Showing papers by "Dalian University of Technology published in 2019"
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TL;DR: This paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain, and develops a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction.
Abstract: Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity. To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via $1D$ convolution. Furthermore, we develop a method to adaptively select kernel size of $1D$ convolution, determining coverage of local cross-channel interaction. The proposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts.
1,048 citations
23 Jan 2019
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Abstract: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).
639 citations
TL;DR: In molecular-catalysis-based AP, much has been attained, but more challenges remain with regard to long-term stability and heterogenization techniques, and an analysis of the advantages, challenges, and stability of molecular catalysts is provided.
Abstract: Molecular catalysis plays an essential role in both natural and artificial photosynthesis (AP). However, the field of molecular catalysis for AP has gradually declined in recent years because of doubt about the long-term stability of molecular-catalyst-based devices. This review summarizes the development history of molecular-catalyst-based AP, including the fundamentals of AP, molecular catalysts for water oxidation, proton reduction and CO2 reduction, and molecular-catalyst-based AP devices, and it provides an analysis of the advantages, challenges, and stability of molecular catalysts. With this review, we aim to highlight the following points: (i) an investigation on molecular catalysis is one of the most promising ways to obtain atom-efficient catalysts with outstanding intrinsic activities; (ii) effective heterogenization of molecular catalysts is currently the primary challenge for the application of molecular catalysis in AP devices; (iii) development of molecular catalysts is a promising way to solve the problems of catalysis involved in practical solar fuel production. In molecular-catalysis-based AP, much has been attained, but more challenges remain with regard to long-term stability and heterogenization techniques.
512 citations
TL;DR: In this article, the shape stability of phase change materials is investigated in thermal management and energy storage systems, and the critical issues in different shape-stabilization strategies and the possible rectifications are discussed.
500 citations
TL;DR: Three strategies for structural engineering of CDs are presented and analyzed, in terms of the tuning of size and crystallinity, and the methodologies for surface modification and heteroatom doping, with a focus on the relationship among the synthesis methods, structure and properties of the concerned CDs.
Abstract: The emergence of carbon dots (CDs) has opened up an exciting new field in the science and technology of carbon nanomaterials and has attracted increasing interest in recent years. Due to their diverse physicochemical properties and favourable attributes, such as quantum confinement effects and abundant surface defects, CDs and their derived hybrids have shown exciting and indispensable prospects in the energy conversion and storage fields. Considering the latest developments, in this review, we comprehensively summarize the classification and structure of CDs. Three strategies for structural engineering of CDs are presented and analyzed, in terms of the tuning of size and crystallinity, and the methodologies for surface modification and heteroatom doping, with a focus on the relationship among the synthesis methods, structure and properties of the concerned CDs. More importantly, the recent advances in energy-oriented applications of CDs, including photo- and electro-catalysis, light-emitting diodes, photovoltaic cells, lithium/sodium ion batteries and supercapacitors, will be systematically highlighted. Finally, we discuss and outline the remaining major challenges and opportunities for CDs in the future.
476 citations
Yale University1, Rice University2, Engineer Research and Development Center3, Clemson University4, Pohang University of Science and Technology5, Georgia Institute of Technology6, University of Cincinnati7, Loughborough University8, Dalian University of Technology9, University of California, Berkeley10, University of New South Wales11
TL;DR: A critical examination of the trajectory of photocatalytic water treatment research is undertaken, assessing the viability of proposed applications and identifying those with the most promising future.
Abstract: Advanced oxidation processes via semiconductor photocatalysis for water treatment have been the subject of extensive research over the past three decades, producing many scientific reports focused on elucidating mechanisms and enhancing kinetics for the treatment of contaminants in water. Many of these reports imply that the ultimate goal of the research is to apply photocatalysis in municipal water treatment operations. However, this ignores immense technology transfer problems, perpetuating a widening gap between academic advocation and industrial application. In this Feature, we undertake a critical examination of the trajectory of photocatalytic water treatment research, assessing the viability of proposed applications and identifying those with the most promising future. Several strategies are proposed for scientists and engineers who aim to support research efforts to bring industrially relevant photocatalytic water treatment processes to fruition. Although the reassessed potential may not live up to initial academic hype, an unfavorable assessment in some areas does not preclude the transfer of photocatalysis for water treatment to other niche applications as the technology retains substantive and unique benefits.
460 citations
TL;DR: A unified framework for a UAV-assisted emergency network is established in disasters by jointly optimized to provide wireless service to ground devices with surviving BSs and multihop UAV relaying to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs.
Abstract: Reliable and flexible emergency communication is a key challenge for search and rescue in the event of disasters, especially for the case when base stations are no longer functioning. Unmanned aerial vehicle (UAV)-assisted networking is emerging as a promising method to establish emergency networks. In this article, a unified framework for a UAV-assisted emergency network is established in disasters. First, the trajectory and scheduling of UAVs are jointly optimized to provide wireless service to ground devices with surviving BSs. Then the transceiver design of UAV and establishment of multihop ground device-to-device communication are studied to extend the wireless coverage of UAV. In addition, multihop UAV relaying is added to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs. Simulation results are presented to show the effectiveness of these three schemes. Finally, open research issues and challenges are discussed.
447 citations
01 Oct 2019
TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Abstract: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.
393 citations
15 Jun 2019
TL;DR: The Attentive Feedback Modules (AFMs) are designed to better explore the structure of objects and produce satisfying results on the object boundaries and achieves state-of-the-art performance on five widely tested salient object detection benchmarks.
Abstract: Recent deep learning based salient object detection methods achieve gratifying performance built upon Fully Convolutional Neural Networks (FCNs). However, most of them have suffered from the boundary challenge. The state-of-the-art methods employ feature aggregation tech- nique and can precisely find out wherein the salient object, but they often fail to segment out the entire object with fine boundaries, especially those raised narrow stripes. So there is still a large room for improvement over the FCN based models. In this paper, we design the Attentive Feedback Modules (AFMs) to better explore the structure of objects. A Boundary-Enhanced Loss (BEL) is further employed for learning exquisite boundaries. Our proposed deep model produces satisfying results on the object boundaries and achieves state-of-the-art performance on five widely tested salient object detection benchmarks. The network is in a fully convolutional fashion running at a speed of 26 FPS and does not need any post-processing.
390 citations
01 Jun 2019
TL;DR: A semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images is proposed, and a novel SPatial Attentive Network (SPANet) is proposed to remove rain streaks in a local-to-global manner.
Abstract: Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of ∼29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes. Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.
387 citations
TL;DR: This paper introduces pose-invariant embedding (PIE) as a pedestrian descriptor and shows that PoseBox alone yields decent re-ID accuracy and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with state-of-the-art approaches.
Abstract: Pedestrian misalignment, which mainly arises from detector errors and pose variations, is a critical problem for a robust person re-identification (re-ID) system. With poor alignment, the feature learning and matching process might be largely compromised. To address this problem, this paper introduces pose-invariant embedding (PIE) as a pedestrian descriptor. First, in order to align pedestrians to a standard pose, the PoseBox structure is introduced, which is generated through pose estimation followed by affine transformations. Second, to reduce the impact of pose estimation errors and information loss during the PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input. The proposed PIE descriptor is thus defined as the fully connected layer of the PBF network for the retrieval task. Experiments are conducted on the Market-1501, CUHK03-NP, and DukeMTMC-reID datasets. We show that PoseBox alone yields decent re-ID accuracy and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with state-of-the-art approaches.
TL;DR: An iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically and results demonstrate that the algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.
Abstract: With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.
01 Oct 2019
TL;DR: This work proposes a novel depth-induced multi-scale recurrent attention network for saliency detection that achieves dramatic performance especially in complex scenarios and boosts its performance by a novel recurrent attention module inspired by Internal Generative Mechanism of human brain.
Abstract: In this work, we propose a novel depth-induced multi-scale recurrent attention network for saliency detection. It achieves dramatic performance especially in complex scenarios. There are three main contributions of our network that are experimentally demonstrated to have significant practical merits. First, we design an effective depth refinement block using residual connections to fully extract and fuse multi-level paired complementary cues from RGB and depth streams. Second, depth cues with abundant spatial information are innovatively combined with multi-scale context features for accurately locating salient objects. Third, we boost our model's performance by a novel recurrent attention module inspired by Internal Generative Mechanism of human brain. This module can generate more accurate saliency results via comprehensively learning the internal semantic relation of the fused feature and progressively optimizing local details with memory-oriented scene understanding. In addition, we create a large scale RGB-D dataset containing more complex scenarios, which can contribute to comprehensively evaluating saliency models. Extensive experiments on six public datasets and ours demonstrate that our method can accurately identify salient objects and achieve consistently superior performance over 16 state-of-the-art RGB and RGB-D approaches.
TL;DR: In this paper, a comprehensive review of published articles addressing passive enhancement of pool boiling using surface modification techniques is provided, including macroscale, microscale, and nanoscale surfaces, as well as multiscale (hybrid-scale), and hybrid-wettability techniques.
15 Jun 2019
TL;DR: A novel adaptive spatially-regularized correlation filters model to simultaneously optimize the filter coefficients and the spatial regularization weight is proposed, which could learn an effective spatial weight for a specific object and its appearance variations, and therefore result in more reliable filter coefficients during the tracking process.
Abstract: In this work, we propose a novel adaptive spatially-regularized correlation filters (ASRCF) model to simultaneously optimize the filter coefficients and the spatial regularization weight. First, this adaptive spatial regularization scheme could learn an effective spatial weight for a specific object and its appearance variations, and therefore result in more reliable filter coefficients during the tracking process. Second, our ASRCF model can be effectively optimized based on the alternating direction method of multipliers, where each subproblem has the closed-from solution. Third, our tracker applies two kinds of CF models to estimate the location and scale respectively. The location CF model exploits ensembles of shallow and deep features to determine the optimal position accurately. The scale CF model works on multi-scale shallow features to estimate the optimal scale efficiently. Extensive experiments on five recent benchmarks show that our tracker performs favorably against many state-of-the-art algorithms, with real-time performance of 28fps.
TL;DR: This work presents BioWordVec: an open set of biomedical word vectors/embeddings that combines subword information from unlabeled biomedical text with a widely-used biomedical controlled vocabulary called Medical Subject Headings (MeSH).
Abstract: Distributed word representations have become an essential foundation for biomedical natural language processing (BioNLP), text mining and information retrieval. Word embeddings are traditionally computed at the word level from a large corpus of unlabeled text, ignoring the information present in the internal structure of words or any information available in domain specific structured resources such as ontologies. However, such information holds potentials for greatly improving the quality of the word representation, as suggested in some recent studies in the general domain. Here we present BioWordVec: an open set of biomedical word vectors/embeddings that combines subword information from unlabeled biomedical text with a widely-used biomedical controlled vocabulary called Medical Subject Headings (MeSH). We assess both the validity and utility of our generated word embeddings over multiple NLP tasks in the biomedical domain. Our benchmarking results demonstrate that our word embeddings can result in significantly improved performance over the previous state of the art in those challenging tasks. Machine-accessible metadata file describing the reported data (ISA-Tab format)
TL;DR: This article constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles and formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes.
Abstract: Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted.
TL;DR: The development of CDs in nanomedicine is reviewed from their use as original imaging agents and/or drug carriers to multifunctional theranostic systems.
Abstract: Carbon dots (CDs), a kind of carbon material discovered accidentally, exhibit unexpected advantages in fluorescence imaging/sensing such as photostability, biocompatibility, and low toxicity. For emerging theranostics, an interdiscipline created by integrating therapy and diagnostics, CDs are good candidates when they are combined with targeted chemo/gene/photodynamic/photothermal therapeutic moieties. Here, the development of CDs in nanomedicine is reviewed from their use as original imaging agents and/or drug carriers to multifunctional theranostic systems. Finally, the challenges and prospects of the next-generation of CD-based theranostics for clinical applications are also discussed.
TL;DR: In this article, a layered B/N co-doped porous carbon (LDC) guided by the intercalator is proposed for the first time as cathode material for high-energy-power ZHSs to efficiently mitigate these issues.
TL;DR: In this article, the authors recognized that electrochemical water splitting is a practical strategy for impelling the transformation of sustainable energy sources such as solar energy from electricity to clean hydrogen fuel.
Abstract: Electrochemical water splitting is recognized as a practical strategy for impelling the transformation of sustainable energy sources such as solar energy from electricity to clean hydrogen fuel. To ...
TL;DR: This paper proposes a parallel diffusion method that ensures the parallelism of diffusion to the utmost extent and achieves a qualitative improvement in efficiency over traditional streaming diffusion methods.
TL;DR: In this paper, the authors analyzed the impact of environmental regulation policies on green productivity growth in OECD countries' industrial sectors using an extended SBM-DDF approach and found that the impact turns to be adverse when the environmental regulation policy is stringent over a certain level.
TL;DR: In this paper, an artificial nano-composite with excellent comprehensive performance by controlling the orientation of one-dimensional (1D) 0.5Ba(Zr0.7Ca0.2Ti0.3)TiO3 nanofibers (BZCT NFs) and adjusting the interaction between BZCTNFs and poly(vinylidene fluoride) (PVDF) matrix via SiO2 buffer layer was proposed.
TL;DR: In this paper, the catalytic oxidation of toluene to CO2 and H2O over nanoflower spinel CoMn2O4 synthesized by the oxalic acid sol-gel method has been investigated.
Abstract: The catalytic oxidation of toluene to CO2 and H2O over nanoflower spinel CoMn2O4 synthesized by the oxalic acid sol–gel method has been investigated, and it demonstrates lower activation energy (35...
TL;DR: In this paper, two triazatruxene (TAT)-based sensitizers, with one containing a flexible Z-type double bond and another a rigid single bond, coded as ZL001 and ZL003, respectively, have been synthesized and applied in DSSCs to probe the energy losses in the process of electron injection.
Abstract: The electron-injection energy losses of dye-sensitized solar cells (DSSCs) are among the fundamental problems hindering their successful breakthrough application. Two triazatruxene (TAT)-based sensitizers, with one containing a flexible Z-type double bond and another a rigid single bond, coded as ZL001 and ZL003, respectively, have been synthesized and applied in DSSCs to probe the energy losses in the process of electron injection. Using time-resolved laser spectroscopic techniques in the kinetic study, ZL003 with the rigid single bond promotes much faster electron injection into the conductive band of TiO2 especially in the locally excited state (hot injection), which leads to higher electron density in TiO2 and a higher Voc. The devices based on ZL003 exhibited a champion power conversion efficiency (PCE) of 13.6% with Voc = 956 mV, Jsc = 20.73 mA cm–2, and FF = 68.5%, which are among the highest recorded results to date on single dye-sensitized DSSCs. An independent certified PCE of 12.4% has been obt...
TL;DR: Wang et al. as discussed by the authors proposed a novel and efficient sunlight-driven phase change materials (PCMs) based on polyethylene glycol (PEG) supported by Ag nanoparticle-functionalized graphene nanosheets (Ag-GNS).
TL;DR: A deep attention-based spatially recursive model that can learn to attend to critical object parts and encode them into spatially expressive representations and is end-to-end trainable to serve as the part detector and feature extractor.
Abstract: Fine-grained visual recognition is an important problem in pattern recognition applications. However, it is a challenging task due to the subtle interclass difference and large intraclass variation. Recent visual attention models are able to automatically locate critical object parts and represent them against appearance variations. However, without consideration of spatial dependencies in discriminative feature learning, these methods are underperformed in classifying fine-grained objects. In this paper, we present a deep attention-based spatially recursive model that can learn to attend to critical object parts and encode them into spatially expressive representations. Our network is technically premised on bilinear pooling, enabling local pairwise feature interactions between outputs from two different convolutional neural networks (CNNs) that correspond to distinct region detection and relevant feature extraction. Then, spatial long-short term memory (LSTMs) units are introduced to generate spatially meaningful hidden representations via the long-range dependency on all features in two dimensions. The attention model is leveraged between bilinear outcomes and spatial LSTMs for dynamic selection on varied inputs. Our model, which is composed of two-stream CNN layers, bilinear pooling, and spatial recursive encoding with attention, is end-to-end trainable to serve as the part detector and feature extractor whereby relevant features are localized, extracted, and encoded spatially for recognition purpose. We demonstrate the superiority of our method over two typical fine-grained recognition tasks: fine-grained image classification and person re-identification.
TL;DR: An overview of the latest development of hydrothermal carbonization in the field of sewage sludge treatment can be found in this paper, where the authors identify the current challenges and knowledge gaps.
Abstract: Hydrothermal carbonization is an important thermochemical conversion process that can be used as an energy-efficient alternative to enhance the dewaterability of sewage sludge and meanwhile to convert sewage sludge into high value-added products, such as clean biofuel, organic fertilizer and precursors of functional materials. This paper presents an overview of the latest development of hydrothermal carbonization in the field of sewage sludge treatment, with a particular focus on critical hydrothermal parameters, physicochemical characteristics of products streams, current understanding on hydrochar formation mechanisms, sewage sludge dewaterability improvement and techno-economic advantages. Recent advances have shown that hydrothermal carbonization of sewage sludge is an exothermal process, which is governed by temperature to a large extent. Both polymerizations of highly reactive intermediates derived from degradation of biopolymers in sewage sludge and solid-solid conversion of their undissolved fractions are regarded as the major mechanisms of hydrochar formation. The high ash content of hydrochar is probably the limiting factor for its potential applications in energy and functional materials. The chemistry in hydrothermal carbonization of sewage sludge, closely related to the process parameters and the chemical composition of sewage sludge, offers huge potential to influence the products distribution and characteristics and the process energetics as desired, which provides a promising opportunity to construct a high-efficiency industrial chain for energy and resources recovery from sewage sludge by a controlled hydrothermal process. This review identifies the current challenges and knowledge gaps, and provides new perspectives for future research efforts targeting at sustainable treatment of sewage sludge by hydrothermal carbonization.
TL;DR: Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed, and a joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution.
Abstract: The shortage of spectrum resources has limited the development of Internet of Things (IoT). Fifth generation (5G) network can flexibly support a variety of devices and services, which makes it possible to combine 5G with IoT. In this paper, a novel multichannel IoT is proposed to dynamically share the spectrum with 5G communication, where an IoT node including transmitter and receiver is designed to perform 5G communication and IoT communication simultaneously. The subchannel sets allocated for 5G communication and IoT communication are defined by two complementary spectrum marker vectors, respectively. Two independent spectrum sequences are generated by calculating the inner products of spectrum marker vectors, presudo-random phases and power scaling vectors. Two time-domain fundamental modulation waveforms generated by the inverse fast Fourier transform of the spectrum sequences are used to modulate 5G data and IoT data, respectively. The receiver can detect the data using the same spectrum marker vectors as the transmitter. The BER performances of the system using binary modulation and cyclic code shift keying modulation in the cases of spectrum marker error and multiple access are analyzed, respectively. A subchannel and power optimization unit is formulated as a joint optimization problem, which seeks to maximize the 5G throughput under the constraints of minimal IoT throughput, maximal power, and maximal interference. An alternative optimization problem is proposed to maximize the IoT throughput while guaranteeing the minimal 5G throughput. A joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution. Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed.