Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Fusion of Capacitively Coupled ECG and Continuous-Wave Doppler Radar for Improved Unobtrusive Heart Rate Measurements

Unobtrusive measurement of vital signs is a research topic of increasing interest, both due to the advances in different technologies and the need for a more patient-centered healthcare. A limitation of such measurements is their sensitivity to motion artifacts, which challenges their use in daily life activities. In this paper, a novel system for the simultaneous acquisition of multichannel capacitively coupled ECG (ccECG) and radar signals is presented, which allows for respiration rate and heart rate (HR) extractions. Furthermore, a combination of signal quality and confidence indicators is proposed, which serves as a first step towards sensor fusion for an increased accuracy and time coverage of HR monitoring. After applying the proposed quality estimation algorithms, sensor fusion methods were performed including quality-based ccECG channel selection, confidence-based radar HR source selection, and Bayesian fusion. Both the system and the proposed algorithms were tested in a total of 125 min of measurements obtained from five different volunteers performing normal office work. An improvement in time coverage (defined as the time in which the HR error is lower than 3 bpm) from 2% to 32.5% was obtained for radar signals with the proposed confidence-based algorithm; the same metric for ccECG increased from 52.2% to 82.1% when automatically selecting the periods with the availability of good quality ccECG signals, while achieving 35.7% coverage in the remaining periods, by using one of the proposed Bayesian fusion methods; errors in each case were reduced accordingly, and a clear division of two sets of signals with different (increased) coverage levels was achieved.
On the Relationship Between Scintillation Anisotropy and Crystal Structure in Pure Crystalline Organic Scintillator Materials

The scintillation anisotropy effect for proton recoil events has been investigated in five pure organic crystalline materials: anthracene, trans-stilbene, p-terphenyl, bibenzyl, and diphenylacetylene (DPAC). These measurements include the characterization of the scintillation response for one hemisphere of proton recoil directions in each crystal. In addition to standard measurements of the total light output and pulse shape at each angle, the prompt and delayed light anisotropies are analyzed, allowing for the investigation of the singlet and triplet molecular excitation behaviors independently. This paper provides new quantitative and qualitative observations that make progress toward understanding the physical mechanisms behind the scintillation anisotropy. These measurements show that the relationship between the prompt and delayed light anisotropies is correlated with a crystal structure, as it changes between the pi-stacked crystal structure materials (anthracene and p-terphenyl) and the herringbone crystal structure materials (stilbene, bibenzyl, and DPAC). The observations are consistent with a model in which there are preferred directions of kinetic processes for the molecular excitations. These processes and the impact of their directional dependences on the scintillation anisotropy are discussed.
Influence on the course line position of errors in the amplitude-phase distribution of the difference channel signals in ILS localizer antenna

The dependency is found between the value of the course line offset relative to the runway axis direction and the errors in the amplitude-phase distribution (APD) of the difference channel signal currents in ILS localizer antenna. The results of a flight test of the course line position are given for the specific realization of the APD in an antenna array.
Influence Of Genetic Polymorphisms In Genes Of Bone Remodeling And Angiogenesis Process In The Apical Periodontitis.

Persistent apical periodontitis (AP) is a situation involving an inflammatory and immune response caused mainly by anaerobic polymicrobial infection of the root canal system and the outcome and follow-up of the root canal treatment has been reported as intimately related to host response. The apical periodontitis repair might be associated with genetic polymorphisms. This study aimed to evaluate the association between HIF1A genetic polymorphisms (rs2301113 and rs2057482) with PAP in Brazilian patients. Subjects with at least 1 year of follow-up after root canal therapy (RCT) were recalled. Sixty-four subjects with signs/symptoms of PAP and 84 subjects with root canal-treated teeth exhibiting healthy perirradicular tissues (healed) were included. Genomic DNA was extracted from saliva and used for HIF1A genotyping by real-time PCR. Genotype and allele frequencies were compared by c2 or Fisher's exact tests and odds ratio was implemented, using Epi Info 3.5. 2. All tests were performed with an established alpha of 0.05. There was no association between allele and genotype distribution for HIF1As polymorphisms and PAP (p>0.05). The genetic polymorphisms in HIF1A were not associated with persistent apical periodontitis.
VHDL-AMS based frequency domain model of four-wire shielded energy cable

This paper proposes a frequency domain methodology for power cable modelling that takes into consideration the cable parameters variation due to skin effect, proximity effect and dielectric losses. To deal with the propagation phenomena, which take place when the frequency increases distributed elements are proposed. A set of functions that fit the parameters measurement curves is used to build a VHDL-AMS based model for each element. The developed method reduces the number of nodes and introduces coupling coefficients between resistances, which is not allowed by PSPICE software. The model is simulated by means of ANSYS Simplorer in the frequency domain. Simulation results show a good agreement with measured ones in a large frequency range up to 100 MHz.
Riemann–Langevin Particle Filtering in Track-Before-Detect

Track-before-detect (TBD) is a powerful approach that consists in providing the tracker directly with the sensor measurements without any predetection. Due to the measurement model nonlinearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in differential-geometric LMC-based filtering to introduce its application to TBD. The benefits of LMC filtering in TBD are illustrated in a challenging low-noise scenario.
Depth Aware Finger Tapping on Virtual Displays

For AR/VR systems, tapping-in-the-air is a user-friendly solution for interactions. Most prior in-air tapping schemes use customized depth-cameras and therefore have the limitations of low accuracy and high latency. In this paper, we propose a fine-grained depth-aware tapping scheme that can provide high accuracy tapping detection. Our basic idea is to use light-weight ultrasound based sensing, along with one COTS mono-camera, to enable 3D tracking of user's fingers. The mono-camera is used to track user's fingers in the 2D space and ultrasound based sensing is used to get the depth information of user's fingers in the 3D space. Using speakers and microphones that already exist on most AR/VR devices, we emit ultrasound, which is inaudible to humans, and capture the signal reflected by the finger with the microphone. From the phase changes of the ultrasound signal, we accurately measure small finger movements in the depth direction. With fast and light-weight ultrasound signal processing algorithms, our scheme can accurately track finger movements and measure the bending angle of the finger between two video frames. In our experiments on eight users, our scheme achieves a 98.4% finger tapping detection accuracy with FPR of 1.6% and FNR of 1.4%, and a detection latency of 17.69ms, which is 57.7ms less than video-only schemes. The power consumption overhead of our scheme is 48.4% more than video-only schemes.
Performance comparison of 2-DOF PID controller based on Moth-flame optimization technique for load frequency control of diverse energy source interconnected power system

The proposed analysis represents a load frequency control technique of two area, diverse-source power system consisting non-identical power plants, based on Moth-flame optimization technique (MFO). It uses a fresh controller namely 2 two degree of freedom proportional-integral-derivative (2-DOF PID) controller. The performance of the proposed controller is checked by an objective function Integral time multiplied by absolute error (ITAE) and then it is compared with the performance of integral and PID controller using different optimization techniques namely genetic algorithm (GA) and cuckoo search algorithm (CSA). The analysis reveals that the robustness of proposed system is much better using 2-DOF PID controller as compared to conventional controllers.
Modeling human-likeness in approaching motions of dual-arm autonomous robots

This paper addresses the problem of obtaining human-like motions with an anthropomorphic dual-arm torso assembled on a mobile platform. The focus is set on the coordinated movements of the robotic arms and the robot base while approaching a table to subsequently perform a bimanual manipulation task. For this, human movements are captured and mapped to the robot in order to compute the human dual-arm synergies. Since the demonstrated synergies change depending on the robot position, a recursive Cartesian-space discretization is presented based on these differences. Thereby, different movements of the arms are assigned to different regions of the Cartesian space. As an application example, a motion-planning algorithm exploiting this information is proposed and used.
High doses of phytase on growth performance and apparent ileal amino acid digestibility of broilers fed diets with graded concentrations of digestible sulfur amino acids

Two experiments of the same design were conducted to determine the influence of phytase on performance and apparent ileal digestibility (AID) of amino acids in broilers fed graded concentrations of digestible sulfur amino acids (dgM+C). Cobb 400 male broilers were allocated to 1 of 10 diets consisting of 5 basal diets formulated at 75, 82, 89, 96, or 103% of the Cobb 400 dgM+C requirements for each feeding phase. Phytase was included in each basal diet at 0 or 2,000 FTU/kg. In experiment 1, 33 birds/pen from hatch to day 42 were fed a 2-phase feeding program with 10 replicate pens/diet. In experiment 2, there were 26 birds/pen from hatch to day 21 and 8 replicate pens/diet. Data were analyzed as a 5 × 2 factorial and means separated using orthogonal contrasts. In experiment 1, overall (hatch to day 42) feed intake (FI) decreased (linear, P < 0.05), body weight gain (BWG) increased (quadratic, P < 0.05), and feed conversion ratio (FCR) was improved (quadratic, P < 0.05) as dgM+C increased to ≥96% in the diet. Phytase increased (P < 0.05) BWG and improved (P < 0.05) FCR, regardless of the percent dgM+C in the diet. In experiment 2, overall (hatch to day 21) BWG increased (quadratic, P < 0.05), and FCR was improved (quadratic, P < 0.05) as dgM+C increased to ≥96% in the diet. Phytase increased FI (P < 0.05) and BWG (P < 0.05) and improved FCR (P < 0.05), regardless of the percent dgM+C in the diet. In the absence of phytase, the AID of all amino acids was greatest (quadratic, P < 0.05) in birds fed between 89 and 96% dgM+C. However, in the presence of phytase the AID of all amino acids was greatest (quadratic, P < 0.05) in birds fed 82% dgM+C and greater at all levels of dgM+C than birds fed diets without phytase (dgM+C × phytase, P < 0.05). In conclusion, phytase improved AID of all amino acids and improved growth performance regardless of the level of dgM+C in the diet.
Screening and Identification of Lassa Virus Entry Inhibitors from an FDA-Approved Drug Library

Currently, there is no approved therapy to treat Lassa fever; therefore, repurposing of approved drugs will accelerate the development of a therapeutic stratagem. In this study, we screened an FDA-approved library of drugs and identified two compounds, lacidipine and phenothrin, which inhibited Lassa virus entry by blocking low-pH-induced membrane fusion. Additionally, both compounds extended their inhibition against the entry of Guanarito virus, and the viral targets were identified as the SSP-GP2 interface. ABSTRACT Lassa virus (LASV) belongs to the Mammarenavirus genus (family Arenaviridae) and causes severe hemorrhagic fever in humans. At present, there are no Food and Drug Administration (FDA)-approved drugs or vaccines specific for LASV. Here, high-throughput screening of an FDA-approved drug library was performed against LASV entry by using pseudotype virus bearing LASV envelope glycoprotein (GPC). Two hit compounds, lacidipine and phenothrin, were identified as LASV entry inhibitors in the micromolar range. A mechanistic study revealed that both compounds inhibited LASV entry by blocking low-pH-induced membrane fusion. Accordingly, lacidipine showed virucidal effects on the pseudotype virus of LASV. Adaptive mutant analyses demonstrated that replacement of T40, located in the ectodomain of the stable-signal peptide (SSP), with lysine (K) conferred LASV resistance to lacidipine. Furthermore, lacidipine showed antiviral activity against LASV, the closely related Mopeia virus (MOPV), and the New World arenavirus Guanarito virus (GTOV). Drug-resistant variants indicated that V36M in the ectodomain of the SSP mutant and V436A in the transmembrane domain of the GP2 mutant conferred GTOV resistance to lacidipine, suggesting the interface between SSP and GP2 is the target of lacidipine. This study shows that lacidipine is a candidate for LASV therapy, reinforcing the notion that the SSP-GP2 interface provides an entry-targeted platform for arenavirus inhibitor design. IMPORTANCE Currently, there is no approved therapy to treat Lassa fever; therefore, repurposing of approved drugs will accelerate the development of a therapeutic stratagem. In this study, we screened an FDA-approved library of drugs and identified two compounds, lacidipine and phenothrin, which inhibited Lassa virus entry by blocking low-pH-induced membrane fusion. Additionally, both compounds extended their inhibition against the entry of Guanarito virus, and the viral targets were identified as the SSP-GP2 interface.
Thai fast food image classification using deep learning

This paper presents a prediction model for classifying Thai fast food images. The model uses a deep learning process that was trained on natural images (GoogLeNet dataset) and was fine-tuned to generate the predictive Thai fast food model. The researchers created a dataset, called the Thai Fast Food (TFF) dataset, which contained 3,960 images. The dataset was divided into eleven groups of food images comprised of omelet on rice, rice topped with stir-fried chicken and basil, barbecued red pork in sauce with rice, stewed pork leg on rice, Thai fried noodle, rice with curried chicken, steamed chicken with rice, shrimp-paste fried rice, fried noodle with pork in soy sauce and vegetables, wide rice noodles with vegetables and meat. The final group comprised dishes which are not members of the other ten groups listed (non-ten-types), but which exist among Thai fast food. The classification average accuracy on a separate test set shows that Thai fast food can be predicted at 88.33%.
Resource delayed release strategy for dynamic and fast end-to-end service provisioning in SDN-enabled OTN over WDM networks

Dynamic end-to-end service provisioning is critical to satisfy various user and carrier requirements such as Bandwidth-on-Demand and Dynamic Bandwidth Adjustment in SDN-enabled OTN over WDM networks. In this context, Service Provisioning Time (SPT) is one of the important metrics, especially in highly dynamic traffic scenarios. In this paper, we propose the Resource Delayed Release strategy to improve the SPT performance by delaying the release of an optical channel when the channel no longer carries any services. Simulation results show that as much as 45∼90% of the SPT is reduced by using RDR strategy under different traffic scenarios, and the blocking probabilities of service requests are also reduced.
Load forecasting based on short-term correlation clustering

Load forecasting is the basis not only of power system stable and safe operation, but also of power demand side intelligent electricity management. Short-term correlation analysis can be used to mine the electricity consumption of a period of time. The analysis of similar electricity consumption can improve the effect of load forecasting. Therefore, this paper proposes a load forecasting method based on short-time correlation clustering. First, a method is proposed to analyze the correlation matrix of power sequences, and then the effects of noise information of the correlation matrix is eliminated. Then, based on the correlation matrix, we group feeders with different electricity consumption by using the fuzzy C-means clustering algorithm. After each feeder is assigned to a special group, we forecast each group load based on artificial neural network. The load forecasting results from each group are summed to obtain total load forecast. Finally, the case studies with instance data verify the load clustering based on short- term correlation analysis for power sequences can improve load forecasting.
Low-latency implementation of CORDIC-based sorted QR decomposition for high-speed MIMO-OFDM system

In this paper, we propose an low-latency coordinate rotation digital computer(CORDIC)-based sorted QR decomposition (SQRD) for high-speed Multiple-Input and Multiple-Output (MIMO) systems. Sorted QRD has been widely adopted to MIMO signal processing to improves the system performance. However, its iterative sorting process after every column operation hinders parallelism and leads to high latency. The proposed CORDIC-based sorted QRD modifies real-value decomposition (RVD) matrix to reduce latency and hardware complexity. It jointly performs sorting processes and CORDIC Givens rotations with adjacent symmetric columns and reduces the numbers of required sorting process and CORDIC stage. The numerical results for a 4 × 4 MIMO show the proposed algorithm reduces the number of CORDIC rotation by 20% in comparison with the conventional sorted QRD. Also, the proposed algorithm requires only 57% of the number of processing stage. The proposed algorithm has been implemented with standard 65 nm cell libraries and achieves reduced latency and complexity compared to the existing results.
Android Malware Detection using Large-scale Network Representation Learning

With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional methods such as signature-based ones cannot defend users from an increasing number of new types of malware or rapid malware behavior changes. In this paper, we propose a new Android malware detection approach based on deep learning and static analysis. Instead of using Application Programming Interfaces (APIs) only, we further analyze the source code of Android applications and create their higher-level graphical semantics, which makes it harder for attackers to evade detection. In particular, we use a call graph from method invocations in an Android application to represent the application, and further analyze method attributes to form a structured Program Representation Graph (PRG) with node attributes. Then, we use a graph convolutional network (GCN) to yield a graph representation of the application by embedding the entire graph into a dense vector, and classify whether it is a malware or not. To efficiently train such a graph convolutional network, we propose a batch training scheme that allows multiple heterogeneous graphs to be input as a batch. To the best of our knowledge, this is the first work to use graph representation learning for malware detection. We conduct extensive experiments from real-world sample collections and demonstrate that our developed system outperforms multiple other existing malware detection techniques.
Two topologies of fractional-order oscillators based on CFOA and RC networks

This paper presents two general topologies of fractional order oscillators. They employ Current Feedback Op-Amp (CFOA) and RC networks. Two RC networks are investigated for each presented topology. The general oscillation frequency, condition and the phase difference between the oscillatory outputs are investigated in terms of the fractional order parameters. Numerical simulations and P-Spice simulation results are provided for some cases to validate the theoretical findings. The fractional order parameters increase the design flexibility and controllability which is proved by the provided experimental results.
Investigating pathogen burden in relation to a cumulative deficits index in a representative sample of US adults

Abstract Pathogen burden is a construct developed to assess the cumulative effects of multiple, persistent pathogens on morbidity and mortality. Despite the likely biological wear and tear on multiple body systems caused by persistent infections, few studies have examined the impact of total pathogen burden on such outcomes and specifically on preclinical markers of dysfunction. Using data from two waves of the National Health and Nutrition Examination Survey, we compared three alternative methods for measuring pathogen burden, composed of mainly persistent viral infections, using a cumulative deficits index (CDI) as an outcome: single pathogen associations, a pathogen burden summary score and latent class analyses. We found significant heterogeneity in the distribution of the CDI by age, sex, race/ethnicity and education. There was an association between pathogen burden and the CDI by all three metrics. The latent class classification of pathogen burden showed particularly strong associations with the CDI; these associations remained after controlling for age, sex, body mass index, smoking, race/ethnicity and education. Our results suggest that pathogen burden may influence early clinical indicators of poor health as measured by the CDI. Our results are salient since we were able to detect these associations in a relatively young population. These findings suggest that reducing pathogen burden and the specific pathogens that drive the CDI may provide a target for preventing the early development of age-related physiological changes.
Bandwidth enhancement of rectangular patch antennas using multiple feeding points: A review

Poor bandwidth performance is a characteristic of microstrip patch antennas and bandwidth enhancement is a most important challenge when designing patch radiators. In this a technique is proposed, which guarantees bandwidth augmentation depending on the number of specular points that are used to feed the patch. The proposed method is based upon the frequency pulling technique which is a common ground regarding the design of bandpass filters. Indicatively, patch antennas with two and four feeding points are presented. Designing a robust configuration is of great importance and is achieved by folding the feeding line on the backside of the patch.