Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of MobilityFirst Future Internet Testbed

Recently, Future Internet research has attracted enormous attentions towards the design of clean slate Future Internet Architecture. A large number of research projects has been established by National Science Foundation's (NSF), Future Internet Architecture (FIA) program in this area. One of these projects is MobilityFirst, which recognizes the predominance of mobile networking and aims to address the challenges of this paradigm shift. Future Internet Architecture Projects, are usually deploying on large scale experimental networks for testing and evaluating the properties of new architecture and protocols. Currently only some specific experiments, like routing and name resolution scalability in MobilityFirst architecture has been performed over the ORBIT and GENI platforms. However, to move from this experimental networking to technology trials with real-world users and applications deployment of alternative testbeds are necessary. In this paper, MobilityFirst Future Internet testbed is designed and deployed on Future Networks Laboratory, University of Science and Technology of China, China. Which provides a realistic environment for MobilityFirst experiments. Next, in this paper, for MF traffic transmission between MobilityFirst networks through current networking protocols (TCP), MobilityFirst Proxies are designed and implemented. Furthermore, the results and experience obtained from experiments over proposed testbed are presented.
False Data Injection Attack Detection Before Decoding in DF Cooperative Relay Network

False data injection (FDI) attacks could happen in decode-and-forward (DF) wireless cooperative relay networks. Although physical integrity check (PIC) can combat that by applying physical layer detection, the detector depends on the decoding results and low signal-to-noise ratio (SNR) further deteriorates the detecting results. In this paper, a physical layer detect-before-decode (DbD) method is proposed, which has low computational complexity with no sacrifice of false alarm and miss detection rates. One significant advantage of this method is the detector does not depend on the decoding results. In order to implement the proposed DbD method, a unified error sufficient statistic (UESS) containing the full information of FDI attacks is constructed. The proposed UESS simplifies the detector because it is applicable to all link conditions, which means there is no need to deal each link condition with a specialized sufficient statistic. Moreover, the source to destination outage probability (S2Dop) of the DF cooperative relay network utilizing the proposed DbD method is studied. Finally, numerical simulations verify the good performance of this DbD method.
MID-INFRARED AND RAMAN SPECTROSCOPY FOR MEDICAL DIAGNOSTICS

The spectroscopy of molecular vibrations using mid-infrared or Raman techniques has been applied to samples of human tissue or body fluids. This review gives examples for its potential in supporting medical diagnostics. Most of the investigations have been performed in the field of internal medicine, namely angiology, hematology, rheumatology, endocrinology, gastroenterology, and nephrology. Further potential applications in neurology, gynecology, obstetrics and dermatology are considered. Although some fundamental issues of the vibrational spectroscopy of biomedical samples still have to be solved, significant progress has been achieved in its clinical application in-vitro. First steps towards in-vivo applications are described.
A Singing Voice Separation Method from Persian Music Based on Pitch Detection Methods

Singing voice separation algorithms have many applications, such as recognizing the lyrics, identifying the singer, and retrieving the music data. Due to its complexity and special rules, the traditional music and the voice of Iranian singers have caused the lack of sufficient research in this field. However, in western music, due to the relatively simple structure of the sounds, it has made it possible for researchers can easily do segmentation based on the recognition of its kind, using the characteristics like the number of uttered words and the recognition of the pitches. In this research, an algorithm is presented for the separation of the singing voice from the music through the recognition of the pitches and based on the characteristic-based method. First, at the stage of recognizing the song’s vocal by segmentation of the speech signal, the input is classified into vocal and non-vocal parts. Then music is recognized from the vocal using the pitch criterion and the characteristics of the ceptral coefficients. Finally, the stage of the separation from the pitch is done and is used for the classification. In this research, an automatic and effective method is presented. In the proposed method, based on the extracted characteristics and the frequency distances in segmented parts, the pitch of the singer’s voice and the process of the separation from the music is done with acceptable accuracy. The quantitative results of this research demonstrate the success of the separation system by the presented method. The precision of the proposed method using a combination of characteristics and the Shuffled frog-leaping algorithm’s metaheuristic algorithm is about 94%. The precision of the proposed method using the dimension reduction through MLP neural system demonstrates is 90%.
Constrained Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge. A number of experiments have indicated that one possible solution is to bound the function space of the discriminator. In practice, when optimizing the standard loss function without limiting the discriminator’s output, the discriminator may suffer from lack of convergence. To be able to reach the Nash equilibrium in a faster way during training and obtain better generative data, we propose constrained generative adversarial networks, GAN-C, where a constraint on the discriminator’s output is introduced. We theoretically prove that our proposed loss function shares the same Nash equilibrium as the standard one, and our experiments on mixture of Gaussians, MNIST, CIFAR-10, STL-10, FFHQ, and CAT datasets show that our loss function can better stabilize training and yield even better high-quality images.
Latency-aware Reliable mmWave Communication via Multi-point Connectivity

The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving low-latency and reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We provide an iterative algorithm for the time-average sum-power-minimization problem by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform a time-average stochastic problem into a sequence of deterministic subproblems. Furthermore, for the robust beamformer design, we consider a pessimistic estimate of the user-specific rate, assuming that a portion of CoMP links would be blocked during the data transmission phase, while ensuring the average latency requirements. The numerical examples illustrate that in the presence of random blockages, the proposed method outperforms baseline scenarios and results in energy-efficient, high-reliability and low-latency mmWave communication.
Theory of end-adsorbed polymer brushes in polymeric matrices

A detailed self‐consistent field theory is used to calculate the properties of end‐adsorbed polymer chains, or polymer ‘‘brushes,’’ in equilibrium with a blend of adsorbing B polymer and nonadsorbing A polymer. The brush properties depend on χab, Na/Nb and β, where χab is the Flory interaction parameter characterizing the thermodynamic interaction between the A and B polymers, Na and Nb are the respective degrees of polymerization of the A and B polymers and kBTβ is the net attractive interaction between the adsorbing chain end and the surface. For χab=0, β≫1 and Na/Nb≫1 an asymptotic limit is reached where the brush properties are completely specified by z*/Rbg, where z* is the integrated surface excess of the adsorbing polymer and Rbg is its unperturbed radius of gyration. In this strong adsorption, dry brush regime the volume fraction of adsorbing polymer at the surface increases rapidly from 0.0 to 0.96 as z*/Rbg increases from 0.0 to 1.6. For z*/Rbg>1.6 the surface volume fraction of the adsorbing po.
Simulation on Robot Arm Manipulator Modeling using Dynamic Kinematic

This simulation shows about analysis of 6 Degrees of Freedom (DOF) Of robotic arm kit generated by Robo Analyzer software. Moreover, this visual simulation consisted of mathematical elements that can animate the mechanical of motion for a robot manipulator. Nowadays, there was a lot of Unmanned Ground Vehicle (UGV) had been equipped with manipulator but the lack of stability in pick and place object was the main problem in safety issues especially in a safety application. For example, safety authorities need to lift picric acid by using less vibration and correct trajectory to avoid an explosion. Therefore, the main objective of this research is to identify the value of the position and orientation of each joint correctly because all these values were needed to be used by the controller to tackle stability issues. Besides, parameters to conduct the simulation can solve forward kinematic of the robot in the form of a 3D model which can determine the suitable motor with enough power and torque that can be used by each of the joints. This Robo Analyzer used a geometrical approach which is first, it used Denavit Hartenberg parameters to determine end effectors. Next, Robo Analyzer used forward and inverse dynamic analysis to get torque values. The simulation results clearly show the effectiveness of Robo Analyzer software to generate a simulation of the coordinate for each joint, joint parameter, and joint torque that is needed to select a suitable motor with enough torque. In other words, the significance of this research is, by using Robo Analyzer, the user can forecast the hardware simulation result before applying it in the real situation because the visual stimulation can animate the motion of the robot from the beginning until its last position.
Unsupervised Hyperspectral Embedding by Learning a Deep Regression Network

This work presents a novel hyperspectral embedding technique by learning a deep regression network in an unsupervised fashion, which aims at reducing the computational complexity and storage-costing of traditional manifold embedding methods as well as improving the representation ability of spectral signatures effectively. The proposed method attempts to learn an explicit and unified nonlinear mapping from all patch-wise correspondences of original hyperspectral data and dimension-reduced products generated by some existing manifold learning approaches. This process can be well performed by means of a deep regression model. The learned model is not only capable of locally capturing the manifold structure of the whole hyperspectral image from densely patch-based random sampling but also better applicable to high-efficient out-of-sample inference. Experimental results conducted on the real hyperspectral data demonstrate the effectiveness and superiority of the proposed hyperspectral embedding technique.
Hyperspectral Band Selection within a Deep Reinforcement Learning Framework

In the last decades, hyperspectral imaging (HSI) has become an appealing field of remote sensing because of its richness in information. However, HSI suffers from redundancy and high computational complexity. To address this challenge, we present Policy Gradient Band Search - an intuitive and straightforward search strategy for dimensionality reduction of hyperspectral data. The family of policy gradient algorithms is a widely used and successful model-free approach in the reinforcement learning framework and is here applied for HSI band selection. In order to exhibit the effectiveness of our method, we evaluate our approach under consideration the spatial-spectral relationship in combination with a 3D-CNN based HSI classification network. Our approach yields satisfying empirical results in three HSI datasets, which are Pavia University, Salinas and Greding. The overall accuracy of 98.14%,96.64%,99.84% was separately accomplished on these datasets while being restricted by utilizing only four chosen bands.
An Electrically Small Antenna in Package Design with Embedded Electronics for RPW Detection

The tracking and detection of Red Palm Weevils (RPWs) are highly desirable for efficient pest control. Bluetooth Low Energy (BLE) tracking system is a good option for real-time monitoring, however, for mounting the system on the insect, it must be miniaturized, particularly the antenna part. In this paper, we propose an electrically small inverted F antenna which is realized on the BLE system package, qualifying it as an antenna in package (AiP) design. The antenna has been designed with the embedded electronics and has a highly compact size of 0.05\lambda\times 0.05\lambda\times 0.025\lambda$. It is very well matched at 2.48 GHz with 17 MHz bandwidth. It demonstrates a maximum detection range of 300 meters.
A Deep Learning Model for Early Detection of Fake News on Social Media*

Fake news detection has recently become an important topic of research. This is due to the impact of fake news on the internet especially on social media. Numerous of the models proposed in the previous studies are based on supervised learning. Therefore, these models are unable to deal with the huge amount of unlabeled data about fake news. Few studies focused on early detection. In this study, we built a semi-supervised learning model to detect fake news on social media at an early stage. By using a semi-supervised learning, we make our model able to deal with the huge amount of unlabeled data on social media. We first built a model to extract users' opinion expressed in comments, then we used CredRank Algorithm to evaluate users' credibility and built a small network of users involved in the spread of a given news. The outputs of these three steps serve as inputs of our news classifier SSLNews. SSLNews is composed of three networks: a shared CNN, an unsupervised CNN and a supervised CNN. We used real world datasets to evaluate our model, Politifact and Gossipcop. When using 25% of labeled data, SSLNews reaches an accuracy of 72.25% on Politifact and 70.35% on Gossipcop. When using data produced in the first 10 minutes of the beginning of the spread of the news, SSLNews reaches an accuracy of 71.10% on Politifact and 68.07% on Gossipcop.
Tripwire Detection in SAR Images Using a Modified Radon Transform

Anti-personnel fragmentation mines and improvised explosive devices often use metallic wires-so-called tripwires-for the trigger mechanism. Depending on environment and vegetation, these wires are hardly visible. Therefore, an airborne synthetic aperture radar (SAR) was developed to assist the process of mine clearance. In this work, an algorithm for the detection and localization of tripwires in SAR images is proposed. A modified Radon transform is employed that relies on the SAR phase information. Instead of simply integrating absolute values over a straight line, the complex-valued data is analyzed in the frequency domain. The result in the transformed image is then given by the maximum amplitude within a given frequency range, causing tripwires to clearly separate from clutter. The functionality of this algorithm is successfully demonstrated by measurements of tripwires attached to a dummy mine in wet grass.
Traffic Condition Estimation Based on Historical Data Analysis

Traffic condition estimation is one of the essential tasks for intelligent transportation systems/services (ITS). In order to provide accurate and timely traffic information, traffic data must be collected adequately at every location or road segment in the traffic network. This requirement is hard to satisfy, however, in practice considering a large Spatio-temporal space of urban traffic system even both fixed-sensor and crowd-sourcing approaches are utilized. In order to resolve this difficulty, this work proposes a framework for analyzing historical data to predict traffic conditions at the road segments where real-time data are missed by leveraging data mining techniques. The experimental results from real-field data collected by our developed system reveal the feasibility and the effectiveness of the proposed approach.
Training Capsnets via Active Learning for Hyperspectral Image Classification

Hyperspectral imaging (HSI) gathers hundreds of images along the electromagnetic spectrum for the same area on the surface of the Earth, collecting a rich amount of spatial and spectral information. Deep learning classifiers have achieved significantly high precision results when analyzing HSI data. In particular, capsule networks (CapsNets) can provide robust classification results, overcoming the limitations of traditional convolutional neural networks (CNNs) by enriching the feature presentation capability and applying dynamic routing mechanisms. As a result, CapsNets are now widely regarded as the state-of-the-art within deep learning field. However, as it is the case for CNNs, the performance of CapsNets strongly depends on the quantity and quality of the available training samples, which in HSI tends to be scarce and noisy. Moreover, obtaining labeled data is expensive and time-consuming, and the high dimensionality of HSI data makes it difficult to accurately design classifiers based on limited training samples. This is mainly due to the strong intra-class variability present in the HSI data. Active learning (AL) can alleviate the aforementioned problems by selecting a small set of highly-representative labeled samples from a pool of unlabeled data, in iterative fashion. This paper presents a new AL-based approach for HSI data classification that integrates the spectral and the spatial information contained in the HSI data and enhances the performance of CapsNets when very limited training samples are available. Code: https://github.com/mhaut/AL-CapsNet-HSI.
A Novel Approach for Hyperspectral Image Superresolution Using Spectral Unmixing and Transfer Learning

Hyperspectral image (HSI) Super-resolution (SR) methods enhance the spatial resolution. In this paper, we propose a novel SR approach for HSIs by making use of spectral unmixing and transfer learning. We first train a deep convolutional neural network (CNN) to learn the mapping between the low-resolution (LR) and high-resolution (HR) natural images and use the same for transfer learning to get the initial estimates of the super-resolved abundances where the input corresponds to LR abundances. To get the better estimates of abundances and hence improve the SR of HSIs, we use a regularization framework in which both the LR and HR abundances are modelled as Inhomogeneous Gaussian Markov field (IGMRF) that serves as the prior. Finally, the SR HSIs are obtained by using a linear mixing model that uses the SR abundances and the endmembers estimated using an appropriate technique. Experiments on synthetic as well as on real HSIs show that the proposed method performs better when compared to other existing approaches. The advantages of the proposed approach are: 1. The method do not require auxiliary image as used in many of the existing methods, 2. Spectral details are better preserved since the SR is carried out in abundance domain, 3. Computational complexity is reduced since the SR is carried out on abundances which are few in number when compared to HSIs.
Deep Feature Selection and Decision Level Fusion for Lungs Nodule Classification

The existence of pulmonary nodules exhibits the presence of lung cancer. The Computer-Aided Diagnostic (CAD) and classification of such nodules in CT images lead to improve the lung cancer screening. The classic CAD systems utilize nodule detector and feature-based classifier. In this work, we proposed a decision level fusion technique to improve the performance of the CAD system for lung nodule classification. First, we evaluated the performance of Support Vector Machine (SVM) and AdaBoostM2 algorithms based on the deep features from the state-of-the-art transferable architectures (such as; VGG-16, VGG-19, GoogLeNet, Inception-V3, ResNet-18, ResNet-50, ResNet-101 and InceptionResNet-V2). Then, we analyzed the performance of SVM and AdaBoostM2 classifier as a function of deep features. We also extracted the deep features by identifying the optimal layers which improved the performance of the classifiers. The classification accuracy is increased from 76.88% to 86.28% for ResNet-101 and 67.37% to 83.40% for GoogLeNet. Similarly, the error rate is also reduced significantly. Moreover, the results showed that SVM is more robust and efficient for deep features as compared to AdaBoostM2. The results are based on 4-fold cross-validation and are presented for publicly available LUNGx challenge dataset. We showed that the proposed technique outperforms as compared to state-of-the-art techniques and achieved accuracy score was 90.46 ± 0.25%.
Generation of Lidar-Predicted Forest Biomass Maps from Radar Backscatter with Conditional Generative Adversarial Networks

This paper studies the generation of LiDAR-predicted aboveground biomass (AGB) maps from synthetic aperture radar (SAR) intensity images by use of conditional generative adversarial networks (cGANs). The purpose is to improve on traditional regression models based on SAR intensity, which are trained with a limited amount of AGB in situ measurements. Although they are costly to collect, data from airborne laser scanning (ALS) sensors are highly correlated with AGB and can replace in situ measurements as the regression target. Thus, the amount of training data increases dramatically, and we can learn an expressive two-stage regression model for SAR backscatter intensity. We propose to model the regression function between SAR intensity and ALS-predicted AGB with a Pix2Pix convolutional neural network for image translation that uses a ResNet-5-based cGAN architecture with the Wasserstein GAN gradient penalty (WGAN-GP) objective function. The synthesized ALS-predicted AGB maps are evaluated qualitatively and quantitatively against real ALS-predicted AGB maps. Our results show that the proposed architecture manages to capture characteristics of the real data, which suggests further use of the ResNet-5 for a SAR intensity regression model of AGB.