Miracle-dz/UniFAR-FTUs huggingface dataset:
Exchanging Bandwidth With Aperture Size in Wireless Indoor Localization - Or Why 5G/6G Systems With Antenna Arrays Can Outperform UWB Solutions

The localization of wireless devices in indoor scenarios presents a major challenge because of multipath propagation. Hence, the majority of the research community has focused on increasing the available bandwidth of localization systems, leading to the emergence of the ultra wide band (UWB) radar. However, the hardware implementation of UWB transceivers is challenging itself and, hence, their utilization in commercial low-cost wireless devices is not to be expected in the near future. Hence, instead of evaluating frequency dependent phases via UWB, the measurement of spatially distributed phases represents a valuable alternative. Therefore, this article presents a comparison of phase-difference-of-arrival (PDOA) and time-of-arrival (TOA) systems. For this purpose, we compare the measurement sensitivity, the effects of multipath propagation, and the hardware complexity. Based on the results, the applicability of typical position estimators is discussed. Thereby, we argue that PDOA-based localization with large receiver arrays appears to be the better choice to localize wireless devices, because it enables highly accurate positioning using narrow band signals without elaborated transmitter–receiver synchronization. To validate this, indoor localization measurements are presented and compared with UWB results in extant literature.
Dielectric Rod Antenna Array With Planar Folded Slot Antenna Excitation

A dielectric rod antenna array fed by slot antenna radiators without the use of metallic waveguide is presented here. Communication systems, especially those related to 5G, are moving up into mm-wave bands where metal losses can become significant and many traditional fabrication and manufacturing techniques become more difficult. Dielectric rod antennas (DRA) are entirely made of dielectric and may be injection-molded or 3D-printed as solid rods, layered rods, or tubes. While the normal approach to feeding DRAs involves some version of metallic waveguide, we show here that the DRA may be integrated with a planar radiator feed and used effectively in an array configuration. The array demonstrated is a <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> array designed for operation at 15 GHz. Each DRA is <inline-formula> <tex-math notation="LaTeX">$6\lambda $ </tex-math></inline-formula> long. The array is fabricated on a 100 mm <inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 100 mm substrate with 52 mm separation between elements. The array radiation efficiency is 80% for an array gain of 19 dBi.
Hybrid Approach for Vehicle Trajectory Prediction Using Weighted Integration of Multiple Models

Prediction of surrounding vehicles accurately is an essential prerequisite for safe autonomous driving. Trajectory prediction methods can be classified into physics-, maneuver-, or learning-based methods. Learning-based methods have been studied extensively in recent years because it effectively exploits the road information and interactions among vehicles. However, learning-based methods perform poorly in unseen environments that were not considered during training and provide unreasonable results such as inconsistent trajectories according to road geometry. In this paper, to address this problem, a hybrid model that combines a learning-based model with physics- and maneuver-based models according to their uncertainties is proposed. The deep ensemble technique is also used to estimate the uncertainty of the learning-based method. Because the deep ensemble tends to show a large variance in unseen environments, this method is used to determine whether to use a hybrid model. The proposed method is trained and validated using the Lyft l5 dataset, the real environment vehicle driving data containing several types of intersections.
Quad-Band Multiport Rectenna for RF Energy Harvesting in Ambient Environment

This article proposes a quad-band multiport harvester that can scavenge ambient radio frequency (RF) energy of available frequency bands (i.e. GSM-900, GSM-1800, 3G and Wi-Fi) efficiently. This design’s novelty is the use of frequency-dependent multiple antenna ports that enables the harvester to fully exploit all available frequency bands, spatial diversity, and polarization for maximizing the harvested RF energy at a lower power density level in an ambient environment. Indistinctly, the proposed antenna consists of 8-port which maintain <inline-formula> <tex-math notation="LaTeX">$0.33\lambda \times 0.33\lambda $ </tex-math></inline-formula> (i.e. wavelength <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> calculated at 0.9 GHz in free space) average area per port and has achieved more than 35% relative bandwidth (BW) with a maximum gain of 6.5 dBi to cover the selected frequency bands. Besides, the proposed quad-band rectifier with a multi-stub impedance matching network is feasible for RF harvesting over the available ambient frequency bands. The proposed rectenna’s measurement results show that the generated average dc output voltage is increased up to 0.750 V at a low RF input power density of −27 dBm for a load resistance of 2.11 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula>. The overall dc rectification efficiency of the 8-port pixel harvester is also assessed and depicted to be 66.52% when the resultant RF input power density level is −27 dBm. Measurement in an ambient outdoor and controlled indoor environment is also conducted, demonstrating that the new 8-port pixel RF harvester can produce a dc output voltage of 0.797 V which is greater than some of the reference RF harvesters.
Fault detection and identification in MVDC Radial and Ring Distribution System by Wavelet decomposition

The use of renewable energy systems (RES), as distributed generation (DG) units, in distribution systems is growing in recent years. DC distribution system is more feasible for the integration of renewable energy-based DGs as compared to the AC Distribution system. In DC distribution protection is the main issue because of low line impedance that causes high fault current. In this paper wavelet decomposition based protection scheme is proposed which identify the fault section in the distribution system and also identify which type of fault is occurred. Voltage and current signals, both are analyzed through wavelet. Sequence voltages are used for the input signal to wavelet decomposition for fault type identification. A communication channel is used between lines relays for the identification of faulty sections in distribution. A bipolar ring and radial type DC distribution systems are modeled on MATLAB/Simulink and different fault events are simulated.
Research on Small Sample Text Classification Based on Attribute Extraction and Data Augmentation

With the development of deep learning and the progress of natural language processing technology, as well as the continuous disclosure of judicial data such as judicial documents, legal intelligence has gradually become a research hot spot. The crime classification task is an important branch of text classification, which can help people related to the law to improve their work efficiency. However, in the actual research, the sample data is small and the distribution of crime categories is not balanced. To solve these two problems, BERT was used as the encoder to solve the problem of small data volume, and attribute extraction network was added to solve the problem of unbalanced distribution. Finally, the accuracy of 90.35% on small sample data set could be achieved, and F1 value was 67.62, which was close to the best model performance under sufficient data. Finally, a text enhancement method based on back-translation technology is proposed. Different models are used to conduct experiments. Finally, it is found that LSTM model is improved to some extent, but BERT is not improved to some extent.
Robust Locomotion Control of a Self-Balancing and Underactuated Bipedal Exoskeleton: Task Prioritization and Feedback Control

This letter presents a study to understand how an underactuated bipedal exoskeleton with an arbitrary human user can exhibit robust dynamic walking behavior despite severe parameter uncertainty and external disturbances. Unlike in the case of classical bipedal robots where each leg possesses 6 or more active joints, it is very challenging to satisfy multiple constraints simultaneously to ensure robust dynamic walking. To overcome this problem, we first propose an optimization algorithm that makes use of a prioritized stack of tasks to satisfy the constraints hierarchically. Furthermore, we synthesized a locomotion controller named ZMP impedance feedback and identified two other state-of-the-art locomotion controllers (admittance control, centroidal momentum control) all of which were built on top of the proposed task prioritization algorithm. In order to verify the validity and robustness of these controllers for a thorough benchmarking, a series of simulation experiments were conducted via MSC ADAMS in which a human-robot coupled model with a 40 kg underactuated exoskeleton and 12 distinct anthropomorphic subjects ($\mathbf {66\sim 102}$ kg) was considered. As the result, all 3 controllers showed adequate performances to address balanced locomotion behavior when used with the proposed task priority-based optimization algorithm. In addition, the proposed ZMP impedance controller showed statistically significant results, indicating its comparatively more robust feature.
A Refined Approach for Classification and Detection of Melanoma Skin Cancer using Deep Neural Network

Although being a less common form of skin cancer, melanoma is the deadliest of all, accounting for around three-quarters of skin cancer-related deaths. The epidemiological learnings at hand clearly show the relationship between solar UV radiations and skin cancer. For curing it on time, the early-stage identification of melanoma is very necessary. Depending on the clinical aspects of melanoma, appropriate microscopic (dermoscopic) and macroscopic (clinical) analysis are enacted to detect the malignant melanoma. Digital image classification of skin lesions is the basis of efficient skin cancer diagnosis that reduces the time spent and pain received by victims in detecting early melanoma. In this paper, we introduced computer supported strategies for the recognition of melanoma skin cancer utilizing multiple image processing tools. Firstly, a skin lesion image acts as an input to this system and then various image processing and classification agents deduce the presence of melanoma. These analysis techniques test for the melanoma warning signs like border, color, size, and shape for segmentation and feature extraction. This piece of work describes the various approaches of image processing to have improved diagnosis of melanoma skin cancer.
DGLFV: Deep Generalized Label Algorithm for Finger-Vein Recognition

With the growing demand for information security, finger-vein recognition has become widespread. However, the robustness of the recognition process becomes a major problem. When identifying unseen categories with traditional finger-vein recognition systems, a few issues remain, such as recognition interference and low efficiency. This paper proposes a Deep Generalized Label Finger-Vein (DGLFV) model to extract feature maps and achieve high-accuracy recognition. The largest rectangular finger-vein region is extracted through image semantic segmentation and the advanced bidirectional traversing and center diffusion method for the known categories. Then we generalize all the unseen categories actively as Class $C+1$ to reduce interference from unregistered users. Furthermore, an adaptive threshold acquisition algorithm is proposed for Label Receiver Operating Characteristic (LROC), so that the procedures of classification, recognition, and verification are unified. Apart from Shandong University Homologous Multi-modal Traits (SDUMLA-HMT), we have conducted additional experiments on our self-built database, Finger Veins of Signal and Information Processing Laboratory (FV-SIPL). The recognition accuracy of the approach proposed in this paper has reached 99.25% and 99.08% testing on FV-SIPL and SDUMLA-HMT, with a low error rate at 1.481% and 2.228% and little time consumption of 0.157s for a single image, which is better than most state-of-the-art finger-vein recognition methods.
Effective Computational Techniques for Generating Electroencephalogram Data

The complexity of the electroencephalogram makes it a significant challenge for physicians and engineers to extract useful information from, process, and classify the electroencephalogram signals. Moreover, the difficulty in conducting clinical experimentation limits the collection of a sufficient number of electroencephalogram data samples for further processing using advanced computational techniques such as deep learning. This complexity and difficulty together with the inflexibility and the subtle linearity of the traditional signal processing techniques motivate us to find innovative techniques to address the problem of insufficient electroencephalogram data. In this paper, a number of computational and statistical techniques to generate electroencephalogram data from a previously done experiment on 30 healthy participants experiencing painful stimuli are applied. We believe this application will benefit the research in the field of biomedical signal processing.
InP@ZnSeS, Core@Composition Gradient Shell Quantum Dots with Enhanced Stability

Utilizing the reactivity difference between TOPSe and TOPS, we synthesized InP@ZnSeS QDs with the composition gradient in a radial direction where ZnSe alleviated lattice strain and ZnS protected QDs from degradation so that we achieved QDs with high QE and photo/chemical stability. In terms of systematic investigation on the relationship between the shell nanostructure and QD stability, we demonstrated that QDs with thick gradient shells exhibited high QE and much enhanced stability against the shell degradation under UV irradiation, ligand exchange, or rigorous purification. This enhanced stability of InP@ZnSeS QDs is attributed to the improved uniformity of composition gradient shells, the efficient confinement of exciton wavefunctions, and the minimized surface oxidation and non-radiative decay via surface states generated by photo-oxidation or ligand exchange. Using InP@ZnSeS QDs with enhanced stability, we were able to demonstrate InP-based colloidal green-emitting QD-LEDs. Although the current stat.
Toward Stopping Incel Rebellion: Detecting Incels in Social Media Using Sentiment Analysis

Incels, which stand for involuntary celibates, refer to online community members who identify themselves as individuals that women are not attracted to them. They are usually involved in misogyny and hateful conversations on social networks, leading to several terrorist attacks in recent years, also known as incel rebellion. In order to stop terrorist acts like this, the first step is to detect incels members in social networks. To this end, user-generated data can give us insights. In previous attempts to identifying incels in social media, users’ likes and fuzzy likes data were considered. However, another piece of information that can be helpful to identify such social network members is users’ comments. In this study, for the first time, we have considered users’ comments to identify incels in the social networks. Accordingly, an algorithm using sentiment analysis was proposed. Study results show that by implementing the proposed method on social media users’ comments, incel members can be identified in social networks with an accuracy of 78.8%, which outperforms the previous work in this field by 10.05%.
Feature Selection as a Tool to Support the Diagnosis of Cognitive Impairments Through Handwriting Analysis

Cognitive Impairments are cognitive deficits that are greater than expected for a person of a given age and level of education, but which do not significantly interfere with the daily life of the people affected. They range from mild to severe and are seen as a risk factor for Alzheimer’s disease, currently the most common neurodegenerative brain disorder worldwide. In a previous study, we presented an experimental protocol comprising different handwriting tasks to be carried out by patients and a healthy control group: the aim was to investigate whether the analysis of the handwriting could be used as a tool to support the diagnosis of this kind of impairment. In the study presented here, we used a well-known and widely-used feature selection approach to determine the most effective features for predicting the symptoms related to cognitive impairments via handwriting analysis. Our intention is to deepen the knowledge about the different cognitive functions affected by the onset of these diseases, as well as to improve the performance of the tools developed to support their diagnosis. The results showed that different sets of highly discriminant features, closely related to the cognitive skills impaired, were selected for the handwriting tasks making up the protocol, thus supporting our hypothesis that their use can be very helpful to support the diagnosis of cognitive impairment.
A Novel Approach for Upper Limb Functionality Assessment Based on Deep Learning and Multimodal Sensing Data

Upper limb rehabilitation is an effective methodology to restore and improve the functionality of patients after multiple medical events, such as strokes, arthroscopic surgery, and breast cancer surgery. High-quality rehabilitation training can promote the independent living of patients, thus enhancing the quality of life and reducing the financial burden. Traditional training sessions have some limitations, including high expenses, low compliance, and inaccurate evaluations. This paper presents a novel approach to assist healthcare professionals in assessing the functionality of upper limbs based on multimodal sensing data and deep learning algorithms. There are five different types of sensing data employed in the proposed approach: accelerometer, angular velocity, device orientation, RGB image, and depth image data. In order to assess the accuracy of training actions, the presented approach applies two machine learning algorithms, which are the dynamic time warping-K-nearest neighbor (DTW-KNN) algorithm and the long short-term memory (LSTM) neural network. The experimental results show that multimodal sensing data can improve the modeling accuracy compared with unimodal sensing data. The LSTM model can achieve better accuracy (96.3%) than DTW-KNN (74.07%) with multimodal sensing data. Moreover, LSTM performs extremely efficiently in modeling high-dimensional sensing data.
Predicting Device Availability in Mobile Crowd Computing using ConvLSTM

The QoS of mobile crowd computing (MCC), in which the public’s smart mobile devices (SMDs) are used for job execution, hampers due to users’ mobility. In this paper, we propose a model to predict SMDs’ availability in a campus-based MCC, where, generally, a set of users are available for a certain period regularly. Predicting the user’s availability before the job submission would help avoid unnecessary job offloading or job loss due to the designated SMD’s early departure. We recorded the real mobility traces of the users connected to a Wi-Fi access point of our research lab. We applied ConvLSTM on the mobility dataset to predict the availability of the SMD. A job submission scenario is simulated. The extensive evaluation of our approach shows that our method has an average accuracy of 78%, making the job submission more reliable.
Measuring Human Decision Confidence from EEG Signals in an Object Detection Task

In this paper, we investigate human decision confidence during image interpretation in an object detection task using electroencephalography (EEG) signals. We develop an EEG dataset acquired from 14 subjects. Five popular EEG features, differential entropy (DE), power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM) and asymmetry (ASM), and two classifiers, a support vector machine (SVM) and a deep neural network with shortcut connections (DNNS), are adopted to measure decision confidence in the object detection task. The classification results indicate that the DE feature with the DNNS model achieves the best accuracy of 47.36% and F1-score of 43.5% for five decision confidence levels. For the extreme confidence levels, the recognition accuracy reaches 83.98%, with an average Fl-score of 80.93%. We also found that the delta band performs better than the other four bands and that the prefrontal area and parietal area might be sensitive brain regions that represent decision confidence in object detection tasks.
Structure and Microwave Properties of Nanocomposites With Embedded Spinel Particles Subjected to High-Temperature Annealing

The nanocomposite materials with particles of ferrites-spinel with ceramic structure are obtained. The nanocomposite materials are prepared on the basis of an opal matrix SiO2. Structure and phase composition of the obtained materials with particles Ni0.5Zn0. 5Fe2O4 and Co0.5Zn0. 5Fe2O4 are investigated. Permittivity in the frequency range from 26 to 38 GHz is measured. It is found that the samples of the same composition with a higher annealing temperature have a greater real part of the permittivity. The samples, in which palladium particles are added, in addition to the particles of nickel-zinc ferrite-spinel, have a lower permittivity than similar nanocomposites without palladium. The dependences of the transmission and reflection coefficients on the magnetic field are measured. It is shown that the phenomena of ferromagnetic resonance and antiresonance are observed in these materials.
A Flexible Vibration Probe Based on PZT Low-Frequency Drive Mode

Precision contact probes have a huge demand in industrial manufacturing due to their high accuracy. However, the passive measurement mode of the existing probe easily causes wear of the probe ball and loss of accuracy during continuous contact scanning. In this paper, a PZT(piezoelectric actuators)-driven flexible probe is proposed with the dynamic time-domain function of the system response from low frequency to high drive frequency. The low-frequency measurement mode is established based on the linear response of the triangle wave. In order to avoid the trajectory nonlinearity of the stylus ball driven by PZT, the nonlinear mapping of the PZT hysteresis model is constructed by the BP neural network, and the linearly weighted particle swarm optimization (PSO) algorithm is used to realize the suppression of the response nonlinearity. Finally, the measurement experiment of the PZT-driven probe is conducted to reveal the measurement characteristics of low-frequency mode as well as measurement capability compared with commercial sensors.