bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating the Performance of Speaker Recognition Solutions in E-Commerce Applications
Authors: Krčadinac Olja , Šošević Uroš , Starčević Dušan
Published: September 2021

Two important tasks in many e-commerce applications are identity verification of the user accessing the system and determining the level of rights that the user has for accessing and manipulating system’s resources. The performance of these tasks is directly dependent on the certainty of establishing the identity of the user. The main research focus of this paper is user identity verification approach based on voice recognition techniques. The paper presents research results connected to the usage of open-source speaker recognition technologies in e-commerce applications with an emphasis on evaluating the performance of the algorithms they use. Four open-source speaker recognition solutions (SPEAR, MARF, ALIZE, and HTK) have been evaluated in cases of mismatched conditions during training and recognition phases. In practice, mismatched conditions are influenced by various lengths of spoken sentences, different types of recording devices, and the usage of different languages in training and recognition phases. All tests conducted in this research were performed in laboratory conditions using the specially designed framework for multimodal biometrics. The obtained results show consistency with the findings of recent research which proves that i-vectors and solutions based on probabilistic linear discriminant analysis (PLDA) continue to be the dominant speaker recognition approaches for text-independent tasks.
A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm
Authors: Wang Yanchao , Tian Ye , Zhu Jinying , She Haotian , Yokoi Hiroshi , Jiang Yinlai , Huang Qiang
Published: September 2021

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.
Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography
Authors: Hsu Chun-Hsien , Wu Ya-Ning
Published: September 2021

Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, we present the application of a nonlinear, nonstationary signal decomposition technique—the empirical mode decomposition (EMD), on MEG data. We discuss the fundamental concepts and importance of nonlinear methods when it comes to analyzing brainwave signals and demonstrate the procedure on a set of open-source MEG facial recognition task dataset. The improved clarity of data allowed further decoding analysis to capture distinguishing features between conditions that were formerly over-looked in the existing literature, while raising interesting questions concerning hemispheric dominance to the encoding process of facial and identity information.
Fabrication and Characterization of Line-by-Line Inscribed Tilted Fiber Bragg Gratings Using Femtosecond Laser
Authors: Li Hongye , Zhao Xiaofan , Rao Binyu , Wang Meng , Wu Baiyi , Wang Zefeng
Published: September 2021

In this paper, we studied the basic characteristics of tilted fiber Bragg gratings (TFBGs), inscribed line-by-line. Experimental results showed that if the TFBGs were located within different planes parallel to the fiber axis, the spectra performed differently. For 2°TFBG, if it was located near the central plane, the Bragg resonance was stronger than ghost mode resonance, and the order reversed if it was located near the boundary between core and cladding. As the tilted angle increased, the range of cladding mode resonance increased. When the tilted angle was larger than 12°, the birefringence effect was observed. Based on the birefringence phenomenon, torsion characteristics were experimentally studied; the sensitivity was about 0.025 dB/degree in the linear variation range. The harmonic order of TFBGs also affected the transmission spectrum. Leaky mode resonance was observed in the 8th order TFBG, and torsion (or polarization) influenced the spectrum of the 8th order TFBG. Our research represented the theory of line-by-line inscribed TFBGs and provided an inscription guidance for TFBGs.
Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning
Authors: Khan Asif , Khalid Salman , Raouf Izaz , Sohn Jung-Woo , Kim Heung-Soo
Published: September 2021

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.
Validity, Reliability and Sensitivity to Change of Three Consumer-Grade Activity Trackers in Controlled and Free-Living Conditions among Older Adults
Authors: Kastelic Kaja , Dobnik Marina , Löfler Stefan , Hofer Christian , Šarabon Nejc
Published: September 2021

Wrist-worn consumer-grade activity trackers are popular devices, developed mainly for personal use. This study aimed to explore the validity, reliability and sensitivity to change of movement behaviors metrics from three activity trackers (Polar Vantage M, Garmin Vivoactive 4s and Garmin Vivosport) in controlled and free-living conditions when worn by older adults. Participants (n = 28; 74 ± 5 years) underwent a videotaped laboratory protocol while wearing all three trackers. On a separate occasion, participants (n = 17 for each of the trackers) wore one (randomly assigned) tracker and a research-grade activity monitor ActiGraph wGT3X-BT simultaneously for six consecutive days. Both Garmin trackers showed excellent performance for step counts, with a mean absolute percentage error (MAPE) below 20% and intraclass correlation coefficient (ICC2,1) above 0.90 (p < 0.05). The MAPE for sleep time was within 10% for all the trackers tested, while it was far beyond 20% for all other movement behaviors metrics. The results suggested that all three trackers could be used for measuring sleep time with a high level of accuracy, and both Garmin trackers could also be used for step counts. All other output metrics should be used with caution. The results provided in this study could be used to guide choice on activity trackers aiming for different purposes—individual use, longitudinal monitoring or in clinical trial setting.
Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump
Authors: Konieczny Jarosław , Stojek Jerzy
Published: September 2021

This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.
Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study
Authors: Chou Jau-Yu , Chang Chia-Ming
Published: September 2021

Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing displacement transducers between the ground and floors in real-world buildings is unrealistic due to lack of reference points and structural scales and complexity. Alternatively, structural displacements can be acquired using computer vision-based motion extraction techniques. These extracted motions not only provide vibrational responses but are also useful for identifying the modal properties. In this study, three methods, including the optical flow with the Lucas–Kanade method, the digital image correlation (DIC) with bilinear interpolation, and the in-plane phase-based motion magnification using the Riesz pyramid, are introduced and experimentally verified using a four-story steel-frame building with a commercially available camera. First, the three displacement acquiring methods are introduced in detail. Next, the displacements are experimentally obtained from these methods and compared to those sensed from linear variable displacement transducers. Moreover, these displacement responses are converted into modal properties by system identification. As seen in the experimental results, the DIC method has the lowest average root mean squared error (RMSE) of 1.2371 mm among these three methods. Although the phase-based motion magnification method has a larger RMSE of 1.4132 mm due to variations in edge detection, this method is capable of providing full-field mode shapes over the building.
Simultaneous Absorbance and Fluorescence Measurements Using an Inlaid Microfluidic Approach
Authors: Creelman Joshua J. , Luy Edward A. , Beland Gabryelle C. H. , Sonnichsen Colin , Sieben Vincent J.
Published: September 2021

A novel microfluidic optical cell is presented that enables simultaneous measurement of both light absorbance and fluorescence on microlitre volumes of fluid. The chip design is based on an inlaid fabrication technique using clear and opaque poly(methyl methacrylate) or PMMA to create a 20.2 mm long optical cell. The inlaid approach allows fluid interrogation with minimal interference from external light over centimeter long path lengths. The performance of the optical cell is evaluated using a stable fluorescent dye: rhodamine B. Excellent linear relationships (R2 > 0.99) are found for both absorbance and fluorescence over a 0.1–10 µM concentration range. Furthermore, the molar attenuation spectrum is accurately measured over the range 460–550 nm. The approach presented here is applicable to numerous colorimetric- or fluorescence-based assays and presents an important step in the development of multipurpose lab-on-chip sensors.
Development and Evaluation of an Improved Apparatus for Measuring the Emissivity at High Temperatures
Authors: Arduini Mariacarla , Manara Jochen , Stark Thomas , Ebert Hans-Peter , Hartmann Jürgen
Published: September 2021

An improved apparatus for measuring the spectral directional emissivity in the wavelength range between 1 µm and 20 µm at temperatures up to 2400 K is presented in this paper. As a heating unit an inductor is used to warm up the specimen, as well as the blackbody reference to the specified temperatures. The heating unit is placed in a double-walled vacuum vessel. A defined temperature, as well as a homogenous temperature distribution of the whole surrounding is ensured by a heat transfer fluid flowing through the gap of the double-walled vessel. Additionally, the surrounding is coated with a high-emitting paint and serves as blackbody-like surrounding to ensure defined boundary conditions. For measuring the spectral directional emissivity at different emission angles, a movable mirror is installed in front of the specimen, which can be adjusted by a rotatable arrangement guiding the emitted radiation into the attached FTIR-spectrometer. The setup of the emissivity measurement apparatus (EMMA) and the measurement procedure are introduced, and the derived measurement results are presented. For evaluating the apparatus, measurements were performed on different materials. The determined emissivities agree well with values published in literature within the derived relative uncertainties below 4% for most wavelengths.
Accent Recognition with Hybrid Phonetic Features
Authors: Zhang Zhan , Wang Yuehai , Yang Jianyi
Published: September 2021

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method.
Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios
Authors: Araluce Javier , Bergasa Luis M. , Ocaña Manuel , López-Guillén Elena , Revenga Pedro A. , Arango J. Felipe , Pérez Oscar
Published: September 2021

Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver’s gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.
StegoFrameOrder—MAC Layer Covert Network Channel for Wireless IEEE 802.11 Networks
Authors: Sawicki Krzysztof , Bieszczad Grzegorz , Piotrowski Zbigniew
Published: September 2021

The proposed StegoFrameOrder (SFO) method enables the transmission of covert data in wireless computer networks exploiting non-deterministic algorithms of medium access (such as the distributed coordination function), especially in IEEE 802.11 networks. Such a covert channel enables the possibility of leaking crucial information outside secured network in a manner that is difficult to detect. The SFO method embeds hidden bits of information in the relative order of frames transmitted by wireless terminals operating on the same radio channel. The paper presents an idea of this covert channel, its implementation, and possible variants. The paper also discusses implementing the SFO method in a real environment and the experiments performed in the real-world scenario.
Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning
Authors: Kolakowski Marcin
Published: September 2021

This paper describes an automated method for the calibration of RSS-fingerprinting-based positioning systems. The method assumes using a robotic platform to gather fingerprints in the system environment and using them for training machine learning models. The obtained models are used for positioning purposes during the system operation. The presented calibration method covers all steps of the system calibration, from mapping the system environment using a GraphSLAM based algorithm to training models for radio map calibration. The study analyses four different models: fitting a log-distance path loss model, Gaussian Process Regression, Artificial Neural Network and Random Forest Regression. The proposed method was tested in a BLE-based indoor localisation system set up in a fully furnished apartment. The results have shown that the tested models allow for localisation with accuracy comparable to those reported in the literature. In the case of the Neural Network regression, the median error of robot positioning was 0.87 m. The median of trajectory error in a walking person localisation scenario was 0.4 m.
The Short-Term Performances of Two Independent Gas Modulated Refractometers for Pressure Assessments
Authors: Forssén Clayton , Silander Isak , Zakrisson Johan , Axner Ove , Zelan Martin
Published: September 2021

Refractometry is a powerful technique for pressure assessments that, due to the recent redefinition of the SI system, also offers a new route to realizing the SI unit of pressure, the Pascal. Gas modulation refractometry (GAMOR) is a methodology that has demonstrated an outstanding ability to mitigate the influences of drifts and fluctuations, leading to long-term precision in the 10−7 region. However, its short-term performance, which is of importance for a variety of applications, has not yet been scrutinized. To assess this, we investigated the short-term performance (in terms of precision) of two similar, but independent, dual Fabry–Perot cavity refractometers utilizing the GAMOR methodology. Both systems assessed the same pressure produced by a dead weight piston gauge. That way, their short-term responses were assessed without being compromised by any pressure fluctuations produced by the piston gauge or the gas delivery system. We found that the two refractometer systems have a significantly higher degree of concordance (in the 10−8 range at 1 s) than what either of them has with the piston gauge. This shows that the refractometry systems under scrutiny are capable of assessing rapidly varying pressures (with bandwidths up to 2 Hz) with precision in the 10−8 range.
VES: A Mixed-Reality Development Platform of Navigation Systems for Blind and Visually Impaired
Authors: Real Santiago , Araujo Alvaro
Published: September 2021

Herein, we describe the Virtually Enhanced Senses (VES) system, a novel and highly configurable wireless sensor-actuator network conceived as a development and test-bench platform of navigation systems adapted for blind and visually impaired people. It allows to immerse its users into “walkable” purely virtual or mixed environments with simulated sensors and validate navigation system designs prior to prototype development. The haptic, acoustic, and proprioceptive feedback supports state-of-art sensory substitution devices (SSD). In this regard, three SSD were integrated in VES as examples, including the well-known “The vOICe”. Additionally, the data throughput, latency and packet loss of the wireless communication can be controlled to observe its impact in the provided spatial knowledge and resulting mobility and orientation performance. Finally, the system has been validated by testing a combination of two previous visual-acoustic and visual-haptic sensory substitution schemas with 23 normal-sighted subjects. The recorded data includes the output of a “gaze-tracking” utility adapted for SSD.
Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning
Authors: Pamuła Teresa , Pamuła Wiesław
Published: September 2021

The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.
Baseline Correction of Acceleration Data Based on a Hybrid EMD–DNN Method
Authors: Chen Zengshun , Fu Jun , Peng Yanjian , Chen Tuanhai , Zhang LiKai , Yuan Chenfeng
Published: September 2021

Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).
Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review
Authors: Palumbo Arrigo , Gramigna Vera , Calabrese Barbara , Ielpo Nicola
Published: September 2021

The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.
BA-MPCUBIC: Bottleneck-Aware Multipath CUBIC for Multipath-TCP
Authors: Mahmud Imtiaz , Lubna Tabassum , Kim Geon-Hwan , Cho You-Ze
Published: September 2021

The Congestion Control Algorithm (CCA) in the Multipath Transmission Control Protocol (MPTCP) is fundamental to controlling the flow of data through multiple subflows (SF) simultaneously. The MPTCP CCA has two design goals: first, always ensure better throughput than single path TCP (SPTCP) flows, and second, collectively, MPTCP SFs going through a shared bottleneck (SB) should occupy bandwidth fairly, i.e., close to the bandwidth occupied by an SPTCP flow. Although several MPTCP CCAs exist, they primarily focus on specific scenarios and could not satisfy the design goals in diverse and dynamic scenarios. Recently, CUBIC has become a widely used CCA for SPTCP for its better compatibility with high-speed internet. CUBIC’s effective implementation in the MPTCP is expected to provide improved throughput and fairer behavior, thus satisfying the design goals. However, although the current multipath CUBIC (MPCUBIC) implementation ensures better fairness, it fails to ensure better throughput. We believe the application of same rule for SFs going through an SB and non-shared bottleneck (NSB) makes it difficult for MPCUBIC to adapt to diverse and dynamically changing network scenarios, thus resulting in poor throughput. Therefore, we present an improved version of MPCUBIC, namely bottleneck-aware MPCUBIC (BA-MPCUBIC), to resolve the throughput issue. First, we deploy an innovative bottleneck detection method that successfully differentiates between an SB and NSB based on round-trip-time, enhanced congestion notification, and packet loss. Then, we implement SPTCP CUBIC and MPCUBIC as the CCAs for SFs going through NSBs and SBs, respectively. Extensive emulation experiments demonstrate that the BA-MPCUBIC successfully detects SBs and NSBs with the highest detection accuracy and the lowest detection time compared with other approaches. Moreover, BA-MPCUBIC successfully satisfies the MPTCP design goals in the considered diverse and dynamic scenarios by ensuring both better throughput and fairness.
The Seismo-Performer: A Novel Machine Learning Approach for General and Efficient Seismic Phase Recognition from Local Earthquakes in Real Time
Authors: Stepnov Andrey , Chernykh Vladimir , Konovalov Alexey
Published: September 2021

When recording seismic ground motion in multiple sites using independent recording stations one needs to recognize the presence of the same parts of seismic waves arriving at these stations. This problem is known in seismology as seismic phase picking. It is challenging to automate the accurate picking of seismic phases to the level of human capabilities. By solving this problem, it would be possible to automate routine processing in real time on any local network. A new machine learning approach was developed to classify seismic phases from local earthquakes. The resulting model is based on spectrograms and utilizes the transformer architecture with a self-attention mechanism and without any convolution blocks. The model is general for various local networks and has only 57 k learning parameters. To assess the generalization property, two new datasets were developed, containing local earthquake data collected from two different regions using a wide variety of seismic instruments. The data were not involved in the training process for any model to estimate the generalization property. The new model exhibits the best classification and computation performance results on its pre-trained weights compared with baseline models from related work. The model code is available online and is ready for day-to-day real-time processing on conventional seismic equipment without graphics processing units.
Study of Forward and Backward Modes in Double-Sided Dielectric-Filled Corrugated Waveguides
Authors: Castillo-Tapia Pilar , Mesa Francisco , Yakovlev Alexander , Valerio Guido , Quevedo-Teruel Oscar
Published: September 2021

This work studies the propagation characteristics of a rectangular waveguide with aligned/misaligned double-sided dielectric-filled metallic corrugations. Two modes are found to propagate in the proposed double-sided configuration below the hollow-waveguide cutoff frequency: a quasi-resonant mode and a backward mode. This is in contrast to the single-sided configuration, which only allows for backward propagation. Moreover, the double-sided configuration can be of interest for waveguide miniaturization on account of the broader band of its backward mode. The width of the stopband between the quasi-resonant and backward modes can be controlled by the misalignment of the top and bottom corrugations, being null for the glide-symmetric case. The previous study is complemented with numerical results showing the impact of the height of the corrugations, as well as the filling dielectric permittivity, on the bandwidth and location of the appearing negative-effective-permeability band. The multi-modal transmission-matrix method has also been employed to estimate the rejection level and material losses in the structure and to determine which port modes are associated with the quasi-resonant and backward modes. Finally, it is shown that glide symmetry can advantageously be used to reduce the dispersion and broadens the operating band of the modes.
Sinusoidal Phase-Modulated Angle Interferometer for Angular Vibration Measurement
Authors: Wang Xianfan , Yang Jianhua , Chen Meng , Miao Lijun , Huang Tengchao
Published: September 2021

Primary angular vibration calibration devices based on laser interferometers play a crucial role in evaluating the dynamic performance of inertial sensing devices. Here, we propose a sinusoidal phase-modulated angle interferometer (SPMAI) to realize angular vibration measurements over a frequency range of 1–1000 Hz, in which the sinusoidal measurement retro-reflector (SMR) and the phase generation carrier (PGC) demodulation algorithm are adopted to track the dynamic angle variation. A comprehensive theoretical analysis is presented to reveal the relationship between demodulation performance of the SPMAI and several factors, such as phase modulation depth, carrier phase delay and sampling frequency. Both the simulated and experimental results demonstrate that the proposed SPMAI can achieve an angular vibration measurement with amplitude of sub-arcsecond under given parameters. Using the proposed SPMAI, the frequency bandwidth of an interferometric fiber-optic gyroscope (IFOG) is successfully determined to be 848 Hz.
Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda
Authors: Cheng Chun-Hong , Wong Kwan-Long , Chin Jing-Wei , Chan Tsz-Tai , So Richard H. Y.
Published: September 2021

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.
A Systematic Mapping Study on Integration Proposals of the Personas Technique in Agile Methodologies
Authors: Losana Patricia , Castro John W. , Ferre Xavier , Villalba-Mora Elena , Acuña Silvia T.
Published: September 2021

Agile development processes are increasing their consideration of usability by integrating various user-centered design techniques throughout development. One such technique is Personas, which proposes the creation of fictitious users with real preferences to drive application design. Since applying this technique conflicts with the time constraints of agile development, Personas has been adapted over the years. Our objective is to determine the adoption level and type of integration, as well as to propose improvements to the Personas technique for agile development. A systematic mapping study was performed, retrieving 28 articles grouped by agile methodology type. We found some common integration strategies regardless of the specific agile approach, along with some frequent problems, mainly related to Persona modelling and context representation. Based on these limitations, we propose an adaptation to the technique in order to reduce the creation time for a preliminary persona. The number of publications dealing with Personas and agile development is increasing, which reveals a growing interest in the application of this technique to develop usable agile software.
Two-Dimensional Cartesian Coordinate System Educational Toolkit: 2D-CACSET
Authors: Castañeda-Miranda Víctor H. , Luque-Vega Luis F. , Lopez-Neri Emmanuel , Nava-Pintor Jesús Antonio , Guerrero-Osuna Héctor A. , Ornelas-Vargas Gerardo
Published: September 2021

Engineering education benefits from the application of modern technology, allowing students to learn essential Science, Technology, Engineering, and Mathematics (STEM) related concepts through hands-on experiences. Robotic kits have been used as an innovative tool in some educational fields, being readily accepted and adopted. However, most of the time, such kits’ knowledge level requires understanding basic concepts that are not always appropriate for the student. A critical concept in engineering is the Cartesian Coordinate System (CCS), an essential tool for every engineering, from graphing functions to data analysis in robotics and control applications and beyond. This paper presents the design and implementation of a novel Two-Dimensional Cartesian Coordinate System Educational Toolkit (2D-CACSET) to teach the two-dimensional representations as the first step to construct spatial thinking. This innovative educational toolkit is based on real-time location systems using Ultra-Wide Band technology. It comprises a workbench, four Anchors pinpointing X+, X−, Y+, Y− axes, seven Tags representing points in the plane, one listener connected to a PC collecting the position of the Tags, and a Graphical User Interface displaying these positions. The Educational Mechatronics Conceptual Framework (EMCF) enables constructing knowledge in concrete, graphic, and abstract levels. Hence, the students acquire this knowledge to apply it further down their career path. For this paper, three instructional designs were designed using the 2D-CACSET and the EMCF to learn about coordinate axes, quadrants, and a point in the CCS.
Modular Robotic Limbs for Astronaut Activities Assistance
Authors: Zhao Sikai , Zhao Jie , Sui Dongbao , Wang Tianshuo , Zheng Tianjiao , Zhao Chuanwu , Zhu Yanhe
Published: September 2021

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method.
Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition
Authors: Budisteanu Elena-Alexandra , Mocanu Irina Georgiana
Published: September 2021

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.
A Blockchain-IoT Platform for the Smart Pallet Pooling Management
Authors: Wu Chun-Ho , Tsang Yung-Po , Lee Carman Ka-Man , Ching Wai-Ki
Published: September 2021

Pallet management as a backbone of logistics and supply chain activities is essential to supply chain parties, while a number of regulations, standards and operational constraints are considered in daily operations. In recent years, pallet pooling has been unconventionally advocated to manage pallets in a closed-loop system to enhance the sustainability and operational effectiveness, but pitfalls in terms of service reliability, quality compliance and pallet limitation when using a single service provider may occur. Therefore, this study incorporates a decentralisation mechanism into the pallet management to formulate a technological eco-system for pallet pooling, namely Pallet as a Service (PalletaaS), raised by the foundation of consortium blockchain and Internet of things (IoT). Consortium blockchain is regarded as the blockchain 3.0 to facilitate more industrial applications, except cryptocurrency, and the synergy of integrating a consortium blockchain and IoT is thus investigated. The corresponding layered architecture is proposed to structure the system deployment in the industry, in which the location-inventory-routing problem for pallet pooling is formulated. To demonstrate the values of this study, a case analysis to illustrate the human–computer interaction and pallet pooling operations is conducted. Overall, this study standardises the decentralised pallet management in the closed-loop mechanism, resulting in a constructive impact to sustainable development in the logistics industry.
State Observer for Linear Systems with Explicit Constraints: Orthogonal Decomposition Method
Authors: Savin Sergei , Balakhnov Oleg , Khusainov Ramil , Klimchik Alexandr
Published: September 2021

In this paper, an orthogonal decomposition-based state observer for systems with explicit constraints is proposed. State observers have been an integral part of robotic systems, reflecting the practicality and effectiveness of the dynamic state feedback control, but the same methods are lacking for the systems with explicit mechanical constraints, where observer designs have been proposed only for special cases of such systems, with relatively restrictive assumptions. This work aims to provide an observer design framework for a general case linear time-invariant system with explicit constraints, by finding lower-dimensional subspaces in the state space, where the system is observable while giving sufficient information for both feedback and feed-forward control. We show that the proposed formulation recovers minimal coordinate representation when it is sufficient for the control law generation and retains non-minimal coordinates when those are required for the feed-forward control law. The proposed observer is tested on a flywheel inverted pendulum and on a quadruped robot Unitree A1.
The State Space Subdivision Filter for Estimation on SE(2)
Authors: Pfaff Florian , Li Kailai , Hanebeck Uwe D.
Published: September 2021

The SE(2) domain can be used to describe the position and orientation of objects in planar scenarios and is inherently nonlinear due to the periodicity of the angle. We present a novel filter that involves splitting up the joint density into a (marginalized) density for the periodic part and a conditional density for the linear part. We subdivide the state space along the periodic dimension and describe each part of the state space using the parameters of a Gaussian and a grid value, which is the function value of the marginalized density for the periodic part at the center of the respective area. By using the grid values as weighting factors for the Gaussians along the linear dimensions, we can approximate functions on the SE(2) domain with correlated position and orientation. Based on this representation, we interweave a grid filter with a Kalman filter to obtain a filter that can take different numbers of parameters and is in the same complexity class as a grid filter for circular domains. We thoroughly compared the filters with other state-of-the-art filters in a simulated tracking scenario. With only little run time, our filter outperformed an unscented Kalman filter for manifolds and a progressive filter based on dual quaternions. Our filter also yielded more accurate results than a particle filter using one million particles while being faster by over an order of magnitude.
Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors
Authors: Moreira Dinis , Barandas Marília , Rocha Tiago , Alves Pedro , Santos Ricardo , Leonardo Ricardo , Vieira Pedro , Gamboa Hugo
Published: September 2021

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.