Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Model-Based Approaches for State-of-Charge Estimation in Batteries

The State-Of-Charge estimation constitutes an essential part of the battery management system. During the early phases, measurement-based methods were used for state-of-charge estimation, which depended on the current and voltage measurements. However, the accuracy of these methods is affected by the presence of noise, dc-bias, etc. In addition, the voltage measurement-based estimation approach is time-consuming due to the delay in battery terminal voltage settling and hence is unsuitable for online SOC computation. To subdue these drawbacks, model-based estimation techniques are employed. This work compares the performance of four commonly used model-based state-of-charge estimation techniques: the Kalman Filter, Extended Kalman Filter, Sigma Point Kalman Filter, and the H∞. The battery model selected for the estimation procedure is Thevenin’s battery model with a single parallel RC branch.
CPW-Fed Compact Circularly Polarized Flexible Antenna for C Band Applications

In this paper, a simple, and compact CPW-fed circularly polarized antenna is presented. The proposed antenna consists of a modified “S” shaped patch which has slots in three different places along with a slot in the ground plane. These slots contribute in increasing the bandwidth of the axial ratio. The antenna has a 3 dB axial ratio bandwidth of 10.47% (4.07 GHz–4.52 GHz) and an impedance bandwidth of 17.53% (3.8 GHz – 4.53 GHz) covering the full region of axial ratio band. Moreover, this antenna is designed using PET paper which makes it flexible in nature and the first flexible antenna in discussed frequency range to the best of author’s knowledge.
Multimodal Simultaneous Amperometry and Electrochemical Impedance Spectroscopy Measurement for Biosensing Applications

Electrochemical techniques are widely used for biosensing applications. Previous work has focused on the development of sensor readout circuits for any one of the electrochemical techniques. However, many sensors face the problem of sensor degradation, which can be estimated using sensor impedance. Amperometry for biosensing applications and impedance spectroscopy for study of sensor impedance can be utilized to study the impact of biofouling on sensor layers. In this paper, we have attempted to develop a sensor readout to concurrently perform amperometry and EIS simultaneously, thus simplifying the overall circuit. In this work, the circuit presented uses the sensing current derived from the Counter Electrode for sensing the impedance at the electrode-electrolyte interface. By mirroring the amperometric glucose sensing currenet concurrent impedance measurement is performed at DC without affecting the amperometric measurement of the sensor.
Improving Performance of NeMo ASR system for Indian Accent English

While many speech recognition applications are quite commonplace now a days, applicability of the recognition techniques to accented languages is still a major challenge. Here, in this paper we have fine-tuned a state-of-the-art convolutional ASR system on regional accented Indian English to improve it’s performance for accented English. We used NVIDIA NeMo1 as our base framework and used QuartzNet 1D Convolutional model to explore their ability to understand language specific phonology. We also fine-tuned the convolutional network on regional accented Indian English. The tests were carried out on a dataset containing male and female English speech samples collected over 13 different regional accents. The experiments were bench-marked against the output generated by well known speech services and the effect of the convolution was found to be positive.
The impact of transfer learning on lung cancer detection using various deep neural network architectures

Lung cancer is the primary reason for death among cancer patients. Early diagnosis of lung cancer in patients has a much higher chance of survival than when lung cancer has spread. In the last few years, computational intelligence-based techniques like deep learning have been designed to diagnose lung cancer at an early stage. This paper combines three types of optimizers with six deep learning models to conduct a performance comparison. This investigation focuses on six models AlexNet, GoogleNet, ResNet, Inception V3, EfficientNet b0, and SqueezeNet. The different models are assessed by comparing their performance with a stochastic gradient with momentum, Adam, and RMSProp optimization techniques. The study showed that CPU training takes time for training without GPU support. According to this study, the google net with Adam as optimizer gives Accuracy-92.08%, Precision-100%, Recall-86.89%, F1score-92.98%, FPR-0%, FNR-13.11%,outperforming the other deep learning architectures. When comparing the computational time for deep learning models, it is observed that Inception V3 takes the most time to train, and AlexNet takes the least time.
Closed WiFi Hotspot - Truly Hidden Network

IEEE 802.11 (WiFi) has evolved in recent years to an extent where it has become an integral part of our life. Despite its popularity, security is one of the major concerns associated with WiFi networks. New vulnerabilities have been found in WiFi systems every year and are patched periodically. There are several hacking tools available to the public for attacking wireless networks. The first logical steps used by these tools include discovering the networks and their associated vulnerabilities. The WiFi networks are not designed to be hidden from eavesdroppers; much useful information about the network is still broadcasted in management frames. The Hidden Network feature, which is present in most Access Points and Mobile Hotspot solutions, does not provide a true hidden feature, and advanced tools exploit it. IEEE 802.11w standard provides integrity to the management frames after secure associations. However, it does not provide confidentiality to all the management frames. This paper proposes a method for creating a closed WiFi Hotspot that can act as a truly hidden network by encrypting Information Elements in Management Frames. We have also shown that the proposed Management Frame Encryption key can be used to secure Pairwise Transient Key (PTK) generation in WiFi networks. Our experimental results clearly show that advanced hacking tools cannot initiate attacks on the proposed Closed WiFi Hotspots. Our experimental results also reveal that there is not much overhead to the connection sequence when implementing the proposed Closed WiFi solution.
On an integral transform and its inverse in nuclear imaging

In a nuclear imaging modality, the goal is to reconstruct the object under study from photon intensity distributions on a detector. However, photon scattering, mainly as a consequence of the Compton effect, considerably affects the image quality of the object. This is why most image reconstruction methods operate only with primary or non-scattered photons. Nevertheless the restored image remains noisy and weak in intensity. In this paper a new relation between the object and photon intensity distributions, generated by photons scattered at various deflection angles is established. It takes the form of an integral transform, compounded from Fourier and Hankel transforms. Most importantly this new transformation is invertible. As a result a novel principle for image reconstruction using scattered photons is derived and may lead to the conception of a new type of imaging device.
Feature Extraction with Locally Preserving Projections Based on Fuzzy K-neighbor

In order to find the nonlinear structure existing in the high-dimensional data set, and improve recognition accuracy, a novel feature extraction method with locally preserving projections (LPP)based on fuzzy K-nearest neighbor (F-LPP) is proposed. Firstly, Euclidean distance between training samples is calculated to determine the membership function between samples. Secondly, the weight matrix S in LPP is defined according to the membership function. Finally, the optimal projection matrix V of training samples was obtained according to LPP algorithm, and the samples are mapped to a low-dimensional space. In addition, the distance of the original samples in low-dimensional space is calculated to recognize and classify the target. Experimental results on crack defect images, welding defect images and binary image database show that the proposed algorithm F-LPP not only has some advantages in dimension reduction, but also greatly improves the classification recognition rate compared with the traditional principal PCA and LPP algorithms.
Moderation of Summertime Heat Island Phenomena via Modification of the Urban Form in the Tokyo Metropolitan Area

AbstractThis study investigated the moderation of the urban heat island via changes in the urban form in the Tokyo metropolitan area (TMA). Two urban scenarios with the same population as that of the current urban form were used for sensitivity experiments: the dispersed-city and compact-city scenarios. Numerical experiments using the two urban scenarios as well as an experiment using the current urban form were conducted using a regional climate model coupled with a single-layer urban canopy model. The averaged nighttime surface air temperature in TMA increased by ~0.34°C in the dispersed-city scenario and decreased by ~0.1°C in the compact-city scenario. Therefore, the compact-city scenario had significant potential for moderating the mean areal heat-island effect in the entire TMA. Alternatively, in the central part of the TMA, these two urban-form scenarios produced opposite effects on the surface air temperature; that is, severe thermal conditions worsened further in the compact-city scenario because.
Singularities of bihamiltonian systems and the multidimensional rigid body

Two Poisson brackets are called compatible if any linear combination of these brackets is a Poisson bracket again. The set of non-zero linear combinations of two compatible Poisson brackets is called a Poisson pencil. A system is called bihamiltonian (with respect to a given pencil) if it is hamiltonian with respect to any bracket of the pencil. The property of being bihamiltonian is closely related to integrability. On the one hand, many integrable systems known from physics and geometry possess a bihamiltonian structure. On the other hand, if we have a bihamiltonian system, then the Casimir functions of the brackets of the pencil are commuting integrals of the system. We consider the situation when these integrals are enough for complete integrability. As it was shown by Bolsinov and Oshemkov, many properties of the system in this case can be deduced from the properties of the Poisson pencil itself, without explicit analysis of the integrals. Developing these ideas, we introduce a notion of linearization of a Poisson pencil. In terms of linearization, we give a criterion for non-degeneracy of a singular point and describe its type. These results are applied to solve the stability problem for a free multidimensional rigid body.
Pricing with Splines

The exponential affine pricing principle is applied to the family of skewed Laplace historical distributions. The risk-neutral distribution is shown to belong to the same family and a closed form pricing formula for a European call is derived. This formula is a direct competitor of the Black-Scholes formula, but involves more parameters, that are location and tail parameters. This approach is extended to exponential affine spline conditional probability density function and stochastic discount factor leading to nonparametric pricing approaches. Finally the time coherency is introduced by means of a Markov specification.
NRC Publications Archive Archives des publications du CNRC

This publication could be one of several versions: author’s original, accepted manuscript or the publisher’s version. / La version de cette publication peut etre l’une des suivantes : la version prepublication de l’auteur, la version acceptee du manuscrit ou la version de l’editeur. For the publisher’s version, please access the DOI link below. / Pour consulter la version de l’editeur, utilisez le lien DOI ci-dessous.
The Dynamic Mixed Hitting-Time Model for Multiple Transaction Prices and Times

We propose a structural model for durations between events and (a vector of) associated marks, using a multivariate Brownian motion. Successive passage times of one latent Brownian component relative to random boundaries define durations. The other, correlated, Brownian components generate the marks. Our model embeds the class of stochastic conditional (SCD) and autoregressive conditional (ACD) duration models, which impose testable restrictions on the relation between the conditional expectation and conditional volatility of durations. We strongly reject the SCD and ACD specifications for both a very liquid and less liquid NYSE-traded stock, and characterize causality relations between volatilities and durations.
A Fixed Point Theorems in L-Fuzzy Quasi-Metric Spaces

At first we considered the L-fuzzy metric space notation which is useful in modeling some phenomena where it is necessary to study the relationship between two probability functions as well observed in Gregori et al. [A note on intuitionistic fuzzy metric spaces. Chaos, Solitons and Fractals 2006; 28: 902-905]. Then we introduced the concept of fixed point theorem in L-fuzzy metric space and finally, showed that every contractive mapping on an L-fuzzy metric space has a unique fixed point.
Research on the Influence of Descriptive and Injunctive Social Norms on the Willingness to Adopt Persuasive Health Information

: [Purpose/significance] To investigate the effect of descriptive and injunctive social norms on promoting people's willingness to adopt health information, introducing social belonging needs and perceived risk to explain the mechanism of this process, and to discuss the moderating role of social distance in it. Thus, we provide health science practitioners with strategies to optimize the effectiveness of message persuasion, while expanding localized research on social norm theory. [Methods/process] Based on the social norm theory to derive the research model, the experimental method and questionnaire were used to collect experimental data, and the Bootstrap method was used to test the mediating and moderating roles. [Result/conclusion] It was found that descriptive social norms were more persuasive than injunctive social norms in Eastern cultures, that the need for social belonging and perceived risk each played a partially mediating role in this process, and that social distance moderated the predictive effect of social norm information on willingness to adopt health information.
S15.3: Is Helicobacter pylori infection a necessary condition for non-cardia gastric cancer?

68 patients with gastric cancer and 360 controls were recruited in a population-based case-control study in Saarland/Germany between 11/1996 and 2/1998. Personal interviews were conducted and serum samples were taken during first hospitalization due to the cancer. Serum samples were analysed for H pylori specific IgG antibodies by ELISA (GAP, Bio-Rad, München, Germany) and for antibodies against the CagA antigen by Western blot (AID, Strasberg, Germany). Cases were compared to controls with respect to serological evidence of H pylori infection and CagA positivity in the entire sample and after (consecutive) exclusion of subgroups among whom H pylori test results might not adequately indicate previous H pylori exposure. Results
A general iterative scheme with applications to convex optimization and related fields

A general iterative scheme including relaxation and a corresponding problem class are presented. Some global convergence results are given. The acceleration of convergence is discussed, The scheme comprises a lot of known iterative methods such as subgradient methods and methods of successive orthogonal projections with relaxation. Applications to convex optimization, convex feasibility problems, systems of convex inequalities, variational inequalities, operator equations and systems of linear equations are given.
Lung ultrasound score-based assessment of postoperative atelectasis in obese patients according to inspired oxygen concentration: A prospective, randomized-controlled study

Background: According to a recent meta-analysis, in patients with a body mass index (BMI) ≥ 30, a high fraction of inhaled oxygen (FiO2) did not increase postoperative atelectasis. However, a high FiO2 generally increases the risk of postoperative atelectasis. Therefore, this study aimed to evaluate the effect of FiO2 on the development of atelectasis in obese patients using the modified lung ultrasound score (LUSS). Methods: Patients were assigned to 4 groups: BMI ≥ 30: group A (n = 21) and group B (n = 20) and normal BMI: group C (n = 22) and group D (n = 21). Groups A and C were administered 100% O2 during preinduction and emergence and 50% O2 during anesthesia. Groups B and D received 40% O2 for anesthesia. The modified LUSS was assessed before and 20 min after arrival to the postanesthesia care unit (PACU). Results: The difference between the modified LUSS preinduction and PACU was significantly higher in group A with a BMI ≥ 30 (P = .006); however, there was an insignificant difference between groups C and D in the normal BMI group (P = .076). Conclusion: High FiO2 had a greater effect on the development of atelectasis in obese patients than did low FiO2; however, in normal-weight individuals, FiO2 did not have a significant effect on postoperative atelectasis.
Diagnostic value of cell-free DNA in thyroid cancer: A systematic review and meta-analysis

Objective: An increasing number of studies have shown the potential diagnostic value of cell-free DNA (cfDNA) as a new biomarker in the management of thyroid cancer (TC); however, the accuracy of research results is inconsistent. This meta-analysis is the first to synthesize published results and evaluate the application value of circulating cfDNA in the diagnosis of TC. Methods: A search strategy was developed according to PICO (P: Patient; I: Intervention; C: Comparison; O: Outcome) principles. We searched 5 databases until October 2022. Original studies that examined cfDNA for the diagnosis of TC and used pathology as the gold standard were included in this meta-analysis. A random-effects model was used to pool the data extracted from individual studies, including the number of patients and the numbers of true positives, false positives, true negatives, and false negatives. Results: A total of 622 patients with TC, 547 patients with benign thyroid nodules, and 98 healthy individuals were included in 20 studies reported in 14 articles. The types of cfDNA included in the research include specific mutations of cfDNA, methylation of cfDNA, the content of cfDNA, and cfDNA index. After rigorous statistical analysis, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.76 (95% confidence interval [CI] 0.62–0.85), 0.87 (95% CI 0.78–0.93), 5.08 (95% CI 3.3–10.3), 0.28 (95% CI 0.17–0.46), 21 (95% CI 9–49), and 0.89 (95% CI 0.86–0.91), respectively. The meta-regression results showed that the number of cfDNAs, cfDNA methylation status, and sample size were the sources of heterogeneity in the specificity of the study. A subgroup analysis showed that the quantitative analysis group (cfDNA level) had a higher diagnostic accuracy than that of the qualitative analysis group (cfDNA methylation, mutation, or integrity index), with a sensitivity of 0.84, specificity of 0.89, and area under the curve of 0.91. Conclusions: The results of this meta-analysis suggest that cfDNA has value as an adjunct for the diagnosis of TC. Quantitative detection of cfDNA can achieve relatively high diagnostic accuracy. However, due to heterogeneity, the test results based on cfDNA for TC should be interpreted with caution.
A linearly constrained wideband adaptive array antenna with orthogonal filter structure

A linearly constrained wideband adaptive array sensor is described whose adaptive processor is realized with a multichannel orthogonal filter structure. It is shown that the transform domain processor utilizing the computationally simple LMS algorithm is capable of convergence speeds which rival the least squares methods which are more expensive to implement. Furthermore, there is no additional cost in terms of the adaptive coefficient requirements (and only a modest increase in nonadaptive computational requirements) when compared to the standard tapped-delay-line structure array utilizing the LMS algorithm. <<ETX>>
A class of self-similar random measure

We describe a class of self-similar random measure that generalizes the class of stable, completely random measure, or, in one dimension, the class of processes with stable independent increments. As with the stable processes, the realizations are purely atomic, but the masses of the atoms are not necessarily independent, but rather characterized by self-similar dependence relations. Indeed, the class can be described most effectively in terms of the point process on the product space for the locations and sizes (‘marksȉ) of the atoms. Then self-similarity reduces to an invariance relation (‘biscale invariance’) on the distribution of this marked point process. The condition can be satisfied when the marked point process is compound Poisson, corresponding to the nonnegative stable processes, but is by no means restricted to this case. An example is given which modifies and extends Ogata's epidemic-type aftershock sequence model for earthquake occurrence.
Toward a Control Theory for Networks

Modelling and algorithm-development tasks in such diverse application areas as autonomous vehicle coordination, sensor networking, and air traffic flow management have led to flourishing interest in network dynamics and control. The design problems of interest in these application areas are very often, at their core, decentralized control problems, and hence we believe strongly that the controls community will (and should) be at the forefront of these new and exciting research directions. What is becoming increasingly apparent, however, is that these new decentralized control problems are difficult ones: they cannot be addressed by the traditional methodology of decentralized control in which the network is viewed as a disturbance impacting robust agents, nor can they be addressed by ad hoc adaptation of centralized-control techniques. Instead, solutions to these exciting control problems must exploit the network communications/sensing, and hence the problems require us to understand in a systematic way the role played by network topology in decentralized control. In our opinion, there is as yet no suitable methodology for designing controllers that take advantage of the network topology, and even very few promising approaches for design. Though the general design of decentralized controllers is undoubtedly a daunting task, our strong belief is that some common characteristics of the modern network applications will permit us to design good controllers for these applications, and in turn give significant insight into the more general design problem. Such modern networks as mobile sensor systems and autonomous-vehicle teams have in common that (1) they are built of agents with highly simplified and often identical internal dynamics, (2) require inter-agent communication to complete a range of tasks, and (3) are subject to severe constraints and topological variation. For this class of communicating-agent networks, there is much hope for understanding the role played by the network topology in permitting control, and for developing high-performance controllers for a range of algorithmic tasks. The articles in this special issue take several approaches toward, and study several aspects of, controller analysis and design for communicating-agent networks. We believe strongly that, together, these articles capture and explore many key challenges in this exciting new direction in control theory.