bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Labeled and Small Loss Selection Semi-Supervised Learning Method for Remote Sensing Image Scene Classification
Authors: Tian Ye , Dong Yuxin , Yin Guisheng
Published: October 2021

The classification of aerial scenes has been extensively studied as the basic work of remote sensing image processing and interpretation. However, the performance of remote sensing image scene classification based on deep neural networks is limited by the number of labeled samples. In order to alleviate the demand for massive labeled samples, various methods have been proposed to apply semi-supervised learning to train the classifier using labeled and unlabeled samples. However, considering the complex contextual relationship and huge spatial differences, the existing semi-supervised learning methods bring different degrees of incorrectly labeled samples when pseudo-labeling unlabeled data. In particular, when the number of labeled samples is small, it affects the generalization performance of the model. In this article, we propose a novel semi-supervised learning method with early labeled and small loss selection. First, the model learns the characteristics of simple samples in the early stage and uses multiple early models to screen out a small number of unlabeled samples for pseudo-labeling based on this characteristic. Then, the model is trained in a semi-supervised manner by combining labeled samples, pseudo-labeled samples, and unlabeled samples. In the training process of the model, small loss selection is used to further eliminate some of the noisy labeled samples to improve the recognition accuracy of the model. Finally, in order to verify the effectiveness of the proposed method, it is compared with several state-of-the-art semi-supervised classification methods. The results show that when there are only a few labeled samples in remote sensing image scene classification, our method is always better than previous methods.
Estimation of Long-Term River Discharge and Its Changes in Ungauged Watersheds in Pamir Plateau
Authors: Wufu Adilai , Yang Shengtian , Chen Yun , Lou Hezhen , Li Chaojun , Ma Ligang
Published: October 2021

The Pamir Plateau is an extremely important water resource area for over 60 million people in Central Asia. With the increasingly significant response of water resources to climate change, timely hydrological predictions for the future supply are necessary. In the plateau, accessing and monitoring the glaciers and their melt outflow are challenging due to the harsh geographic environments. Unmanned aerial vehicles (UAVs) combined with remote sensing technologies offer great potential for providing information to improve water resources management and decision-making. In this study, we integrated UAV and satellite remote sensing data, and applied a water balance model to estimate monthly and annual river discharges for the ten river sections in the Eastern Pamir Plateau, China from 1999 to 2020. We found that the glacier area in the controlled basins of these sections has decreased by approximately 63% from 1999 to 2020. Basins with smaller glacier areas are more sensitive to climate change. The ten river sections are characterized by decreasing trends in monthly river discharge, with an average reduction of −21.05%. The annual variation of total runoff and glacial meltwater discharge is consistent with the monthly variation of discharge, and the average discharge from glacier meltwater accounts for 83% of the total runoff. We conclude that the overall decreasing trend of discharge is closely related to the recession of glaciers. Under the background of climate warming in the region, glaciers are no longer sufficient to support the increase in river discharge, which has passed its peak value and shows a decreasing trend.
Metop First Generation AVHRR FRAC SST Reanalysis Version 1
Authors: Pryamitsyn Victor , Petrenko Boris , Ignatov Alexander , Kihai Yury
Published: October 2021

The first full-mission global AVHRR FRAC sea surface temperature (SST) dataset with a nominal 1.1 km resolution at nadir was produced from three Metop First Generation (FG) satellites: Metop-A (2006-on), -B (2012-on) and -C (2018-on), using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) SST enterprise system. Historical reprocessing (‘Reanalysis-1’, RAN1) starts at the beginning of each mission and continues into near-real time (NRT). ACSPO generates two SST products, one with global regression (GR; highly sensitive to skin SST), and another one with piecewise regression (PWR; proxy for depth SST) algorithms. Small residual effects of orbital and sensor instabilities on SST retrievals are mitigated by retraining the regression coefficients daily, using matchups with drifting and tropical moored buoys within moving time windows. In RAN, the training windows are centered at the processed day. In NRT, the same size windows are employed but delayed in time, ending four to ten days prior to the processed day. Delayed-mode RAN reprocessing follows the NRT with a two-month lag, resulting in a higher quality and a more consistent SST record. In addition to its completeness, the newly created Metop-FG RAN1 SST dataset shows very close agreement with in situ data (including the fully independent Argo floats), well within the NOAA specifications for accuracy (global mean bias; ±0.2 K) and precision (global standard deviation; 0.6 K) in a ~20% clear-sky domain (percent of clear-sky SST pixels to the total of ice-free ocean). All performance statistics are stable in time, and consistent across the three platforms. The Metop-FG RAN1 data set is archived at the NASA JPL PO.DAAC and NOAA NCEI. This paper documents the newly created dataset and evaluates its performance.
Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating
Authors: Uddin Md Zia , Seeberg Trine M. , Kocbach Jan , Liverud Anders E. , Gonzalez Victor , Sandbakk Øyvind , Meyer Frédéric
Published: October 2021

The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.
Applicability of Geomorphic Index for the Potential Slope Instability in the Three River Region, Eastern Tibetan Plateau
Authors: Liu Feng , Yao Xin , Li Lingjing
Published: October 2021

Geomorphic indices (e.g., the normalized channel steepness index (Ksn) and the stream length-gradient index (SL)) highlight changes in fluvial shapes and gradients. However, the application of these indices was seldom used to identify potential landslide zones. In this study, we used the Ksn and SL indices to detect the significant variations in the stream power along river reaches, which are anomalies associated with landslides, in the Zengqu River watershed, the upper reaches of the Jinsha River. Most of the landslide anomalies originate along the trunk and surrounding tributaries below the knickpoint of the mainstream. This suggests an erosional wave is migrating upstream throughout the drainage area. The fluvial incision may generate over-steepened hillslopes, which could fail in the future. In addition, the divide asymmetry index (DAI) predicts the direction of the divide as the headwaters migrate toward lower relief, higher elevation surfaces. Landslides are expected to occur as the unstable divide migrates. The proposed methodology can benefit the detection and characterization of potential landslide zones. It should improve hazard and risk analysis and the identification of drainage network areas associated with landslides.
HEAD Metamodel: Hierarchical, Extensible, Advanced, and Dynamic Access Control Metamodel for Dynamic and Heterogeneous Structures
Authors: Kashmar Nadine , Adda Mehdi , Ibrahim Hussein
Published: October 2021

The substantial advancements in information technologies have brought unprecedented concepts and challenges to provide solutions and integrate advanced and self-ruling systems in critical and heterogeneous structures. The new generation of networking environments (e.g., the Internet of Things (IoT), cloud computing, etc.) are dynamic and ever-evolving environments. They are composed of various private and public networks, where all resources are distributed and accessed from everywhere. Protecting resources by controlling access to them is a complicated task, especially with the presence of cybercriminals and cyberattacks. What makes this reality also challenging is the diversity and the heterogeneity of access control (AC) models, which are implemented and integrated with a countless number of information systems. The evolution of ubiquitous computing, especially the concept of Industry 4.0 and IoT applications, imposes the need to enhance AC methods since the traditional methods are not able to answer the increasing demand for privacy and security standards. To address this issue, we propose a Hierarchical, Extensible, Advanced, and Dynamic (HEAD) AC metamodel for dynamic and heterogeneous structures that is able to encompass the heterogeneity of the existing AC models. Various AC models can be derived, and different static and dynamic AC policies can be generated using its components. We use Eclipse (xtext) to define the grammar of our AC metamodel. We illustrate our approach with several successful instantiations for various models and hybrid models. Additionally, we provide some examples to show how some of the derived models can be implemented to generate AC policies.
Meander Thin-Film Biosensor Fabrication to Investigate the Influence of Structural Parameters on the Magneto-Impedance Effect
Authors: Sayad Abkar , Uddin Shah Mukim , Chan Jianxiong , Skafidas Efstratios , Kwan Patrick
Published: October 2021

Thin-film magneto-impedance (MI) biosensors have attracted significant attention due to their high sensitivity and easy miniaturization. However, further improvement is required to detect weak biomagnetic signals. Here, we report a meander thin-film biosensor preparation to investigate the fabrication parameters influencing the MI effect. Specifically, we hypothesized that an optimal film thickness and sensing area size ratio could be achieved to obtain a maximum MI ratio. A meander multilayer MI biosensor based on a NiFe/Cu/NiFe thin-film was designed and fabricated into 3-, 6-, and 9-turn models with film thicknesses of 3 µm and 6 µm. The 9-turn biosensor resembled the largest sensing area, while the 3- and 6-turn biosensors were designed with identical sensing areas. The results indicated that the NiFe film thickness of 6 µm with a sensing area size of 14.4 mm2 resembling a 9-turn MI biosensor is the optimal ratio yielding the maximum MI ratio of 238%, which is 70% larger than the 3- and 6-turn structures. The 3- and 6-turn MI biosensors exhibited similar characteristics where the MI ratio peaked at a similar value. Our results suggest that the MI ratio can be increased by increasing the sensing area size and film thickness rather than the number of turns. We showed that an optimal film thickness to sensing area size ratio is required to obtain a high MI ratio. Our findings will be useful for designing highly sensitive MI biosensors capable of detecting low biomagnetic signals.
Supporting Visualization Analysis in Industrial Process Tomography by Using Augmented Reality—A Case Study of an Industrial Microwave Drying System
Authors: Zhang Yuchong , Omrani Adel , Yadav Rahul , Fjeld Morten
Published: October 2021

Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this article, we propose a complete data processing and visualizing workflow regarding a specific case—microwave tomography (MWT) controlled industrial microwave drying system. Furthermore, we present the up-to-date augmented reality (AR) technique to support the corresponding data visualization and on-site analysis. As a pioneering study of using AR to benefit IPT systems, the proposed AR module provides straightforward and comprehensible visualizations pertaining to the process data to the related users. Inside the dataflow of the case, a time reversal imaging algorithm, a post-imaging segmentation, and a volumetric visualization module are included. For the time reversal algorithm, we exhaustively introduce each step for MWT image reconstruction and then present the simulated results. For the post-imaging segmentation, an automatic tomographic segmentation algorithm is utilized to reveal the significant information contained in the reconstructed images. For volumetric visualization, the 3D generated information is displayed. Finally, the proposed AR system is integrated with the on-going process data, including reconstructed, segmented, and volumetric images, which are used for facilitating interactive on-site data analysis for domain users. The central part of the AR system is implemented by a mobile app that is currently supported on iOS/Android platforms.
A Sensor Fault Detection Scheme as a Functional Safety Feature for DC-DC Converters
Authors: Schmidt Simon , Oberrath Jens , Mercorelli Paolo
Published: October 2021

DC-DC converters are widely used in a large number of power conversion applications. As in many other systems, they are designed to automatically prevent dangerous failures or control them when they arise; this is called functional safety. Therefore, random hardware failures such as sensor faults have to be detected and handled properly. This proper handling means achieving or maintaining a safe state according to ISO 26262. However, to achieve or maintain a safe state, a fault has to be detected first. Sensor faults within DC-DC converters are generally detected with hardware-redundant sensors, despite all their drawbacks. Within this article, this redundancy is addressed using observer-based techniques utilizing Extended Kalman Filters (EKFs). Moreover, the paper proposes a fault detection and isolation scheme to guarantee functional safety. For this, a cross-EKF structure is implemented to work in cross-parallel to the real sensors and to replace the sensors in case of a fault. This ensures the continuity of the service in case of sensor faults. This idea is based on the concept of the virtual sensor which replaces the sensor in case of fault. Moreover, the concept of the virtual sensor is broader. In fact, if a system is observable, the observer offers a better performance than the sensor. In this context, this paper gives a contribution in this area. The effectiveness of this approach is tested with measurements on a buck converter prototype.
The Direct Effect of Magnetic Tape® on Pain and Lower-Extremity Blood Flow in Subjects with Low-Back Pain: A Randomized Clinical Trial
Authors: Selva-Sarzo Francisco , Fernández-Carnero Samuel , Sillevis Rob , Hernández-Garcés Héctor , Benitez-Martinez Josep-Carles , Cuenca-Zaldívar Juan-Nicolás
Published: October 2021

Low-back pain has a high impact on the world population, and solutions are in demand. The behavior of specific physiological processes has been modified using magnetic fields, whether for pain relief, bone consolidation, or improvement of vascularization. The use of tape with magnetic properties could help in these cases. A double-blind randomized clinical trial was designed to use Magnetic Tape® versus placebo Kinesio tape. Blood flow variables were evaluated using pulsed power Doppler ultrasound. Resistance index, pulsatility index, systolic velocity, and diastolic velocity were measured. The pressure pain threshold was measured using algometry in 22 subjects. The results reveal significant differences between the groups for the pulsation index variable (8.06 [5.16, 20.16] in Magnetic Tape® versus 5.50 [4.56, 6.64] in Kinesio tape) and lower (0.98 [0.92, 1.02] for Magnetic Tape® versus 0.99 [0.95, 1.01] for Kinesio tape) in the resistance index variable. The pressure pain threshold variable presented significant differences at multiple levels. The application of Magnetic Tape® causes immediate effects on blood flow and pain and could be a technique of choice for pain modulation. Further studies would be necessary.
Raman Amplification Optimization in Short-Reach High Data Rate Coherent Transmission Systems
Authors: Tan Mingming , Iqbal Md Asif , Nguyen Tu T. , Rosa Paweł , Krzczanowicz Lukasz , Phillips Ian. D. , Harper Paul , Forysiak Wladek
Published: October 2021

We compared the transmission performances of 600 Gbit/s PM-64QAM WDM signals over 75.6 km of single-mode fibre (SMF) using EDFA, discrete Raman, hybrid Raman/EDFA, and first-order or second-order (dual-order) distributed Raman amplifiers. Our numerical simulations and experimental results showed that the simple first-order distributed Raman scheme with backward pumping delivered the best transmission performance among all the schemes, notably better than the expected second-order Raman scheme, which gave a flatter signal power variation along the fibre. Using the first-order backward Raman pumping scheme demonstrated a better balance between the ASE noise and fibre nonlinearity and gave an optimal transmission performance over a relatively short distance of 75 km SMF.
Automatically Attributing Mobile Threat Actors by Vectorized ATT&CK Matrix and Paired Indicator
Authors: Kim Kyoungmin , Shin Youngsup , Lee Justin , Lee Kyungho
Published: October 2021

During the past decade, mobile attacks have been established as an indispensable attack vector adopted by Advanced Persistent Threat (APT) groups. The ubiquitous nature of the smartphone has allowed users to use mobile payments and store private or sensitive data (i.e., login credentials). Consequently, various APT groups have focused on exploiting these vulnerabilities. Past studies have proposed automated classification and detection methods, while few studies have covered the cyber attribution. Our study introduces an automated system that focuses on cyber attribution. Adopting MITRE’s ATT&CK for mobile, we performed our study using the tactic, technique, and procedures (TTPs). By comparing the indicator of compromise (IoC), we were able to help reduce the false flags during our experiment. Moreover, we examined 12 threat actors and 120 malware using the automated method for detecting cyber attribution.
Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge
Authors: Daher Ali Walid , Rizik Ali , Muselli Marco , Chible Hussein , Caviglia Daniele D.
Published: October 2021

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
Differential Soft Sensor-Based Measurement of Interactive Force and Assistive Torque for a Robotic Hip Exoskeleton
Authors: Wang Sun’an , Zhang Binquan , Yu Zhenyuan , Yan Yu’ang
Published: October 2021

With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure.
VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service
Authors: Detti Andrea , Nakazato Hidenori , Martínez Navarro Juan Antonio , Tropea Giuseppe , Funari Ludovico , Petrucci Luca , Sánchez Segado Juan Andrés , Kanai Kenji
Published: October 2021

Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.
Symmetric Connectivity of Underwater Acoustic Sensor Networks Based on Multi-Modal Directional Transducer
Authors: Qiao Gang , Liu Qipei , Liu Songzuo , Muhammad Bilal , Wen Menghua
Published: October 2021

Topology control is one of the most essential technologies in wireless sensor networks (WSNs); it constructs networks with certain characteristics through the usage of some approaches, such as power control and channel assignment, thereby reducing the inter-nodes interference and the energy consumption of the network. It is closely related to the efficiency of upper layer protocols, especially MAC and routing protocols, which are the same as underwater acoustic sensor networks (UASNs). Directional antenna technology (directional transducer in UASNs) has great advantages in minimizing interference and conserving energy by restraining the beamforming range. It enables nodes to communicate with only intended neighbors; nevertheless, additional problems emerge, such as how to guarantee the connectivity of the network. This paper focuses on the connectivity problem of UASNs equipped with tri-modal directional transducers, where the orientation of a transducer is stabilized after the network is set up. To efficiently minimize the total network energy consumption under constraint of connectivity, the problem is formulated to a minimum network cost transducer orientation (MNCTO) problem and is provided a reduction from the Hamiltonian path problem in hexagonal grid graphs (HPHGG), which is proved to be NP-complete. Furthermore, a heuristic greedy algorithm is proposed for MNCTO. The simulation evaluation results in a contrast with its omni-mode peer, showing that the proposed algorithm greatly reduces the network energy consumption by up to nearly half on the premise of satisfying connectivity.
Hybrid Dissolved-Oxygen and Temperature Sensing: A Nanophotonic Probe for Real-Time Monitoring of Chlorella Algae
Authors: Fallahi Chegeni Niloofar , Ijadi Maghsoodi Parto , Habibi Mahsa , Zare-Behtash Hossein , Majles Ara Mohammad Hossein , Heydari Esmaeil
Published: October 2021

Dissolved-oxygen concentration and temperature are amongst the crucial parameters required for the precise monitoring of biological and biomedical systems. A novel hybrid nanocomposite probe for real-time and contactless measurement of both dissolved-oxygen concentration and temperature, based on a combination of downconverting phosphorescent molecules of platinum octaethylporphyrin and lanthanide-doped upconverting nanoparticles immobilized in a host of polystyrene, is here introduced. Chlorella algae are employed here as a model to demonstrate the hybrid nanophotonic sensor’s capability to monitor the aforementioned two parameters during the photosynthesis process, since these are among the parameters impacting their production efficiency. These algae have attracted tremendous interest due to their potential to be used for diverse applications such as biofuel production; however, feasibility studies on their economic production are still underway.
Multimodal Network Architecture for Shared Situational Awareness amongst Vessels
Authors: Seferagić Amina , Haxhibeqiri Jetmir , Pilozzi Paolo , Hoebeke Jeroen
Published: October 2021

To shift the paradigm towards Industry 4.0, maritime domain aims to utilize shared situational awareness (SSA) amongst vessels. SSA entails sharing various heterogeneous information, depending on the context and use case at hand, and no single wireless technology is equally suitable for all uses. Moreover, different vessels are equipped with different hardware and have different communication capabilities, as well as communication needs. To enable SSA regardless of the vessel’s communication capabilities and context, we propose a multimodal network architecture that utilizes all of the network interfaces on a vessel, including multiple IEEE 802.11 interfaces, and automatically bootstraps the communication transparently to the applications, making the entire communication system environment-aware, service-driven, and technology-agnostic. This paper presents the design, implementation, and evaluation of the proposed network architecture which introduces virtually no additional delays as compared to the Linux communication stack, automates communication bootstrapping, and uses a novel application-network integration concept that enables application-aware networks, as well as network-aware applications. The evaluation was conducted for several IEEE 802.11 flavors. Although inspired by SSA for vessels, the proposed architecture incorporates several concepts applicable in other domains. It is modular enough to support existing, as well as emerging communication technologies.
An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN
Authors: Afzaal Usman , Bhattarai Bhuwan , Pandeya Yagya Raj , Lee Joonwhoan
Published: October 2021

Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.
Characterization of COVID-19’s Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain
Authors: Rodríguez González Ana Belén , Wilby Mark R. , Vinagre Díaz Juan José , Fernández Pozo Rubén
Published: October 2021

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.
Audio-Tactile Rendering: A Review on Technology and Methods to Convey Musical Information through the Sense of Touch
Authors: Remache-Vinueza Byron , Trujillo-León Andrés , Zapata Mireya , Sarmiento-Ortiz Fabián , Vidal-Verdú Fernando
Published: October 2021

Tactile rendering has been implemented in digital musical instruments (DMIs) to offer the musician haptic feedback that enhances his/her music playing experience. Recently, this implementation has expanded to the development of sensory substitution systems known as haptic music players (HMPs) to give the opportunity of experiencing music through touch to the hearing impaired. These devices may also be conceived as vibrotactile music players to enrich music listening activities. In this review, technology and methods to render musical information by means of vibrotactile stimuli are systematically studied. The methodology used to find out relevant literature is first outlined, and a preliminary classification of musical haptics is proposed. A comparison between different technologies and methods for vibrotactile rendering is performed to later organize the information according to the type of HMP. Limitations and advantages are highlighted to find out opportunities for future research. Likewise, methods for music audio-tactile rendering (ATR) are analyzed and, finally, strategies to compose for the sense of touch are summarized. This review is intended for researchers in the fields of haptics, assistive technologies, music, psychology, and human–computer interaction as well as artists that may make use of it as a reference to develop upcoming research on HMPs and ATR.
Modified Thermocouple Sensor and External Reference Junction Enhance Accuracy in Indoor Air Temperature Measurements
Authors: Lundström Hans , Mattsson Magnus
Published: October 2021

Indoor air temperature belongs to the most important climatic variables in indoor climate research, affecting thermal comfort, energy balance, and air movement in buildings. This paper focuses on measurement errors when using thermocouples in indoor temperature measurements, with special attention on measurements of air temperature. We briefly discuss errors in thermocouple measurements, noting that, for temperatures restricted to indoor temperature ranges, a thermocouple Type T performs much better than stated in “standards”. When thermocouples are described in the literature, industrial applications are primarily considered, involving temperatures up to several hundred degrees and with moderate demands on accuracy. In indoor applications, the temperature difference between the measuring and the reference junction is often only a few degrees. Thus, the error contribution from the thermocouple itself is almost immeasurable, while the dominant error source is in the internal reference temperature compensation in the measuring instrument. It was shown that using an external reference junction can decrease the measurement error substantially (i.e., down to a few hundredths of a degree) in room temperature measurements. One example of how such a device may be assembled is provided. A special application of room temperature measurements involves measuring indoor air temperature. Here, errors, due to radiation influence on the sensor from surrounding surfaces, were surprisingly high. The means to estimate the radiative influence on typical thermocouples are presented, along with suggestions for modification of thermocouple sensors to lower the radiation impact and thereby improve the measurement accuracy.
A New Current-Shaping Technique Based on a Feedback Injection Mechanism to Reduce VCO Phase Noise
Authors: del Pino Suárez Francisco Javier , Khemchandani Sunil Lalchand
Published: October 2021

Inductor-capacitor voltage controlled oscillators (LC-VCOs) are the most common type of oscillator used in sensors systems, such as transceivers for wireless sensor networks (WSNs), VCO-based reading circuits, VCO-based radar sensors, etc. This work presents a technique to reduce the LC-VCOs phase noise using a new current-shaping method based on a feedback injection mechanism with only two additional transistors. This technique consists of keeping the negative resistance seen from LC tank constant throughout the oscillation cycle, achieving a significant phase noise reduction with a very low area increase. To test this method an LC-VCO was designed, fabricated and measured on a wafer using 90 nm CMOS technology with 1.2 V supply voltage. The oscillator outputs were buffered using source followers to provide additional isolation from load variations and to boost the output power. The tank was tuned to 1.8 GHz, comprising two 1.15 nH with 1.5 turns inductors with a quality factor (Q) of 14, a 3.27 pF metal-oxide-metal capacitor, and two varactors. The measured phase noise was −112 dBc/Hz at 1 MHz offset. Including the pads, the chip area is 750 × 850 μm2.
A Miniature, Fiber-Optic Vibrometer for Measuring Unintended Acoustic Output of Active Hearing Implants during Magnetic Resonance Imaging
Authors: Fierens Guy , Walraevens Joris , Peeters Ronald , Verhaert Nicolas , Glorieux Christ
Published: October 2021

Making use of magnetic resonance imaging (MRI) for diagnostics on patients with implanted medical devices requires caution due to mutual interactions between the device and the electromagnetic fields used by the scanner that can cause a number of adverse events. The presented study offers a novel test method to quantify the risk of unintended output of acoustically stimulating hearing implants. The design and operating principle of an all-optical, MRI safe vibrometer is outlined, followed by an experimental verification of a prototype. Results obtained in an MRI environment indicate that the system can detect peak displacements down to 8 pm for audible frequencies. Feasibility testing was performed with an active middle ear implant that was exposed to several pulse sequences in a 1.5 Tesla MRI environment. Magnetic field induced actuator vibrations, measured during scanning, turned out to be equivalent to estimated sound pressure levels between 25 and 85 dB SPL, depending on the signal frequency. These sound pressure levels are situated well below ambient sound pressure levels generated by the MRI scanning process. The presented case study therefore indicates a limited risk of audible unintended output for the examined hearing implant during MRI.
On the Use of Ridge Gap Waveguide Technology for the Design of Transverse Stub Resonant Antenna Arrays
Authors: Benavides-Vazquez Javier , Vazquez-Roy Jose-Luis , Rajo-Iglesias Eva
Published: October 2021

This paper presents some considerations on the design of a novel antenna consisting of the combination of a transverse stubs (TS) array excited by Ridge Gap Waveguides (RGWs), as well as a discussion of the experimental results obtained from a prototype that was manufactured and measured. A combination of Continuous Transverse Stubs (CTSs) is used as the starting point. Subsequently, the CTSs are modified to include some metallic blockers that split each CTS into a combination (array) of shorter TSs. This is performed in order to excite each individual TS column using a different RGW; thus, ensuring a close to uniform field distribution in the transverse plane of the TS arrays. Hence, the directivity of the antenna is increased. As a series-feed configuration is considered, the antenna keeps a resonant behaviour, having a narrow-band response. A Corporate Feeding Network (CFN) using the aforementioned RGW technology placed in the same layer as the rest of the antenna is included in the design. The radiating area of the antenna is, finally, 5.88λ0×7.12λ0 with a simulated peak gain of 26.2 dBi and a Side Lobe Level (SLL) below −13 dB. A prototype is manufactured and tested. The simulated and measured radiation patterns maintain similar shapes to those of the simulations, with very similar angular widths in both main planes, although the frequency corresponding to the highest directivity changes to 31.8 GHz. A matching bandwidth of 517 MHz and a gain of 24.5 is, finally, achieved at that frequency.
A Joint Model Provisioning and Request Dispatch Solution for Low-Latency Inference Services on Edge
Authors: Prasad Anish , Mofjeld Carl , Peng Yang
Published: October 2021

With the advancement of machine learning, a growing number of mobile users rely on machine learning inference for making time-sensitive and safety-critical decisions. Therefore, the demand for high-quality and low-latency inference services at the network edge has become the key to modern intelligent society. This paper proposes a novel solution that jointly provisions machine learning models and dispatches inference requests to reduce inference latency on edge nodes. Existing solutions either direct inference requests to the nearest edge node to save network latency or balance edge nodes’ workload by reducing queuing and computing time. The proposed solution provisions each edge node with the optimal number and type of inference instances under a holistic consideration of networking, computing, and memory resources. Mobile users can thus be directed to utilize inference services on the edge nodes that offer minimal serving latency. The proposed solution has been implemented using TensorFlow Serving and Kubernetes on an edge cluster. Through simulation and testbed experiments under various system settings, the evaluation results showed that the joint strategy could consistently achieve lower latency than simply searching for the best edge node to serve inference requests.
Deep Learning-Based Analysis of Face Images as a Screening Tool for Genetic Syndromes
Authors: Geremek Maciej , Szklanny Krzysztof
Published: October 2021

Approximately 4% of the world’s population suffers from rare diseases. A vast majority of these disorders have a genetic background. The number of genes that have been linked to human diseases is constantly growing, but there are still genetic syndromes that remain to be discovered. The diagnostic yield of genetic testing is continuously developing, and the need for testing is becoming more significant. Due to limited resources, including trained clinical geneticists, patients referred to clinical genetics units must be accurately selected. Around 30–40% of genetic disorders are associated with specific facial characteristics called dysmorphic features. As part of our research, we analyzed the performance of classifiers based on deep learning face recognition models in detecting dysmorphic features. We tested two classification problems: a multiclass problem (15 genetic disorders vs. controls) and a two-class problem (disease vs. controls). In the multiclass task, the best result reached an accuracy level of 84%. The best accuracy result in the two-class problem reached 96%. More importantly, the binary classifier detected disease features in patients with diseases that were not previously present in the training dataset. The classifier was able to generalize differences between patients and controls, and to detect abnormalities without information about the specific disorder. This indicates that a screening tool based on deep learning and facial recognition could not only detect known diseases, but also detect patients with diseases that were not previously known. In the future, this tool could help in screening patients before they are referred to the genetic unit.
Optical Detection of SARS-CoV-2 Utilizing Antigen-Antibody Binding Interactions
Authors: Ahmad Mahmoud Al , Mustafa Farah , Panicker Neena , Rizvi Tahir A.
Published: October 2021

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus disease (COVID-19) pandemic, is sweeping the world today. This study investigates the optical detection of SARS-CoV-2, utilizing the antigen-antibody binding interactions utilizing a light source from a smart phone and a portable spectrophotometer. The proof-of-concept is shown by detecting soluble preparations of spike protein subunits from SARS-CoV-2, followed by detection of the actual binding potential of the SARS-CoV-2 proteins with their corresponding antigens. The measured binding interactions for RBD and NCP proteins with their corresponding antibodies under different conditions have been measured and analyzed. Based on these observations, a “hump or spike” in light intensity is observed when a specific molecular interaction takes place between two proteins. The optical responses could further be analyzed using the principle component analysis technique to enhance and allows precise detection of the specific target in a multi-protein mixture.
Minimum Length Scheduling for Multi-Cell Full Duplex Wireless Powered Communication Networks
Authors: Iqbal Muhammad Shahid , Sadi Yalcin , Coleri Sinem
Published: October 2021

Wireless powered communication networks (WPCNs) will be a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. These MTC networks will be deployed by using low-power transceivers and a very limited set of transmission configurations. We investigate a novel minimum length scheduling problem for multi-cell full-duplex wireless powered communication networks to determine the optimal power control and scheduling for constant rate transmission model. The formulated optimization problem is combinatorial in nature and, thus, difficult to solve for the global optimum. As a solution strategy, first, we decompose the problem into the power control problem (PCP) and scheduling problem. For the PCP, we propose the optimal polynomial time algorithm based on the evaluation of Perron–Frobenius conditions. For the scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. Through extensive simulations, we demonstrate a 50% reduction in the schedule length by using the proposed algorithm in comparison to unscheduled concurrent transmissions.