Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Massive MIMO Relay Systems With Variable-Resolution ADCs/DACs Over Spatially Correlated Channels

The use of low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) is recognized as a practical technique to reduce power consumption and hardware cost for massive multiple-input and multiple-output (MIMO) systems. In this context, by leveraging on the additive quantization noise model (AQNM), this paper investigates the performance of massive MIMO relay systems with a variable-resolution ADC/DAC-based architecture, where each ADC and DAC use arbitrary bits for quantization. To be specific, we first derive the closed-form expression of achievable rate over spatially correlated Rayleigh fading channels by using perfect channel state information (CSI), maximal ratio combining (MRC), and maximal ratio transmission (MRT). Afterwards, we extend the work to the case of imperfect CSI. The analytical expressions reveal some insights of key parameters, i.e., spatial channel correlation, large-scale fading coefficients, estimate errors, and the resolution of ADCs/DACs. Finally, simulation results validate our theoretical analyses and confirm that the achievable rate reduces as the spatial correlation rises. Besides, we provide the condition under which coarse DACs (ADCs) and receive spatial correlation (transmit spatial correlation) dominate the loss of achievable rate. Moreover, provided that the total number of quantization bits is constant, we find that the achievable rate is optimal over spatially correlated channels when all ADC and DAC adopt the same quantization bits from the perspective of statistic.
High-Selectivity Filtering Patch Antennas Based on MultiPath Coupling Structures

In this article, we present a design method of high-selectivity filtering antennas based on a dual-probe feeding structure. The first stage of the filtering circuit consists of two or three coupled resonators which are then coupled to the radiating patch acting as the last resonator of the filter. Cross-coupling is used to obtain transmission zero, and thus the selectivity of the filtering antennas is improved. A cascaded triplet (CT) filtering patch antenna, which produces a radiation zero at the high-frequency edge of the passband, and a cascaded quadruplet (CQ) filtering patch antenna, which produces radiation zeros at both edges of the passband, are designed for verification. The process of designing the filtering patch antenna is given in detail. The designed CT filtering patch antenna has a bandwidth of 1.029–1.17 GHz and a radiation zero at 1.19 GHz. The designed CQ filtering patch antenna has a bandwidth of 1.029–1.17 GHz and two radiation zeros at 1.012 and 1.19 GHz, respectively. The peak gain of the CT filtering patch antenna is 9.35 dBi and that of the CQ filtering patch antenna is 9.16 dBi. A dual-polarized CQ filtering patch antenna is fabricated, showing a good performance in measurement.
Deep learning-based classification of environmental sounds

Sound classification has been a major research topic for many years. Sound classifying models and intelligent sound recognition systems are based on the analysis of human auditory characteristics. The use cases of those characteristics are countless: context awareness, surveillance systems, crime detection etc. Deep learning models, such as convolutional neural networks (CNNs), have been shown very useful for classifying image datasets. We approach the sound classification problem via image classification. To that end, we represent sound files with their image representations, namely mel spectrogram, tonal centroid, spectral contrast and chromagram, and train a CNN deep learning model on these image representations. The proposed method achieves the mean accuracy of 73%, using 10-fold cross validation. This result is, considering the nature of the dataset, and the fact that environmental sounds are much more cumbersome to classify compared with music and speech, very satisfactory. The experimental results also show that the deep learning approach outperforms in accuracy fully-connected NN (59% accuracy) by a large margin.
Online Teaching Methodologies in Higher Education Credit Mobility Courses: ErasmusX pilot project

Based on Erasmus credit mobility programs, ErasmusX is a project envisaged to offer students the possibility to add value to international exchange periods by combining online certified studies with further professional or academic experiences abroad. However, differences between national higher education systems and teaching styles can lead to issues concerning the recognition of credits, which makes necessary to establish common basic procedures. For this purpose, five different European Higher Education Institutions which have traditionally based their academic organization in the face to face credit system, have joined to develop on-line certified courses on several pre-defined areas following common practices. Bearing in mind this purpose, this communication focuses on the proposal of key pedagogical models, approaches and strategies to consider when designing online courses for mobility students. The revision of existing practices underline that online teaching requires not only an adjustment of the role of professors and students, but also a different structure of the courses based on a flexible online instructional design. The methods and models revised in this preliminary study point to the need of incorporating a collaborative online teaching approach with processed educational technology in English mediated instruction.
Design and Implementation for Deep Learning Based Adjustable Beamforming Training for Millimeter Wave Communication Systems

Millimeter wave (mmWave) provides extremely high throughput owing to their high bandwidth utilization over higher frequencies. To compensate for the severe loss and attenuation, beamforming training is used to determine the optimal beam direction and thereby improve directional transmission power. However, the training overhead will be significantly increased with narrower beams, particularly under conventional exhaustive schemes. Therefore, we propose a learning-based adjustable beam number training (LABNT) scheme to intelligently and flexibly select beam training candidates considering different user mobility and hybrid line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. In LABNT, several deep learning networks are parallelly constructed and connected to a reinforcement learning network to determine training candidates dynamically based on performance rewards. A novel enhanced feature selection method, that is, uniformly distributed mutual information, is developed based on the correlation of historical results to select the beam inputs for each deep learning network. In simulations, the proposed LABNT outperforms other existing schemes in terms of beam alignment accuracy, training latency, and system throughput. Moreover, following the IEEE 802.11ad/ay protocols, we implement realistic mmWave beamforming training on a programmable hardware platform with integrated 60 GHz wireless-gigabit (WiGig) transceiver devices. The experimental results on the WiGig platform demonstrate that the proposed LABNT scheme can achieve real-time performance with approximately Gbps transmission throughput and millisecond-level training overhead.
EMD-Based Noise-Robust Method for Speech/Pause Segmentation

The article presents a noise-robust method for speech/pause segmentation based on empirical mode decomposition. The method has been developed on the basis of a combined analysis of zero-crossing rate and short-term energy using empirical mode decomposition at the stage of preprocessing. Based on the results of preliminary processing, a set of new investigated signals, containing the most reliable information about the boundaries of the beginning and the end of informative sections of noisy speech, has been formed. The effect of the decomposition method and the influence of fragment duration of the investigated signals on the segmentation efficiency of noisy speech at different signal-to-noise ratio levels, from 20 to -5 dB with a step size of 5 dB, were assessed. The research results have shown a decrease in the values of the first and second kind errors during the segmentation of noisy speech signals using the proposed noise-robust method.
The Line-Structured Light Plane Calibration based on the Plücker Line

Most of the traditional methods of light plane calibration obtain the light plane equation by extracting the feature points, but the efficiency of obtaining the feature points is low and the number is insufficient. To improve the calibration accuracy, this paper proposes a method to solve the light plane according to the Plücker matrix. This method improves the extraction accuracy of the centerline of the light bar by avoiding the intersection of the light bar with the chessboard on the target reference object and converts the center line into the Plücker matrix form. The Plücker line of world coordinated system is obtained by plane homograph. According to the external parameters, the Plücker line of camera coordinated system is obtained, the multiple Plücker lines and the plane equation are established to establish a constraint equation set, and the light plane equation parameters are obtained through SVD decomposition. Experimental results show that the optical plane composite accuracy of this method is less than 0. 05mm, and its accuracy and robustness are better than the cross-ratio method.
Cooperative NOMA, Prototyping and Experimental Evaluation Using SDR

In this paper, we developed a prototype of power domain downlink non-orthogonal multiple access (NOMA) using software defined radio (SDR). Our key focus was to experimentally evaluate the utility of dedicated relay-cooperative NOMA under diverse channel conditions. Furthermore, instead of using traditional power allocation mechanisms, users are categorized based on quality of service (QoS) requirements. For extensive comparative analysis, a series of experiments are performed to evaluate over-the-air performance of cooperative NOMA. The results indicate that relay-cooperative NOMA outperforms non-cooperative NOMA and yields significant performance gains.
Fano-Assisted Tunable X-Band Microwave Ring Resonator

Control of the resonance frequency of microwave cavities is of great interest in many applications such as sensing and wavelength filtering. However, this task normally relies on active control via functional materials (i.e., polymers and ferrites, or a combination of both) excited by an external electric or magnetic field. In this article, instead, we propose an innovative whispering gallery mode cavity resonator with tunable bandwidth for sensing and wavelength filtering applications. The proposed approach exploits Fano-type resonances (FTRs) to improve the cavity performance in the microwave regime. We have measured a ~74% increase in the loaded quality factor compared to regular ring cavities, with only 1.9-dB additional loss. In addition, by carefully choosing the size of the transmitting and receiving monopole antennas placed inside the cavity and their relative angular spacing, we can easily tune the cavity response. The extra degree-of-freedom introduced by such variable angular spacing, responsible for the onset of FTRs, allows the cavity quality factor $Q$ to be continuously tuned from 385 to 670.
Performance Enhancement of P300 Detection by Multiscale-CNN

The P300-based spelling system is one of the most popular brain–computer interface applications. Its success largely depends on performance, including the information transmission rate (ITR) and detection rate (i.e., accuracy). To achieve good performance, we proposed a multiscale convolutional neural network (MS-CNN) model that consists of seven layers. First, an upfront data set was used to train the MS-CNN, aiming to obtain a subject-unspecific model (universal model) for P300 detection. Second, this universal model was adapted by a portion of data derived from a subject to update the model to obtain a subject-specific model by incorporating a transfer learning technique. We applied the proposed model in the brain–computer interface (BCI) Controlled Robot Contest at the 2019 World Robot Conference, and our performance was the best among the teams in the contest. In the contest, ten healthy young subjects were randomly assigned by the contest committee to assess the model. Our model achieved the best P300 detection performance (higher accuracy with less repetition time). The ITR for the subject-unspecific case was 13.49 bits/min, while the ITR for the subject-specific case was 12.13 bits/min when the repetitions were fewer than six. It is believed that our method may pave a promising path for taking a further step toward efficient implementation of the P300-based spelling system.
Beam Aggregation-Based mmWave MIMO-NOMA: An AI-Enhanced Approach

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) with hybrid beamforming enables high data rate broadband services for 5G. To further improve the spectral efficiency and connectivity, non-orthogonal multiple access (NOMA) has been considered to be integrated with mmWave MIMO. Nonetheless, the existing power-domain mmWave MIMO-NOMA with narrow analog beams suffers from beam misalignments which deteriorate the data rates of the misaligned users as well as the system fairness. Thereby, we propose a beam aggregation-based mmWave MIMO-NOMA scheme to loosen the requirement of beam alignment and improve the system fairness. The proposed scheme generates virtual beams with wider beamwidth by aggregating adjacent analog beams. NOMA transmissions are utilized within each aggregated virtual beam. Then, we propose a non-orthogonal multiuser precoding scheme to ensure the fairness within the aggregated beam by maximizing the minimum achievable rates of the grouped users. To address this issue, a max-min problem is proposed and artificial intelligence (AI) technology is exploited to solve this non-trivial problem. The problem is firstly converted to an equivalent penalized minimization problem and then an unsupervised deep neural network (DNN) is trained to map the instantaneous channel coefficients to the precoders in a data-driven fashion. Specifically, we explicitly introduce the transmit power as the DNN input to achieve better generalization in different signal-to-noise ratio regions. Performance evaluations reveal that the proposed scheme can achieve significant gain on the max-min data rate compared with the conventional scheme.
Performance Analysis of URLL Random-Access NOMA-Enabled IoT Networks with Short Packet and Diversity Transmissions

Recently, non-orthogonal multiple-access (NOMA) has been proposed to improve spectrum-efficiency and throughput of 5G cellular networks, and is also considered a key-enabler for ultra-reliable and low-latency (URLL) communications. Moreover, the Internet-of-Things (IoT) paradigm has emerged to provide massive-connectivity for intelligent devices and systems, which entail spectrum-efficient transmission schemes. Hence, exploiting NOMA for URLL transmissions in IoT networks is inevitable. Furthermore, random-access (RA) techniques are also considered essential to enable massive URLL IoT networks, since they reduce signaling overhead and packet latency, especially when massive numbers of clustered IoT devices with sporadic traffic behaviour are considered. In this paper, the performance of uplink RA-NOMA in URLL IoT networks with short packet and diversity transmissions is analyzed. Specifically, network metrics-such as average packet latency, reliability, and GoodPut-are mathematically derived. Additionally, the effect of transmission diversity, and number of data bits per blocklength on the different network metrics has been extensively evaluated, illustrating several tradeoffs between the different network metrics as well as highlighting the importance of carefully selecting the network parameters to satisfy the URLL requirements.
Interference Cancellation Aided Hybrid Beamforming for mmWave Multi-User Massive MIMO Systems

In large scale multiple-input multiple-output (MIMO) systems (or massive MIMO systems), hybrid beamforming is a promising technique due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. In this paper, we investigate the downlink millimeter wave (mmWave) multi-user massive MIMO system and propose an interference cancellation (IC) framework on hybrid beamforming design. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Specifically, for the first proposed algorithm, we use zero-forcing (ZF) to cancel inter-user interference and use SIC to cancel intra-user interference. For the second one, SIC is used to cancel inter-user interference and ZF is used to cancel intra-user interference. Both inter-user interference and intra-user interference are suppressed by SIC in the third algorithm. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed SIC-aided hybrid beamforming algorithms outperforms the existing approaches in terms of spectral efficiency (SE) at the cost of computational complexity for the SIC procedure. Moreover, the results indicate that the proposed algorithms can achieve good SE performance with 2-bit finite resolution phase shifters and channel estimation error.
Dog Nose-Print Identification Using Deep Neural Networks

Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of single-person households. This trend has resulted in a growing interest in problems requiring solutions, such as missing pets and false insurance claims. Traditional non-biometric-based methods cannot address these problems. This paper proposes a novel deep-learning model that can extract discriminative features through dog nose-print patterns for individual identification. We present a robust baseline for how individual dogs can be identified. The proposed dog nose network (DNNet) is a convolutional neural network (CNN)-based Siamese network structure comprising feature extraction and self-attention modules. Moreover, there is no need for a separate scanning device because it uses popular mobile devices to acquire the dataset. Besides high recognition performance, the proposed method also ensures simplicity and efficiency. The proposed method achieves better recognition performance than state-of-the-art methods for the collected dog nose-print dataset. It achieves recognition performance superior to state-of-the-art methods for the collected dog nose-print dataset. Using multiple datasets through cross-validation, we acquired an average identification accuracy of 98.972% with the Rank-1 approach. Additional performance benefits were demonstrated through the receiver operating characteristic (ROC) curve, t-distributed stochastic neighbor embedding (t-SNE), and confusion matrix.
ActiveIter: Meta Diagram Based Active Learning in Social Networks Alignment

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training instances, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter (Active Iterative Alignment), is introduced in this paper. The model ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.
A Network Based On Deep Interest Network for Taobao Click-Through Rate Prediction

Improving the prediction accuracy of advertising click-through rate (CTR) is a very important task in the field of computing advertising. The higher the accuracy of click-through rate prediction, the more accurate the information provided to users will be, and the better the promotion effect of merchants will be, which will increase the profits of media platforms, DSP companies and merchants. In this paper, we propose a new model based on DIN (Deep Interest Network) [1], which can make better use of data structure and achieve diversity and local interest to users through a network structure similar to attention. activation. Facts have proved that this method is effective and significantly better than traditional models. Experiments show that the model can bring more interpretability and obtain good AUC (area under the curve) performance.
An Optically Transparent Near-Field Focusing Metasurface

We propose a novel optically transparent reflection-type metasurface based on indium tin oxide (ITO) material for simultaneously achieving high transmission of visible light and near-field focusing (NNF) of microwave, demonstrating its potential for wireless power transfer (WPT) and harvesting applications. By achieving high impedance of the metasurface, this work overcomes the main challenge in designing metasurface with lossy metal materials, i.e., optimizing the tradeoff between phase shift characteristics and efficiency loss. We propose a new element with two degrees of freedom to ensure that the phase shift range can reach 350° while keeping <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert $ </tex-math></inline-formula> less than −2.5 dB. In addition, we adopt the grid ground (GND) instead of the complete GND plane to further improve the light transmittance. Based on the above considerations, we design two types of metasurfaces for deployments in ambient wireless energy harvesting (plane-wave feeding) and WPT (horn feeding), respectively. Its NNF transfer efficiency can reach more than 60% of the metasurface based on good conductor materials. The relative bandwidth with 50% transfer efficiency can reach 34.5% (4.9–6.9 GHz). We fabricate an ITO-based prototype of the metasurface with the dimension of <inline-formula> <tex-math notation="LaTeX">$342 \times 342 \times 4.4$ </tex-math></inline-formula> mm<sup>3</sup> (<inline-formula> <tex-math notation="LaTeX">$6.6 \times 6.6 \times 0.08\lambda _{0}^{3}$ </tex-math></inline-formula>) with the sheet impedance of <inline-formula> <tex-math notation="LaTeX">$1~\Omega $ </tex-math></inline-formula>/sq and a light transmittance of 60%. We also perform near-field scanning measurements to verify that the focusing position is accurate. Finally, through WPT and harvesting tests, we achieve a WPT and receiving efficiency (from power source to receiving antenna) of 12.6% and a rectification efficiency of 55%, confirming the practicability and effectiveness of the proposed work.