Miracle-dz/UniFAR-FTUs huggingface dataset:
Absorption Enhancement of GaAs Slab with Geometrically Varying Periodic Array of SiO2 Nanostructures

In this work we present a systematic study on absorption enhancement in thin-film GaAs slabs having two-dimensional periodic arrays of SiO2 nanostructures. Nanostructures in the form of circle, square and triangles having dimensions ranging from 50 nm to 120 nm are considered to be periodically embedded into GaAs slabs having thicknesses ranging from 100 nm to 500 nm. Finite difference time domain based analysis of these structures, which essentially constitute two-dimensional photonic crystals, show that the peak absorption characteristics of the enhanced absorption profile are in fact intricately related to the curvature of the nanostructure. Increased optical path length with increasing curvature results in the highest absorption in circular arrays, whereas about 20% lower peak absorption is obtained in thin-films having triangular arrays. A quantitative estimate of curvature is presented in this work, which appears to be in direct correlation with the peak absorptions obtained for different nanostructures. Also for small curvatures of the nanostructures, their size variation appears to have less of an effect on the overall absorption profile. Moreover, absorption characteristics of the thin film appear to be less dependent on its thickness if the curvature of the nanostructures is decreased. For all geometrical shapes however, increasing film thickness results in an increase of the bandwidth of the absorption profile. It is envisaged that the results presented here will serve as guidelines for photon-management employing easily realizable photonic structures in high-efficiency thin-film solar cells.
A Fiber-Si3N4 Composite Nanoforest with High 7.6 to $11.6\ \mu\mathrm{m}$ Absorption for MEMS Infrared Sensors

In this work, a fiber-Si3N4 composite nanoforest with high absorption in 7.6 to $11.6\ \mu \mathrm{m}$ wavelength range is presented. Especially, when thickness of a Si3N4 coating reaches 300 nm, the nanoforest can achieve an average absorption as high as 88.12%. Such a composite nanoforest is fabricated based on an extremely simple process, including spin-coating and plasma treating of a polyimide layer, followed by deposition of a Si3N4 film. The process is fully compatible with conventional micromachining, thus the nanoforest can be integrated onto MEMS infrared (IR) sensors as an additional absorber. Furthermore, with such a composite nanoforest-based absorber, the IR sensors are expected to achieve higher performance, especially for human IR sensing.
Code Confusion Confrontation Method Based on Feature Comparison

With the maturity of code hardening technology, more and more malicious applications have adopted the reinforcement technology to avoid the detection of anti-virus engines, which brings great challenges for the identification and analysis of malicious code. The paper through an in-depth analysis of common detection avoidance techniques, the technical principle of reinforcement is studied, and a code confusion countermeasure method based on feature comparison is constructed. The method takes the unobfuscated function in the target application as a reference, extracts the target application characteristics by constructing the dependency graph of the program code, and then performs code similarity query in the software gene library system constructed in this paper, and analyzes based on the query result. The code segment is similar or identical to the obfuscated code, and finally restores the confusing variable or string name based on the variable mapping relationship before and after the confusion, thereby achieving effective recovery of the obfuscated code.
Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition

In recent times, with the advancement of digital imaging, automatic facial recognition has been intensively studied for adults, while less for neonates. Due to the miniature facial structure and facial attributes, newborn facial recognition remains a challenging area. In this paper, an automatic video-based Neonatal Face Attributes Recognition (NFAR) approach in a hierarchical framework is proposed by coalescing the intensity-based method, pose estimation, and novel dedicated neonatal Face Feature Selection (FFS) algorithm. The intensity-based method is used for face detection, followed by the facial pose estimation algorithm and FFS are dedicated to neonatal pose and face feature recognition, respectively. In this study, video-data of 19 neonates’ were collected from the Children’s Hospital affiliated to Fudan University, Shanghai, to evaluate the proposed NFAR approach. The results show promising performance to detect the neonatal face, pose estimation (−45°, 45°), and facial features (nose, mouth, and eyes) recognition. The NFAR approach exhibits a sensitivity, accuracy, and specificity of 98.7%, 98.5%, and, 95.7% respectively, for the newborn babies at the frontal (0°) facial region. The neonatal face and its attributes recognition can be expected to detect neonate’s medical abnormalities unobtrusively by examining the variation in newborn facial texture pattern.
Classifying Speech of ASD Affected and Normal Children Using Acoustic Features

Children affected with autism spectrum disorder (ASD) produce speech that consists of distinctive acoustic patterns, as compared to normal children. Hence, acoustic analyses can help classifying speech of ASD affected children from that of normal children. In this study, the aim is to identify those discriminating characteristics of speech production that help classification between speech of children with ASD and normal children. Two separate datasets were recorded for this study: the English speech of children affected with ASD and the English speech of normal children. Comparative analyses of acoustic features derived for both datasets are carried out. Changes in the speech production characteristics are examined in three parts. Firstly, changes in the excitation source features F0 and strength of excitation (SoE) are analyzed. Secondly, changes in the vocal tract filter features the formants (F1 to F5) and dominant frequencies (FD1, FD2) are analyzed. Thirdly, changes in the combined source-filter features signal energy and zero-crossing rate are analyzed. Different combinations of the feature sets are then classified using three different classifiers for validation of results: SVM, KNN and ensemble classifiers. Performance evaluation is carried using different combinations of features sets and classifiers. Results up to 97.1% are obtained for classification accuracy between speech of ASD affected children and normal children, using a combination of feature set with SVM classifier. The results are better than other similar few studies. This study should be helpful in developing an automated system for identffying ASD speech, in future.
Point Cloud Classification Model Based on a Dual-Input Deep Network Framework

The disorder, sparseness, irregularity, noise, and background of point clouds cause significant challenges in point cloud classification tasks. In such tasks, deep learning methods based on raw point cloud data have recently achieved good performance on simulated data. However, many methods experience problems when applied to realistic data containing much noise and complex background information. This paper proposes an end-to-end dual-input network (DINet) point cloud classification model based on deep learning. In the proposed model, a feature extractor obtains high-dimensional features, a feature comparator aggregates and disperses homogeneous and heterogeneous point clouds, respectively, in the feature space, and a feature analyzer completes the task. The two-channel data input facilitates a universal DINet framework that is flexible and extends to other models generalized across different datasets. DINet brings improvements in performance, presenting an overall accuracy value of 81.3% and average accuracy value of 79.6% in experiments conducted on the real-world ScanObjectNN dataset. The code for the proposed point cloud classification model is available at https://github.com/zhairf/DINet.
A Novel Facial Expression Intelligent Recognition Method Using Improved Convolutional Neural Network

Human facial expression is the core carrier of feedback. Facial expression recognition(FER) has been introduced into mickle fields, such as auxiliary medical care, safe driving, marketing assistance, distance education. However, in the real production process, facial expression image samples collected in different scenarios have problems such as complex backgrounds, which causes the FER model to train very slowly, low recognition rate, and insufficient generalization, so it cannot meet the actual production requirements. As the originator of the clustering algorithm, fuzzy C-means clustering(FCM) algorithm has stable performance and good results. It is applied to the convolutional layer of a convolutional neural network(CNN) to obtain a convolution kernel with an initial value, so as to extract the expression image features in the training set and the test set. This can solve the problem of random initialization of the convolution kernel. Based on the CNN, this paper introduces FCM to optimize the feature extraction (FE) capability of the model, and proposes a novel FER algorithm using an improved CNN(F-CNN). Because traditional CNN has problems such as irrational layer settings and too many parameters. The proposed F-CNN first adjusts the CNN network structure to improve the nonlinear expression ability of CNN. Then, replace the Softmax classifier that comes with CNN with a support vector machine (SVM) to improve the model’s classification ability. The comparison experiments with other models show that the improved model improve the FER rate. The introduced FCM algorithm can effectively improve the model’s FE performance and shorten the time of F-CNN during training. On the whole, F-CNN has reference value.
Maximum Efficiency Point Tracking in Inductive Links: Series Versus Parallel Receiver's Compensation

The postregulation method of an inductive power link consists of adjusting its output voltage directly in the receiver by using a feedback dc-dc converter, thus without having to rely on back telemetry. In postregulated systems, the maximum efficiency point (MEP) of the link can be tracked by adjusting the transmitter voltage amplitude in closed-loop. In this paper, we analyze how the series and parallel receiver compensation affect differently the closed-loop control of the MEP tracking. The theoretical analysis proves that the MEP cannot be achieved with a series compensation but it can be attained with a parallel compensation. Additionally, the theoretical analysis predicts how any more complex resonant structure affects the attainability of the MEP. Finally, the analysis is validated by simulations and measurements. The proof-of-concept system postregulates the output voltage at 5 V and achieves the MEP of 40% in measurements while delivering 50 mW to the load, working at 13.56 MHz with 1-cm-air-gap and a 25mmx25mm receiver.
Various Optimization Algorithm used in CRN

Cognitive radio is a next generation technology of wireless communication system and the limitations of conventional wireless networks are overcome by cognitive techniques. The main objective behind CR was to improve the management and utilization of spectrum resources. Software defined Radios (SDR) use cognitive radio and enhances optimization algorithm that can modify radio transmission parameters as per conditions of environment parameter Number of researchers has used various optimization algorithms in CR for improving its results. In this paper, we have given review on work done on various optimization algorithms in Cognitive radio network for different purpose. The main algorithms covered in this paper are Firefly algorithm, Particle swarm optimization, Genetic algorithm, Ant colony optimization and Support vector machine. All these algorithms were used in CRN for different purposes like to solve NP-hard problem, to tune transmission parameters in multicarrier cognitive systems, to propose a routing algorithm, to optimize the weighted coefficient vector and global decision threshold assigned to each cognitive users, used for centralized CRN, to solve the optimization problem of channel assignment as a linear programming, etc.
Probabilistic Analysis on Successive Cancellation-Assisted Transmitter Identification in SFN With Randomly Distributed Co-Channel Interferers

This paper investigates the detection error of radio frequency (RF)-watermark type transmitter identification (TxID) signal in a single frequency network (SFN). Based on the Cramer-Rao bound (CRB) of TxID detection, closed-form failure probabilities for TxID detection are derived. In order to reflect the practical network condition, the interference from Poisson-distributed out-of-guard interval transmitters is accounted for TxID detection failure probability (TDFP). The proposed approach consequently examines the possible TDFP gain that can be obtained by applying successive preamble cancellation to the TxID detection. Numerical results reveal that the performance of the preamble cancellation-assisted technique is dependent on the threshold signal-to-interference noise ratio (SINR) for preamble signal detection. Nevertheless, the assistance of preamble cancellation is verified to guarantee more than 4 dB improvement of TxID detection capability under practical preamble signal configurations.
Voltage-Double RF Rectifier using Inductive Matching Network

An RF rectifier based on a Greinacher voltage doubler is presented in this paper. A typical Greinacher voltage doubler mainly consists of a series and a shunt diodes. In this approach, an inductance is deliberately placed to the shunt diode to provide an additional input matching parameter such that a better control of the input impedance results in better reflection coefficient compared to a typical matching approach. A demonstration of the validity of this configuration is designed for the 2.45 GHz ISM band and the fabricated PCB is less than 4 cm2. Measured peak efficiency of 65.3% with a corresponding dc output voltage of 2.98 V is achieved at an input power of 8 dBm. Measured efficiency is as good as a rectifier without voltage doubler and the dc output voltage is sufficient for most power management modules. Measured peak dc output voltage is 7.30 V for an input power of 20 dBm.
SCSCDaylight: Network Monitoring Tools for Software-Defined Networks Based on Opendaylight

Monitoring is a foundation for network management. Timely and accurate network statistics is essential to critical network management task, such as traffic engineering, service-level agreement (SLA), anomaly detection and troubleshooting. However, in traditional networks, monitoring is difficult to achieve due to the lack of support by the equipment. Recently, software defined network (SDN) separates the network control plane from the data forwarding plane, and manages the network by the centralized controller which can access the underlying device data flexibly. In this paper, a Software-Defined Networks monitoring tools-SCSCDaylight is implemented based on opendaylight, which is an open source SDN controller platform. The tools support network topology partition, multiple controller collaboration, two-way link speed and packet loss rate measurement. And the network simulation is used to evaluate this system. The experimental results show that the tools can accurately implement the measurement of link speed and packet loss rate.
Fluid Flow Sensors Design Based on Electromagnetic Drag Effect

In this paper, we describe the possibility of establishing the effect of electromagnetic drag on the microwave range. We are conducting an initial study of the use of this effect for novel fluid flow sensors designing. Two different experimental stands on circular and rectangular waveguides are considered. The study is carried out using a vector network analyzer at frequencies of 8-12 GHz (X-band). Distilled water is used as a moving medium. The experiment is optimized on the basic parameters (including the temperature of the liquid) to obtain the maximum magnitude of the target observation effect. As an optimization criterion, the difference of the arrival phase of two coherent waves propagating in identical media is used, one of which has a translational motion, and the second is at rest. In conclusion, findings are presented describing the main optimization results. The principle of detecting the effect of drag on guided waves in the transmission lines is set. As a sensor test experiment, a scheme using a signal analyzer is provided.
A New Alzheimer's Disease Classification Technique from Brain MRI images

Alzheimer's Disease (AD) is one of the most common forms of dementia that leads to memory loss mostly in elderly people. Treatment at the early stage of the disease will lessen the progression rate of the disease and its pathogenesis. Computer Aided Diagnosis (CAD) is gaining popularity in recent years for the detection of AD. In this study, we report a novel strategy for AD classification using brain Magnetic Resonance (MR) Images. Ensemble Empirical Mode Decomposition (EEMD) was used to decompose the image. From each decomposed image, the statistical features and fractal based features were extracted. Feature reduction has been performed using Principal Component Analysis (PCA). The proposed method was examined on publicly available data sets (OASIS). Three different classifiers namely Logistic regression, NaiveBayes, and support vector machine was used in this work. The classification accuracy of each classifier was reported and compared with the existing methods. The average accuracy achieved was 92.34%. The developed approach seems to be promising to develop reliable and accurate devices for AD detection, and classification.
Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

High-accuracy microseismic phase picking is fundamental to microseismic signal processing. Phase picking methods based on deep learning show great potential dealing with low signal to noise ratio (SNR) data but need enormous training data. However, it’s not easy to obtain a big size of field datasets and label them manually to train the neural network. In this paper, a novel method is proposed by applying feature extraction (Gammatone Feature) and neural networks to pick phases automatically. The feature extraction scheme aims to train the neural network using a relatively small size of training datasets. To test the performances of the proposed method, synthetic datasets were obtained by numerical simulation and used to train the neural network. Both synthetic datasets and field datasets were used to test the neural network for picking P- and S-phases of microseismic events. The results of phase picking illustrate that: (1) feature extraction scheme in the training stage can help reduce the size of training datasets; (2) The neural network can be trained well just by synthetic data and phase picking results are accurate and satisfying when the method was tested by both synthetic and field datasets.
Personal Oral Health Advisor Using Multimodal Sensing and Machine Learning with Smartphones and Cloud Computing

The mouth is considered a mirror of the overall health of the body and yet there is no personal device available today that can be used at home to monitor oral health. While this is true across the world, the need is even more acute in emerging markets. For instance, there is only one dentist for every 150000 people in rural India. The objective of this project is to create a cost-effective, multimodal, personal oral sensing device that automatically classifies sensed data and provides interactive advice about oral health. Based on surveys of dentists and doctors about the types of information that would be useful, this paper presents POHA, a handheld home device that integrates image, video, smell, temperature, and 3D orientation sensing, and communicates with a Smartphone via USB and Bluetooth. POHA scans the mouth and teeth of a person while they perform a brushing-like motion. This research work developed image and smell classification models by training machine learning algorithms with hundreds of images collected from the web and air quality values of various mouth odors obtained with a low-cost gas sensor. Initial experiments show greater than 80% accuracy in detecting dental cavities, gingivitis, and bad breath. This project also involved developing a Smartphone application that provides users real-time views of sensed data, shows classification results and advice, collects health information, and stores tagged sensor data in a central cloud database. This first-of-its-kind device fits easily into one's daily routine, provides preventive advice, and encourages seeing a dentist or a doctor. POHA also provides doctors for the first time with tagged daily oral sensing data from millions of people that would be invaluable for medical research in detecting, preventing, and predicting numerous diseases that have oral manifestations.
High Efficiency Hybrid Single-Phase Voltage-Source Inverter for PV Applications

Grid-connected low-power PV systems are becoming more and more popular inside the energy market. Such a systems, are commonly installed in residential and small commercial buildings, having as a main objective the reduction of the energy bill. Efficiency is a key word in the design of PV systems since this parameter contributes with economic benefits for the investors. Among low power systems, microinverters are designed to inject the generated energy from a single PV panel to the electrical grid. A single photovoltaic panel produces a voltage around 35 V, therefore microinverters are generally designed using two stages. First stage boost and regulate the output DC voltage while the second stage produces the injected AC current. Single-phase H-bridge inverter is the basic topology used in the second stage of the isolated microinverters. In this paper, a modified hybrid high-frequency H-bridge inverter is proposed using a combination between MOSFET’s and IGBT’s according with the low and high frequency pulse width modulated signals. The proposal is evaluated by means of numerical simulations and compared with transformerless topologies considering the total harmonic distortion and efficiency as the comparative parameters.
Cost-Aware Edge Resource Probing for Infrastructure-Free Edge Computing: From Optimal Stopping to Layered Learning

To meet the stringent requirement of artificial intelligence applications, such as face recognition and video streaming analytics, a resource-constrained device can offload its task to nearby resource-rich devices in edge computing. Resource awareness, as a prime prerequisite for offloading decision-making, is critical for achieving efficient collaborative computation performance. In this paper, we consider cost-aware edge resource probing (CERP) framework design for infrastructure-free edge computing wherein a task device self-organizes its resource probing for informed computation offloading. We first propose a multi-stage optimal stopping formulation for the problem, and derive the optimal probing strategy which reveals a nice multi-threshold structure. Accordingly, we then devise a data-driven layered learning mechanism for more practical and complicated application environments. Layered learning enables the task device to adaptively learn the optimal probing sequence and decision thresholds at runtime, aiming at deriving a good balance between the gain of choosing the best edge device and the accumulated cost of deep resource probing. We further conduct thorough performance evaluation of the proposed CERP schemes using both extensive numerical simulations and realistic system prototype implementation, which demonstrate the superior performance of CERP in the diverse application scenarios.