in PSD value of 0.029 for <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> rhythm as compared to other existing techniques.
Community Evolutional Network for Situation Awareness Using Social Media

Social media is important for situational awareness during a disaster. During a disaster, the situation of emergence often changes over time and hence the topics of social media messages generated by social media users also change accordingly. Few studies quantitatively describe the topic evolution of social media during a disaster and the corresponding relationship between topic evolution and disaster process. We address this problem using co-word network analysis and present a new method based on the community evolution of the co-word network to analyze topic evolution over time in social media. The method uses communities of the co-word network in social media to represent topics. Based on the theory of community evolution, a community evolutional network is proposed to support and quantify the evolution of the topics. We implemented the proposed method in a case study, “July 2012 Beijing flood” using the Sina Weibo dataset. Results show that our method can well quantify the evolution process of topics and validate the effectiveness of our method in real-world applications. The method can facilitate the understanding of public expression dynamics during a disaster and be used to reveal the process and stages of a disaster.
Investigation of Neural Network Approaches for Unified Spectral and Prosodic Feature Enhancement

Most speech enhancement (SE) systems focus on the spectral feature or raw-waveform enhancement. However, many speech-related applications rely on other features rather than the spectral features, such as the intensity and fundamental frequency (f0). Therefore, a unified feature enhancement for different types of features is worth investigating. In this work, we train our neural network (NN)-based SE system in a manner that simultaneously minimizes the spectral loss and preserves the correctness of the intensity and f0 contours extracted from the enhanced speech. The idea is to introduce an NN-based feature extractor to the SE framework that imitates the feature extraction of Praat. Then, we can train the SE system by minimizing the combined loss of the spectral feature, intensity, and f0. We investigate three bidirectional long short-term memory (BLSTM)-based unified feature enhancement systems: fixed-concat, joint-concat, and multi-task. The results of the experiments on the Taiwan Mandarin hearing in a noise test dataset (TMHINT) demonstrate that all three systems show improved intensity and f0 extraction accuracy without sacrificing the perceptual evaluation of the speech quality and short-time objective intelligibility scores compared with the baseline SE system. Further analysis of the experimental results shows that the improvement mostly comes from better f0 contours under difficult conditions such as low signal-to-noise ratio and nonstationary noises. Our work demonstrates the advantage of the unified feature enhancement and provides new insights for SE.
Quantum-Message-Passing Receiver for Quantum-Enhanced Classical Communications

For space-based laser communications, when the mean photon number per received optical pulse is much smaller than one, there is a large gap between communications capacity---measured in bits communicated per pulse---achievable with a receiver that detects (converts from optical to electrical domain) each modulated pulse one at a time, versus with the quantum-optimal joint-detection receiver that acts on a long codeword comprised of $n$ modulated pulses; an effect often termed superadditive capacity. The action of this receiver cannot be described as the detection of each individual pulse, interspersed with classical feedforward and soft-information post-processing. In this paper, we consider the simplest scenario where a large superadditive capacity is known: a pure-loss channel with a coherent-state binary phase-shift keyed (BPSK) modulation. The two BPSK states can be mapped conceptually to two non-orthogonal states of a single qubit, described by an inner product that is a function of the mean photon number of each BPSK pulse. Using this map, we derive an explicit construction of the quantum circuit of a joint-detection receiver based on a recent idea of belief-propagation with quantum messages (BPQM) [arXiv:1607.04833]. We analyze this scheme rigorously and show that it achieves the quantum limit of minimum average error probability in discriminating $8$ (BPSK) codewords of a length-$5$ binary linear code with a tree factor graph. We quantify its performance improvement over the (Dolinar) receiver that optimally detects one pulse at a time. Our result suggests that a BPQM-receiver might attain the Holevo capacity, the quantum limit of classical communication capacity, of this BPSK-modulated pure-loss channel. This suggests a new application for a small, special-purpose, photonic quantum computer capable of so-called cat-basis universal qubit logic.
SINGAN: Singing Voice Conversion with Generative Adversarial Networks

Singing voice conversion (SVC) is a task to convert the source singer's voice to sound like that of the target singer, without changing the lyrical content. So far, most of the voice conversion studies mainly focus only on the speech voice conversion that is different from singing voice conversion. We note that singing conveys both lexical and emotional information through words and tones. It is one of the most expressive components in music and a means of entertainment as well as self expression. In this paper, we propose a novel singing voice conversion framework, that is based on Generative Adversarial Networks (GANs). The proposed GAN-based conversion framework, that we call SINGAN, consists of two neural networks: a discriminator to distinguish natural and converted singing voice, and a generator to deceive the discriminator. With GAN, we minimize the differences of the distributions between the original target parameters and the generated singing parameters. To our best knowledge, this is the first framework that uses generative adversarial networks for singing voice conversion. In experiments, we show that the proposed method effectively converts singing voices and outperforms the baseline approach.