The purpose of this paper is to present in fully quantum terms the fundamentals of quantum Fourier optics in a completely coherent and comprehensive method with several key applications. This paper’s physical and mathematical models for various elements of a typical spatial optical signal processing apparatus create a new paradigm and will be of utmost importance to advance many quantum optical science and engineering fields. The results in this paper will, significantly advance the state of the art of quantum Fourier optics.

This paper provides a solution for a linear command governor (CG) that employs invariant and constraint-admissible ellipsoid. The motivation is to substitute the typical polyhedral set used in almost all CG schemes with the ellipsoidal one, which is much easier to construct. However the price for this offline computational efficiency is that the size of the feasible set can be relatively small, and the online computational burden is heavier than that of polyhedral set based CGs. The proposed solution overcomes these two weaknesses and offers a very attractive alternative to polyhedral set based CG. Two numerical examples with comparison to earlier solutions from the literature illustrate the effectiveness of the proposed algorithm.

Greenhouse gasses (GHGs) play an important role in controlling local air pollution as well as climate change. In the present study, we retrieved column-averaged dry-air (X) mole fractions of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier Transform Infrared Spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this paper, the diurnal variation and the time series of daily averaged XCO2, XCH4, and XCO covering the period December 2020 to May 2021 are analyzed. The maximum values of XCO2, XCH4, and XCO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb respectively. Less diurnal (XCO2 ~0.44 ppm; XCH4=0.004 ppm, and XCO=4.84 ppb) but clear seasonal changes are observed during the study period. The XCH4 and XCO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient ‘r’ between the EM27/SUN retrieved XCH4 and XCO against S5P/TROPOMI are 0.75 and 0.94 respectively.

The paper presents the sizing and profiling of charging systems for electric vehicles integrated into a smart grid in the town of Campobasso, in Molise. After having described and examined the main variables that characterize the system, the work focuses on the profiling of the electrical power absorbed by the building and the loads deriving from the charging stations of electric vehicles. The intensive use of software (especially MATLAB) and databases available online has been essential for the analysis of the random and stochastic values of the model developed. 4 different scenarios are studied, varying according to the number of charging electric vehicles. These scenarios are analyzed for three different typical days of winter, summer and mid-season. The different scenarios are compared qualitatively and quantitatively through the introduction of specific KPIs, which indicate the value of the power and the energy consumption of each specific asset, as can be seen in the results. Finally, various hypotheses are proposed regarding the integration of a storage system and the shifting of electric vehicles charges, for a more efficient energy exchange between the building and the network.

This paper presents a novel cascode cross coupled dynamic comparator that has good coomon-mode behaviour. Performance metrics such as delay, offset, rms noise, energy consumed per bit conversion are calculated andcompared with state of the art work.

Financial sectors play a significant role in the agriculture domain to improve economic growth. Technological advancements attracted all stakeholders in agriculture to make use of it effectively. Agriculture, the oldest sector of human civilization, can enjoy rapid growth and sustainability by effectively utilizing current technology’s financial and communication applications. According to the old farm regulations, Agri-Fintech—a catchall phrase for using technology to finance farmers and the value chain—could be the key to increasing the profitability of farms and farmers. Through intelligent data intervention, market linkages, partnerships, and a physical strategy, lending to farmers, Farmer Producer Organizations(FPO), and other value chain actors have been demonstrated to be scalable. A significant borrower from banks and other financial institutions is the agriculture sector. This work explains how Agri-Techs become Agri-Fintech and what role banks play.

Large intelligent surface-based transceivers (LISBTs), in which a spatially continuous surface is being used for signal transmission and reception, have emerged as a promising solution for improving the coverage and data rate of wireless communication systems. To realize these objectives, the acquisition of accurate channel state information (CSI) in LISBT-assisted wireless communication systems is crucial. In this paper, we propose a channel estimation scheme based on a parametric physical channel model for line-of-sight dominated communication in millimeter and terahertz wave bands. The proposed estimation scheme requires only five pilot signals to perfectly estimate the channel parameters assuming there is no noise at the receiver. In the presence of noise, we propose an iterative estimation algorithm that decreases the channel estimation error due to noise. The training overhead and computational cost of the proposed scheme do not scale with the number of antennas. The simulation results demonstrate that the proposed estimation scheme significantly outperforms other benchmark schemes.

Wheezes are adventitious respiratory sounds commonly present in patients with respiratory conditions. The presence of wheezes and their time location are relevant for clinical reasons, such as understanding the degree of bronchial obstruction. Conventional auscultation is usually employed to analyze wheezes, but remote monitoring has become a pressing need during recent years. Automatic respiratory sound analysis is required to reliably perform remote auscultation. In this work we propose a method for wheeze segmentation. Our method starts by decomposing a given audio excerpt into intrinsic mode frequencies using empirical mode decomposition. Then, we apply harmonic-percussive source separation to the resulting audio tracks and get harmonic-enhanced spectrograms, which are processed to obtain harmonic masks. Subsequently, a series of empirically derived rules are applied to find wheeze candidates. Finally, the candidates stemming from the different audio tracks are merged and median filtered. In the evaluation stage, we compare our method to three baselines on the ICBHI 2017 Respiratory Sound Database, a challenging dataset containing various noise sources and background sounds. Using the full dataset, our method outperforms the baselines, achieving an F1 of 41.9%. Our method’s performance is also better than the baselines across several stratified results focusing on five variables: recording equipment, age, sex, body-mass index, and diagnosis. We conclude that, contrary to what has been reported in the literature, wheeze segmentation has not been solved for real life scenario applications. Adaptation of existing systems to demographic characteristics might be a promising step in the direction of algorithm personalization, which would make automatic wheeze segmentation methods clinically viable.

Rotor position information obtained through a position sensor is crucial for proper field-oriented control (FOC) of permanent magnet synchronous machine based high dynamic energy conversion applications. In applications such as transportation and industrial, PMSM energy conversion systems are subjected to harsh environmental conditions such as vibration, shock, and thermal shock causing mechanical interfaces holding positions sensors to fail, introducing error to the position measurement. A dynamic position sensor offset error (DPSOE), being such a failure mode has the potential to degrade system torque output or more adversely, reverse machine torque output that may cause catastrophic outcomes. This paper evaluates different DPSOE scenarios, presents an approach to model the failure mode for analysis and proposes two novel and robust DPSOE detection methods for PMSM drive systems. The proposed detection methods are analytically proven and supported by simulation and experimental results under multiple operating conditions proving robustness.

Applications of X-ray computed tomography (CT) for porosity characterization of engineering materials often involve an extended data analysis workflow that includes CT reconstruction of raw projection data, binarization, labeling and mesh extraction. It is often desirable to map the porosity in larger samples but the computational challenge of reducing gigabytes of raw data to porosity information poses a critical bottleneck. In this work, we describe algorithms and implementation of an end-to-end porosity mapping code that processes raw projection data from a synchrotron CT instrument into a porosity map and visualization in the form of triangular face mesh. Towards this objective, we report the development of a novel subset reconstruction scheme for X-ray CT using filtered backprojection and a convolutional neural network that allows us to reconstruct arbitrarily-shaped subsets of a tomography object. We build upon this scheme to implement the complete code for porosity mapping. The code first detects possible voids by performing a coarse reconstruction on down-sampled projections and then improves the shape of those voids with higher detail offered by reconstructing selected subsets from the original raw data. We report measurements of the time taken by this code to perform complete processing from raw data to a triangular face mesh for several visualization scenarios on a single highperformance workstation equipped with GPU. We show that we can now visualize local porosity within a 8 gigavoxel CT volume (12 gigabytes raw data) within 1 to 2 minutes and a 64 gigavoxel CT volume (100 gigabytes of raw data) within 3 to 7 minutes.

We propose a new method and software for measuring blood vessel diameters from fundus photographs. In this method the edge of the blood vessel is the two minimum points in the luminance profile and diameter is distance between minimum points. We noticed the relation between W-shape of luminance profile at blood vessel transversal line and cylindrical shape, and analyzed the validity of this method using the diffuse reflection model of a cylinder shape based on the optics. The simulation image by the diffuse reflection model of the cylinder was similar to that of the blood vessel images. A newly developed software using the minimum points can measure repeated measurement in a series of photographs at the same point of the same person. The data of the repeated measurement of each person showed stability, and the coefficient of variation showed no affection of different diameter and arteriovenous distinction. Hence, we believe that the minimum points are valid as blood vessel edges. The software that measures these blood vessel edges has an extremely simple measurement procedure, possesses few errors, and is easy to automate. This method is relevant because the central reflex, which has not been utilized previously, optically represents the cylindrical three-dimensional structure of blood vessels.

Explainability is becoming increasingly crucial in machine learning studies and, as the complexity of the model increases, so does the complexity of its explanation. However, the higher the complexity of the problem the higher the amount of information it may provide, and this information can be exploited to generate a more precise explanation of how the model works. One of the most valuable ways to recover such relation between input and output is to extract counterfactual explanations. In binary classification, counterfactuals allow us to find minimal changes from an observation to another one belonging to the opposite class. But how do counterfactuals work in multi-class problems? In this article, we propose a novel methodology to extract multiple counterfactual explanations (MUCH, MUlti Counterfactual via Halton sampling) from an original Multi-Class Support Vector Data Description algorithm (MC-SVDD). To evaluate the performance of the proposed method, we extracted a set of counterfactual explanations from three state-of-the-art datasets achieving satisfactory results that pave the way to a range of real-world applications, for example disease prevention.

Automated chest X-ray analysis has a great potential for diagnosing thorax diseases since errors in diagnosis have always been a concern among radiologists. Being a multi-label classification problem, achieving accurate classification still remains challenging. Several studies have focused on accurately segmenting the lung regions from the chest X-rays to deal with the challenges involved. The features extracted from the lung regions typically provide precise clues for diseases like nodules. However, such methods ignore the features outside the lung regions, which have been shown to be crucial for diagnosing conditions like cardiomegaly. Therefore, in this work, we explore a dual-branch a network-based framework that relies on features extracted from the lung regions as well as the entire chest x-rays. The proposed framework uses a novel network named R-I UNet for segmenting the lung regions. The dual-branch network in the proposed framework employs two fine-tuned AlexNet models to extract discriminative features, forming two feature vectors. Each of these feature vectors is fed into a recurrent neural network consisting of a stack of gated recurrent units with skip connections. Finally, the resulting feature vectors are concatenated for classification. The R-I UNet has been evaluated on the JSRT and Montgomery datasets, while the dual-branch classification network has been evaluated on the NIH ChestXray14 dataset. The proposed models achieve state of-the-art performance for both segmentation and classification tasks on the above benchmark datasets.

Blockchain is a distributed ledger technology (DLT) to manage data in a decentralised way. During the last years, interoperability has become one of the main challenges within blockchain research as blockchains increasingly require integration between each other. Indeed, blockchains work by design in silos of information as interoperability is not a native feature. The main efforts in the field are focused on blockchains, such as Bitcoin and Ethereum. However, interoperability in DLT remains as an almost untouched area of work as they introduce additional requirements focusing on privacy and identity. Although there are some interoperability solutions for DLT, they are either high-level design proposals not providing concrete implementations or focus on interoperability issues between business applications and blockchain platforms. In this paper we propose a gateway-based platform-to-platform interoperability solution for DLT, which comprises a detailed solution design and a reference implementation. The proposal was assessed through the development of a social security case scenario and the evaluation through two interoperability frameworks. A reference implementation was built using two DLT: Hyperledger Fabric and Corda. The experimental results shows that it is possible to achieve technical interoperability between two heterogeneous DLT platforms using a gateway-based interoperability solution, relaxing decentralisation, data privacy, identity and authorisation management properties.

The skin’s ability to sense its environment is vital to activities of daily living. Cutaneous sensory perception diagnostics allow for the early detection and symptom tracking of tactile dysfunction. However, lack of access to healthcare and the limited frequency of current screening tools can leave skin sensation impairments undiscovered or unmonitored. This work presents a smartphone application for Cutaneous Hand Assessment with a Smartphone Interface (CHASI) to establish Vibrational Perception Thresholds (VPT). CHASI’s vibrational output and force measurement abilities are also characterized. An 18-participant cross-sectional study, with both normative subjects and subjects with sensation impairment, compares the monofilament test with smartphone established VPT (SE- VPT). We find a high positive correlation between SE-VPT and monofilament scores (rs=0.83, p = 0.00014). We also investigate the sensitivity of our proposed SE-VPT method to the motions and forces applied to the touchscreen. We find that variations in force do not alter the practical significance of the monofilament correlation. These results further the smartphone as a potential diagnostic and monitoring tool for hand health.

This research paper presents an advanced robotic system designed for efficient pick-and-place of deformable poultry pieces from cluttered bins. The system incorporates a novel architecture with seamless integration of various modules, enabling the robot to handle deformable poultry with precision. It introduces a comprehensive evaluation approach to assess the system’s performance, considering perception, state modeling, planning and control, gripping and manipulation. The experiments were conducted on two different samples of chicken pieces with varying weights and shapes, under complex and simple scenarios. Performance indicators, failure categories, and cycle time were used for evaluation. The evaluation revealed an overall success rate of 49.4% for picking and placing chicken pieces, with failure rates of 21.8% for perception, 30.7% for gripping, and 11% for manipulation modules. These results highlight areas of improvement, particularly in object detection, grasp pose estimation in clutter, and gripper designs for deformable products, to create a robust pick-and-place solution. The proposed robotic system and evaluation method hold immense potential for revolutionizing the meat processing industry and other food processing sectors, making automation more efficient and adaptable to meet the increasing demand in the food industry.

Quantum control of Bose-Einstein-Condensates (BEC) had been found for one and two dimension cases. Firstly in this paper, we want to control BEC at electro-magnetic field in three dimension optical lattice theoretically. The trapped particles in constructed lattices can be made and controlled by optical pulse, and known as molasses. It is evident that quantum control theory is worked for physical particles in BEC status. It is a survey with system control and quantum particle physics. Future work is to focus on collaborating with real laboratory.

This work offers a generic example for the visualization of uncertainty propagated through nonlinear algorithms by automatic differentiation. Scientists use increasingly elaborate algorithms to assess and analyze empirical data. Uncertainty estimates — error bars — are crucial for scientific analysis, but many modern computational analysis methods do not propagate uncertainty from their inputs to their outputs. Our example treatment of PCA combines the classic notion of linearization for error propagation with the power of modern automatic differentiation, and can be applied to other methods, too. We provide a visualization technique to go alongside this approach: Animations along orbits of the probability distribution provide an additional visual channel to represent uncertainty even in plots that are already visually dense as point estimates.