Setup Parameters Research Articles

A procedure for the characterization of a music instrument vibro-acoustic fingerprint: the case of a contemporary violin

Violins are wooden musical instruments, whose quality is mainly evaluated on the basis of their aesthetics, as well as depending on the historical relevance of their makers. However, their acoustic quality remains a key evaluation parameter for performers and listeners. The instrument perceived quality, in turn, depends on one side, on the player, the environmental conditions and on the listeners’ psychoacoustic factors. On the other side, the quality of a violin depends on its materials, constructive and set-up parameters, that impact on the vibro-acoustical characteristics of the instrument. This work investigates a procedure for the vibro-acoustic characterization of a violin, here called vibro-acoustic fingerprint, as an example of vibro-acoustical characterization of a wooden music instrument. The procedure was applied, as a case study, to an Italian contemporary violin, built on the basis of a Guarneri del Gesù model in the year 2011.

Digital image processing for atmospheric monitoring at Colombian Andes

ABSTRACT As an alternative to the current technologies, we explored the feasibility of using low cost and massive use of digital cameras as photometric sensors to retrieve the atmospheric total optical depth (τ) in the urban area of a city in the Colombian Andes. This study proposes a simple way to estimate τ from digital processing of images of the Sun based on the Beer-Bouguer-Lambert law Langley’s linear fitting for the colour levels in channels red, green, and blue registered by the pixels of cameras’ sensors. From February to March 2022, the τ values retrieved from the images were correlated to the retrieved values from a solar spectral radiometer (SSR). We found that τ is sensible to the featured changes in the local atmosphere and to the cameras’ exposure parameters setup. Under conditions of partly clear sky, around 80% (r > 0.8) of the τ values from cameras showed a linear correspondence to those retrieved from SSR system. Its spectral dependency (τ _red < τ _green < τ _blue) is in accordance with the physical phenomena in light-atmosphere interaction. The results suggest that the methodology applied can be used for monitoring the atmosphere at any geographical location in the world.

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

A hybrid energy storage system (HESS) by integrating Lithium-Ion Battery and Wind Turbine System for Electric Vehicle is designed and implemented. An advanced model of lithium ion/wind turbine HESS model is developed to improve the battery’s charging capacity. The behavior of this model is tested by using Machine Learning Algorithms to identify remaining battery capacity of the energy storage components. The Machine learning Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) Decision Tree (DT) and Linear Regression (LR) are implemented which helps in predicting the State of Charge (SoC) in the electric vehicle to estimate the battery’s lifetime. The SoC of the EV without HESS is calculated and compared with the SoC of HESS implemented EV model. Sensors are used to measure the current and voltage parameters of the Lithium-Ion Battery setup (Internal Battery) and Storage Battery (External Battery). The measured data is sent to the LPC2148 ARM microcontroller Board. The data that is obtained from LPC2148 is given as input to the machine learning algorithms which are executed in the Computer. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square (RMSE), R-Square Error (R2E) and Mean Square Error (MSE) are calculated and the SoC values are predicted. The Random Forest algorithm produces better results for the HESS model as the Error values produced are less than 2.5%. The battery capacity of the electric vehicle has been improved by using external battery that is charged by wind turbine. Thus, the driving range of the electric vehicle implemented with HESS is increased twice when compared to conventional electric vehicle [1].

Melanoma Espial Employing Deep Learning Applied to Mobilenet

Abstract: The most recent advancements in smartphone-based skin cancer diagnosis software enable convenient, portable methods for melanoma risk assessment and diagnosis for skin cancer early detection due to the issue of trade-offs. [12] The majority of skin cancer detection applications do the image analysis on the server due to the (time complexity and error rate) limitations of running a machine learning algorithm for image analysis on a smartphone. In this work, we use the MobileNet v2 deep learning model to examine the performance of skin cancer picture identification and classification on Android devices. [10] We evaluate the effectiveness of a number of factors, including parameter setup, picture acquisition technique, computer and Android-based image analysis, and object identification and classification methods. Melanoma and skin cancer are utilized to evaluate the effectiveness of the suggested approach. [15] The measurement uses the testing techniques' running times, sensitivity, accuracy, and specificity. The optimum parameter for the MobileNet v2 model on Android utilizing photos from the smartphone camera gives 91.346% accuracy for classification, according to the experiment's findings. The Android app's ability to recognize objects and classify them made it possible to analyze skin cancer. Android-based image analysis maintains the computation time threshold that indicates user comfort and matches the computer's performance accuracy for high-quality photos. [18] These results drove the creation of an Android app for disease detection processing that makes use of the camera on smartphones and seeks to achieve high-accuracy real-time detection and classification.

On the field uniformity of reverberation chambers – a global sensitivity analysis study

PurposeGlobal sensitivity analysis (SA) by means of Sobol’ indices enhanced with different surrogate modeling techniques is performed in this work. The purpose is to investigate the influence of measurement uncertainties and the environment characteristics themselves on the desired field uniformity in reverberation chambers (RCs). This yields an efficient apparatus for the stirring and chamber design process.Design/methodology/approachThe technique of Sobol’ indices, as a candidate of global SA methods, is suitable for high fluctuations due to its robustness, which can be addressed to the stochastic nature of the RC environment. The aim of using surrogate modeling techniques is to compute the indices efficiently with a moderate number of required simulations. The powerfulness of this approach is introduced in a simple numerical example in which the physical phenomena can be identified more straightforwardly.FindingsThis method can provide useful knowledge in the lower frequency range, where the ideal properties of the electromagnetic field in RCs cannot be established, and the importance of the setup parameters can vary from configuration to configuration. In addition, it can serve as a basis for setup adaptation during parallelized electromagnetic compatibility tests, which would result in a more time- and cost-saving option in industrial applications in the future.Originality/valueDespite the previous attempts, a profound investigation of multiple setup parameters is still a hot topic. The main contribution of this work is the extension of the application area of the method of Sobol’ indices to RCs, which has not been done so far.

A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas. Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.

Modulating the trapping and manipulation of semiconductor particles using Bessel beam optical tweezers

We show that the optical trapping and manipulation of spherical-shaped semiconductor (germanium) particles can be strongly modulated and controlled using optical tweezers based on a highly focused Bessel beam. Depending on the setup parameters, the germanium beads can be stably trapped outside the optical axis, present a characteristic oscillatory dynamics near the focal plane or even be repelled from the focus. This intricate behavior was studied by varying relevant parameters such as the laser power, the radius of the Bessel beam and the focal height of the trap. The results show that Bessel beam optical tweezers using germanium beads are very versatile tools for applying pico- to femto-Newton constant or oscillatory forces on systems, opening perspectives for new applications in soft matter science and correlated fields, as well as in the construction of microdevices.

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications.

Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM's application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4-10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.

Recursive least-squares algorithm based on a third-order tensor decomposition for low-rank system identification

A recently developed third-order tensor (TOT) decomposition-based method has proved to be working very well in linear system identification problems that target the estimation of long length impulse responses. This technique exploits the decomposition of the impulse response based on the nearest Kronecker product and low-rank approximations, but it also owns a specific advantage. Its main feature is related to the way of handling the rank of a third-order tensor, which is controlled in terms of the rank of a matrix and, as a result, is limited to small values. In this paper, we further develop a recursive least-squares (RLS) algorithm based on the TOT decomposition technique. The resulting solution combines the estimates provided by three shorter adaptive filters, thus gaining in terms of both performance and complexity. The three component filters are updated in parallel, which represents an important practical advantage, in terms of the modularity of implementation. Also, as compared to the conventional RLS algorithm, a significant reduction of the computational complexity can be achieved for the common setup of the decomposition parameters. Besides, since the length of the adaptive filter highly influences the main performance criteria (e.g., convergence, tracking, and accuracy), the proposed RLS version based on the TOT decomposition outperforms both the conventional algorithm and a previously developed RLS-based solution that involves a second-order decomposition. These performance gains mainly reflect in a faster tracking capability and a better accuracy of the estimate (i.e., lower misalignment). Simulations performed in the framework of echo cancellation support the performance features of the proposed tensorial RLS-based algorithm.

Boosting Quantum Key Distribution via the End-to-End Loss Control

With the rise of quantum technologies, data security increasingly relies on quantum cryptography and its most notable application, quantum key distribution (QKD). Yet, current technological limitations, in particular, the unavailability of quantum repeaters, cause relatively low key distribution rates in practical QKD implementations. Here, we demonstrate a remarkable improvement in the QKD performance using end-to-end line tomography for the wide class of relevant protocols. Our approach is based on the real-time detection of interventions in the transmission channel, enabling an adaptive response that modifies the QKD setup and post-processing parameters, leading, thereby, to a substantial increase in the key distribution rates. Our findings provide everlastingly secure efficient quantum cryptography deployment potentially overcoming the repeaterless rate-distance limit.

Convex Programming-Based Robust Optimization Procedure for Blast-Excited Structures with Bounded Uncertainty

Application of the robust optimization (RO) technique to ensure the least possible deviation of structural performance is gaining increasing attention in recent years. However, most RO studies have been conducted in the probabilistic domain, where sufficient uncertainty information is available to construct probability distribution functions of response parameters. But, in many cases, such as for structures under underground blast excitation, hazard parameters like charge weight and charge center distance do not have any definite probability density function. Only their ranges of variations are known to an engineer. Hence, it is more logical to model these parameters as uncertain-but-bounded (UBB) type, which requires only bounds of variations to characterize the uncertainty. The available RO studies in the UBB-domain mainly deal with static loads. Studies on time-variant dynamic loads, such as blast load, are scarce in the existing literature. Also, blast load time-histories show record-to-record variations in their signatures, even with the same setup of hazard parameters. This aspect cannot be considered by the conventional convex programming approaches of RO in the UBB domain. To address all these issues, a new RO procedure is proposed in the present study, which is based on the dual response surface method. The proposed RO procedure can transform the complex simulation-based RO procedure under dynamic blast load to an equivalent deterministic problem with explicit constraints. Thereby, the RO can be easily solved by a gradient-based optimizer. The effectiveness of the proposed approach is elucidated by the complex real-world problems of a multistory building and an underground reinforced concrete bunker. The results are compared with conventional RO approaches. The results show that the proposed approach consistently yields more economic as well as robust solutions in a computationally efficient way.

Dynamic Mechanical Analysis of Cementitious Composites: Test Method Optimization and Materials Characterization

Dynamic mechanical analysis (DMA) applies an oscillating force to a specimen and analyzes the rheological and viscoelastic properties of a material. Since this method is used to characterize the frequency-dependent viscoelastic properties, it is mostly used for polymers, which have high sensitivity to the loading frequency. Cementitious composites are brittle and require more attention to specimen preparation and experimental settings to obtain reliable results. This work develops a DMA test protocol for cementitious composites which includes analyzing the effect of preload test, frequency pretest, and amplitude sweep on properties of cementitious composites. We propose a statistical method to analyze the variation between samples and determine the outliers among the specimens with the same mix to obtain valid test results from a sample group. The dynamic results obtained by using the proposed method are also discussed to serve as the baseline. The experimental setup parameters and statistical analysis presented in this work can be a robust foundation for the DMA analysis of cementitious composites.

The Lobster Eye Imager for Astronomy Onboard the SATech-01 Satellite

The Lobster Eye Imager for Astronomy (LEIA), a pathfinder of the Wide-field X-ray Telescope of the Einstein Probe mission, was successfully launched onboard the SATech-01 satellite of the Chinese Academy of Sciences on 2022 July 27. In this paper, we introduce the design and on-ground test results of the LEIA instrument. Using state-of-the-art Micro-Pore Optics (MPO), a wide field of view of 346 square degrees (18.°6 × 18.°6) of the X-ray imager is realized. An optical assembly composed of 36 MPO chips is used to focus incident X-ray photons, and four large-format complementary metal-oxide semiconductor (CMOS) sensors, each of size 6 cm × 6 cm, are used as the focal plane detectors. The instrument has an angular resolution of 4′–8′ (in terms of FWHM) for the central focal spot of the point-spread function, and an effective area of 2–3 cm2 at 1 keV in essentially all the directions within the field of view. The detection passband is 0.5–4 keV in soft X-rays and the sensitivity is 2–3 × 10−11 erg s−1 cm−2 (about 1 milliCrab) with a 1000 s observation. The total weight of LEIA is 56 kg and the power is 85 W. The satellite, with a design lifetime of 2 yr, operates in a Sun-synchronous orbit of 500 km with an orbital period of 95 minutes. LEIA is paving the way for future missions by verifying in flight the technologies of both novel focusing imaging optics and CMOS sensors for X-ray observation, and by optimizing the working setups of the instrumental parameters. In addition, LEIA is able to carry out scientific observations to find new transients and to monitor known sources in the soft X-ray band, albeit with limited useful observing time available.

Promoting cooperation of agents through aggregation of services in trust models

In agent systems, cooperation plays a key role that can only be achieved trusting other agents. However, in many application areas of agents, the lack of enough frequent direct experiences is a major problem to find the right partner to cooperate with. The exchange of indirect information such as recommendations about third parties intends to address this problem. Since an increase of recommendation exchanges will produce more accuracy in trusting decisions, any way of promoting the exchange of recommendations would be welcome in order to extend the use of Trust models. In this paper, we present a method of exchanging recommendations to be used by such trusting agents which pursues an increase of cooperation (in the form of more recommendations exchanges). We used ART testbed domain as an illustrative example of application, where agents act as providers and consumers of painting appraisals, and we propose the pair of interacting agents to agree about a joint pair of reputation requests that would immediately satisfy both agents instead of expecting future benefits of sharing reputation in an altruistic way. We show under what circumstances (different parameter setups) an implementation of our proposal behaves better (in terms of quality and cost) compared to no-recommendations and the way recommendations take place in ART Testbed.

Wear Behavior of AZ61 Matrix Hybrid Composite Fabricated via Friction Stir Consolidation: A Combined RSM Box–Behnken and Genetic Algorithm Optimization

Friction stir consolidation (FSC) is a promising manufacturing process for metal matrix hybrid composites (MMHC) with excellent mechanical properties. The originality of this study involves the exploration of the fabrication technique (FSC), the selection of materials and the optimization of wear behavior via a systematic investigation of the process parameters. The aim of this study was to optimize and investigate the wear behavior of MMHCs fabricated using FSC. The optimum sample was nominated for thermogravimetric analysis (TGA) and wear morphology analysis using SEM imaging. Material compositions of 7.5%wt of SiC, 7.5%wt of ZrO2 and 85%wt of AZ61 were considered for the experimental investigation. The RSM Box–Behnken design followed by a genetic algorithm (GA) was implemented to optimize the process parameters of sliding distance, speed and load at 350 m, 500 m and 650 m; 220 rpm, 240 rpm and 260 rpm; and 20 N, 30 N and 40 N, respectively. The RSM Box–Behnken result showed that the minimum wear rate of 0.008 mg/m was obtained at 350 m, 20 N and 240 rpm, whereas GA predicted the optimum parametric setup at 350 m, 20 N and 220 rpm. Additionally, TGA showed the material’s thermal stability from 375 °C to 480 °C. Generally, MMHCs exhibited a promising wear performance, proving the effectiveness of the FSC.

Force-induced fluorescence spectrum shift of spiropyran-based polymer for mechano-response sensing

As spiropyran exhibits mechanophore response, i.e., becomes vibrantly fluorescent under an applied force, its change has been measured primarily by fluorescence intensity. However, fluorescence intensity, which is an extensive property, is strongly affected by specimen and set-up parameters, and the corresponding measurements therefore require laborious and assumptive corrections. Herein, spiropyran is covalently linked to poly(dimethylsiloxane) and an in-situ opto-mechanical set-up is developed to monitor fluorescence spectrum evolution under deformation. Peak wavelength, which is an intensive property, is shown to be a superior mechano-response indicator that offers high accuracy/reliability, allows one to establish a 1:1 correspondence to strain (or stress) without corrections, and is independent of size. To the best of our knowledge, this is the first report of peak wavelength as mechano-response indicator for SP and its first systematic in-situ study. In summary, peak wavelength is shown to successfully reflect the force applied to polymer specimens without the need for corrections and exhibit considerable resistance to cycling-induced fatigue, while analysis of the finite extensibility limit reveals that the change of peak wavelength is closely related to the forces exerted directly on the polymer chain.

Three‐Point Bending Behavior of Foam‐Filled Conventional and Auxetic 3D‐Printed Honeycombs

Cellular hexagonal (conventional) and re‐entrant (auxetic) honeycombs are applicable in automotive, construction, and protective engineering. Auxetic structures own excellent energy absorption and flexural behavior due to their special deformation under loading. This work explores the performance of additively manufactured polylactic acid (PLA)‐ and thermoplastic polyurethane (TPU)‐based hexagonal and re‐entrant honeycombs under flexural loading via experimental three‐point bending (TPB) tests and finite‐element analysis (FEA). 3D‐printed conventional and auxetic cellular structures are filled with polyurethane (PU) foam and their energy absorption capacity and flexural modulus are compared with hollow structures. The results reveal that TPU‐based structures’ energy absorption capacity and flexural modulus improve significantly, whereas the PLA‐based structures’ performance deteriorates when filled with PU foam. Moreover, re‐entrant honeycombs are better reinforced with foam in comparison to the hexagonal honeycombs, as the re‐entrant's unit cell is more spacious than the hexagonal unit cell. Finally, parametric studies are performed via FEA to investigate the influence of geometric parameters of structures and flexural loading setup on the performance of the honeycombs, showing that structures with thicker struts and higher cell angle can act stiffer under TPB. The outcomes of this research indicate the promising performance of foam‐filled TPU‐based auxetic structures.

Impact of experimental setup parameters on the measurement of articular cartilage optical properties in the visible and short near-infrared spectral bands.

There is increasing research on the potential application of diffuse optical spectroscopy and hyperspectral imaging for characterizing the health of the connective tissues, such as articular cartilage, during joint surgery. These optical techniques facilitate the rapid and objective diagnostic assessment of the tissue, thus providing unprecedented information toward optimal treatment strategy. Adaption of optical techniques for diagnostic assessment of musculoskeletal disorders, including osteoarthritis, requires precise determination of the optical properties of connective tissues such as articular cartilage. As every indirect method of tissue optical properties estimation consists of a measurement step followed by a computational analysis step, there are parameters associated with these steps that could influence the estimated values of the optical properties. In this study, we report the absorption and reduced scattering coefficients of articular cartilage in the spectral band of 400-1400 nm. We assess the impact of the experimental setup parameters, including surrounding medium, sample volume, and scattering anisotropy factor on the reported optical properties. Our results suggest that the absorption coefficient of articular cartilage is sensitive to the variation in the surrounding medium, whereas its reduced scattering coefficient is invariant to the experimental setup parameters.

Localization of acoustic sources in water tunnel with a demodulation technique

The prediction of the underwater radiated noise of a vessel is still an important issue during the design process of a ship. Due to the influence of shipping noise on marine life and for acoustic stealth in military applications, this problem has seen increased interest for decades. The preferred way to estimate the future radiated noise level is to perform measurements at the model scale and extrapolate the results obtained to full scale. DGA Hydrodynamics owns its cavitation tunnel with low background noise that allows such measurements. The test section of this cavitation tunnel looks like a rectangular corridor filled with demineralized water. Among all the parameters of the experimental set-up that influence the accuracy of the acoustic measurements, the position of the acoustic source could be difficult to estimate, especially at low frequencies. In this frequency domain, acoustic propagation could be described advantageously by the modal theory. In this communication, we propose to use the modal theory and the associated demodulation techniques to estimate the position of the source inside the test section. The first part of the paper presents the modal theory and the developed method. Simulations have been carried out to estimate the performances of such methods. The results of this simulation and analysis are then presented. The method appears as an efficient way to estimate the position of the source with, nevertheless, some limitations in frequency and number of measured modes in the demodulation technique.

Microscopy with heralded Fock states.

We consider a microscopy setting where quantum light is used for illumination. Spontaneous parametric down conversion (SPDC) is used as a source of a heralded single photon, which is quantum light prepared in a Fock state. We present analytical formulas for the spatial mode tracking along with the heralded and non-heralded mode widths. The obtained analytical results are supported by numerical calculations and the following discussion taking into account realistic setup parameters such as finite-size optics and finite-size single-photon detectors. This allows us to observe that the diffraction limit can be approached with simultaneous alleviation of the photon loss leading to increased signal-to-noise ratio - a factor limiting practical applications of quantum light. Additionally, it is shown that the spatial resolution can be manipulated by carefully preparing the amplitude and phase of the spatial mode profile of the single photon at the input to the microscope objective. Here, the spatial entanglement of the biphoton wavefunction or adaptive optics can be applied for spatial mode shaping. Analytical dependencies between the incident and focused spatial mode profiles parameters are provided.