bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems
Authors: Lorincz Josip , Ramljak Ivana , Begušić Dinko
Published: January 2022

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.
MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation
Authors: Li Jie , Wang Zhixing , Qi Bo , Zhang Jianlin , Yang Hu
Published: January 2022

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.
Automatic Fungi Recognition: Deep Learning Meets Mycology
Authors: Picek Lukáš , Šulc Milan , Matas Jiří , Heilmann-Clausen Jacob , Jeppesen Thomas S. , Lind Emil
Published: January 2022

The article presents an AI-based fungi species recognition system for a citizen-science community. The system’s real-time identification too — FungiVision — with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset — Danish Fungi 2020 (DF20) — with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities.
A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique
Authors: Alghofaili Yara , Rassam Murad A.
Published: January 2022

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.
A Collision Relationship-Based Driving Behavior Decision-Making Method for an Intelligent Land Vehicle at a Disorderly Intersection via DRQN
Authors: Yu Lingli , Huo Shuxin , Li Keyi , Wei Yadong
Published: January 2022

An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily. A collision relationship-based driving behavior decision-making method via deep recurrent Q network (CR-DRQN) is proposed for intelligent land vehicles. First, the collision relationship between the intelligent land vehicle and surrounding vehicles is designed as the input. The collision relationship is extracted from the observed states with the sensor noise. This avoids a CR-DRQN dimension explosion and speeds up the network training. Then, DRQN is utilized to attenuate the impact of the input noise and achieve driving behavior decision-making. Finally, some comparative experiments are conducted to verify the effectiveness of the proposed method. CR-DRQN maintains a high decision success rate at a disorderly intersection with partially observable states. In addition, the proposed method is outstanding in the aspects of safety, the ability of collision risk prediction, and comfort.
Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters
Authors: Zaborowicz Maciej , Zaborowicz Katarzyna , Biedziak Barbara , Garbowski Tomasz
Published: January 2022

Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.
A Study on Distortion Estimation Based on Image Gradients
Authors: Chin Sin Chee , Chow Chee-Onn , Kanesan Jeevan , Chuah Joon Huang
Published: January 2022

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.
Internet of Things for Smart Community Solutions
Authors: Singh Dhananjay , Divan Mario , Singh Madhusudan
Published: January 2022

The term IoT (Internet of Things) constitutes the quickly developing advanced gadgets with highest computing power with in a constrained VLSI design space [...]
A Novel Two-Axis Differential Resonant Accelerometer Based on Graphene with Transmission Beams
Authors: Xiao Yang , Hu Feng , Zhang Yuchen , Zheng Jiaxing , Qin Shiqiao
Published: January 2022

In this paper, a novel two-axis differential resonant accelerometer based on graphene with transmission beams is presented. This accelerometer can not only reduce the cross sensitivity, but also overcome the influence of gravity, realizing fast and accurate measurement of the direction and magnitude of acceleration on the horizontal plane. The simulation results show that the critical buckling acceleration is 460 g, the linear range is 0–89 g, while the differential sensitivity is 50,919 Hz/g, which is generally higher than that of the resonant accelerometer reported previously. Thus, the accelerometer belongs to the ultra-high sensitivity accelerometer. In addition, increasing the length and tension of graphene can obviously increase the critical linear acceleration and critical buckling acceleration with the decreasing sensitivity of the accelerometer. Additionally, the size change of the force transfer structure can significantly affect the detection performance. As the etching accuracy reaches the order of 100 nm, the critical buckling acceleration can reach up to 5 × 104 g, with a sensitivity of 250 Hz/g. To sum up, a feasible design of a biaxial graphene resonant accelerometer is proposed in this work, which provides a theoretical reference for the fabrication of a graphene accelerometer with high precision and stability.
Mix Frame Visual Servo Control Framework for Autonomous Assistive Robotic Arms
Authors: Arif Zubair , Fu Yili
Published: January 2022

Assistive robotic arms (ARAs) that provide care to the elderly and people with disabilities, are a significant part of Human-Robot Interaction (HRI). Presently available ARAs provide non-intuitive interfaces such as joysticks for control and thus, lacks the autonomy to perform daily activities. This study proposes that, for inducing autonomous behavior in ARAs, visual sensors integration is vital, and visual servoing in the direct Cartesian control mode is the preferred method. Generally, ARAs are designed in a configuration where its end-effector’s position is defined in the fixed base frame while orientation is expressed in the end-effector frame. We denoted this configuration as ‘mixed frame robotic arms’. Consequently, conventional visual servo controllers which operate in a single frame of reference are incompatible with mixed frame ARAs. Therefore, we propose a mixed-frame visual servo control framework for ARAs. Moreover, we enlightened the task space kinematics of a mixed frame ARAs, which led us to the development of a novel “mixed frame Jacobian matrix”. The proposed framework was validated on a mixed frame JACO-2 7 DoF ARA using an adaptive proportional derivative controller for achieving image-based visual servoing (IBVS), which showed a significant increase of 31% in the convergence rate, outperforming conventional IBVS joint controllers, especially in the outstretched arm positions and near the base frame. Our Results determine the need for the mixed frame controller for deploying visual servo control on modern ARAs, that can inherently cater to the robotic arm’s joint limits, singularities, and self-collision problems.
A TDMA-Based MAC Protocol for Mitigating Mobility-Caused Packet Collisions in Vehicular Ad Hoc Networks
Authors: Latif Muhammad Bilal , Liu Feng , Liu Kai
Published: January 2022

An autonomous driving environment poses a very stringent requirement for the timely delivery of safety messages in vehicular ad hoc networks (VANETs). Time division multiple access (TDMA)-based medium access control (MAC) protocols are considered a promising solution because of their time-bound message delivery. However, in the event of mobility-caused packet collisions, they may experience an unpredicted and extended delay in delivering messages, which can cause catastrophic accidents. To solve this problem, a distributed TDMA-based MAC protocol with mobility-caused collision mitigation (MCCM-MAC) is presented in this paper. The protocol uses a novel mechanism to detect merging collisions and mitigates them by avoiding subsequent access collisions. One vehicle in the merging collisions retains the time slot, and the others release the slot. The common neighboring vehicles can timely suggest a suitable new time slot for the vacating vehicles, which can avoid access collisions between their packet transmissions. A tie-breakup mechanism is employed to avoid further access collisions. Simulation results show that the proposed protocol reduces packet loss more than the existing methods. Consequently, the average delay between the successfully delivered periodic messages is also reduced.
Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing
Authors: Javadi S. Hamed , Guerrero Angela , Mouazen Abdul M.
Published: January 2022

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.
Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
Authors: Kim Minki , Kang Sunwon , Lee Byoung-Dai
Published: January 2022

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.
An Ultra-Fast TSP on a CNT Heating Layer for Unsteady Temperature and Heat Flux Measurements in Subsonic Flows
Authors: Bitter Martin , Hilfer Michael , Schubert Tobias , Klein Christian , Niehuis Reinhard
Published: January 2022

In this paper, the authors demonstrate the application of a modified Ru(phen)-based temperature-sensitive paint which was originally developed for the evaluation of unsteady aero-thermodynamic phenomena in high Mach number but short duration experiments. In the present work, the modified TSP with a temperature sensitivity of up to −5.6%/K was applied in a low Mach number long-duration test case in a low-pressure environment. For the demonstration of the paint’s performance, a flat plate with a mounted cylinder was set up in the High-Speed Cascade Wind Tunnel (HGK). The test case was designed to generate vortex shedding frequencies up to 4300 Hz which were sampled using a high-speed camera at 40 kHz frame rate to resolve unsteady surface temperature fields for potential heat-transfer estimations. The experiments were carried out at reduced ambient pressure of p∞ = 13.8 kPa for three inflow Mach numbers being Ma∞=[0.3;0.5;0.7]. In order to enable the resolution of very low temperature fluctuations down to the noise floor of 10−5 K with high spatial and temporal resolution, the flat plate model was equipped with a sprayable carbon nanotube (CNT) heating layer. This constellation, together with the thermal sensors incorporated in the model, allowed for the calculation of a quasi-heat-transfer coefficient from the surface temperature fields. Besides the results of the experiments, the paper highlights the properties of the modified TSP as well as the methodology.
The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value
Authors: Digman Matthew F. , Cherney Jerry H. , Cherney Debbie J. R.
Published: January 2022

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.
Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry Sector
Authors: Montalcini Camille Marie , Voelkl Bernhard , Gómez Yamenah , Gantner Michael , Toscano Michael J.
Published: January 2022

Tracking technologies offer a way to monitor movement of many individuals over long time periods with minimal disturbances and could become a helpful tool for a variety of uses in animal agriculture, including health monitoring or selection of breeding traits that benefit welfare within intensive cage-free poultry farming. Herein, we present an active, low-frequency tracking system that distinguishes between five predefined zones within a commercial aviary. We aimed to evaluate both the processed and unprocessed datasets against a “ground truth” based on video observations. The two data processing methods aimed to filter false registrations, one with a simple deterministic approach and one with a tree-based classifier. We found the unprocessed data accurately determined birds’ presence/absence in each zone with an accuracy of 99% but overestimated the number of transitions taken by birds per zone, explaining only 23% of the actual variation. However, the two processed datasets were found to be suitable to monitor the number of transitions per individual, accounting for 91% and 99% of the actual variation, respectively. To further evaluate the tracking system, we estimated the error rate of registrations (by applying the classifier) in relation to three factors, which suggested a higher number of false registrations towards specific areas, periods with reduced humidity, and periods with reduced temperature. We concluded that the presented tracking system is well suited for commercial aviaries to measure individuals’ transitions and individuals’ presence/absence in predefined zones. Nonetheless, under these settings, data processing remains a necessary step in obtaining reliable data. For future work, we recommend the use of automatic calibration to improve the system’s performance and to envision finer movements.
ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge
Authors: Avgeris Marios , Spatharakis Dimitrios , Dechouniotis Dimitrios , Leivadeas Aris , Karyotis Vasileios , Papavassiliou Symeon
Published: January 2022

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.
Investigation on Applicability and Limitation of Cosine Similarity-Based Structural Condition Monitoring for Gageocho Offshore Structure
Authors: Kim Byungmo , Oh Jaewon , Min Cheonhong
Published: January 2022

The key to coping with global warming is reconstructing energy governance from carbon-based to sustainable resources. Offshore energy sources, such as offshore wind turbines, are promising alternatives. However, the abnormal climate is a potential threat to the safety of offshore structures because construction guidelines cannot embrace climate outliers. A cosine similarity-based maintenance strategy may be a possible solution for managing and mitigating these risks. However, a study reporting its application to an actual field structure has not yet been reported. Thus, as an initial study, this study investigated whether the technique is applicable or whether it has limitations in the real field using an actual example, the Gageocho Ocean Research Station. Consequently, it was found that damage can only be detected correctly if the damage states are very similar to the comparison target database. Therefore, the high accuracy of natural frequencies, including environmental effects, should be ensured. Specifically, damage scenarios must be carefully designed, and an alternative is to devise more efficient techniques that can compensate for the present procedure.
Analysis of Web-Based IoT through Heterogeneous Networks: Swarm Computing over LoRaWAN
Authors: Afzal Samira , De Biase Laisa C. C. , Fedrecheski Geovane , Pereira William T. , Zuffo Marcelo K.
Published: January 2022

The Internet of Things (IoT) leverages added valued services by the wide spread of connected smart devices. The Swarm Computing paradigm considers a single abstraction layer that connects all kinds of devices globally, from sensors to super computers. In this context, the Low-Power Wide-Area Network (LPWAN) emerges, spreading out connection to the IoT end devices. With the upsides of long-range, low power and low cost, LPWAN presents major limitations regarding data transmission capacity, throughput, supported packet length and quantity per day limitation. This situation makes LPWAN systems with limited interoperability integrate with systems based on REpresentational State Transfer (REST). This work investigates how to connect web-based IoT applications with LPWANs. The analysis was carried out studying the number of packets generated for a use case of REST-based IoT over LPWAN, specifically the Swarm OS over LoRaWAN. The work also presents an analysis of the impact of using promising schemes for lower communication load. We evaluated Constrained Application Protocol (CoAP), Static Context Header Compression (SCHC) and Concise Binary Object Representation (CBOR) to make transmission over the restricted links of LPWANs possible. The attained results show the reduction of 98.18% packet sizes while using SCHC and CBOR compared to HTTP and JSON by sending fewer packets with smaller sizes.
Smart Bio-Impedance-Based Sensor for Guiding Standard Needle Insertion
Authors: Kudashov Ivan , Shchukin Sergey , Al-harosh Mugeb , Shcherbachev Andrew
Published: January 2022

A venipuncture is the most common non-invasive medical procedure, and is frequently used with patients; however, a high probability of post-injection complications accompanies intravenous injection. The most common complication is a hematoma, which is associated with puncture of the uppermost and lowermost walls. To simplify and reduce complications of the venipuncture procedure, and as well as automation of this process, a device that can provide information of the needle tip position into patient’s tissues needs to be developed. This paper presents a peripheral vascular puncture control system based on electrical impedance measurements. A special electrode system was designed to achieve the maximum sensitivity for puncture identification using a traditional needle, which is usually used in clinical practice. An experimental study on subjects showed that the electrical impedance signal changed significantly once the standard needle entered the blood vessel. On basis of theoretical and experimental studies, a decision rule of puncture identification based on the analysis of amplitude-time parameters of experimental signals was proposed. The proposed method was tested on 15 test and 9 control samples, with the results showing that 97% accuracy was obtained.
Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray
Authors: Khan Irfan Ullah , Aslam Nida , Anwar Talha , Alsaif Hind S. , Chrouf Sara Mhd. Bachar , Alzahrani Norah A. , Alamoudi Fatimah Ahmed , Kamaleldin Mariam Moataz Aly , Awary Khaled Bassam
Published: January 2022

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.
A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks
Authors: Yang Daoguang , Karimi Hamid Reza , Gelman Len
Published: January 2022

Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.
Air Damping Analysis of a Micro-Coriolis Mass Flow Sensor
Authors: Zeng Yaxiang , Sanders Remco , Wiegerink Remco J. , Lötters Joost C.
Published: January 2022

A micro-Coriolis mass flow sensor is a resonating device that measures small mass flows of fluid. A large vibration amplitude is desired as the Coriolis forces due to mass flow and, accordingly, the signal-to-noise ratio, are directly proportional to the vibration amplitude. Therefore, it is important to maximize the quality factor Q so that a large vibration amplitude can be achieved without requiring high actuation voltages and high power consumption. This paper presents an investigation of the Q factor of different devices in different resonant modes. Q factors were measured both at atmospheric pressure and in vacuum. The measurement results are compared with theoretical predictions. In the atmospheric environment, the Q factor increases when the resonance frequency increases. When reducing the pressure from 1 bar to 0.1 bar, the Q factor almost doubles. At even lower pressures, the Q factor is inversely proportional to the pressure until intrinsic effects start to dominate, resulting in a maximum Q factor of approximately 7200.
Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography
Authors: Kim Sehyeon , Shin Dae Youp , Kim Taekyung , Lee Sangsook , Hyun Jung Keun , Park Sung-Min
Published: January 2022

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group.
Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings
Authors: Valsson Steinar , Arandjelović Ognjen
Published: January 2022

With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examined the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present the qualitative assessment of the spatial information learned by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of the performance of machine-learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.
Sensitivity Increase in Headspace Analysis of Hydrocarbons in Water by Using Online Selective Elimination of Gas Extractant
Authors: Rodinkov Oleg V. , Pisarev Alexey Y. , Moskvin Leonid N. , Bugaichenko Aleksandra S. , Nesterenko Pavel N.
Published: January 2022

In this study, a novel approach in headspace gas chromatographic analysis using the selective absorption of the gas extractant during concentration of the analytes was developed. The carbon dioxide used as the gas extractant was removed from the sample flow by passing it through a column packed with microdispersed sodium hydroxide granules. The analytical capabilities of the suggested method were illustrated by the determination of aliphatic and aromatic hydrocarbons in water. We established that this method allows the preconcentration of analytes in the gas phase to be increased proportionally to the volume ratios of the gas extractant before and after absorption, while the analyte limits of detection decrease 30-fold. For example, benzene can be detected in water at a concentration of 0.5 μg/L.
Targeted UHPLC-ESI-MS/MS Analysis of Selected Neurotransmitters, Tryptophan and Its Metabolite Kynurenine in Tau Transgenic Rat Brain Tissue: A Pivotal Study
Authors: Piestansky Juraj , Forgacsova Andrea , Olesova Dominika , Galba Jaroslav , Mikus Peter , Majerova Petra , Kovac Andrej
Published: January 2022

Neurotransmitters (NT) are widely distributed in the central nervous system. These molecules are important for many physiological processes and the function of the immune system. Imbalance of NT are linked to numerous neurological disorders and diseases, including tauopathies. Here, a targeted approach based on on-line combination of ultra-high performance liquid chromatography with tandem mass spectrometry was validated and applied to the quantitative analysis of nine NT (acetylcholine, choline, aspartic acid, asparagine, glutamic acid, glutamine, pyroglutamate, γ-aminobutyric acid, N-acetyl-L-aspartic acid), tryptophan and its metabolite kynurenine in brain tissue samples of a rat model for tauopathy. The applied analytical method was characterized by excellent validation parameters for all analytes, such as limits of detection in the range of 0.01–1.70 µg/mL, regression coefficients of the calibration curves ≥ 0.9946, intra-day and inter-day precision expressed as coefficient of variation in the range of 0.6–11.9% and 0.6–14.4%, and accuracy in the range of 87.6–107.1% and 87.2–119.6%. Our analytical approach led to the identification of increased levels of choline and γ-aminobutyric acid in pons, and elevated concentration levels of pyroglutamate in medulla oblongata. These findings indicate that NT could play a valuable role in the study and clarification of neuroinflammation and neurodegenerative diseases.
Hypericum spp.—An Overview of the Extraction Methods and Analysis of Compounds
Authors: Ion Valentin , Ielciu Irina , Cârje Anca-Gabriela , Muntean Daniela Lucia , Crişan Gianina , Păltinean Ramona
Published: January 2022

The Hypericum genus contains one of the few genera of flowering plants that contains a species with authorization for marketing as a traditional medicine, H. perforatum. Due to the fact that this is a large genus, comprising numerous species, a large amount of interest has been shown over the years in the study of its various pharmacological activities. The chemical composition of these species is quite similar, containing compounds belonging to the class of phloroglucinol derivatives, naphthodianthrones, phenols, flavonoids and essential oils. Taking all of this into consideration, the present study aims to offer an overview of the species of the genus from the point of view of their extraction techniques and analysis methods. An extensive study on the scientific literature was performed, and it revealed a wide range of solvents and extraction methods, among which ethanol and methanol, together with maceration and ultrasonication, are the most frequent. Regarding analysis methods, separation and spectral techniques are the most employed. Therefore, the present study provides necessary data for future studies on the species of the genus, offering a complete overview and a possible basis for their development.
Novel Applications of Microextraction Techniques Focused on Biological and Forensic Analyses
Authors: D’Ovidio Cristian , Bonelli Martina , Rosato Enrica , Tartaglia Angela , Ulusoy Halil İbrahim , Samanidou Victoria , Furton Kenneth G. , Kabir Abuzar , Ali Imran , Savini Fabio , Locatelli Marcello , de Grazia Ugo
Published: January 2022

In recent years, major attention has been focused on microextraction procedures that allow high recovery of target analytes, regardless of the complexity of the sample matrices. The most used techniques included liquid-liquid extraction (LLE), solid-phase extraction (SPE), solid-phase microextraction (SPME), dispersive liquid-liquid microextraction (DLLME), microextraction by packed sorbent (MEPS), and fabric-phase sorptive extraction (FPSE). These techniques manifest a rapid development of sample preparation techniques in different fields, such as biological, environmental, food sciences, natural products, forensic medicine, and toxicology. In the biological and forensic fields, where a wide variety of drugs with different chemical properties are analyzed, the sample preparation is required to make the sample suitable for the instrumental analysis, which often includes gas chromatography (GC) and liquid chromatography (LC) coupled with mass detectors or tandem mass detectors (MS/MS). In this review, we have focused our attention on the biological and forensic application of these innovative procedures, highlighting the major advantages and results that have been accomplished in laboratory and clinical practice.
Invasive Alien Plants and Invasion Risk Assessment on Pingtan Island
Authors: Luo Minxian , Xiao Lifang , Chen Xuhui , Lin Kaiqin , Liu Bao , He Zongming , Liu Jinfu , Zheng Shiqun
Published: January 2022

Pingtan Island is the largest island in Fujian Province and the fifth largest island in China. The invasion of a large number of alien plants has had a profound impact on the local ecological environment. Because the harm caused by alien invasive plants varies greatly between different ecosystems and even in different habitats, the risk assessment index system suitable for one region may not be suitable for other regions. Therefore, it is necessary to establish a risk assessment index system for invasive alien plants on Pingtan Island. Alien plant communities in different habitats were studied by means of quadrat investigation and professional literature review. Some invasive alien species were selected and compiled into a list of invasive alien plants on Pingtan Island, and their species composition, origin, flora, life forms, and habitats were statistically grouped. There were 104 species in 80 genera and 37 families of alien invasive plants. Asteraceae, Fabaceae, Amaranthaceae, and Poaceae were the main families, accounting for 26.7%, 6.7%, 6.7% and 5.8% of the total species, respectively. The geographical components of families and genera have obvious tropical properties, accounting for 51.3% and 66.6% of the total species, respectively. These originated mainly from South America and North America, accounting for 45.5% and 30.1% of the total frequency, respectively. Annual herbs, biennial herbs, and perennial herbs accounted for 84.6% of the total species. Based on a DPSIR conceptual model and an AHP method, an invasion risk assessment of 104 invasive alien plants was conducted. The ecological adaptability, habitat distribution and landscape impact of species were considered in the selection of indicators and the formulation of standards. A total of 23 high-risk invasive species were identified at level I, 37 medium-risk invasive species at level II, and 44 low-risk invasive species at level III. Lantana camara L. had the highest risk score (49), followed by Cenchrus echinatus L. (45), Spartina alterniflora Loisel. (45), and Panicum repens L. (43.5). Suggestions are put forward to prevent the invasion of alien plants on Pingtan Island and to provide a theoretical basis for promoting the healthy and stable development of the ecological environment on the island.