Search results for: variational quantum classifier

Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Maria Hussin, Malik Zawwar Hussain, Mubashrah Saddiqa

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We present a trigonometric scheme to approximate a circular arc with its two end points and two end tangents/unit tangents. A rational cubic trigonometric Bézier curve is constructed whose end control points are defined by the end points of the circular arc. Weight functions and the remaining control points of the cubic trigonometric Bézier curve are estimated by variational approach to reproduce a circular arc. The radius error is calculated and found less than the existing techniques.

Keywords: control points, rational trigonometric Bézier curves, radius error, shape measure, weight functions

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Authors: Wonkyung Na, Namhun Kim, Heeyeop Chae

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Quantum dots (QDs) are getting attentions due to their excellent optical properties in display, solar cell, biomolecule detection and lighting applications. Energy band gap can be easilty controlled by controlling their size and QDs are proper to apply in light-emitting-diode(LED) and lighting application, especially. Typically cadmium (Cd) containing QDs show a narrow photoluminescence (PL) spectrum and high quantum yield. However, Cd is classified as a hazardous materials and the use of Cd is being tightly regulated under 100ppm level in many countries.InP and CuInS2 (CIS) are being investigated as Cd-free QD materials and it is recently demonstrated that the performance of those Cd-free QDs is comparable to their Cd-based rivals.Due to a broad emission spectrum, CuInS2 QDs are also proper to be applied to white LED.4 For the lighting applications, the QD should be made in forms of color conversion films. Various film processes are reported with QDs in polymer matrixes. In this work, we synthesized the CuInS2 (CIS) QDs and QD embedded polystyrene color conversion films were fabricated for white color emission with electro-spinning process. As a result, blue light from blue LED is converted to white light with high color rendering index (CRI) of 72 by the color conversion films.

Keywords: CuInS2/ZnS, electro-spinning, color conversion films, white light emitting diodes

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Pejman Hosseinioun, Abolghasem Ghasempour, Elham Gholami, Hamed Sarbazi

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Considering the development in global semiconductor technology, it is anticipated that gadgets such as diodes and resonant transistor tunnels (RTD/RTT), Single electron transistors (SET) and quantum cellular automata (QCA) will substitute CMOS (Complementary Metallic Oxide Semiconductor) gadgets in many applications. Unfortunately, these new technologies cannot disembark the common Boolean logic efficiently and are only appropriate for liminal logic. Therefor there is no doubt that with the development of these new gadgets it is necessary to find new MacDraw technologies which are compatible with them. Resonant transistor tunnels (RTD/RTT) and circuit MacDraw with enhanced computing abilities are candida for accumulating Nano criterion in the future. Quantum cellular automata (QCA) are also advent Nano technological gadgets for electrical circuits. Advantages of these gadgets such as higher speed, smaller dimensions, and lower consumption loss are of great consideration. QCA are basic gadgets in manufacturing gates, fuses and memories. Regarding the complex Nano criterion physical entity, circuit designers can focus on logical and constructional design to decrease complication in MacDraw. Moreover Single electron technology (SET) is another noteworthy gadget considered in Nano technology. This article is a survey in future of Nano technology in digital MacDraw.

Keywords: nano technology, resonant transistor tunnels, quantum cellular automata, semiconductor

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Authors: S. Ghammamy, M. Mirzaabdollahiha

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Quantum mechanical calculations of energies, geometries, and vibrational wavenumbers of fluorous 1,3-dion compounds are carried out using density functional theory (DFT/B3LYP) method with LANL2DZ basis sets. The calculated HOMO and LUMO energies show that charge transfer occurs in the molecules. The thermodynamic functions of fluorous 1,3-dion compounds have been performed at B3LYP/LANL2DZ basis sets. The theoretical spectrograms for F NMR spectra of fluorous 1,3-dion compounds have also been constructed. The F NMR nuclear shieldings of fluoride ligands in fluorous 1,3-dion compounds have been studied quantum chemical.

Keywords: density function theory, natural bond orbital, HOMO, LOMO, fluorous

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Thomas Prevenslik

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Weakly Interacting Massive Particle (WIMP) experiments suggesting dark matter does not exist are consistent with the argument that the long-standing galaxy rotation problem may be resolved without the need for dark matter if the redshift measurements giving the higher than expected galaxy velocities are corrected for the redshift in cosmic dust. Because of the ubiquity of cosmic dust, all velocity measurements in astronomy based on redshift are most likely overstated, e.g., an accelerating Universe expansion need not exist if data showing supernovae brighter than expected based on the redshift/distance relation is corrected for the redshift in dust. Extensions of redshift corrections for cosmic dust to other historical astronomical observations are briefly discussed.

Keywords: alternative theories, cosmic dust redshift, doppler effect, quantum mechanics, quantum electrodynamics

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Authors: S. V. Kuznetsov, M. N. Mayakova, V. Yu. Proydakova, V. V. Pavlov, A. S. Nizamutdinov, O. A. Morozov, V. V. Voronov, P. P. Fedorov

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Increase in the share of electricity received by conversion of solar energy results in the reduction of the industrial impact on the environment from the use of the hydrocarbon energy sources. One way to increase said share is to improve the efficiency of solar energy conversion in silicon-based solar panels. Such efficiency increase can be achieved by transferring energy from sunlight-insensitive areas of work of silicon solar panels to the area of their photoresistivity. To achieve this goal, a transition to new luminescent materials with the high quantum yield of luminescence is necessary. Improvement in the quantum yield can be achieved by quantum cutting, which allows obtaining a quantum yield of down conversion of more than 150% due to the splitting of high-energy photons of the UV spectral range into lower-energy photons of the visible and near infrared spectral ranges. The goal of present work is to test approach of excitation through sensibilization of 4f-4f fluorescence of Yb3+ by various RE ions absorbing in UV and Vis spectral ranges. One of promising materials for quantum cutting luminophores are fluorides. In our investigation we have developed synthesis of nano- and submicron powders of calcium fluoride and strontium doped with rare-earth elements (Yb: Ce, Yb: Pr, Yb: Eu) of controlled dimensions and shape by co-precipitation from water solution technique. We have used Ca(NO3)2*4H2O, Sr(NO3)2, HF, NH4F as precursors. After initial solutions of nitrates were prepared they have been mixed with fluorine containing solution by dropwise manner. According to XRD data, the synthesis resulted in single phase samples with fluorite structure. By means of SEM measurements, we have confirmed spherical morphology and have determined sizes of particles (50-100 nm after synthesis and 150-300 nm after calcination). Temperature of calcination appeared to be 600°C. We have investigated the spectral-kinetic characteristics of above mentioned compounds. Here the diffuse reflection and laser induced fluorescence spectra of Yb3+ ions excited at around 4f-4f and 4f-5d transitions of Pr3+, Eu3+ and Ce3+ ions in the synthesized powders are reported. The investigation of down conversion luminescence capability of synthesized compounds included measurements of fluorescence decays and quantum yield of 2F5/2-2F7/2 fluorescence of Yb3+ ions as function of Yb3+ and sensitizer contents. An optimal chemical composition of CaF2-YbF3- LnF3 (Ln=Ce, Eu, Pr), SrF2-YbF3-LnF3 (Ln=Ce, Eu, Pr) micro- and nano- powders according to criteria of maximal IR fluorescence yield is proposed. We suppose that investigated materials are prospective in solar panels improvement applications. Work was supported by Russian Science Foundation grant #17-73- 20352.

Keywords: solar cell, fluorides, down-conversion luminescence, maximum quantum yield

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Adel Belayadi, Ahmed Mougari, Boualem Bourahla

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Molecular junctions are currently considered a promising style in the miniaturization of electronic devices. In this contribution, we provide a tight-binding model to investigate the quantum transport properties across-molecular junctions sandwiched between 2D-graphene nanoribbons in the zigzag direction. We investigate, in particular, the effect of embedded atoms such as Gold and Silicon across the molecular junction. The results exhibit a resonance behavior in terms of incident Fermi levels, depending on the molecular junction type. Additionally, the transport properties under a perpendicular magnetic field exhibit an oscillation for the transmittance versus the magnetic field strength.

Keywords: molecular junction, 2D-graphene nanoribbons, quantum transport properties, magnetic field

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Authors: Walid Tawfik, W. Askam Farooq, Sultan F. Alqhtani

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Quantum dots (QDots) are nanometer-sized crystals, less than 10 nm, comprise a semiconductor or metallic materials and contain from 100 - 100,000 atoms in each crystal. QDots play an important role in many applications; light emitting devices (LEDs), solar cells, drug delivery, and optical computers. In the current research, a fundamental wavelength of Nd:YAG laser was applied to analyse the impurities in homemade cadmium selenide (CdSe) QDots through laser-induced plasma (LIPS) technique. The CdSe QDots were fabricated by using hot-solution decomposition method where a mixture of Cd precursor and trioctylphosphine oxide (TOPO) is prepared at concentrations of TOPO under controlled temperatures 200-350ºC. By applying laser energy of 15 mJ, at frequency 10 Hz, and delay time 500 ns, LIPS spectra of CdSe QDots samples were observed. The qualitative LIPS analysis for CdSe QDs revealed that the sample contains Cd, Te, Se, H, P, Ar, O, Ni, C, Al and He impurities. These observed results gave precise details of the impurities present in the QDs sample. These impurities are important for future work at which controlling the impurity contents in the QDs samples may improve the physical, optical and electrical properties of the QDs used for solar cell application.

Keywords: cadmium selenide, TOPO, LIPS spectroscopy, quantum dots

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Authors: Juyon Lee, Myoungjin Park, Jonghoon Kim, Jaekook Ha, Chanhee Lee

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We demonstrated a new positive aging methods of QLEDs devices that can apply in large size inkjet printing display. Conventional positive aging method using photo-curable resin remains unclear mechanism of the phenomenon and also there are many limitations to apply large size panels in commercial process. Through the photo acid generator (PAG) in ETL Ink, we achieved 90% of the efficiency of the conventional method and up to 1000h life time stability (T80). This techniques could be applied to next generation of QLEDs panels and also can prove the working mechanism of positive aging in QLED related to modification of ZnMgO NPs.

Keywords: quantum dots, QLED, printing, positive aging, ZnMgO NPs

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Authors: Loai AbdAllah, Mahmoud Kaiyal

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Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

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Authors: Y. Lu, L. Liu, J. Guo

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Monolayer graphene and dichaclogenide semiconductor materials attract extensive research interest for potential nanoelectronics applications. The ultimate scaling limit of double gate MoS2 Field-Effect-Transistors (FETs) with a monolayer thin body is examined and compared with ultra-thin-body Si FETs by using self-consistent quantum transport simulation in the presence of phonon scattering. Modelling of phonon scattering, quantum mechanical effects, and self-consistent electrostatics allows us to accurately assess the performance potential of monolayer MoS2 FETs. The results revealed that monolayer MoS2 FETs show 52% smaller Drain Induced Barrier Lowering (DIBL) and 13% Smaller Sub-Threshold Swing (SS) than 3 nm-thick-body Si FETs at a channel length of 10 nm with the same gating. With a requirement of SS<100mV/dec, the scaling limit of monolayer MoS2 FETs is assessed to be 5 nm, comparing with 8nm of the ultra-thin-body Si counterparts due to the monolayer thin body and higher effective mass which reduces direct source-to-drain tunnelling. By comparing with the ITRS target for high performance logic devices of 2023; double gate monolayer MoS2 FETs can fulfil the ITRS requirements.

Keywords: nanotransistors, monolayer 2D materials, quantum transport, scaling limit

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Authors: Bruna G. M. Araújo, Pedro M. M. Q. Cruz

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In this letter, we review the literature of the major concepts that govern Gaussian quantum information. As we work with quantum information and computation with continuous variables, Gaussian states are needed to better describe these systems. Analyzing a single ion locked in a Paul trap we use the interaction picture to obtain a toolbox of Gaussian operations with the ion-laser interaction Hamiltionian. This is achieved exciting the ion through the combination of two lasers of distinct frequencies corresponding to different sidebands of the external degrees of freedom. First we study the case of a trap with 1 mode and then the case with 2 modes. In this way, we achieve different continuous variables gates just by changing the external degrees of freedom of the trap and combining the Hamiltonians of blue and red sidebands.

Keywords: Paul trap, ion-laser interaction, Gaussian operations

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Mohamed Shalaby, Yasser Kamal, Negm Shawky

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Mutually unbiased bases (MUBs) have an important role in the field of quantum computation and information. A complete set of these bases can be constructed when the system dimension is the power of the prime. Constructing such complete set in composite dimensions is still an open problem. Recently, the concept of weak mutually unbiased bases (WMUBs) in composite dimensions was introduced. A complete set of such bases can be constructed by combining the MUBs in each subsystem. In this paper, we present a comparative study between MUBs and WMUBs in the context of complex projective t-design. Explicit proofs are presented.

Keywords: complex projective t-design, finite quantum systems, mutually unbiased bases, weak mutually unbiased bases

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Authors: Mitsunobu Iwasaki, Akifumi Iseda

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Yttrium aluminum garnet doped with cerium (Ⅲ) ions (Y3Al5O12:Ce3+, YAG:Ce3+) has attracted a great attention because it can efficiently convert the blue light into a very broad yellow emission band, which produces white light emitting diodes and is applied for panel displays. To improve the brightness and resolution of the display, a considerable attention has been directed to develop fine phosphor particles. We have prepared YAG:Ce3+ nanophosphors by environmental-friendly wet process. The peak maximum of absorption spectra of surface plasmon of Ag nanopaticles are close to that of the excitation spectra (460 nm) of YAG:Ce3+. It can be expected that Ag nanoparticles supported onto the surface of YAG:Ce3+ (Ag-YAG:Ce3+) enhance the absorption of Ce3+ ions. In this study, we have prepared Ag-YAG:Ce3+ nanophosphors and investigated their photoluminescent properties. YCl3･6H2O and AlCl3･6H2O with a molar ratio of Y:Al=3:5 were dissolved in ethanol (100 ml), and CeCl3•7H2O (0.3 mol%) was further added to the above solution. Then, NaOH (4.6×10-2 mol) dissolved in ethanol (50 ml) was added dropwise to the mixture under reflux over 2 hours, and the solution was further refluxed for 1 hour. After cooling to room temperature, precipitates in the reaction mixture were heated at 673 K for 1 hour. After the calcination, the particles were immersed in AgNO3 solution for 1 hour, followed by sintering at 1123 K for 1 hour. YAG:Ce3+ were confirmed to be nanocrystals with a crystallite size of 50-80 nm in diameter. Ag nanoparticles supported onto YAG:Ce3+ were single nanometers in diameter. The excitation and emission spectra were 454 nm and 539 nm at a maximum wavelength, respectively. The emission intensity was maximum for Ag-YAG:Ce3+ immersed into 0.5 mM AgCl (Ag-YAG:Ce (0.5 mM)). The absorption maximum (461 nm) was increased for Ag-YAG:Ce3+ in comparison with that for YAG:Ce3+, indicating that the absorption was enhanced by the addition of Ag. The external and internal quantum efficiencies became 11.2 % and 36.9 % for Ag-YAG:Ce (0.5 mM), respectively. The emission intensity and absorption maximum of Ag-YAG:Ce (0.5 mM)×n (n=1, 2, 3) were increased with an increase of the number of supporting times (n), respectively. The external and internal quantum efficiencies were increased for the increase of n, respectively. The external quantum efficiency of Ag-YAG:Ce (0.5 mM) (n=3) became twice as large as that of YAG:Ce. In conclusion, Ag nanoparticles supported onto YAG:Ce3+ increased absorption and quantum efficiency. Therefore, the support of Ag nanoparticles enhanced the photoluminescent properties of YAG:Ce3+.

Keywords: plasmon, quantum efficiency, silver nanoparticles, yttrium aluminum garnet

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Authors: Tokuei Sako

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Laser-induced current in a quasi-one-dimensional nanostructure has been studied by a model of a few electrons confined in a 1D electrostatic potential coupled to electrodes at both ends and subjected to a pulsed laser field. The time-propagation of the one- and two-electron wave packets has been calculated by integrating the time-dependent Schrödinger equation directly by the symplectic integrator method with uniform Fourier grid. The temporal behavior of the resultant light-induced current in the studied systems has been discussed with respect to the lifetime of the quasi-bound states formed when the static bias voltage is applied.

Keywords: pulsed laser field, nanowire, electron wave packet, quantum dots, time-dependent Schrödinger equation

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Authors: Dilip Saikia, Suparna Bhattacharjee, Nirab Adhikary

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In this piece of work, we have presented the synthesis and characterization of CdTe/ZnS core/shell (CS) quantum dots (QD). CS QDs are used as a fluorescence probe to design a simple cost-effective and ultrasensitive sensor for the detection of toxic Hg(II) in an aqueous medium. Mercaptosuccinic acid (MSA) has been used as a capping agent for the synthesis CdTe/ZnS CS QD. Photoluminescence quenching mechanism has been used in the detection experiment of Hg(II). The designed sensing technique shows a remarkably low detection limit of about 1 picomolar (pM). Here, the CS QDs are synthesized by a simple one-pot aqueous method. The synthesized CS QDs are characterized by using advanced diagnostics tools such as UV-vis, Photoluminescence, XRD, FTIR, TEM and Zeta potential analysis. The interaction between CS QDs and the Hg(II) ions results in the quenching of photoluminescence (PL) intensity of QDs, via the mechanism of excited state electron transfer. The proposed mechanism is explained using cyclic voltammetry and zeta potential analysis. The designed sensor is found to be highly selective towards Hg (II) ions. The analysis of the real samples such as drinking water and tap water has been carried out and the CS QDs show remarkably good results. Using this simple sensing method we have designed a prototype low-cost electronic device for the detection of Hg(II) in an aqueous medium. The findings of the experimental results of the designed sensor is crosschecked by using AAS analysis.

Keywords: photoluminescence, quantum dots, quenching, sensor

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Authors: S. Slimani, B. Djellouli

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Novel materials and innovative device structures has become necessary for the future of CMOS. High mobility materials like Ge is a very promising material due to its high mobility and is being considered to replace Si in the channel to achieve higher drive currents and switching speeds .Various approaches to circumvent the scaling limits to benchmark the performance of nanoscale MOSFETS with different channel materials, the optimized structure is simulated within nextnano in order to highlight the quantum effects on DG MOSFETs when Si is replaced by Ge and SiO2 is replaced by ZrO2 and HfO2as the gate dielectric. The results have shown that Ge MOSFET have the highest mobility and high permittivity oxides serve to maintain high drive current. The simulations show significant improvements compared with DGMOSFET using SiO2 gate dielectric and Si channel.

Keywords: high mobility, high-k, quantum effects, SOI-DGMOSFET

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

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Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Chung-Ming Lo, Chung-Chien Lee

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In many countries, the lifetime prevalence of shoulder pain is up to 70%. In America, the health care system spends 7 billion per year about the healthy issues of shoulder pain. With respect to the origin, up to 70% of shoulder pain is attributed to rotator cuff lesions This study proposed a computer-aided diagnosis (CAD) system to assist radiologists classifying rotator cuff lesions with less operator dependence. Quantitative features were extracted from the shoulder ultrasound images acquired using an ALOKA alpha-6 US scanner (Hitachi-Aloka Medical, Tokyo, Japan) with linear array probe (scan width: 36mm) ranging from 5 to 13 MHz. During examination, the postures of the examined patients are standard sitting position and are followed by the regular routine. After acquisition, the shoulder US images were drawn out from the scanner and stored as 8-bit images with pixel value ranging from 0 to 255. Upon the sonographic appearance, the boundary of each lesion was delineated by a physician to indicate the specific pattern for analysis. The three lesion categories for classification were composed of 20 cases of tendon inflammation, 18 cases of calcific tendonitis, and 18 cases of supraspinatus tear. For each lesion, second-order statistics were quantified in the feature extraction. The second-order statistics were the texture features describing the correlations between adjacent pixels in a lesion. Because echogenicity patterns were expressed via grey-scale. The grey-scale co-occurrence matrixes with four angles of adjacent pixels were used. The texture metrics included the mean and standard deviation of energy, entropy, correlation, inverse different moment, inertia, cluster shade, cluster prominence, and Haralick correlation. Then, the quantitative features were combined in a multinomial logistic regression classifier to generate a prediction model of rotator cuff lesions. Multinomial logistic regression classifier is widely used in the classification of more than two categories such as the three lesion types used in this study. In the classifier, backward elimination was used to select a feature subset which is the most relevant. They were selected from the trained classifier with the lowest error rate. Leave-one-out cross-validation was used to evaluate the performance of the classifier. Each case was left out of the total cases and used to test the trained result by the remaining cases. According to the physician’s assessment, the performance of the proposed CAD system was shown by the accuracy. As a result, the proposed system achieved an accuracy of 86%. A CAD system based on the statistical texture features to interpret echogenicity values in shoulder musculoskeletal ultrasound was established to generate a prediction model for rotator cuff lesions. Clinically, it is difficult to distinguish some kinds of rotator cuff lesions, especially partial-thickness tear of rotator cuff. The shoulder orthopaedic surgeon and musculoskeletal radiologist reported greater diagnostic test accuracy than general radiologist or ultrasonographers based on the available literature. Consequently, the proposed CAD system which was developed according to the experiment of the shoulder orthopaedic surgeon can provide reliable suggestions to general radiologists or ultrasonographers. More quantitative features related to the specific patterns of different lesion types would be investigated in the further study to improve the prediction.

Keywords: shoulder ultrasound, rotator cuff lesions, texture, computer-aided diagnosis

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Authors: Suleiman Bashir Adamu, Lawan Sani Taura

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We study the role of boundary conditions via -symmetric quantum mechanics, where denotes parity operator and denotes time reversal operator. Using the one-dimensional Schrödinger Hamiltonian for a free particle in an infinite square well, we introduce symmetric boundary conditions. We find solutions of the 1D Klein-Gordon equation for a free particle in an infinite square well with Hermitian boundary and symmetry boundary conditions, where in both cases the energy eigenvalues and eigenfunction, respectively, are obtained.

Keywords: Eigenvalues, Eigenfunction, Hamiltonian, Klein- Gordon equation, PT-symmetric quantum mechanics

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Authors: Francesca Schiavello

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This work aims to create a hybrid algorithm, combining Quantum Approximate Optimization Algorithm (QAOA) with an Evolutionary Algorithm (EA) in the place of traditional gradient based optimization processes. QAOA’s were first introduced in 2014, where, at the time, their algorithm performed better than the traditional best known classical algorithm for Max-cut graphs. Whilst classical algorithms have improved since then and have returned to being faster and more efficient, this was a huge milestone for quantum computing, and their work is often used as a benchmarking tool and a foundational tool to explore variants of QAOA’s. This, alongside with other famous algorithms like Grover’s or Shor’s, highlights to the world the potential that quantum computing holds. It also presents the reality of a real quantum advantage where, if the hardware continues to improve, this could constitute a revolutionary era. Given that the hardware is not there yet, many scientists are working on the software side of things in the hopes of future progress. Some of the major limitations holding back quantum computing are the quality of qubits and the noisy interference they generate in creating solutions, the barren plateaus that effectively hinder the optimization search in the latent space, and the availability of number of qubits limiting the scale of the problem that can be solved. These three issues are intertwined and are part of the motivation for using EAs in this work. Firstly, EAs are not based on gradient or linear optimization methods for the search in the latent space, and because of their freedom from gradients, they should suffer less from barren plateaus. Secondly, given that this algorithm performs a search in the solution space through a population of solutions, it can also be parallelized to speed up the search and optimization problem. The evaluation of the cost function, like in many other algorithms, is notoriously slow, and the ability to parallelize it can drastically improve the competitiveness of QAOA’s with respect to purely classical algorithms. Thirdly, because of the nature and structure of EA’s, solutions can be carried forward in time, making them more robust to noise and uncertainty. Preliminary results show that the EA algorithm attached to QAOA can perform on par with the traditional QAOA with a Cobyla optimizer, which is a linear based method, and in some instances, it can even create a better Max-Cut. Whilst the final objective of the work is to create an algorithm that can consistently beat the original QAOA, or its variants, due to either speedups or quality of the solution, this initial result is promising and show the potential of EAs in this field. Further tests need to be performed on an array of different graphs with the parallelization aspect of the work commencing in October 2023 and tests on real hardware scheduled for early 2024.

Keywords: evolutionary algorithm, max cut, parallel simulation, quantum optimization

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Authors: Dmytro Zubov, Francesco Volponi

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In this paper, D-Wave quantum computing Ising model is used for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data respectively. Ising model’s real-valued weights and dimensionless coefficients are calculated using daily mean air temperatures from 119 places around the world, as well as sea level (Aburatsu, Japan). In comparison with current methods, this approach is better suited to predict heat wave values because it does not require the estimation of a probability distribution from scarce observations. Proposed forecast quantum computing algorithm is simulated based on traditional computer architecture and combinatorial optimization of Ising model parameters for the Ronald Reagan Washington National Airport dataset with 1-day lead-time on learning sample (1975-2010 yr). Analysis of the forecast accuracy (ratio of successful predictions to total number of predictions) on the validation sample (2011-2014 yr) shows that Ising model with three qubits has 100 % accuracy, which is quite significant as compared to other methods. However, number of identified heat waves is small (only one out of nineteen in this case). Other models with 2, 4, and 5 qubits have 20 %, 3.8 %, and 3.8 % accuracy respectively. Presented three-qubit forecast model is applied for prediction of heat waves at other five locations: Aurel Vlaicu, Romania – accuracy is 28.6 %; Bratislava, Slovakia – accuracy is 21.7 %; Brussels, Belgium – accuracy is 33.3 %; Sofia, Bulgaria – accuracy is 50 %; Akhisar, Turkey – accuracy is 21.4 %. These predictions are not ideal, but not zeros. They can be used independently or together with other predictions generated by different method(s). The loss of human life, as well as environmental, economic, and material damage, from extreme air temperatures could be reduced if some of heat waves are predicted. Even a small success rate implies a large socio-economic benefit.

Keywords: heat wave, D-wave, forecast, Ising model, quantum computing

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Authors: Lae-Jeong Park

Abstract:

The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: pedestrian detection, color segmentation, false positive, feature extraction

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Authors: Gong Zhilin, Jing Yang, Jian Yin

Abstract:

The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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