Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspects of Time Series Analysis with Entropies and Complexity Measures

This exploratory work looks on time series complexity, for direct applications in change detection of the structure and properties of the signals. The objective of the paper was to compare the results of the evaluation of the complexity of time series data, obtained with measures based on entropy and data complexity indices. As test signals, determinist, random and chaotic signals are considered, in an independent and mixed probabilistic approach. Complexity descriptors are based on entropies, as Renyi, Tsallis, Multiscale technique, and two data complexity indices, as Lempel-Ziv complexity and Lyapunov exponent. High values of complexity measures are expected for all cases where random or chaotic components are dominant, i.e. greater amplitudes than the determinist components. The complexity measures are evaluated in terms of monotony, sensitivity at the length of time series, and change detection capability of the structure of the analyzed signal. The results of the experiments based on computer-based simulations are presented with fuzzy labels and show a variety of results, i.e. good results for some cases and small for others. Such results suggest an aggregate criterion for change detection, with at least two terms, one based on entropy and another one on complexity indices.
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning

Diabetic Retinopathy is a primary complication of diabetes which more often than not, affects both eyes and anyone with type-1 or type-2 diabetes can develop it. A Diabetic patient should undergo eye tests periodically as the pace of development of this condition is slow. A Dataset of Fundus Photographs of retina is considered. Thus, there is a notable value in automatically categorizing the Fundus Photographs. Therefore, to get a consolidated and objective medical diagnosis, this paper proposes a transfer learning based approach for Diabetic Retinopathy categorization. The resizing of the dataset is performed, which converts the varied images into 224x224 format. The images are augmented using AUGMIX and pooled using GeM. Then, we have used pretrained models, namely SEResNeXt32x4d and EfficientNetb3. The pretraining of the aforementioned neural networks has been done on the ImageNet dataset. Then, the Diabetic Retinopathy images are migrated to these models. Based on the dataset already available, the output is ultimately split up into 5 levels according to the seriousness of the degree of DR. The experimental results show that the training accuracy of this method can reach as high as 0.91. Hence, the retina images of the Diabetic and Healthy patients can be easily classified using our proposed methodology, consequently reducing the number of reviews by medical professionals.
Isotopomer-Selective Overtone Spectroscopy of Jet-Cooled Benzene by Ionization Detected IR + UV Double Resonance: The N = 2 CH Chromophore Absorption of 12C6H6 and 13C12C5H6 near 6000 cm-1

Employing our recently introduced IR + UV double resonance scheme for obtaining mass-resolved infrared spectra, we have recorded the isotopomer selected N = 2 CH chromophore absorption of 1 2 C 6 H 6 and 1 3 C 1 2 C 5 H 6 near 6000 cm - 1 in a supersonic jet expansion of the benzene isotopomer mixture at natural abundance. The 1 3 C 1 2 C 5 H 6 spectra are the first of this kind reported in the literature. For 1 3 C 1 2 C 5 H 6 , a preliminary analysis yields an approximate decay time of vibrational excitation τ ≥ 130 fs, which is presumably due to strong anharmonic Fermi resonances between CH-stretching and CH-bending modes. The 1 2 C 6 H 6 spectrum is compatible with a proposed model of intramolecular vibrational redistribution with a distinct hierarchy of time scales: the CH-stretching state is the IR chromophore state coupled to the IR field. With a decay time of τ 100 fs, vibrational excitation is redistributed to a first tier of vibrational states, probably CH-stretching/ bending combination bands coupled by strong Fermi resonances. Vibrational excitation is then further redistributed with τ 0.35 ps to a second tier of states by weaker anharmonic resonances. The observed line widths give a lower bound for the decay time into the dense background manifold, τ > 1.3 ps. Although the experimental jet spectra of 1 2 C 6 H 6 are in qualitative agreement with previously published calculated spectra, they disagree in finer details.
Carbon–ceramic (AlN) interfaces from liquid quench ab initio molecular dynamics simulations

We report on the structure of carbon–ceramic interface models obtained by quenching a carbon liquid in between two identical crystalline surfaces of aluminium nitride using ab initio molecular dynamics simulations. Two kinds of neutral and non-polar surfaces [ and ] were studied. The models obtained in both cases show typical disordered graphitic carbon domains with well-defined anisotropic nanotextures, while a bulk carbon system quenched in the same conditions leads to a nearly isotropic amorphous solid. The graphene layers of the nanocomposite with surfaces develop parallel to the latter, showing weak interface bonding. On the contrary, they develop normally to the surfaces, with many covalent bonds connecting each graphene layer to both surfaces. Finally, by looking at the mobility of carbon atoms during the quench simulations, we show that carbon actually gets bonded to surfaces much before the temperature reaches its melting point. This indicates that the solidification of the carbon phase is strongly catalysed by the presence of surfaces. It does not happen when surfaces are used. In this case, no clear distinction can be observed between the self-diffusivity coefficients of carbon in the nanocomposite and those in the bulk carbon system, whatever the temperature.
Channel Estimation Method Based on Transformer in High Dynamic Environment

As the relative movement speeds of the communication parties increase, the Doppler frequency offset gradually increases, and the speed of channel state information(CSI) change also increases, which limits the performance of traditional channel estimation algorithms. To solve the above problems, we propose a channel estimation structure based on Transformer. Convolutional Neural Network (CNN) is used to extract the feature vectors of channel response and Transformer is used for channel estimation. Utilize Transformer’s deep learning capabilities to better track channel variation characteristics in highly dynamic environments. By simulation, we get results that compared with traditional channel estimation methods, the performance of the proposed channel estimation method is improved significantly under high dynamic environment.
A Joint Detection and Recognition Approach to Lung Cancer Diagnosis From CT Images With Label Uncertainty

Automatic lung cancer diagnosis from computer tomography (CT) images requires the detection of nodule location as well as nodule malignancy prediction. This article proposes a joint lung nodule detection and classification network for simultaneous lung nodule detection, segmentation and classification subject to possible label uncertainty in the training set. It operates in an end-to-end manner and provides detection and classification of nodules simultaneously together with a segmentation of the detected nodules. Both the nodule detection and classification subnetworks of the proposed joint network adopt a 3-D encoder-decoder architecture for better exploration of the 3-D data. Moreover, the classification subnetwork utilizes the features extracted from the detection subnetwork and multiscale nodule-specific features for boosting the classification performance. The former serves as valuable prior information for optimizing the more complicated 3D classification network directly to better distinguish suspicious nodules from other tissues compared with direct backpropagation from the decoder. Experimental results show that this co-training yields better performance on both tasks. The framework is validated on the LUNA16 and LIDC-IDRI datasets and a pseudo-label approach is proposed for addressing the label uncertainty problem due to inconsistent annotations/labels. Experimental results show that the proposed nodule detector outperforms the state-of-the-art algorithms and yields comparable performance as state-of-the-art nodule classification algorithms when classification alone is considered. Since our joint detection/recognition approach can directly detect nodules and classify its malignancy instead of performing the tasks separately, our approach is more practical for automatic cancer and nodules detection.
A High-Efficient Power Converter for Thermoelectric Energy Harvesting

Self-power technique is a vital key for stand-alone applications whereas battery replacement may be impossible. For wearable applications, extracting energy from the ambient temperature is one of the best solutions among the other energy harvesting methods such as solar, wireless waves, and temperature. In this paper, a high-efficient power dc-dc converter with maximum power point tracking (MPPT) and zero-current switching (ZCS) based on digital counters is proposed for thermoelectric energy harvesting. The proposed technique is able to adapt to a wide range of temperature differences. The integrated ZCS module plays an essential role in reducing the loss induced by inaccurately controlling the high-side switch. Besides, the maximum power extracted from the thermal energy source is monitored with the MPPT module. The power converter was simulated using CMOS 600nm Nuvoton technology. From the simulation results, it shows that when employing a thermoelectric generator with a temperature gradient of 3 Celsius degrees, the converter is capable of providing a maximum power of 112μW with a high-efficient of 66%.
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning

HTTP Adaptive Streaming (HAS) is the most common approach for delivering video content over the Internet. The requirement to encode the same content at different quality levels (i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios (41% in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.
EMC Simulation of Conducted Emissions Produced by a DC- DC converter

The Conducted Emissions (CE) measurement is mandatory for a product to enter mass production. This paper presents LTspice and Matlab tools application for EMC Simulation, namely of CE - LISN (Line Impedance Stabilization Network) method described in CISPR 25 Standard. Based on analog simulation (without any layout information or mechanical models) optimizations are made for a DC-DC converter, to reduce the levels of CE.
IR-QNN Framework: An IR Drop-Aware Offline Training of Quantized Crossbar Arrays

Resistive Crossbar Arrays present an elegant implementation solution for Deep Neural Networks acceleration. The Matrix-Vector Multiplication, which is the corner-stone of DNNs, is carried out in <inline-formula> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> compared to <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> steps for digital realizations of <inline-formula> <tex-math notation="LaTeX">$O(log_{2}(N))$ </tex-math></inline-formula> steps for in-memory associative processors. However, the IR drop problem, caused by the inevitable interconnect wire resistance in RCAs remains a daunting challenge. In this article, we propose a fast and efficient training and validation framework to incorporate the wire resistance in Quantized DNNs, without the need for computationally extensive SPICE simulations during the training process. A fabricated four-bit Au/Al<sub>2</sub>O<sub>3</sub>/HfO<sub>2</sub>/TiN device is modelled and used within the framework with two-mapping schemes to realize the quantized weights. Efficient system-level IR-drop estimation methods are used to accelerate training. SPICE validation results show the effectiveness of the proposed method to capture the IR drop problem achieving the baseline accuracy with a 2% and 4% drop in the worst-case scenario for MNIST dataset on multilayer perceptron network and CIFAR 10 dataset on modified VGG and AlexNet networks, respectively. Other nonidealities, such as stuck-at fault defects, variability, and aging, are studied. Finally, the design considerations of the neuronal and the driver circuits are discussed.
A Residual BiLSTM Model for Named Entity Recognition

As one of the most powerful neural networks, Long Short-Term Memory (LSTM) is widely used in natural language processing (NLP) tasks. Meanwhile, the BiLSTM-CRF model is one of the most popular models for named entity recognition (NER), and many state-of-the-art models for NER are based on it. In this paper, we propose a new residual BiLSTM model and perform it with a conditional random field (CRF) layer together on NER tasks. Based on the most popular BiLSTM-CRF model, we replace the BiLSTM with our residual BiLSTM blocks to encode words or characters. We evaluate our model on Chinese and English datasets. We utilize both word2vec and BERT to generate word or character vectors. Furthermore, we conduct experiments to compare the performance of NER by using different structures of residual blocks. The experimental results show that our model can improve the performance of both Chinese and English NER effectively without introducing any external knowledge.
A Novel Density-Based Performance Evaluation Index for Multipath Components Clustering

The machine learning algorithms applied in multipath components (MPCs) clustering should be evaluated by an appropriate performance index. In this paper, a novel performance evaluation index for MPCs clustering is proposed, which is based on the density of clusters instead of cluster distance. The proposed index improves the traditional S_Dbw validity index by taking into account the intra-cluster density obtained according to the Graham scanning method and Green’s formula, which can be applied to MPCs with arbitrary distribution characteristics. The proposed index is used to evaluate the performance of different machine learning algorithms, such as K-means and Gaussian mixture model (GMM) algorithms, which shows a more accurate result than other traditional indexes. In addition, the utility of the proposed index is verified by the measured MPCs data involving both delay and angle information. The evaluation result shows that the variational Bayesian GMM (VB-GMM) algorithm outperforms the K-means and expectation maximization GMM (EM-GMM) algorithms in MPCs clustering.
Vibrational spectroscopy of HOD in liquid D2O. III. Spectral diffusion, and hydrogen-bonding and rotational dynamics

Time-resolved infrared spectroscopy has the potential to provide unprecedented information about molecular dynamics in liquids. In the case of water, one of the most exciting techniques being developed is transient hole-burning. From experiments on dilute HOD in D2O one can obtain the transition frequency time-correlation function for the OH stretch vibration, finding that it decays on a time scale of between 0.5 and 1 ps. In this paper we provide a molecular-level interpretation of this spectral diffusion time-correlation function. First, we verify that for hydrogen-bonded HOD molecules the instantaneous OH frequency is highly correlated with the distance to the (hydrogen-bonded) D2O molecule. Second, we show that the instantaneous OH frequency is highly correlated with whether or not the HOD molecule has a hydrogen bond. Finally, we show that the short-time dynamics of the spectral diffusion time-correlation function is due to hydrogen-bond stretching motions, while the longer-time decay observed in the.
Learning the Connectivity: Situational Graph Convolution Network for Facial Expression Recognition

Previous studies recognizing expressions with facial graph topology mostly use a fixed facial graph structure established by the physical dependencies among facial landmarks. However, the static graph structure inherently lacks flexibility in non-standardized scenarios. This paper proposes a dynamic-graph-based method for effective and robust facial expression recognition. To capture action-specific dependencies among facial components, we introduce a link inference structure, called the Situational Link Generation Module (SLGM). We further propose the Situational Graph Convolution Network (SGCN) to automatically detect and recognize facial expression in various conditions. Experimental evaluations on two lab-constrained datasets, CK+ and Oulu, along with an in-the-wild dataset, AFEW, show the superior performance of the proposed method. Additional experiments on occluded facial images further demonstrate the robustness of our strategy.
Transformation of Hematite into Magnetite During Dissimilatory Iron Reduction—Conditions and Mechanisms

Magnetite formation during the reduction of nanoparticulate hematite by Shewanella putrefaciens 200R is investigated in media of variable composition, at circumneutral pH and with lactate as electron donor. The relative rates of production of dissolved Fe(II) and Fe(III), aqueous speciation, plus chemical gradients control whether or not magnetite forms in the experiments. High bicarbonate concentrations result in the precipitation of magnetite, presumably by enhancing the non-reductive dissolution of hematite, hence causing the simultaneous production of soluble Fe(III) and Fe(II) in the incubations. Magnetite formation is inhibited when hematite dissolution is slowed down by adsorption of oxyanions (phosphate and sulfate) at the mineral surface, when the reduction of soluble Fe(III) is enhanced by increasing the cell density or adding an electron shuttle (AQS), or when aqueous Fe(II) is complexed by ferrozine. In experiments where hematite suspensions with and without bacteria are separated by a dialysis membrane, magnetite formation occurs mainly in the cell-free portion of the reaction system. Most likely, precipitation of magnetite is favored in the cell-free suspension because of a higher soluble Fe(III) to Fe(II) ratio. The formation of magnetite in the absence of cells further implies that its nucleation is not catalyzed by the bacterial surfaces.
Visual Measurements at Small Scales: Guidelines to Reduce Uncertainties down to a Few Nanometers

Vision-based measurement is a widespread solution to characterize, classify and control micro and nano robotic systems. Achieving multi-degrees-of-freedom measurements with nanometer uncertainties using a common experimental setup has recently been demonstrated which appears highly interesting for many applications. Nevertheless, succeeding in setting the experimental set-up to reproduce such performances still appears not so easy, due to the difficulty to understand what are the influence quantities. To address this key issue, the paper firstly synthesizes the different approaches and good practices used in the literature where several influence quantities have mainly been investigated independently. Secondly, an experimental set-up is used to study the influence of several key quantities onto position uncertainties. These two kinds of investigations, conducted to propose practical guidelines to get position uncertainties down to 5 nm with typical experimental setups used for micro and nano robotics purposes. The experiments also show that measurement precision is experimentally estimated at a few nanometers by measurement reproducibility over several days.
Multicast Traffic Throughput Maximization through Dynamic Modulation and Coding Scheme Assignment in Wireless Sensor Networks

The paper studies the following question: to what extent traffic throughput of a wireless sensor network (WSN) can be increased when dynamic assignment of modulation and coding schemes (MCS) is applied. We assume a TDMA-based scheme that is capable of changing the MCS used by the broadcasting nodes from a time slot to a time slot of the frame. The traffic throughput is measured by the size of the frame (the smaller the size the larger the throughput) that is necessary to carry the assumed volumes of data produced cyclically by the multicast traffic sources (e.g., sensors). Since we consider multi-hop networks, this means that the per-cycle data volume produced by each source must be delivered to all nodes in its multicast routing tree within one frame time-span. To answer this question we introduce a mixed-integer programming formulation of the related optimization, and use it in a numerical study that illustrates possible gains of dynamic MCS assignment. The presented optimization model is novel since only its unicast counterparts have been considered so far.
A Unified Mean Square Consensus Criterion for Stochastic Multi-Agent Systems With ROUs and RONs Under Impulse Time Windows

This paper probes into the leader-following mean square consensus problem of stochastic multi-agent systems with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs) under impulse time windows (ITWs). A new concept named average left endpoint interval (ALEI) related to ITWs is proposed, which is inspired by the average impulsive interval. Based on ALEI and Lyapunov stability theory, a unified mean square consensus criterion for both stabilizing and interference impulses is attained. The introduction of ALEI allows for a larger upper bound of left endpoint interval and smaller lower bound of left endpoint interval, which makes the preset of ITWs more flexible and less conservative. The validity of the theoretical results is verified by the numerical simulation.
Simulation, Fabrication and Characterization of Zinc Oxide TFT

In this paper, ZnO thin film transistors were simulated and fabricated. The TFTs fabricated are of bottom gate configuration. Initially, the structure of the TFT is simulated by using Silvaco TCAD tool and the input and output characteristics are determined and the theoretical threshold voltage of the device is also calculated. Material characterization techniques such as SEM, XRD and EDS were performed to optimize the gate oxide layer and the oxide layer. The different materials are used in the fabrication of the thin film are Aluminium (Al) as the gate, source, and drain material. Aluminium oxide (Al$_{2} O_{3}$) as the gate oxide layer and Zinc Oxide (ZnO) as channel layers. PVD techniques such as sputtering and electron beam deposition are used to deposit the gate layer, gate oxide layer and the channel layer. Thermal evaporation is used to deposit the drain and source layer. After the deposition, the device is tested at a probing station to find out the input and output characteristics and verify if they match with the simulated values obtained from TCAD simulations.