Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-based spectrum sensing with copulas for cognitive radios

In this study, an energy-based spectrum sensing method combined with copula theory is proposed for cognitive radio systems. In the proposed spectrum sensing model, cognitive radio users first make their own local spectrum decision with energy-based spectrum sensing. Then, they forward their decision to the fusion center. In the fusion center, this decision is compared with the threshold value determined by copula theory and global spectrum decision is made. The test statistic at the fusion center were obtained with the Neyman Pearson approach. Thus, the fusion rule was created for the fusion center and necessary simulation studies were performed. According to the results of the simulation studies, the proposed detection method showed better results than the traditional energy based detection method.
Numerical modelling for investigation and optimization of a sensor for sensitive partial discharge detection on high-voltage XLPE cables

Based on experimental investigations a theoretical model of a sensor for partial discharge (PD) detection on high-voltage cross-linked polyethylene (XLPE)-insulated cables respectively joints has been developed. The model handles radial symmetric sensors and is based on the Finite-Difference Time-Domain (FDTD) method in which a cylindrical coordinate system is exploited. The calculation provides field values at each node of the FDTD grid. The field pattern in the cable and sensor at different time steps is used to investigate the coupling mechanism of the sensor. It is also used for parametric studies of the sensor, e. g. the dependence of the sensitivity on geometrical characteristics of the sensor, which is investigated theoretically as well as experimentally. Results of computer simulation are in a good agreement with experimental data derived for such sensors.
Electromyography and Inertial Motion Sensors Based Wearable Data Acquisition System for Stroke Patients: A Pilot Study

Development of wearable data acquisition systems with applications to human-machine interaction (HMI) is of great interest to assist stroke patients or people with motor disabilities. This paper proposes a hybrid wireless data acquisition system, which combines surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. It is designed to interface wrist extension with external devices, which allows the user to operate devices with hand orientations. A pilot study of the system performed on four healthy subjects has successfully produced two different control signals corresponding to wrist extensions. Preliminary results show a high correlation (0.42-0.75) between sEMG and IMU signals, thus proving the feasibility of such a system. Results also show that the developed system is robust as well as less susceptible to external interferences. The generated control signals can be used to perform real-time control of different devices in daily-life activities, such as turning ON/OFF of lights in a smart home, controlling an electric wheelchair, and other assistive devices. Such a system will help decrease the dependency of disabled people on their caretakers and empower them to perform their daily-life activities independently.
Prediction of EMG Activation Profiles from Gait Kinematics and Kinetics during Multiple Terrains

Continuous myoelectric prediction of intended limb dynamics has the ability to provide transparent control of a prosthesis by the user. However, the impact on these models of adding a human user into the control loop is less clear. Here, the ability of a User Response Model (URM) to continuously predict EMG activity from gait kinematics and kinetics collected during three mobility tasks (level-ground walking, stair ascent, and stair descent) was examined. Multiple-input, multiple-output NARX-based URMs were developed with two outputs (ankle plantarflexor and dorsiflexor) and variable inputs (ankle kinetics, and shank and/or ankle kinematics). Accuracy in predicting the tibialis anterior and medial gastrocnemius EMG was comparable across URMs regardless of the number of inputs. Stair descent had the lowest accuracy among the mobility tasks. No significant differences in normalized root-mean-square error and cross-correlation were found between URMs with five and nine inputs. A URM that continuously predicts EMG activity from gait kinetics and kinematics could be used to simulate human-in-the-loop myoelectric control of a transtibial prosthesis and examine the stability of the system to changes in the environment or due to control errors.
'Pyramiding': Efficient Identification of Rare Subjects

The need to economically identify rare subjects within large, poorly-mapped search spaces is a frequently-encountered problem for social scientists and managers. It is notoriously difficult, for example, to identify the best new CEO for our company, or the best three lead users to participate in our product development project. Mass screening of entire populations or samples becomes steadily more expensive as the number of acceptable solutions within the search space becomes rarer. Pyramiding can be significantly more efficient under many conditions. Pyramiding is a search process based upon the view that people with a strong interest in a topic or field tend to know people more expert than themselves. In this paper we empirically explore the efficiency of pyramiding searches relative to mass screening in search spaces where there is only one best solution. In four experiments, we find that pyramiding in each case identifies the best solution within the search space using an average of only 30% of the effort required by mass screening. Based on our findings, we propose conditions under which pyramiding will be a more efficient approach to exploring a search space than screening.
UGV Demo II reuse technology for AHS

The primary goal of the Advanced Research Project Agency (ARPA) unmanned ground vehicle (UGV) Demo II program is to demonstrate the utility of advanced unmanned systems to conduct tasks that enhance the Department of Defense force structure. Some of the key technologies being developed include perception, autonomous navigation, and passive stereo vision and an active scanning laser rangefinder, both for obstacle detection. A good portion of this technology has direct relevance to the US Department of Transportation's Automated Highway System (AHS) program. The program focuses on a planned evolution to an automated highway, starting from today's vehicle-highway system. The envisioned AHS would operate properly equipped vehicles under full automated control on dedicated lanes. This paper presents each applicable reuse capability, including supporting hardware and software required. We also note any special "lessons learned" during integration and/or operation of the specific technology. Reuse technology is mapped to the AHS minimum functions such as lateral vehicle control.
Research on Intelligent Ship Path Planning Optimization Based on Chaos Particle Swarm Optimization

Under the background of the popular research field of intelligent ship path planning, this paper aims to optimize the application of particle swarm algorithm in intelligent ship path planning. Aiming at the problem of particle swarm optimization easily falling into local optimality in ship path planning, this paper introduces chaotic interference, and obtains an improved Chaotic Particle Swarm Optimization (Based on Chaotic Particle Swarm Optimization, hereinafter referred to as BCPSO). BCPSO increases the inertia weight of the particle swarm algorithm in the later stage to improve the probability of the particle swarm algorithm jumping out of the local optimum to obtain the global optimum, thereby improving the global search ability of the particle swarm algorithm in intelligent ship path planning. Through simulation experiments, compared with two similar algorithms in terms of convergence and global search ability, it is verified that the algorithm in this paper has better convergence and global optimization ability, and it has a good effect to make intelligent ship path planning and collision avoidance decision-making.
Angular Velocity Profiles of Upper Limb Joint Synergies in Reaching Movements: a pilot study*

The spatiotemporal kinematic synergy, a coupling of multiple degrees of freedom (DoF), runs through human activities of daily living (ADL). And it is an entry point for exploring the central nervous system’s (CNS) control process of musculoskeletal system by analyzing the time-varying kinematic synergy. The aim of this study was to find more physiological properties from the angular velocity profiles of synergy. Ten healthy right-handed subjects were asked to reach target button at different locations. During reaching movement, the motion data of five right upper limb joints were recorded, and the synergistic patterns were extracted by PCA algorithm. Our results showed that the combinations of the first four synergies were sufficient to explain raw data. As far as possible to exclude the effects of individual and information differences, we found shoulder flexion/extension and elbow flexion/extension made distinct contribution in a period of time to the control procedure performed by CNS after targets were confirmed. Our preliminary results implied that reaching movements required comparatively constant scheduling of shoulder horizontal abduction/adduction, shoulder internal/external rotation and wrist ulnar/radial deviation by CNS, while scheduling of SFE and EFE depends on the objectives.Clinical relevance— The findings of this paper may provide a novel dynamic control evidence based on CNS for realizing near-natural control of assistive devices in motor rehabilitation area.
Marine CO 2 system variability along the Inside Passage of the Pacific Northwest coast of North America determined from an Alaskan ferry

Abstract. Information on marine CO2 system variability has been limited along the Inside Passage of the Pacific Northwest coast of North America despite the region’s rich biodiversity, abundant fisheries, and developing aquaculture industry. Beginning in 2017, the Alaska Marine Highway System M/V Columbia has served as a platform for surface underway data collection while conducting twice weekly ~1600-km transits between Bellingham, Washington and Skagway, Alaska. This dataset allowed for the assessment of marine CO2 system patterns along the Inside Passage, including quantification of the relative importance of key drivers in shaping pCO2 variability. Surface water pH and aragonite saturation state (Ωarag) were determined using the pCO2 data with alkalinity from a regional salinity-based relationship, which was evaluated with discrete seawater samples and underway pH measurements. Low pH and corrosive (Ωarag
Methyl mercaptan MAK Value Documentation , supplement – Translation of the German version from 2019

The German Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area has re-evaluated the maximum concentration at the work place (MAK value) of methyl mercaptan [74-93-1]. Available publications and unpublished study reports are described in detail. No new data are available that would be relevant for the derivation of a MAK value for methyl mercaptan. Therefore, the MAK value of 0.5 ml/m3 is retained based on slight behavioural changes at 2 ml/m3 in a 90-day inhalation study in rats. The MAK value of 0.5 ml/m3 for methyl mercaptan is supported by a limited inhalation study with 3 volunteers exposed to ethanethiol, showing irritation and other symptoms after repeated exposure to ethanethiol in a concentration of 3.9 ml/m3, but not after 0.39 ml/m3. The behavioural changes in rats exposed to methyl mercaptan are interpreted as a result of the odour nuisance or the local irritation. Therefore, methyl mercaptan is classified in Peak Limitation Category I with an excursion factor of 1 as no studies in humans are available. In a screening study for repeated exposure and reproductive toxicity with sodium methanethiolate no foetotoxic effects were observed up to the highest dose tested of 45 mg/kg body weight, however, the teratogenicity was not examined. Methyl mercaptan remains assigned to Pregnancy Risk Group D. Skin contact is not expected to contribute significantly to systemic toxicity. There are no data on sensitization. Methyl mercaptan and sodium methanethiolate are neither mutagenic nor clastogenic. The MAK Collection for Occupational Health and Safety 2020, Vol 5, No 4 1 MAK Value Documentations – Methyl mercaptan MAK value (1969) 0.5 ml/m3 (ppm) ≙ 1.0 mg/m3 Peak limitation (2018) Category I, excursion factor 1 Absorption through the skin –
Are There Reliable Qualitative Individual Differences

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Clustering and Retrieval of Tele -Lecture Content

: Nowadays video lecturing is becoming popular due to its various advantages than classroom learning. Many institutes and organizations are using this method for learning. So there is an enormous amount of data available in video lecturing form. To extract the exact video and exact information through this collection of information from videos is a tedious task. In this paper we introduce the techniques for automatically retrieving the information from video files to collect it as a metadata for those files. For efficient retrieval of text from videos we use the OCR (Optical Character Recognition) tool to extract text from slides and ASR (Automatic Speech Recognition) tool for retrieval of speech information by the speaker. First of all we do segmentation and classification of video files for extracting the key frames. Then the OCR and ASR tool is used for collecting the information and it will be stored as a metadata for the file. At last, we provide the efficient browsing for these videos by using the clustering and ontology concept.
Interactive cutting and tearing in projective dynamics with progressive cholesky updates

We propose a new algorithm for updating a Cholesky factorization which speeds up Projective Dynamics simulations with topological changes. Our approach addresses an important limitation of the original Projective Dynamics, i.e., that topological changes such as cutting, fracturing, or tearing require full refactorization which compromises computation speed, especially in real-time applications. Our method progressively modifies the Cholesky factor of the system matrix in the global step instead of computing it from scratch. Only a small amount of overhead is added since most of the topological changes in typical simulations are continuous and gradual. Our method is based on the update and downdate routine in CHOLMOD, but unlike recent related work, supports dynamic sizes of the system matrix and the addition of new vertices. Our approach allows us to introduce clean cuts and perform interactive remeshing. Our experiments show that our method works particularly well in simulation scenarios involving cutting, tearing, and local remeshing operations.
Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human

The in vivo estimation of α-motoneuron (MN) properties in humans is crucial to characterize the effect that neurorehabilitation technologies may elicit over the composite neuro-musculoskeletal system. Here, we combine biophysical neuronal modelling, high-density electromyography and convolutive blind-source separation along with numerical optimization to estimate geometrical and electrophysiological properties of in vivo decoded human MNs. The proposed methodology implements multi-objective optimization to automatically tune ionic channels conductance and soma size of MN models for minimizing the error between several features of simulated and in vivo decoded MN spike trains. This approach will open new avenues for the closed-loop control of motor restorative technologies such as wearable robots and neuromodulation devices.Clinical Relevance— This work proposes a non-invasive framework for the in vivo estimation of person-specific α-motoneuron properties. This will enable predicting neuronal adaptations in response to neurorehabilitation therapies in the intact human.
Real-time Stress Detection Model and Voice Analysis: An Integrated VR-based Game for Training Public Speaking Skills

This paper describes a work in progress Virtual Reality (VR) based serious game, integrated with an algorithmic classification model for detecting stress during public speaking in real-time by analysing the speaker's voice. The designed VR game offers real-time virtual social support/feedback for the training of public speaking skills. We developed a stress detection model that recognises the stress and an altered normal confident state based on 24 actors' voice expressions from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Three different classifiers models were constructed for each actor by extracting and identifying overall significant voice features. The results show that random forest classification using features Amplitude Envelope (AE), Root-Mean-Square (RMS) and Mel-Frequency Cepstral Coefficients (MFCCs) provides high accuracy for detection of stress, while many more features can possibly be explored.
Neural Screen Space Rendering of Direct Illumination

Neural rendering is a class of methods that use deep learning to produce novel images of scenes from more limited information than traditional rendering methods. This is useful for information scarce applications like mixed reality or semantic photo synthesis but comes at the cost of control over the ﬁnal appearance. We introduce the Neural Direct-illumination Renderer (NDR), a neural screen space renderer capable of rendering direct-illumination images of any geometry, with opaque materials, under distant illuminant. The NDR uses screen space buffers describing material, geometry, and illumination as inputs to provide direct control over the output. We introduce the use of intrinsic image decomposition to allow a Convolutional Neural Network (CNN) to learn a mapping from a large number of pixel buffers to rendered images. The NDR predicts shading maps, which are subsequently combined with albedo maps to create a rendered image. We show that the NDR produces plausible images that can be edited by modifying the input maps and marginally outperforms the state of the art while also providing more functionality.
Development and Evaluation of AccuPower COVID-19 Multiplex Real-Time RT-PCR Kit and AccuPower SARS-CoV-2 Multiplex Real-Time RT-PCR Kit for SARS-CoV-2 Detection in Sputum, NPS/OPS, Saliva and Pooled Samples

Rapid and accurate detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for the successful control of the current global COVID-19 pandemic. The real-time reverse transcription polymerase chain reaction (Real-time RT-PCR) is the most widely used detection technique. This research describes the development of two novel multiplex real-time RT-PCR kits, AccuPower (R) COVID-19 Multiplex Real-Time RT-PCR Kit (NCVM) specifically designed for use with the ExiStation 48 system (comprised of ExiPrep 48 Dx and Exicycler 96 by BIONEER, Korea) for sample RNA extraction and PCR detection, and AccuPower (R) SARS-CoV-2 Multiplex Real-Time RT-PCR Kit (SCVM) designed to be compatible with manufacturers` on-market PCR instruments. The limit of detection (LoD) of SCVM was 2 copies/{micro} L and the LoD of the NCVM was 120 copies/mL for both the gene and the SARS-CoV-2 gene (N gene and RdRp gene). The AccuPower (R) kits demonstrated high precision with no cross reactivity to other respiratory-related microorganisms. The clinical performance of AccuPower (R) kits was evaluated using the following clinical samples: sputum and nasopharyngeal/oropharyngeal swab (NPS/OPS) samples. Overall agreement of the AccuPower (R) kits with a Food and Drug Administration (FDA) approved emergency use authorized commercial kit (STANDARD M nCoV Real-Time Detection kit, SD BIOSENSOR, Korea) was above 95% (Cohen`s kappa coefficient [≥] 0.95), with a sensitivity of over 95%. The NPS/OPS specimen pooling experiment was conducted to verify the usability of AccuPower (R) kits on pooled samples and the results showed greater than 90% agreement with individual NPS/OPS samples. The clinical performance of AccuPower (R) kits with saliva samples was also compared with NPS/OPS samples and demonstrated over 95% agreement (Cohen`s kappa coefficient > 0.95). This study shows the BIONEER NCVM and SCVM assays are comparable with the current standard confirmation assay and are suitable for effective clinical management and control of SARS-CoV-2.
The Vorbix Burner: A New Approach to Gas Turbine Combustors

The vorbix burner (acronym for Vortex Burning and Mixing) represents a new approach to a practical gas turbine combustor design. The concept exploits the Rayleigh instability of swirling flows to enhance the mixing and combustion rates. The combination of a two-stage fuel system with a piloted combustor leads to a unique high rate technique for fuel prevaporization within the combustor proper. This paper presents the fundamental concepts in the definition of the vorbix combustor and the results of exploratory tests conducted on can (tubular) and annular vorbix combustors. The results indicate that this type of combustor has unique performance characteristics that include excellent stability and high combustion efficiency over wide excursions in operating fuel air ratios in addition to substantially reduced emission levels during high power operation.Copyright © 1975 by ASME
Dimensional characterizations of gender diversity are associated with higher polygenic propensity for cognitive performance in a neurodiverse sample

Both sex and gender are characteristics that play a key role in risk and resilience in health and well-being. Current research lacks the ability to quantitatively describe gender and gender diversity, and is limited to endorsement of categorical gender identities, which are contextually and culturally dependent. A more objective, dimensional approach to characterizing gender diversity will enable researchers to advance the health of gender-diverse people by better understanding how genetic factors interact to determine health outcomes. To address this research gap, we leveraged the Gender Self-Report (GSR), a questionnaire that captures multiple dimensions of gender diversity. We then performed polygenic score associations with brain-related traits like cognitive performance, personality, and neuropsychiatric conditions. The GSR was completed by N = 818 independent adults with or without autism in the SPARK cohort, and GSR factor analysis identified two factors: Binary (divergence from gender presumed by designated sex to the opposite) and Nonbinary (divergence from male and female gender norms) Gender Diversity (BGD and NGD, respectively). We performed polygenic associations (controlling for age, sex, and autism diagnostic status) in a subset of N = 452 individuals and found higher polygenic propensity for cognitive performance was associated with greater BGD (B = 0.017, p = 0.049) and NGD (B = 0.036, p = 0.002), and higher polygenic propensity for educational attainment was also associated with greater NGD (B = 0.030, p = 0.015). We did not observe any significant associations with personality or neuropsychiatric polygenic scores in this sample. Overall, our results suggest cognitive processes and gender diversity share overlapping genetic factors, indicating the biological utility of the GSR while also underscoring the importance of quantitatively measuring gender diversity in health research contexts.