Miracle-dz/UniFAR-FTUs huggingface dataset:
DSF: A Distributed SDN Control Plane Framework for the East/West Interface

The ever-increasing number of network-capable devices places a massive burden on modern networks. Communication infrastructure should provide quality-of-service essentials in terms of high-bandwidth capacity, scalability, resiliency, and security. Programmable networks are viewed as the prevailing method of encountering the challenges introduced by the accelerated expansion. The ability of software-defined networking (SDN) to separate the control plane from the data plane and enable the programmability of the network creates new ways to architect the network. The centralization of control logic introduces complexities in large-scale, distributed networks such as performance bottlenecks and reliability. Distributed SDN controllers have been proposed to overcome the performance concerns. The lack of a communication standard among distributed controllers, referred to as the East/West interface, presents a challenge in the adoption of SDN in large-scale, distributed networks. In this paper, we propose Distributed SDN control plane Framework (DSF) - a framework for the East/West interface for heterogeneous, distributed SDN controllers to synchronize topologies using a standardized data-centric real-time publish/subscribe paradigm known as the Data Distribution Service (DDS). Distributed control plane architectures are proposed using DSF: flat, hierarchical, and T-model. The DSF interface is implemented on multiple SDN control plane platforms to evaluate performance: Floodlight and Open Network Operating System (ONOS) controllers. Test cases with different configurations are designed for performance evaluation of the proposed interface in homogeneous and heterogeneous SDN control planes. In addition, a performance comparison is presented of DSF-based ONOS controllers versus Atomix-based ONOS cluster solutions.
An Adaptive Statistical Multi-grid DInSAR Technique for Studying Multi-scale Earth Surface Deformation Phenomena

In this study, we show the potential of an adaptive quad-tree-based decomposition method applied to Differential Synthetic Aperture Radar (DInSAR) data. Specifically, the proposed method exploits a multi-resolution scheme for the phase unwrapping of sequences of DInSAR interferograms and allows one to produce DInSAR deformation products at different scales of resolution. The selection of the used multi-grid is based on the analysis of the statistical properties of a sequence of interferometric phase, allowing to recognizing major deformation areas where phase unwrapping operations can be performed more efficiently, with a computational improvement and without losing significant information.
Harbor Detection in SAR Images Based on Multidirectional One-Dimensional Scanning

In SAR image target detection, harbor detection can help the detection of harbor targets and maritime traffic planning. In this paper, we propose a harbor detection method of SAR images based on multidirectional one-dimensional scanning. Take the candidate points along the coastline and the multidirectional one-dimensional scanning is performed. Using the distribution characteristics of land, sea and dock in the one-dimensional vector, training a convolutional neural network to classify the candidate points into harbor and non-harbor feature points. Then we get the harbor feature points map reflecting the distribution of harbors. The Sentinel-1 spaceborne SAR images covering a coastal region are used to verify the proposed method. The experimental results show the effectiveness and accuracy of the proposed method.
Differential d-Vectors for RSS based Localization in Dynamic IoT Networks

Wireless Fidelity (Wi-Fi) Received Signal Strength (RSS) fingerprints are frequently used for localization in Internet of Things (IoT) environment. However, RSS fingerprinting based localization methods’ accuracy degrades due to device heterogeneity and temporal variations (dynamic variations) in the target environment. In this paper, we propose a smart device localization method that addresses the dynamic RSS variations using differential d-vectors. The d-vector is the signature of a particular location and is persistent even with dynamic RSS variations. We compute the performance of the proposed d-vectors on two popular real-world datasets, and the obtained results outperform state-of-the-art fingerprinting methods that address the heterogeneity and temporal variations of the RSS values.
Initial NovaSAR-1 Data Processing and Imagery Evaluation

NovaSAR-1 is a low-cost spaceborne Synthetic Aperture Radar (SAR) technology demonstrator developed by Surrey Satellite Technology Ltd, UK (SSTL), and Airbus Defence and Space Ltd, with funds of the UK Government [1]. After the successful launch of NovaSAR-1 satellite in September 2018 and completion of the commission phase in August 2019, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) are able to task, capture and access the unique S-band NovaSAR-1 imagery under the partnership of Australia's 10% capacity share in this new satellite mission [2], [3]. In the paper, we introduce the NovaSAR-1 data processing, and present initial results of NovaSAR-1 data performance evaluation and the recent emergency observations of extensive Australian bushfires.
Exemplar-Based Emotive Speech Synthesis

Expressive text-to-speech (E-TTS) synthesis is important for enhancing user experience in communication with machines using the speech modality. However, one of the challenges in E-TTS is the lack of a precise description of emotions. Previous categorical specifications may be insufficient for describing complex emotions. The dimensional specifications face the difficulty of ambiguity in annotation. This work advocates a new approach of describing emotive speech acoustics using spoken exemplars. We investigate methods to extract emotion descriptions from the input exemplar of emotive speech. The measures are combined to form two descriptors, based on capsule network (CapNet) and residual error network (RENet). The first is designed to consider the spatial information in the input exemplary spectrogram, and the latter is to capture the contrastive information between emotive acoustic expressions. Two different approaches are applied for conversion from the variable-length feature sequence to fixed-size description vector: (1) dynamic routing groups similar capsules to the output description; and (2) recurrent neural network's hidden states store the temporal information for the description. The two descriptors are integrated to a state-of-the-art sequence-to-sequence architecture to obtain an end-to-end architecture that is optimized as a whole towards the same goal of generating correct emotive speech. Experimental results on a public audiobook dataset demonstrate that the two exemplar-based approaches achieve significant performance improvement over the baseline system in both emotion similarity and speech quality.
Density Functional Study of the Adsorption of Methanol and its Derivatives on Boron Nitride Nanotubes

Changes in the structural and electronic properties of chemically modified boron nitride nanotubes (BNNTs) using methanol and its derivatives including CH3CH2CH2OH CH3CH2OH, (ph)CH2CH2OH, CH2COOH and (CN)CH2CH2OH were investigated using density functional theory calculations. The study results showed that molecules of methanol can be chemically adsorbed on top of a sidewall B atom with an adsorption energy of −0.67 eV, which is stronger than that of carbon nanotubes. When using different derivatives of methanol, the adsorption energies and charge transfer from the adsorbate to the BNNT depending on the electron-withdrawing or electron-donating capability of the subgroups within the derivatives. Subgroups with strong electron-withdrawing capability generally lead to transfer less charge and smaller adsorption energy. The calculated density of state shows that the electronic properties of the BNNT are only slightly changed by the chemical modification. However, preservation of the electronic properties of BNNTs coupled with the enhanced solubility suggests that chemical modification of BNNTs with either methanol or its derivatives may be an effective way for purification of the BNNTs.
In-Network Computing Powered Mobile Edge: Toward High Performance Industrial IoT

Recently, the industrial Internet of Things (IoT) has quickly become a disruptive force reshaping how we live and work. Compared to the consumer Internet, the industrial IoT puts forward much higher performance requirements in terms of network and computing capacity. The industrial IoT system needs to process tons of data generated by millions of IoT sensors in real-time. Recently, with the advent of programmable network devices (e.g., SmartNIC, programmable switch), the in-network computing (INC) paradigm has received a large amount of attention. INC refers to offload application-specific tasks from end-host to network devices. Benefiting from the line-rate processing capacity of network devices, the INC paradigm presents superior performance in terms of high-throughput low-latency computing. Therefore, INC is considered a promising technique to meet performance requirements in the industrial IoT. However, while network devices are capable of performing application-specific tasks, they are still far from the universal computing platform. In this article, we propose an INC powered mobile edge architecture, where we offload lightweight critical tasks to the INC devices and leave the rest to the MEC. To identify critical tasks, we introduce the complex event processing (CEP) tool in our architecture. Also, we present two industrial IoT use cases to evaluate the feasibility of our architecture.
Persian Music Source Separation in Audio-Visual Data Using Deep Learning

In applications such as noise removal, speech separation, music reconstruction or integration, and many others, it is important to decompose a single-channel recorded sound into its sources. In this paper, we deal with the separation of single-channel audio sources and cocktail party problem in audio data using supervised learning. The basis of this method is using deep learning to separate audio signals. The data contains audio-visual videos and Persian musical instruments sounds which have been selected as audio sources. The proposed algorithm is the first method implemented based on deep learning to separate the sound sources of Persian musical instruments. In this method, first the audio part of the video signal is extracted, then the audio signal, which is a mixture of sound sources, is separated and decomposed by a deep neural network model. According to the evaluations, the proposed method has good quantitative and qualitative results for separation two audio sources. In addition, this method is a comprehensive solution that can be used for a wide range of audio sources and a combination of more than two audio sources.
The Phase Transition Analysis for Random Regular Exact (s, c, k)—SAT Problem

The length of each clause in a regular <inline-formula> <tex-math notation="LaTeX">$(s, c, k)-CNF$ </tex-math></inline-formula> formula is <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. Each argument occurrences <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> times, among them, positive occurrences <inline-formula> <tex-math notation="LaTeX">$(c * s)$ </tex-math></inline-formula> times and negative occurrences <inline-formula> <tex-math notation="LaTeX">$(s-c*s)$ </tex-math></inline-formula> times, where <inline-formula> <tex-math notation="LaTeX">$0 < c < 1$ </tex-math></inline-formula>. A regular exact <inline-formula> <tex-math notation="LaTeX">$(s, c, k)-SAT$ </tex-math></inline-formula> question refers to whether there is a set of Boolean variable assignment such that exactly one literal in each clause of the regular <inline-formula> <tex-math notation="LaTeX">$(s, c, k)-CNF$ </tex-math></inline-formula> formula is true. Obviously, the problem is a NP-hard problem. To understand the hardness and the distribution of satisfiable solutions of regular exact <inline-formula> <tex-math notation="LaTeX">$(s, c, k)-SAT$ </tex-math></inline-formula> problem, we introduce a random instance generation model, use the first moment and second moment methods to analyze the satisfiable phase transition of the solutions. Set <inline-formula> <tex-math notation="LaTeX">${s^{*}}$ </tex-math></inline-formula> is the phase transition point, we show that the stochastic regular exact <inline-formula> <tex-math notation="LaTeX">$(s, c, k)-SAT$ </tex-math></inline-formula> problem instance is satisfiable with high probability if <inline-formula> <tex-math notation="LaTeX">$s < {s^{*}}$ </tex-math></inline-formula> and unsatisfiable with high probability if <inline-formula> <tex-math notation="LaTeX">$s > {s^{*}}$ </tex-math></inline-formula>, among them, <inline-formula> <tex-math notation="LaTeX">$s^{*}$ </tex-math></inline-formula> is a function about a parameter <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula>. Further, we discuss the phase transition point of the random regular exact <inline-formula> <tex-math notation="LaTeX">$(s, r, k)-SAT$ </tex-math></inline-formula> problem– the difference between the positive and negative occurrences of the variable is <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> - is <inline-formula> <tex-math notation="LaTeX">${s^{*}}$ </tex-math></inline-formula>. Finally, through several groups of experimental data to verify, the experimental results are consistent with the theory, and prove the correctness of the theory.
CNN-Based Tropical Cyclone Track Forecasting from Satellite Infrared Images

In this study, a deep convolutional neural network (CNN) was developed to forecast the movement direction of tropical cyclones (or typhoons) over the Northwestern Pacific basin from Himawari-8 (H-8) satellite images. 2250 infrared images which captured 97 typhoon cases between 2015 and 2018 were used to train the CNN model. By using images from Channels 13 and 15 as input into the CNN model, the mean error of the typhoon movement angle reaches up to 27.8°, which shows the great potential of deep learning in tropical cyclone track prediction.
Detection and classification of gastric precancerous diseases using deep learning

nowadays, there are different types of endoscopic images due to the dramatic development of medical imaging technologies and according to the sensitivity of this issue, proper diagnosis and classification of different kinds of diseases is very important. Gastrointestinal tract of the patients can be observed by endoscopy and this technology can be used to diagnose gastric diseases. Gastric lesions are divided into three categories: Advanced Gastric Cancer (AGC), Early Gastric Cancer (EGC), and Gastric Precursor Disease (GPD). Early detection of these types of lesions can be helpful for cancer prevention and for this reason, detection accuracy is of great importance. Since endoscopic procedures are operator-dependent and human factors based processes, there is a high probability of error which by designing an intelligent system based on convolutional neural networks, different types of lesions and diseases can be classified more accurately. In the proposal system, the convolutional neural network model is trained by designing a convolutional neural network architecture and using data augmentation, dropout, noise and weight decay techniques. Using performance criteria such as accuracy, cost, and confusion matrix, the performance of this model is evaluated. Finally, the proposed model is tested on a dataset of 3673 images of gastric diseases such as Erosion, polyps and ulcers. The results show 89% classification accuracy of this model.
Develop Large-Area Autumn Crop Type Product Using a Deep Learning Strategy

Accurate crop type map over large area at high resolution plays an important role in national food production security. However, there are not so far large-area crop cover data product, mainly because the traditional classification methods are highly dependent on the complicated process, including data pre-process, feature-selection and various classifier decision, which results in high labor-cost and time-consumption. To overcome these challenges, we creatively introduce a deep learning strategy to produce a large-area autumn crop type map. Using a convolution neural network model to train standard GF-1 medium resolution images (16m) from Liaoning Province, China, we generated a basic model. Then, we applied the basic model to predict the autumn crop within Liaoning region, and further extensively predicted the autumn crop (rice and corn) within Jilin Province. And both provinces received satisfied overall accuracy with 89.9% for Liaoning and 84.1% for Jilin. It can conclude that the deep learning strategy is an effective solution to large-area crop mapping, which would pave a way to develop the large-area crop type product annually to meet with the requirement of the policy-making, research and agriculture management.
Blind Symbol Rate and Frequency Offset Estimation for PCMA Signals via Cyclic Correlations

Many blind detection algorithms for paired carrier multiple access (PCMA) signals in satellite communications require information about the signal and channel parameters. A blind receiver needs to obtain knowledge of at least some of these prior to the symbol detection process. In this paper, schemes for the blind estimation of the symbol rates and carrier frequencies of the two overlapping co-channel signals in PCMA are proposed. The methods exploit the cyclic statistics of the PCMA signal for the estimation of the desired parameters. Effects of the mutual interference of the carriers in the estimation process are analyzed and the performance of the proposed methods is evaluated via numerical simulations.
A GPU Accelerated Contourlet Method for Detecting Changes Due to Fire Using Remote Sensing

Remotely sensed images are suitable resources to examine the extent and impact of fire on the land-scape. This paper presents a multiresolution approach to detect changes due to fire. A large number of change detection methods utilizing wavelet features have been developed. However, usage of contourlets is limited in context of change detection application. The aim of this paper is to investigate and propose a texture based model utilizing contourlets in parallel-processing framework to detect changes due to fire. To achieve near real time processing, the proposed algorithm is implemented on NVIDIA's Graphical Processing Unit. The results are compared with the changes detected by different thresholding methods in the feature space. It is found that the proposed method exhibits high change detection accuracy with better edge continuity. The results show that the GPU implementation improves the performance in terms of execution time as compared to the sequential implementation.
Combating the Impact of Jittering in UAV-based Sensing Systems Using Deep Denoising Network

In this paper, we exploit the deep learning based technologies to mitigate the impact of unmanned aerial vehicle (UAV) jittering on wireless sensing performance. In recent years, UAV has been widely utilized for remote sensing applications due to its high flexibility and maneuverability. However, the mobility and vibration of the UAV’s body may cause the jittering effect which can severely degrade the sensing performance. To our best knowledge, the impact of UAV jittering has not been fully examined in literature so far. To alleviate this problem, we propose to leverage adversarial denoising autoencoder (ADAE) for corrupted signal reconstruction. To validate the effectiveness of our proposed scheme, we consider a device-free human sensing scenario in which a UAV is used to sense surrounding human activity by analyzing the received signal strength (RSS). Experiments demonstrate that the proposed ADAE based scheme can effectively reduce the impact of UAV jittering, recovering up to 97% of the performance loss due to the UAV jittering.
Improvement of CNN-Based Road Extraction from Satellite Images via Morphological Image Processing

In this paper, we propose to improve the recall of CNN-based road extraction from satellite images by means of label thickening and thinning. With the thickened road labels, the CNN is led to extract roads more aggressively, preserving the topological information of roads. After inference, the predicted segment maps need to be thinned back to the original width. The proposed technique has been evaluated with an existing road extraction dataset in various degrees of thickening. Throughout the experiments, the relaxed recall score has been successfully improved by the proposed technique, reducing the number of false-negative pixels. However, at the same time, it has been observed that the number of false-positive pixels also increases slightly. Overall, it has been visually observed that the topological information of roads such as connectivity is better extracted by the proposed technique.
Intelligent fast cell association scheme based on deep Q-learning in ultra-dense cellular networks

To support dramatically increased traffic loads, communication networks become ultra-dense. Traditional cell association (CA) schemes are time-consuming, forcing researchers to seek fast schemes. This paper proposes a deep Q-learning based scheme, whose main idea is to train a deep neural network (DNN) to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is associated. In the training stage, the intelligent agent continuously generates samples through the trial-and-error method to train the DNN until convergence. In the application stage, state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user distribution. Simulations demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional schemes. Meanwhile, performance metrics, such as capacity and fairness, can be guaranteed.
Self-Paced Learning with Superpixelwise Features for Hyperspectral Image Classification

We explore self-paced boost learning (SPBL) with superpix-elwise features for hyperspectral image classification (HIC). Firstly, we conduct feature extraction using superpixelwise principal component analysis (SuperPCA), which reduces the dimensionality of hyperspectral images considering the project discrepancy in different homogeneous regions. Secondly, we perform classification by SPBL on the extracted features, where the learning just focuses on the pixels to be classified and does not make their spatial neighbours involved. SPBL embraces the power of self-paced learning on classifying from simple to complex and that of boost learning on classifying in a robust fashion. Our method is not deep learning grounded and the training does not demand high computing resources. The experimental results on two public hyperspectral image datasets demonstrate that our method is competitive with several prominent ones.
Design of the Triple Band Micro-Strip Antenna With AMC Reflector

In this paper, a multi-band antenna composed of irregular metal plate, input feeders and symmetrical defect ground is designed. The antenna radiation enhancement is improved by placing a rectangular artificial magnetic conductor array with zero phase reflection points corresponding to the multi-band antenna frequencies at a fixed distance below. The three impedance bandwidths of the antenna are 2.13-2.58GHz, 3.43-3.64GHz and 5.7-6.0GHz respectively, which effectively cover the 2.4/5.8GHz WLAN band and 2.5/3.5/3.8GHz WIMAX band. By adding AMC reflector, the performance of the antenna in the working frequency band is significantly improved, and a new frequency band is generated at 7.54-8.63 GHz.