Miracle-dz/UniFAR-FTUs huggingface dataset:
New Network Based on D-LinkNet and ResNeXt for High Resolution Satellite Imagery Road Extraction

DlinkNet[1] (LinkNet With Pretrained Encoder and Dilated Convolution) has been proved to be an effective method for road extraction in remote sensing fields as it won the champion in the DeepGlobe's Road Extraction Challenge. However, as the number of hyperparameters increases (such as the number of channels, filter size, etc.), the difficulty and computational overhead of network design will increase. Focused on this problem, this paper put forward effective ideas to improve D-LinkNet: (1) Applying ResNeXt as its backbone instead of ResNet to rebuild D-LinkNet; (2) Replacing initial block with stem block in the beginning of the network. The results of road extraction which was trained with our own dataset was evaluated with IoU scores. The evaluation results shows that the improved network has higher IoU scores than D-LinkNet when maintaining the model complexity and number of parameters.
Linear-PoseNet: A Real-Time Camera Pose Estimation System Using Linear Regression and Principal Component Analysis

Neural networks-based camera pose estimation systems rely on fine tuning very large networks to regress the camera position and orientation with very complex training procedure. In this paper, we explore the following question: do we need to fine tune and train such complex networks to reach the desired accuracy? We show that we can reach comparable or better accuracy for the single image indoor localization systems with using only one layer of ridge regression and pretrained features of ResNet-50 architecture with training time less than a second on CPU instead of hours of GPU training needed by the state of the art. For outdoor scenes, we show that using only 3 fully connected layers on top of pretrained ResNet50 features without fine-tuning can perform well compared to the state of the art with only minutes of training. For more complexity reduction, we show that downsampling the pretrained ResNet-50 features by more than 10 times using principal component analysis (PCA) has a little effect on the performance but can save both training time and storage space.
Deep Manifold Learning Network for Hyperspectral Image Classification

Deep neural networks have achieved great success in the field of image processing. The feature representation of RGB image can be easily obtained in spatial domain. Different from this, hyperspectral image (HSI) is a kind of high-dimensional data that contains rich spectral information. To explore the manifold structure in HSI, a new deep learning model termed deep manifold learning network (DMLN) was proposed in this paper. In DMLN, a graph based loss function is designed to combine the exploration of manifold structure and the extraction of deep abstract information, which can obtain the discriminant features by iteratively enhancing the compactness of intraclass samples and the separation of interclass samples. Experimental results on two real-world HSI data sets demonstrate the proposed DMLN outperformed some the state-of-the-art methods.
Air-Filled Substrate Integrated Waveguide (AFSIW) Cavity-Backed Slot Antenna for 5G Applications

A cavity-backed slot antenna based on air-filled substrate integrated waveguide (AFSIW) with the technology of multilayer printed circuit board (PCB) is proposed for the 5th generation (5G) mobile communication system at 28 GHz for the first time. The whole antenna is made of five layers and its size is $10\ \text{mm} \times 9\ \text{mm}$. The top and bottom layers are Rogers RT6002 and the medium layer is based on an air-filled substrate integrated waveguide. According to simulation results, compared with the antenna which has the same structure but based on a substrate integrated waveguide (DFSIW), the antenna proposed in this paper increases the gain by 5.8 dB (4.8 dB), the total efficiency by 92.0% (69.7%) and the bandwidth by 990 MHz (207 MHz).
Spatial Coupling In Turbo Equalization

In this paper we consider spatial coupling in turbo equalization and demonstrate that the code design trade-off between the performance in waterfall and error floor regions can be avoided. We introduce three coupling schemes and compare their performances, where the first method introduces coupling between the encoder and the channel, while the second uses a spatially coupled (SC) code. In the third scheme we use both a coupled code and couple between the code and the channel. We show by computer simulations that, with spatial coupling, we can have good performance in both the error floor and the waterfall region with reasonable decoding latency by using a window decoder. We show this for both the maximum a posteriori (MAP) and linear minimum mean square (MMSE) equalizers.
A Deep Music Recommendation Method Based on Human Motion Analysis

Despite its applications in automatic video editing and automatic music composition, the problem of music recommendation from dance motions has seldom been explored. In order to solve this problem, this work proposes a deep music recommendation algorithm based on dance motion analysis and evaluate it through quantitative measures. For quantitative evaluation, this work implements a LSTM-AE based music recommendation method which learns the correspondences between motion and music. In experiments, the two methodologies are compared and the motion analysis based methods outperform their rival by large margins. This work also proposes a quantitative measure of accurately recommended music genre. The proposed motion analysis based method achieves a recommendation accuracy of 91.3% using late fusion of joint and limb features.
ArMor: A Data Analytics Scheme to identify malicious behaviors on Blockchain-based Smart Grid System

The next-generation energy system, i.e., Smart Grid (SG), empowers the real-time transfer of information using advanced metering infrastructure (AMI) and smart meter (SM) between end-consumers and grid. It accelerates various services such as automatic meter reading, time-of-use (TOU) pricing, demand-response management, and many more. Though it has growing security and privacy concerns and the detection of malicious activity is a critical security task that sacrifices the overall Quality-of-Service (QoS) of SG and Quality-of-Experience (QoE) for customers. To address the aforementioned issues, we propose a data analytics Scheme ArMor for malicious activity detection on the blockchain (BC)-based SG system. The ArMor detects data integrity issues in real-time like false data injection attack and SM failure. Here, we proposed a unique ARIMA-based malicious activity detection model and classified the customer. Then, we proposed a Smart Contract (SC)-based incentive mechanism for utility providers handling the malicious activity at their end. It prevents the entry of malicious data into the SG system as transactional data once stored in BC, it is secured using SC. The obtained results are compared against parameters like prediction accuracy, latency, and data storage cost compared to the state-of-the-art approaches to designate the efficacy of the proposed scheme.
Modeling Interference to Reuse Millimeter-wave Spectrum to In-Building Small Cells Toward 6G

In this paper, we present an analytical model to characterize co-channel interference between small cell base stations (SBSs), each enabled with dual-transceiver operating at 28 GHz and 60 GHz millimeter-wave bands, in a multistory building. Subject to satisfying a predefined co-channel interference limit, we then deduce a minimum distance in both intra-floor and inter-floor levels between co-channel SBSs. The optimal size of a 3-dimensional (3D) cluster of SBSs and the corresponding 3D spatial reuse factor are defined such that both millimeter-wave spectrums can be reused fully to each 3D cluster of SBSs within the building. We derive system-level average capacity, spectral efficiency, and energy efficiency metrics for an arbitrary number of L buildings of SBSs within a macrocell coverage. With extensive system-level numerical and simulation results, we show that the 3D spatial reuse of multi-millimeter-wave spectrums to each 3D cluster of SBSs per building can achieve the expected average spectral efficiency and average energy efficiency requirements for the sixth-generation (6G) mobile networks.
Hyperspectral Images Denoising Based on Mixtures of Factor Analyzers

This paper presents two hyperspectral image (HSI) denoising methods, mixtures of factor analyzers (MFA) and wavelet-based MFA (WMFA). MFA uses a Gaussian mixture model to segment the original HSI into different parts, where each part follows Gaussian distribution and then utilizes a factor analyzer to get a low-rank factor loading matrix, and finally uses the inverse transformation of the matrix to get the denoised hyperspectral dataset. WMFA uses the MFA in the wavelet domain to remove the noise in HSI. The proposed methods are evaluated by using both simulated and real hyperspectral datasets.
Classification system study of soybean leaf disease based on deep learning

Accurate and automatic identification of leaf diseases can help farmers to formulate early response actions to reduce economic losses. A deep learning-based classification system against soybean leaf disease was proposed in this paper. The dataset was derived from multi-blade real crop images taken directly by small UAVs at different time periods and under different illumination conditions. First, for the collected leaf images, SLIC method was used to segment the image of plant leaves by classification system. Then, fine-tuning and transfer learning strategies was adopted to expand training of deep neural networks, as well as data enlargement and Dropout techniques were used to avoid over-fitting. Finally, test results of real data sets are presented quantitatively and qualitatively by means of visualization. The results showed that the classification accuracy can be as high as 99.04% when using Inception-v3 combined with 75% fine-tune parameter strategy.
Resource Allocation in Downlink Multicarrier NOMA under a Fairness Constraint

The problem of resource allocation in multicarrier non-orthogonal multiple access (NOMA) systems is non-deterministic polynomial time (NP)-hard and requires exhaustive search, which has prohibitive computational complexity. Computationally-efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into two sub-problems, namely subcarrier assignment and power allocation. The optimal users’ channel condition relationship is derived in terms of effective system sum-rate, and a computationally-efficient ideal partner search algorithm (IPSA) is proposed. Further, optimal power allocation (OPA) is studied in terms of fairness, and an OPA coefficient is theoretically derived based on the channel condition ratio of two NOMA users. Numerical results show that IPSA with OPA outperforms IPSA with fractional transmit power allocation (FTPA), the strongest user-weakest user (SUWU) scheme and user grouping (UG) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER. Further, the computational complexity of IPSA with OPA is much lower than that of exhaustive search.
Adaptive Sidelobe Suppression of SAR Images with Arbitrary Doppler Centroids and Bandwidths

Sidelobe suppression is an issue of interest in SAR imaging. In order to obtain low-sidelobe SAR images, various sidelobe suppression methods have been proposed. SVA (Spatially Variant Apodization) method can both maintain image resolution and suppress sidelobes. However, this method assumes that the Doppler centroid is zero and the Doppler bandwidth is close to the pulse repetition frequency (PRF). Actually, when the radar has a radial velocity, the Doppler centroid may deviate from zero and the Doppler bandwidth may be significantly smaller than the PRF. In such case, the method has poor performance in suppressing sidelobes. This new algorithm proposed in this paper not only inherits the advantage that the SVA maintains image resolution, but also considers arbitrary Doppler centroids and bandwidths. The result of simulated and real SAR images indicates the advantage of this new algorithm over the traditional methods.
In search of functionality for efficient inhibition of mild steel corrosion both in HCl and H2SO4

While the organic ammonium compounds are known to be effective against mild steel corrosion in HCl, these cationic salts are not as effective in sulphuric acid media. A new series of tertiary diallylamines containing C12, C14, C16 and C18 alkyl hydrophobes, N-hexadecylpyrrolidine and their corresponding ammonium salts were found to be among a rare class of inhibitors in arresting mild steel corrosion in HCl as well as in H2SO4 media effectively. While all the inhibitor molecules exhibited excellent inhibition efficiencies (%IEs) (∼99%) in 1M HCl, ammonium salts containing alkyne moiety at 400 ppm provided 93-97%IE even in 7·7M HCl. The inhibitors (400 ppm) containing diallyl moiety demonstrated the %IEs in the range 80-97% in 0·5M H2SO4. The interactions of the π electrons in the diallyl moiety with the low lying vacant d-orbitals of iron help the inhibitor molecules to undergo adsorption and interfere with the anodic dissolution process.
Reconstruction of Compressed Sensing MRI: An Experimental Review among Priors- and Deep Learning- based Algorithms

Magnetic resonance imaging (MRI) is the most effective and reliable imaging process to visualize the internal organs of the human body for clinical purposes and medical research. Although MRI does not use any radiation effect, it takes a long time to acquire an MRI image that makes discomfort to the patients, especially children and persons who have anxiety. Compressed sensing (CS) is a recently used technique for allowing faster acquisition of MRI. Since CS generates random under-sampling k-space measurements due to allowing faster acquisition, a non-linear algorithm is required for reconstructing a perfect MRI image. In this work, a comparative study is performed among single priors-, combined priors-, and deep learning-based non-linear algorithms for the reconstruction of MRI from CS measurements. Experiments are conducted to perform a comparative study that shows the significant aspects of the algorithms in terms of visual quality and quantitative measurements compared with each other. The results of the experiment proved the superiority of the combined priors-based method when compared with the single priors- and deep learning-based method.
Urban Scenes Change Detection Based on Multi-Scale Irregular Bag of Visual Features for High Spatial Resolution Imagery

Remote sensing scene change detection (SCD) is to detect whether and what changes have occurred in the semantic category of corresponding scenes for a long time at the semantic level. This can provide detailed land use/land cover change information for Urban planning and environmental monitoring. Previous studies take regular patches divided by uniform grid sampling as scene units. This may lead to mosaic phenomenon, and use fixed Window to extract features, ignoring the multi-scale features of ground objects, while extracting scene features. To solve the problems, the multi-scale irregular bag of visual features (MIBVF) framework is proposed for high spatial resolution (HSR) imagery SCD. In this paper, we integrate image classification of the physical characteristics from remote sensing data with the socio-economic attributes from open source geographic data. Road network data is used to preserve the geological significance and semantic integrity of urban scenes, and multi-scale window sampling is used to solve the problem of different object sizes. To confirm the feasibility of the proposed method, experiments with multi-temporal images of the Pudong area in Shanghai indicate that the proposed method achieves a clearly higher change detection accuracy than current state-of-the-art methods.
Optic Disc Segmentation in Fundus Images using Operator Splitting Approach

The optic disc (OD) is a critical anatomical pattern in retinal fundus images. The expertise to identify OD in fundus images play a significant part in automatic analysis frameworks. Any changes that occur in these anatomical structures result in retinal abnormalities. Hence it is essential to segment retinal features for detecting retinal diseases. Segmentation of the OD is significant for screening retinal disorder such as diabetic retinopathy(DR) and glaucoma. OD identification is a relatively easy task in standard retinal images, but Identifying this region in DR retinal images is difficult as Exudates-(EX) and OD appear the same features. In this paper, a novel technique using an operator splitting approach(OSA) for segmenting OD in fundus images with poor edges is proposed. A local database of 100 images collected from Silchar Medical College and Hospital(SMCH), Assam, and accuracy of 96% is obtained, which is better than other states of the art methods.
Optimal Power Allocation for Secure Non-orthogonal Multiple Access Transmission

Non-orthogonal multiple access (NOMA) has been considered as a promising scheme for enabling ultra-high throughput transmission and massive-connectivity in next generation wireless systems. In this paper, we investigate the secrecy-based NOMA transmission for encountering the eavesdropping attack. Exploiting the NOMA-users simultaneous transmission as an artificial jamming, we investigate the joint optimization of NOMA-users’ power allocations and the secrecy-provisioning, with the objective of the effective secure throughput of NOMA-users while ensuring the fairness among them. Despite the non-convexity of the formulated joint optimization problem, we explore its hidden feature and design a search algorithm to compute the optimal solution. Numerical results are provided to validate the performance of our proposed algorithm.1
Detection of Rail Fasteners from Aerial Images Using Deep Convolution Neural Networks

Reliable rail infrastructure helps in ensuring a state's economic stability. Timely detection of missing rail fasteners is crucial in avoiding fatal accidents and plays a significant role in rail safety assurance. In this paper we propose the use of one-stage RetinaNet deep convolution network architecture to detect rail fasteners on rail tracks by analyzing the aerial imagery. Quantitative assessment was conducted in a one km railway stretch by acquiring aerial images of size $5472\times 3078$, using a DJI Phantom 4 from a height of 100m. However, the background complexity renders it as a demanding task. Results demonstrate that the learned deep features promise a robust method, if trained on a larger dataset and can help identify rail anomalies which may require imminent repair.
Multiscale Convolution Network with Region-Based Max Voting for Hyprrsprctral Imagrs Classificatton

Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional handcrafted feature extraction methods, convolutional neural network (CNN) can automatically learn hierarchical features with discriminative information. However, two issues exist in applying CNN to HSI classification. One issue is how to represent the land covers at multiscale, the other is how to solve the “salt and pepper” noises caused by pixel-based CNN classification. To solve these issues, in this paper, a multiscale CNN is proposed to extract multiscale features for HSI classification, and then a region-based max voting scheme is applied to the classification map to solve the “salt and pepper” noises. Experiments on two classical data sets demonstrate that the proposed method is effective for HSI classification, especially for images with large scale changes.
Hyperspectral Target Detection via Multiple Instance LSTM Target Localization Network

Modeling target detection problem given inaccurate annotations as a multiple instance learning (MIL) problem is an effective way for addressing the ground truth uncertainties of remotely sensed hyperspectral imagery. In this paper, we propose a hyperspectral target detection method based on 1D convolution neural network (1DCNN) feature extraction and long short term memory network (LSTM) under the MIL framework, where the LSTM features for each hyperspectral pixel is further refined by a scoring network as to discriminate the real target instance from the inaccurately labeled hyperspectral regions. The proposed method has achieved superior results on both simulated data and real hyperspectral data over the state-of-the-art methods, showing the prospects for further investigation.
Drift mobilities in amorphous charge‐transfer complexes of trinitrofluorenone and poly‐n‐vinylcarbazole

Hole and electron drift mobilities have been measured in thin films of charge‐transfer complexes of 2,4,7‐trinitro‐9‐ fluorenone (TNF) with poly‐n‐vinylcarbazole (PVK). The film compositions ranged from pure PVK to mixtures of TNF : PVK in a 1.1 : 1 molar ratio with respect to the monomer unit. Measurements have also been made on amorphous films of pure TNF. The drift mobilities are extremely low (< 10−6 cm2/V sec) and are strongly field and temperature dependent. The field dependence is due to the measured activation energies decreasing as the square root of the applied field from zero‐field values of about 0.7 eV for both carriers. The field dependence of the mobilities is similar to that observed for conductivity modified by the Poole‐Frenkel mechanism; however, several objections make this model unacceptable for this case. At this time, no satisfactory explanation of the observed field dependence can be given. As the TNF concentration is increased, the hole mobility decreases rapidly, while the electron mobility increases. The results suggest that charge carriers are highly localized with transport taking place by a thermally assisted intermolecular hopping process as for the case of small ppolaron motion. Hole transport is apparently due to hopping via uncomplexed carbazole states, while electron transport is due to hopping between TNF sites which may be complexed or uncomplexed. Estimates of the charge‐carrier localization obtained from the exponential dependence of mobility on the site separation give characteristic distances of 1.1 × 10−8 cm for holes and 1.8 × 10−8 cm for electrons.