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In this paper, we investigate the detection problem for a massive machine-type communication (mMTC) system that has correlated user activities. Two deep learning assisted algorithms are proposed to exploit the user activity correlation to facilitate channel estimation and user identification. Due to the dependency among user activities, conventional element-wise minimum mean square error (MMSE) denoiser used in the orthogonal approximate message passing (OAMP) algorithm cannot achieve satisfying performance during the two-step iterative process. Therefore, we propose a deep learning modified OAMP (DL-mOAMP) algorithm, which iteratively modifies the user activity ratio via exploiting the user activity correlation in the MMSE denoiser based on the estimated sequence during each OAMP iteration. Moreover, given a specific false alarm probability, a constant threshold employed in the conventional user identification is not optimal in the presence of user activity correlation. Thus, we propose a neural network framework that is dedicated to the user identification (DL-mOAMP-UI algorithm), which minimizes the missed detection probability under a pre-determined false alarm probability. Numerical results show that the proposed DL-mOAMP algorithm provides a substantial mean squared error performance gain compared to the conventional OAMP algorithm and the DL-mOAMP-UI algorithm can further improve the user identification accuracy of an mMTC system.

Numerous studies have shown deep hashing can facilitate large-scale image retrieval since it employs neural networks to learn feature representations and binary codes simultaneously. Despite supervised deep hashing has made great achievements under the guidance of label information, it is hardly applicable to a real-world image retrieval application because of its reliance on extensive human-annotated data. Furthermore, the pair-wise or triplet-wise unsupervised hashing can hardly achieve satisfactory performance due to the absence of local similarity of image pairs. To solve those problems, we propose a novel unsupervised deep hashing framework to learn compact binary codes, which takes the quadruplet forms as input units, called Unsupervised Deep Quadruplet Hashing with Isometric Quantization (UDQH-IQ). Specifically, by introducing the rotation invariance of images, the novel quadruplet-based loss is designed to explore the underlying semantic similarity of image pairs, which could preserve local similarity with its neighbors in Hamming space. To decrease the quantization errors, Hamming-isometric quantization is exploited to maximize the consistency of semantic similarity between binary-like embedding and corresponding binary codes. To alleviate redundancy in different bits, an orthogonality constraint is developed to decorrelate different bits in binary codes. Experimental results on three benchmark datasets indicate that our UDQH-IQ achieves promising performance.

Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.

With the advance of Convolutional Neural Network, deep hashing methods have shown the great promising performance in large-scale image retrieval. Without depending on extensive human-annotated data, unsupervised hashing is more applicable to image retrieval tasks compared to supervised methods. However, due to the lack of fine-grained supervised signals and multi-similarity constraints, most state-of-the-art unsupervised deep hashing algorithms cannot ensure the correct fine-grained similarity ranking for image pairs. In this paper, we propose a novel unsupervised deep multi-similarity hashing framework to learn compact binary codes by jointly exploiting global-aware and spatial-aware representations, called Unsupervised Deep Multi-Similarity Hashing with Semantic Structure (UDMSH). Specifically, to obtain distinguishing characteristics, we develop a sub-network by jointly learning global semantic structures from Convolutional Neural Network (CNN) and inherent spatial structures from Fully Convolutional Network (FCN). By computing the cosine distance for deep features from image pairs, we construct a similarity matrix with semantic structure, then utilize this matrix to guide hash code learning process. Based on it, we carefully design a multi-level pairwise loss to preserve the correct fine-grained similarity ranking. Furthermore, we introduce Hamming-isometric mapping into unsupervised hashing framework to decrease the quantization errors. Extensive experiments on three widely used benchmarks prove that our proposed UDMSH outperforms several state-of-the-art unsupervised hashing with respect to different evaluation metrics.

One of the major challenges of learning to hash in large-scale image retrieval is the projective transformation from raw image to binary space with preserving semantic similarity. Recently, several deep hashing methods show many excellent properties compared with traditional hashing based on hand-designed representation. However, most of the existing hashing models only pay attention to the semantic similarity between image pairs, ignoring the ranking information of retrieval results, which limits its performance. In this paper, a novel deep hashing framework, named Deep Top Similarity Hashing with Class-wise loss (DTSH-CW), is proposed to preserve semantic similarity between top images of ranking list and query images. In this proposed framework, CNNs architecture with batch normalization module is adopted to extract deep semantic characteristics. With integrating the position information of images, a top similarity loss is carefully designed to ensure the similarities between top images of ranking list and query images. Unlike pair-wise or triplet-wise loss, by directly leveraging the class labels, a cubic constraint based on Gaussian distribution is introduced to optimize objective function so as to maintain semantic variations of different classes. Furthermore, in order to solve discrete optimization problem, Two-Stage strategy is developed to provide efficient model training. Quantities of comparison experiments on three multi-label benchmark datasets show that our proposed DTSH-CW achieves promising performance compared to several state-of-the-art hashing methods.

In this article, we propose a two-tier communication strategy to facilitate data collection in unmanned aerial vehicle (UAV)-enabled massive Internet of Things (IoT) systems through introducing ground access points (APs) to serve between the UAV and IoT devices. In the first tier of our proposed strategy, all IoT devices transmit their packets to their local APs via a multi-channel ALOHA-based random access scheme, while in the second tier, APs deliver their aggregated data to the UAV through coordinated time division multiple access. Thus, our introduced APs not only liberate the UAV from the potential massive IoT congestion but also facilitate the design of UAV’s trajectory based on the location of APs. To examine the performance of our strategy, we propose a tractable framework to analyze the average system throughput. We reveal that the average two-tier throughput of each AP monotonically increases with its maximum achievable throughput in the second tier, while the increasing slope becomes steeper with a higher traffic load mean in the first tier. Then, we formulate the joint design of UAV’s trajectory and resource allocation as a non-convex optimization problem to maximize the average system throughput while considering the heterogeneous quality of service requirement of each AP. To solve this problem, a low-complexity iterative algorithm is devised based on successive convex approximation. Numerical results demonstrate the substantial average system throughput gain achieved by our proposed strategy and design in the context of massive access, compared to the baseline schemes in the literature.

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on the effective channel and its estimation performance in the delay-Doppler (DD) domain, the total average transmit power, and the effective noise covariance matrix. When the channel state information (CSI) is available at the transceiver, we analyze the minimum squared error (MSE) of data detection and propose an optimal transmitter window to minimize the detection MSE. The proposed optimal transmitter window can be interpreted as a mercury/water-filling power allocation scheme, where the mercury is firstly filled before pouring water to pre-equalize the time-frequency (TF) domain channels. When the CSI is not available at the transmitter but can be estimated at the receiver, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver, which can effectively enhance the sparsity of the effective channel in the DD domain. Thanks to the enhanced DD domain channel sparsity, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in both channel estimation and data detection compared with that of rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation and data detection performance over the conventional rectangular window design.

The key of fine-grained image categorization is to locate discriminative regions and feature extraction from these regions correspond to subtle visual traits. Some of the current methods use the attention mechanism to identify the discriminative region, but ignore that there is still a large amount of non-foreground noise information in these regions. In this work, we propose a Subtler Mixed Attention Network (SMA-Net), which contains two modules: 1) Discriminative region location module uses the channel attention mechanism to construct a feature pyramid network to locate the discriminative regions. And use the positive effect of classification to screen a group of the most discriminative regions and learn through rank to learn. 2) Mixed attention module (MAM) of feature extraction that can focus on subtler and differentiated regions. We divide the feature map into intervals according to regions, and learn attention features according to regional orientation. Then the attention maps are multiplied to the input feature map for adaptive features reinforce. At the same time, MAM is a lightweight module that can be easily integrated into advanced networks without increasing too much calculation. We validated our SMA-Net through substantial experiments on Caltech-UCSD Birds (CUB-200-2011), Stanford Cars, CIFAR-10, Fish4Knowledge and Flower17. In particular, the accuracy on two widely used fine-grained datasets, CUB-2011 and Stanford Cars, reached 87.71% and 94.37%, respectively.

Extractive distillation (ED) can be used to separate mixtures with low relative volatilities that are even close to unity. It is widely adopted for the separation of aromatics and nonaromatics in t...

Fine-grained recognition is a very challenging issue, since it is difficulty to mine discriminative and subtle feature for objects with similar visual appearance. Because massive manual annotations (e.g., bounding box for discriminative regions) are time-consuming and labor-consuming, existing methods designed single form of attention model outputted discriminative regions in a weakly supervised way. In this paper, we proposed a novel method named a Spatial-Channel Aware Attention Filters (SCAF) to address the weakly supervised fine-grained recognition problem. Compared with the previous attention models, SCAF can obtain attentions-aware features from two dimensions, i.e., the spatial location of image and the channel of feature maps. With the proposed SCAF, the model can enhance the discriminative regions on both spatial and channel dimensions simultaneously. In addition, the multi-channel network multi-level structure are designed to extract richer regional features. Moreover, focal loss is introduced to balance the samples’ distribution of fine-grained image dataset. Comprehensive and comparable experiments are conducted in publicly available datasets, and the experimental results show that our method can achieve the state-of-the-art performance on fine-grained recognition tasks. For instance, we achieve 99.370%, 80.749% accuracy on two underwater datasets respectively, i.e., Fish4Knowlege and Wild Fish.

Weather recognition is a significant technique for many potential computer vision applications in our daily lives. Generally, most existing works treat weather recognition as a single-label classification task, which cannot describe the weather conditions comprehensively due to the complex co-occurrence dependencies between different weather conditions. In this paper, we propose a novel Graph Convolution Networks with Attention (GCN-A) model for multi-label weather recognition. To our best knowledge, this is the first attempt to introduce GCN into weather recognition. Specifically, we employ GCN to capture weather co-occurrence dependencies via a directed graph. The graph is built over weather labels, where each node (weather label) is represented by word embeddings of a weather label. Furthermore, we design a re-weighted mechanism to build weather correlation matrix for information propagation among different nodes in GCN. In addition, we develop a channel-wise attention module to extract informative semantic features of weather for effective model training. Compared with the state-of-the-art methods, experiment results on two widely used benchmark datasets demonstrate that our proposed GCN-A model achieves promising performance.

In this study, a new approach using a novel ensemble classification algorithm based on ECG morphological features is proposed for accurate detection of heart ventricular and atrial abnormalities. First, the raw ECG signal is preprocessed and the main character waves are detected. Second, a combination of ECG morphological features is proposed and extracted from the selected ECG segments. The proposed feature set contains morphological parameters, morphological visual pattern of QRS complex, and principle components of the third level and fourth level of a four-level Sym8 wavelet-decomposed ECG waveform. Next, a novel ensemble classification algorithm, with the key idea of integrating the knowledge acquired by several popular classification algorithms for this task into an ensemble system, is proposed so that the accuracy and robustness over various arrhythmia types could be improved. Finally, the features are applied to the proposed ensemble classification algorithm for abnormality detection. The proposed approach achieved an overall accuracy of 98.68% when it was validated on fifteen heartbeat types from the MIT-BIH arrhythmia database (MITDB), according to the Association for Advancement of Medical Instrumentation (AAMI) standard. The classification accuracies of the six main types – normal beat (N), right bundled branch blocks beat (R), left bundled branch blocks beat (L), atrial premature beat (A), premature ventricular contractions beat (V), and paced beat (P) are 98.75%, 99.77%, 99.70%, 94.81%, 98.57%, and 99.94%, respectively. The proposed approach proves a solid result in comparison with component classification algorithms as well as recent peer works.

Person re-identification (Re-ID) has attracted more attention in computer vision tasks recently and achieved high accuracy in some public available datasets in a supervised manner. The performance drops significantly when datasets are unlabeled, which limits the scalability of Re-ID algorithms in practical applications. Despite some unsupervised methods are proposed to address the scalability problem of Re-ID, it’s hard to learn discriminative feature representations due to the lack of pairwise labels in different camera views. To overcome this problem, we propose an end-to-end network named Adaptive Attention-Aware Network for unsupervised person re-identification. Specifically, we propose a novel adaptive attention-aware module that could be easily embedded into Re-ID architecture. The proposed module focuses on learning strong expressive relationship among channels of feature maps, and alleviating the key problems of Re-ID, e.g., occlusion and local deformation. In addition, we extract the camera-invariant features by adopting camera-style transfer feature learning since matching pairs in Re-ID suffers from appearance changes under different camera views. Besides, unsupervised hard negative mining is introduced to learn large intra-person appearance variance and discriminate high inter-person appearance similarity in an unlabeled target dataset with an auxiliary labeled dataset. Comprehensive experiments on three public available Re-ID datasets demonstrate that our method can achieve the state-of-the-art results of unsupervised Re-ID and is competitive with supervised learning.

For multi-label image retrieval based on deep hashing, the ultimate challenge is to map from the original image to binary space while preserving high-level semantic similarity. Recently, many supervised deep hashing approaches for multi-label image retrieval have been proposed to generate high-quality binary codes. However, most such methods are only introduced to learn simple similarity based on these image characteristics, therein ignoring complex multilevel semantic similarity with fine-grained features. In this paper, we propose a framework named deep hashing with fine-grained feature learning (DH-FFL) to preserve complex multilevel semantic similarity between multi-label image pairs. In this proposed model, compact bilinear pooling convolutional neural networks (CNNs) with normalization are adopted to extract fine-grained feature descriptors. In addition, a novel multilevel contrastive loss is designed to preserve multilevel semantic similarity by introducing a zero-loss parameter. Moreover, a multi-label classification loss is used to maintain the unique semantic structure of each image and maximize the distinguishing ability of binary codes. Comprehensive experiments on three benchmark datasets show that the proposed DH-FFL achieves promising performance compared with other state-of-the-art multi-label image retrieval applications.

Magnetically recyclable Zn0.95−xCr0.05MnxAl2O4 nanoparticles were synthesized via hydrothermal and heat treatment techniques. The crystal structure, morphology, and photoelectrochemical and magnetic properties of the samples were characterized by x-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), x-ray energy-dispersive spectroscopy (XEDS), ultraviolet–visible (UV–Vis) spectroscopy, photoluminescence spectra (PL), vibrating-sample magnetometer (VSM), electrochemical impedance spectra (EIS) and transient photocurrent responses, and the photocatalytic activity of the samples were evaluated by the photodegradation of methyl orange (MO) dye under simulated sunlight irradiation. The experimental results show that Zn0.95−xCr0.05MnxAl2O4 nanoparticles possess a cubic spinel structure without impurity phases. Cr and Mn ions are successfully substituted in the lattice site of Zn2+ and enter the ZnAl2O4 matrix. The morphology of the samples exhibits irregular spherical or ellipsoid particles with small particle size and good dispersion. The average crystallite size and the bandgap decrease with an increase in Mn ion concentration. Meanwhile, the co-doped samples exhibit obvious room-temperature ferromagnetism, the photoluminescence intensity decreases and a luminescence quenching phenomenon occurs. In addition, Zn0.95−xCr0.05MnxAl2O4 nanoparticles exhibit excellent photocatalytic activity and possess better photocatalytic reusability in degradation of MO aqueous solution.

In recent years, deep convolutional features have been applied to discriminative correlation filters-based methods, which have achieved impressive performance in tracking. Most of them utilize hierarchical features from a certain layer. However, this is not always sufficient to learn target appearance changes and to suppress the background interference in complicated interfering factors (e.g., deformation, fast motion, low resolution, and rotations). In this paper, we propose an adaptive multi-branch correlation filter tracking method, by constructing multi-branch models and using an adaptive selection strategy to improve the accuracy and robustness of visual tracking. Specially, the multi-branch models are introduced to tolerate temporal changes of the object, which can serve different circumstances. In addition, the adaptive selection strategy incorporates both foreground and background information to learn background suppression. To further improve the tracking performance, we propose a measurement method to handle tracking failures from unreliable samples. Extensive experiments on OTB-2013, OTB-2015, and VOT-2016 datasets show that the proposed tracker has comparable performance compared to state-of-the-art tracking methods. Especially, on the OTB-2015, our method significantly improves the baseline with a gain of 5.5% in overlap precision.

The emerging orthogonal time frequency space (OTFS) modulation technique has shown its superiority to the current orthogonal frequency division multiplexing (OFDM) scheme, in terms of its capabilities of exploiting full time-frequency diversity and coping with channel dynamics. The optimal maximum a posteriori (MAP) detection is capable of eliminating the negative impacts of the inter-symbol interference in the delay-Doppler (DD) domain at the expense of a prohibitively high complexity. To reduce the receiver complexity for OTFS scheme, this paper proposes a variational Bayes (VB) approach as an approximation of the optimal MAP detection. Compared to the widely used message passing algorithm, we prove that the proposed iterative algorithm is guaranteed to converge to the global optimum of the approximated MAP detector regardless the resulting factor graph is loopy or not. Simulation results validate the fast convergence of the proposed VB receiver and also show its promising performance gain compared to the conventional message passing algorithm., Comment: Submitted to IEEE journal

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes., Accepted for publication, IEEE TCOM

With the development of multimedia technology, 3D model has been applied in many fields such as mechanical design, construction industry, entertainment industry, medical treatment and so on. The number of 3D model is becoming more and more in our lives. Therefore, effective automatic management and classification of 3D models become more and more important. In this paper, we propose a dual-meta-learner model based on LSTM to learn the exact optimization algorithm used to train another two learner neural network classifier in the few-shot regime. The parametrization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while it can also achieve a general initialization of the learner (classifier) network that allows for quick convergence of training. Our method attains state-of-the-art performance by significant margins.

LaSr2Mn2O7, a Ruddlesden-Popper type bilayered perovskite, was investigated as oxygen catalyst for oxygen reduction reaction (ORR) and active electrode material for electrochemical capacitors in KOH solutions. XPS results show that Mn takes a mixed Mn2+, Mn3+ and Mn4+ valence state with an average valence of 3.4. As catalyst towards ORR, LaSr2Mn2O7 and LaSr2Mn2O7/acetylene black composite favor four electron pathways for ORR. LaSr2Mn2O7/C composite shows a comparable onset potential for ORR to that of Pt/C electrode. The improved ORR activity of LaSr2Mn2O7/acetylene black composite is attributed to the synergy between catalytic LaSr2Mn2O7 and conductive acetylene black. As electrode material for electrochemical capacitors, LaSr2Mn2O7 exhibits a maximum specific capacitance of 167.2 F/g with 62% faradic contribution at a scan rate of 1 mV/s in cyclic voltammetry test.

Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.

ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types - normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance.

Anomaly detection over HTTP traffic has attracted much attention in recent years, which plays a vital role in many domains. This article proposes an efficient machine learning approach to detect anomalous HTTP traffic that addresses the problems of existing methods, such as data redundancy and high training complexity. This algorithm draws on natural language processing (NLP) technology, uses the Word2vec algorithm to deal with the semantic gap, and implements Term Frequency-Inverse Document Frequency (TF-IDF) weighted mapping of HTTP traffic to construct a low-dimensional paragraph vector representation to reduce training complexity. Then we employs boosting algorithm Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to build an efficient and accurate anomaly detection model. The proposed method is tested on some artificial data sets, such as HTTP DATASET CSIC 2010, UNSW-NB15, and Malicious-URLs. Experimental results reveal that both the boosting algorithms have high detection accuracy, high true positive rate, and low false positive rate. Compared with other anomaly detection methods, the proposed algorithms require relatively short running time and low CPU memory consumption.

Recently, deep neural networks have received intense interests in polarimetric synthetic aperture radar (PolSAR) image classification. However, its success is subject to the availability of large amounts of annotated data which require great efforts of experienced human annotators. Aiming at improving the classification performance with greatly reduced annotation cost, this paper presents an active deep learning approach for minimally supervised PolSAR image classification, which integrates active learning and fine-tuned convolutional neural network (CNN) into a principled framework. Starting from a CNN trained using a very limited number of labeled pixels, we iteratively and actively select the most informative candidates for annotation, and incrementally fine-tune the CNN by incorporating the newly annotated pixels. Moreover, to boost the performance and robustness of the proposed method, we employ Markov random field (MRF) to enforce class label smoothness, and data augmentation technique to enlarge the training set. We conducted extensive experiments on four real benchmark PolSAR images, and experiments demonstrated that our approach achieved state-of-the-art classification results with significantly reduced annotation cost.

In this paper, we propose a transmission control scheme for the approximate message passing (AMP)-based joint user identification and channel estimation in massive connectivity networks. In the proposed transmission control scheme, a transmission control function is designed to determine a user’s transmission probability, when it has a transmission demand. By employing a step transmission control function for the proposed scheme, we derive the channel distribution experienced by the receiver to describe the effect of transmission control on the design of AMP algorithm. Based on that, we modify the AMP algorithm by designing a minimum mean squared error (MMSE) denoiser, to jointly identify the user activity and estimate their channels. We further derive the false alarm and missed detection probabilities to characterize the user identification performance of the proposed scheme. Closed-form expressions of the average packet delay and the network throughput are obtained. Furthermore, we optimize the transmission control function to maximize the network throughput. We demonstrate that the proposed scheme can significantly improve the user identification and channel estimation performance, reduce the average delay, and boost the throughput, compared to the conventional scheme without transmission control.

Wurtzite structure Ni and Co co-doped Zn0.97−xNi0.03CoxS (x = 0.01, 0.03 and 0.05) nanorods were successfully synthesized by a hydrothermal route with ethylenediamine. The effects of Co doping concentration on the crystal microstructure, morphology, optical and magnetic properties were systematically investigated by x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), energy dispersive spectrometry (EDS), UV–visible spectra (UV–VIS), photoluminescence (PL) and a vibrating sample magnetometer (VSM). The morphology of as-prepared samples is one-dimensional wurtzite rod-like with good dispersion. It is demonstrated that the Ni and Co co-doped ZnS nanorods lead to an increased visible light absorption, slightly increased band gap, and decreased PL intensity. Blue shift of the band gap occurs in doped Zn0.97−xNi0.03CoxS nanorods. PL spectra shows an obvious ultraviolet emission peak at 375 nm and a weaker side band at 730 nm, implying the excellent single-phase ZnS. Notably, two major blue emissions band around 497 nm and 576 nm are quenched sharply with the increase of Co doping concentration. The CIE chromaticity diagram results show that the prepared Zn0.97−xNi0.03CoxS samples exhibit excellent light-emitting ability. All samples possess ferromagnetic behavior at room temperature. The saturation magnetization of Zn0.97−xNi0.03CoxS nanorods with an appropriate amount of Co decreases obviously, reaching a minimum at 3% Co.

A series of Co-doped Zn1-xCoxMn2O nanocrystals with a spinel structure were successfully prepared by hydrothermal method, and the influence of Co doping concentration on the microstructure, morphology, elemental composition, and optical and photocatalytic properties of the samples was characterized. The experimental results show that all samples exhibit a tetragonal structure, Co2+ ions are successfully substituted for the lattice site of Zn2+ to generate ZnMn2O4 nanocrystals, and the crystalline size decreases as Co-doped concentration increase. The morphologies are loose hollow microsphere structures. The band gap of Zn1−xCoxMn2O samples is smaller than that of pure ZnMn2O4 and has been red shifted. The photocatalytic activity of doped Zn1−xCoxMn2O4 samples is obviously higher than that of pure ZnMn2O4 samples for the photodegradation of MO under visible light irradiation. All these results demonstrate that Co-doped spinel ZnMn2O4 nanocrystals are a meaningful choice for photocatalytic degradation of the pollutants.

In this paper, the MnO2/three-dimensional carbon nanotubes-graphene nanocomposite was synthesized on Cu foil substrate by chemical vapor deposition and subsequent thermal decomposition of manganese acetyllacetonate precursors. The nanocomposite material was directly used as a binder-free electrode to investigate the supercapacitive performance. The results indicate that the appropriate defects and MnO2 nanoparticles are introduced simultaneously into the carbon nanotubes-graphene architecture in one-step. The specific capacitance of MnO2/three-dimensional carbon nanotubes-graphene/Cu electrode is up to 365 F g−1 at a current density of 1 A g−1 in 1.0 M Na2SO4 solution, which is 9 times higher than that of electrode without MnO2 modification. After 1000 cycles at 1 A g−1, the capacity retention is still maintained at 97.0%, suggesting potential applications in high-performance energy storage devices. The excellent electrochemical performances of nanocomposite electrode can be attributed to the significantly synergistic effects between the lasting electronic conductivity of three-dimensional carbon nanotubes-graphene/Cu with the appropriate defects and the pseudocapacitance of the MnO2 nanoparticles.

Oxygen reduction reaction (ORR) plays a critical role in many energy conversion and storage processes. Therein, a comparative study of the electrocatalytic activity for ORR in 0.1 mol/L KOH solution was conducted using layered perovskite-like LaSr3Fe3O10 and LaSr3Fe3O10-graphene oxide (GO) composite as electrodes. Linear sweep voltammetry (LSV) results show that the LaSr3Fe3O10-GO hybrid exhibits higher current density, a more positive onset potential (−0.15 V vs. Hg/HgO) in comparison with LaSr3Fe3O10. The value of the overall transferred electrons for both catalysts implies a dominant two electron process for ORR. Both catalysts under alkalic conditions exhibit a two-step Tafel slope, suggesting a change in the reaction mechanism for ORR. The composite electrode exhibits a higher ORR current density, but inferior durability performances in relative to the LaSr3Fe3O10 electrode.

With the continuous growth of air traffic flow, some airports in China or other countries begin to construct Closely Spaced Parallel Runways (CSPR) to meet the capacity requirement of civil aviation. In this paper, the simulation and calculation method of flight trajectory is developed, as well as the collision risk calculation method and wake vortex encounter risk calculation method. New methods for departure and go-around procedures are proposed to achieve approximate segregated parallel operation in an attempt to obtain approximately the same results on closely spaced parallel runways as on widely spaced parallel runways. By comparing with the independent parallel departure and segregated parallel operation in ICAO Doc. 9643, it is found that the lateral separation between aircraft in this proposal is not less than the separations in the other two cases. Based on the simulation calculation of flight trajectories under different conditions, the probabilities of collision conflict and encountering wake vortex are lower than those in current operation plan. The proposed plan has no special requirements in pilot operation, control command procedures, airport facilities, or meteorological observation and prediction, so it is convenient to promote and implement at the airports with closely spaced parallel runways. Keywords: Air traffic control, Air transportation, Closely spaced parallel runways, Segregated parallel operation, Wake vortex encounter, Wake vortex separation standards

Person re-identification plays a critical part in many surveillance applications. Due to complicated illumination environments and various viewpoints, it is still a challenging problem to extract robust features. To solve this issue, we propose a novel deep feature ranking scheme. Our main contribution is to rank achieved deep features, which are obtained by classic deep learning model, and set the sort order number as our feature vector, named as ordinal deep features (ODFs). Person re-identification results are acquired by ranking person candidates by measuring distance based on ODFs. Since applying for rank orders rather than original feature values, our method achieves robust results, especially under the situation of viewpoints shift. Comprehensive experiments are carried out to demonstrate the significance of the proposed feature. Meanwhile, comparative experiments are applied over the publicly available dataset, our method achieves promising performance and outperforms the state of the art methods. Moreover, we applied the proposed feature in the scenario of image classification and discussed the effectiveness.

Pure ZnS and Ni2+-doped ZnS nanorods (Zn1-xNixS, x=0, 0.01, 0.03, 0.05 and 0.07, mole fraction, %) were synthesized by hydrothermal method. The effects of Ni2+ doping on the phase-structure, morphology, elemental composition and optical properties of the samples were investigated by X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), X-ray energy dispersive spectrometry (EDS) and ultraviolet-visible spectroscopy (UV-Vis), respectively. The photocatalytic activity of Zn1-xNixS nanorods was evaluated by the photodegradation of organic dyes Rhodamine B (RhB) in aqueous solution under UV light irradiation. The results show that all samples exhibit wurtzite structure with good crystallization. The morphologies are one-dimensional nanorods with good dispersion, and the distortion of the lattice constant occurs. The band gap of Zn1-xNixS samples is smaller than that of pure ZnS, thus red shift occurs. Ni2+-doped ZnS nanocrystals can enhance photocatalytic activities for the photodegradation of RhB. Especially, Zn0.97Ni0.03S sample exhibits better photocatalytic performance and photocatalytic stability for the decomposition of RhB.

The fruit of Xanthium strumarium L. (Xanthii Fructus), a traditional Chinese medicine commonly used to treat rhinitis, need to be processed to reduce their toxicity before clinical use. The chemical components and colour features of Xanthii Fructus change during processing, and thus these changes could be used to evaluate the processing degree and quality of Xanthii Fructus. In the present study, high performance liquid chromatography equipped with a diode array detector (HPLC-DAD) was used to quantify the chemical components in Xanthii Fructus. A computer vision system (CVS) was also established to determine three kinds of colour spaces, including red-green-blue (RGB), L*a*b*, and HSV (hue, saturation and value), and the colour values were used to predict the chemical component content using machine learning. A support vector machine (SVM) and an artificial neural network (ANN) were employed to establish regression models between the colour values and the chemical composition contents. The different colour values were chosen as inputs of the model, and the contents of each chemical component were considered as outputs, respectively. The experimental results showed that a CVS combined with an SVM could be a cost-efficient, easy-to-build, and rapid detection system for on-line monitoring of Xanthii Fructus processing compared with those obtained using an ANN.

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data. This paper focuses on top- $k$ frequent columns publication on sensitive data, with high result utility under differential privacy. Existing works directly select frequent columns from all columns (called one-phase scheme), which is far from ideal due to the large privacy consumption or misjudgments for columns with frequencies close to the frequency of the $k$ th frequent column (called near- $k$ -fluctuation-column). This paper presents a new solution Two Phase Selection (TPS) to carefully choose frequent columns in two phases. The main idea is to classify columns into two distinct categories based on whether it is one near- $k$ -fluctuation-column or not. Frequent columns are chosen from the two categories using different techniques, which is totally different from existing solutions without classifying. Furthermore, by analyzing the distribution of near- $k$ -fluctuation-columns, we introduce a block-centric column-choosing method privacy-free-mechanism (PFM). By partitioning columns into blocks, PFM makes the privacy consumption proportional to the number of blocks, instead of frequent columns. Extensive experiments on real datasets show that our proposals outperform the state-of-the-art techniques for top- $k$ column publication.

The Sn1-xCrxS2 (x = 0, 0.01, 0.03, 0.05 and 0.07) nanoflowers with different molar ratios were prepared via hydrothermal at a low temperature of 180 °C. All samples were characterized by XRD, FT-IR, SEM, EDS, TEM, XPS, UV–vis and PL. The Sn1-xCrxS2 nanoflowers are single-phase wurtzite structure with good crystallinity, which is confirmed by the XRD, and FT-IR results. XPS and EDS results demonstrate that Cr is present in the valence of Cr3+ ions. Based on the SEM and TEM analysis, the size of doped samples is nearly flower-like ~1.5 µm. The band gap of Sn0.95Cr0.05S2 (2.24 eV) is the largest in the doped samples, which displays the most similar to that of the pure SnS2 nanoflowers (2.27 eV). The photocatalytic activity of the Cr doped SnS2 nanoflowers is evaluated towards the decolorization of rhodamine B under visible irradiation with good cyclability and stability. Moreover, the main active species are • OH and h+. Accordingly, Cr doped SnS2 displays super photocatalytic performance in dyes decomposition showing the lowest intensity in the PL spectra with doped optimum 5% Cr concentration.

Non-orthogonal multiple access (NOMA) has been widely recognized as a promising multiple access scheme for realizing next generation wireless communications. Unlike existing NOMA schemes assuming perfectly time synchronized user’s signals received at the base station (BS), in this paper, we investigate the achievable rates of uplink NOMA with asynchronized transmission. By invoking Szego’s Theorem, we derive both the upper- and lower-bounds of the achievable rates of asynchronized NOMA (aNOMA) systems. In particular, we reveal that the derived lower-bound is essentially the achievable rate for conventional synchronized NOMA systems, which indicates that the asynchronization is not necessarily a foe. More specifically, we show that aNOMA systems are superior to conventional NOMA systems in terms of the achievable rates with non-sinc shaping pulses. Important insights are also unveiled based on the derived bounds. Simulation results confirm the validity of our derived analysis and demonstrate considerable achievable rates gains of aNOMA systems over conventional NOMA systems.

In this paper, we develop a novel downlink transmission scheme for vehicular networks based on integrated sensing and communication (ISAC) signals and orthogonal time frequency space (OTFS) modulation. Based on the ISAC-OTFS signals, the roadside units (RSUs) are capable of estimating various motion-related parameters, such as the locations and speeds of vehicles based on the reflected echoes, which are further exploited for predicting the delay-Doppler domain downlink channel parameters. Consequently, the RSU can create effective transmit beamformers which can compensate the channel path loss and Doppler shift before transmitting the ISAC signal while the vehicles can bypass the channel estimation for downlink transmission. Through numerical results, we verify the effectiveness of the proposed scheme.

This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals. We study the joint design of the beamformer at an access point (AP) and the phase shifts and the power harvesting schedule at an IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the capability of IRS elements to harvest wireless power for realizing self-sustainability. Subsequently, we propose a computationally-efficient alternating algorithm to obtain a suboptimal solution to the design problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and self-sustainability in IRS-assisted systems; 2) the performance gain achieved by the proposed scheme is improved with an increasing number of IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sum-rate of an ideal case with continuous phase shifts., submitted to IEEE Global Commun. Conf. (GLOBECOM), Taiwan, Dec. 2020

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI., Accepted by IEEE Globecom 2020; Journal version "Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications" has been accepted by IEEE TWC. arXiv admin note: substantial text overlap with arXiv:2009.05231

In this paper, we propose the use of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)- based orthogonal frequency division multiple access (OFDMA) communication systems. The proposed scheme exploits both the rich beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV’s trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sum-rate. The existence of an IRS introduces both frequencyselectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channel gain and propose a parametric approximation approach to establish a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem. Simulation results unveil the promising sum-rate gain achieved by the deployment of an IRS in UAVbased communication systems.

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.

Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications. However, the movement of UAVs impose challenge for accurate beam alignment between the UAV and the ground user equipment (UE). In this letter, we propose a deep learning-based location-aware predictive beamforming scheme to track the beam for UAV communications in a dynamic scenario. Specifically, a long short-term memory (LSTM)-based recurrent neural network (LRNet) is designed for UAV location prediction. Based on the predicted location, a predicted angle between the UAV and the UE can be determined for effective and fast beam alignment in the next time slot, which enables reliable communications between the UAV and the UE. Simulation results demonstrate that the proposed scheme can achieve a satisfactory UAV-to-UE communication rate, which is close to the upper bound of communication rate obtained by the perfect genie-aided alignment scheme., 4 pages, 4 figures, submitted to IEEE Wireless Communications Letters

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI., Accepted by IEEE Transactions on Wireless Communications

With traditional embedded memories, such as eFlash and SRAM, facing major challenge of scaling beyond 28 nm, STT-MRAM stands out from competing emerging NVM technologies as the preferred technology for both embedded flash and SRAM replacements. Having high data retention, reflow compatibility and high endurance, MRAM is ready for production to replace eFlash at 28 nm node and below. On the other end of the spectrum, by tuning magnetic properties of magnetic tunnel junction storage layers, MRAM can achieve high speed/endurance, and offer higher density compared with embedded SRAM at advance nodes.

In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system., Accepted by IEEE Transactions on Communications, 18 pages, 12 figures

With the semi-conductor technology gradually approaching its physical and heat limits, recent supercomputers have adopted major architectural changes to continue increasing the performance through more power-efficient heterogeneous many-core systems. Examples include Sunway TaihuLight that has four Management Processing Element (MPE) and 256 Computing Processing Element (CPE) inside one processor and Summit that has two central processing units (CPUs) and 6 graphics processing units (GPUs) inside one node. Meanwhile, current high-resolution Earth system models that desperately require more computing power, generally consist of millions of lines of legacy codes developed for traditional homogeneous multi-core processors and cannot automatically benefit from the advancement of supercomputer hardware. As a result, refactoring and optimizing the legacy models for new architectures become a key challenge along the road of taking advantage of greener and faster supercomputers, providing better support for the global climate research community and contributing to the long-lasting society task of addressing long-term climate change. This article reports the efforts of a large group in the International Laboratory for High-Resolution Earth System Prediction (iHESP) established by the cooperation of Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM), Texas A & M University and the National Center for Atmospheric Research (NCAR), with the goal of enabling highly efficient simulations of the high-resolution (25-km atmosphere and 10-km ocean) Community Earth System Model (CESM-HR) on Sunway TaihuLight. The refactoring and optimizing efforts have improved the simulation speed of CESM-HR from 1 SYPD (simulation years per day) to 3.4 SYPD (with output disabled), and supported several hundred years of pre-industrial control simulations. With further strategies on deeper refactoring and optimizing for a few remaining computing hot spots, we expect an equivalent or even better efficiency than homogeneous CPU platforms. The refactoring and optimizing processes detailed in this paper on the Sunway system should have implications to similar efforts on other heterogeneous many-core systems such as GPU-based high-performance computing (HPC) systems.

In this paper, we consider the application of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)-based orthogonal frequency division multiple access (OFDMA) communication systems, which exploits both the significant beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV's trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sum-rate while taking into account the heterogeneous quality-of-service (QoS) requirement of each user. The existence of an IRS introduces both frequency-selectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channels and propose a parametric approximation approach to establish an upper and a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem and its performance is compared with the benchmark performance achieved by solving the upper bound problem. Simulation results unveil the small gap between the developed bounds and the promising sum-rate gain achieved by the deployment of an IRS in UAV-based communication systems., Comment: 32 pages, 12 figures, submitted to IEEE Transactions on Wireless Communications

Spinel structure Co-doped $$\hbox {ZnMn}_{{2}}\hbox {O}_{{4}}$$ nanocrystals were successfully synthesized by a hydrothermal method. The effects of Co-doping concentration on the structure and electrochemical properties of the samples were investigated. The experimental results manifest that all samples exhibit a single-phase with a tetragonal structure, and morphologies are regular hollow microspheres. Cyclic voltammetry curves for all samples are similar to a rectangular shape with symmetric nature and no obvious redox peak. Galvanostatic charge–discharge curves were triangular and symmetric. Impedance spectra revealed that $$\hbox {Zn}_{1-x}\hbox {Co}_{{x}}\hbox {Mn}_{{2}}\hbox {O}_{{4}}$$ possess low resistance. Better electrochemical properties of the $$\hbox {ZnMn}_{{2}}\hbox {O}_{{4}}$$ electrode could be obtained when the Co-doping ratio is 0.3. $$\hbox {Zn}_{0.7}\hbox {Co}_{0.3}\hbox {Mn}_{{2}}\hbox {O}_{{4}}$$ exhibits much higher specific capacitance ( $$306\hbox { F g}^{-1}$$ ) at a scan rate of $$5\hbox { mV s}^{-1}$$ , and shows excellent cycling stability and retains 98.2% of its initial capacitance after 1000 cycles. The enhanced capacitive performance in this work can be attributed to the incorporation of Co ions doped into the $$\hbox {ZnMn}_{{2}}\hbox {O}_{{4}}$$ host lattice.

In this paper, we propose a deep learning aided list approximate message passing (AMP) algorithm to further improve the user identification performance in massive machine type communications. A neural network is employed to identify a suspicious device which is most likely to be falsely alarmed during the first round of the AMP algorithm. The neural network returns the false alarm likelihood and it is expected to learn the unknown features of the false alarm event and the implicit correlation structure in the quantized pilot matrix. Then, via employing the idea of list decoding in the field of error control coding, we propose to enforce the suspicious device to be inactive in every iteration of the AMP algorithm in the second round. The proposed scheme can effectively combat the interference caused by the suspicious device and thus improve the user identification performance. Simulations demonstrate that the proposed algorithm improves the mean squared error performance of recovering the sparse unknown signals in comparison to the conventional AMP algorithm with the minimum mean squared error denoiser., Comment: 6 pages, 4 figures, accepted by IEEE GLOBECOM 2019