Accurate recognition of early-stage pneumoconiosis presents significant challenges due to the irregular morphology, diffuse distribution, and small size of pulmonary lesions. Existing 2D methods struggle to focus on lesion-level 3D characteristics and inter-slice correlations in localized weak lesion regions, resulting in incomplete feature extraction and inaccurate calculation of lesion volume. To obtain complete 3D fine-grained lesion features in the entire lung, this paper proposes an early pneumoconiosis recognition network (EPRNet) to enhance fine-grained feature acquisition abilities and discover inter-slice correlations, thereby improving early pneumoconiosis recognition accuracy in a more structured and flexible manner. Specifically, to obtain the fine-grained 3D features of early pneumoconiosis more comprehensively, a distance-similarity graph encoding module is proposed to construct and encode the relationships of the distributed tiny lesions within CT slices, integrating the spatial positions and the corresponding feature similarities of the lesions to improve the accuracy of pneumoconiosis feature representation. To adaptively preserve accurate graph representations of the correlations of the lesions across inter-CT slices, a hierarchical dynamic-scored adaptive pooling module is proposed to discover the potential long distance correlations between cross-slices, obtaining spatial semantic information of diffused lesions in the entire lung. Experimental results based on multiple datasets demonstrate that EPRNet achieves state-of-the-art performance while exhibiting better generalization. The ablation experiments also prove the effectiveness of each module in EPRNet.

Ultrasound imaging and biochemical examinations are the primary methods for diagnosing Hashimoto's thyroiditis (HT). However, neither of them is sufficient to accurately diagnose HT alone. Most existing multimodal models for HT diagnosis focus primarily on extracting and concatenating features from different modalities, which are ineffective due to the dimensional imbalance of the features between the textual and image data. To address this issue, we propose a novel Multimodal Collaborative Fusion Learning (MCFL) approach, which can enhance and recalibrate the biochemical indicators using ultrasound images, effectively improving the significance and specificity of biochemical indicators for the diagnosis of HT. Specifically, MCFL first constructs a novel INNet to convert the image-level characteristics of the HT ultrasound image into two numerical indicators, i.e., the Local prominent inflammatory (Lpi) and the Global diffuse lesion (Gdl), unifying image data and textual data into a single representation space. Then, a decision tree-based optimization strategy is employed to supervise the training of INNet, interactively recalibrating biochemical indicators with the guidance of the two numerical indicators mentioned above and obtaining a more accurate feature representation of HT. Finally, based on the deep Q-learning framework, a reward mechanism is established to guide the HT diagnostic process, in which the experience replay mechanism and the $\epsilon $ -greedy strategy are utilized collaboratively to improve the accuracy and robustness of the model. Extensive experiments are conducted on a multimodal dataset from multiple medical centers, and the results demonstrate that MCFL achieves state-of-the-art performance, setting a new benchmark.

Wi-Fi is the de facto standard for providing wireless access to the Internet using the 2.4GHz ISM (Industrial Scientific Medical) band. LoRa (Long Range) is specially designed for Low-Power, Wide-Area Networks (LPWANs) and has a broad range of applications in Internet of Things. Tens of billions of mobile devices (e.g., smartphones) are manufactured with limited types of wireless radio, making it challenging to access the data in the heterogeneous IoT devices. To address this challenge, we propose a method that enables LoRa devices to establish connections and engage in communication with Wi-Fi networks. A key observation of this study is that when a LoRa frame collides with an ongoing Wi-Fi transmission, the Wi-Fi receiver captures and retains the LoRa data. By analyzing the decoded Wi-Fi payload, we can retrieve the LoRa data, and this method remains fully compatible with existing commodity Wi-Fi hardware. Moreover, evaluations with Universal Software Radio Peripheral (USRP) and commodity devices demonstrate reliable wireless communication from LoRa to Wi-Fi networks with a high reliability in frame reception and low frame error rates across various indoor and outdoor environments.

Real-time semantic recognition is crucial for virtual reality (VR) applications, but the efficient fusion of multi-modal data poses significant challenges under resource-constrained VR scenarios. While integrating millimeter-wave (mmWave) radar point clouds with vision data offers a promising solution, existing methods often suffer from excessive data overhead and degraded accuracy due to redundant and noisy information. To address this limitation, this paper presents VR-PCT, a multi-modal transformer for edge-client collaborative VR semantic recognition that fuses mmWave radar point cloud and vision data for VR applications. VR-PCT introduces a novel collaborative design where VR clients perform lightweight semantic region detection while VR edge processes multi-modal VR semantic recognition. Through efficient edge-client collaboration, VR-PCT optimizes the transmission of mmWave point cloud and vision data by transmitting only the VR semantic region of vision data instead of the entire video. Additionally, it incorporates adaptive cross-modal data selection and fusion strategies to achieve real-time semantic recognition while significantly reducing data redundancy. Across 22 participants engaged in four experimental scenes utilizing VR devices from three different manufacturers, our evaluation demonstrates that VR-PCT achieves 97.6% recognition accuracy while reducing transmission overhead by 81.5% compared to existing approaches. These results highlight the effectiveness of VR-PCT in enabling efficient and accurate multi-modal VR semantic recognition for VR applications. The code and data of VR-PCT are released onhttps://github.com/luoyumei1-a/VR-PCT.

Ultrasound imaging can reveal the typical changes in thyroid tissue caused by Hashimoto's Thyroiditis (HT), which plays a crucial role in HT diagnosis. Clinicians should perform ultrasound imaging from multiple anatomical planes to obtain a series of thyroid images, thereby collecting comprehensive information about HT lesions from different perspectives. However, ultrasound images lacking typical HT characteristics or containing extensive non-HT regions may hinder the identification of localized lesions, thereby increasing the complexity of diagnosis. In addition, the ability to capture synergistic interactions among multiple lesions in critical ultrasound images is crucial for improving the diagnostic accuracy of HT. To address these challenges, a novel weakly supervised multi-instance learning model, HTMIL, is proposed for HT diagnosis, requiring only patient-level data annotation. HTMIL consists of a Global-Neighbor extraction Layer (GNL) and a Cross-Aggregation Layer (CAL). Specifically, a Global-Neighbor Awareness (GNA) module is proposed in GNL to allow HTMIL to focus on key localized lesions and ignore noise from unrelated regions in HT ultrasound images, thus improving the effectiveness of extracting focused features from localized lesions. The Adaptive Bilinear Pooling (ABP) module is introduced in CAL to capture the interaction features between key localized lesions and other characteristics of HT in critical ultrasound images, thus achieving synergistic diagnosis and further increasing diagnostic accuracy. HTMIL achieves state-of-the-art (SOTA) performance with 87.67% accuracy and 91.73% AUC on a multicenter HT dataset, and its robustness is further validated on a public dataset.

Underwater images often suffer from color distortion, reduced contrast, and blurriness due to light refraction, absorption, and scattering. In this paper, we propose a coarse-to-fine deepPyramid network forUnderwaterImageEnhancement (PyUIE). Specifically, PyUIE begins by decomposing the input image into high- and low-frequency components using a Laplacian pyramid. The low-frequency residual, which primarily contains lighting and color information, is processed with a lightweight deterministic color mapping network to correct global illumination and color distortions. Concurrently, the high-frequency components containing the fine details are enhanced in a coarse-to-fine manner, such that each higher scale is guided by the reconstruction from the adjacent lower scale. This hierarchical strategy effectively mitigates the risk of over-enhancement by avoiding excessive modifications to the high-frequency components. Additionally, we implement a multi-scale supervised training strategy, enabling the model to learn and reconstruct features across multiple scales, which enhances its ability to capture diverse details and improves its generalization and robustness. Extensive experiments demonstrate that our method successfully restores fine details and small structures in underwater images while producing vivid and visually appealing colors, thereby outperforming existing enhancement methods in both qualitative and quantitative evaluations. The code is available athttps://github.com/ttttllt/PyUIE.git.

Few-shot knowledge graph completion (FKGC) aims to infer missing triples for long-tail relationships using a small set of References. Existing FKGC models focus mainly on entity representation aggregation, heavily relying on interactions between central entities and their neighbors. However, real-world knowledge graphs contain relations with multiple semantics, and existing models struggle to capture the diverse semantic information of the relations in different contexts. To address this issue, we propose a novel FKGC model, context-aware relational learning and multidimensional matching (CRL-MM). First, CRL-MM enhances the representation of task relations by obtaining semantic information in different scenarios based on the semantic similarity between task relations and background relations. Second, unlike previous models, which rely mainly on neighborhood relations to capture relation information, CRL-MM considers the entity pair and its neighborhood as a unified contextual whole, aggregating neighborhood information through adaptive task relations and paired entity awareness to improve entity encoding. In addition, during the matching phase, we design a matching network from multiple dimensions, which includes not only the similarity score of the entity pairs but also the triple rationality score to further improve the generalizability of the model. Extensive experiments on public benchmark datasets show that CRL-MM outperforms state-of-the-art methods, and the ablation experiments also demonstrate the effectiveness of each module of the proposed CRL-MM.

Cross-Technology communication (CTC) has emerged as a pivotal solution for enabling communication between heterogeneous wireless devices, particularly in scenarios involving high-power and long-range networks. This paper introduces an innovative approach called WeRa, which leverages IEEE 802.11n technology to emulate LoRa waveforms, thus achieving efficient CTC from Wi-Fi to LoRa. WeRa utilizes orthogonal frequency division multiplexing (OFDM) technology in 802.11n to emulate LoRa chirp signals. To address the unique characteristics of LoRa signals, we propose a subcarrier selection scheme to enhance the communication performance between commercial Wi-Fi devices and commercial LoRa devices. During the Wi-Fi emulation process, frequency offsets are inevitable due to technical constraints. To mitigate this, we implement a fixed frequency compensation mechanism for the emulated signals based on thorough analysis. To tackle errors introduced by the cyclic prefix (CP), we build upon the boundary-flipping method proposed in WeBee and introduce a dynamic mode-flipping technique, effectively reducing the interference of CP on the emulated signals. We successfully implemented a prototype of WeRa on commercial Wi-Fi devices, the USRP platform, and commercial LoRa devices. Extensive experiments demonstrate WeRa’s robust performance, achieving a throughput of 119.683 kbps for efficiently transmitting complete LoRa frames while maintaining a symbol error rate below 0.1.

Image Color Aesthetics Assessment (ICAA) focuses on evaluating the aesthetic quality of color composition within images. This task involves analyzing and quantifying the visual appeal of color arrangements, taking into account factors such as harmony, contrast, and balance, to provide an objective assessment of color aesthetics. In this paper, we investigate the application of the State Space Model (Mamba) to the ICAA task, with a focus on exploring the perceptual capabilities of vision Mamba. To this end, we propose a novel framework that employs Mamba as the backbone to extract informative patterns from images, leveraging its global receptive field and linear complexity with respect to input length. Instead of relying solely on a final-layer feature, followed by fully connected layers for quality prediction, we exploit a comprehensive set of multi-scale features, thereby constructing a richer global representation of color information. To fuse these multi-scale features effectively, we introduce a Weighted Feature Fusion Module (WFFM), which adaptively assigns weights to emphasize salient color-related information. We conduct extensive experiments on two benchmark datasets, ICAA17K and SPAQ, successfully demonstrating its effectiveness for the ICAA task. The code is available at https://github.com/qinghaoya/icaa-mamba.

Pulse modulation based communication techniques have enabled various Internet of Things (IoT) applications, such as smart meters and automotive systems. However, the existing pulse modulators rely on platform-specific hardware components, leading to limited extensibility and hardware dependency when supporting diverse variants such as Pulse Position Modulation (PPM) and Pulse Amplitude Modulation (PAM). This paper introduces NN-Pulse, an innovative neural networkdefined pulse modulator designed to enhance extensibility and flexibility, ensuring compatibility with multiple pulse modulation schemes. Specifically, NN-Pulse realizes the pulse modulation process using fundamental neural network modules with carefully tailored weights, via the proposed spike neural network (SNN)-based position selection module and transposed convolutional layers for phase modulation. Evaluations show that NNPulse generates PPM and PAM signals with bit error ratios of 0.6% and 0.2%, respectively. Moreover, the time consumption of modulating a pulse symbol via NN-Pulse is only$1.4 \mu \mathrm{s}$, outperforming traditional methods by 47 times.

Cross-Technology Communication (CTC) is a cornerstone for seamless interoperability in heterogeneous wireless environments, enabling diverse devices to coexist and cooperate effectively. In this paper, we present Wi-Fi-to-X, designed to leverage deep learning techniques to generate waveforms that are compatible with multiple communication protocols, allowing seamless data transmission between Wi-Fi and other wireless technologies such as ZigBee, LoRa, and Bluetooth. This approach enables devices operating under different wireless standards to communicate effectively without requiring hardware modifications or protocol standardization. By training a specialized neural network on simulations of Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM), we have improved the efficiency and reliability of signal processing in CTC, Wi-Fi-to-X achieves robust signal modulation and demodulation across disparate technologies, enabling communication from Wi-Fi to other IoT devices, including ZigBee, LoRa, and Bluetooth. We evaluated both USRP and commodity devices, demonstrated that Wi-Fi-to-X can achieve concurrent wireless communication from Wi-Fi to other IoT devices.

As more users seek generative AI (GAI) models to enhance work efficiency, GAI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize GAI models provided by the service provider, they cannot be certain that the model’s quality matches the provider’s claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multiagent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on noninteractive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an Age of Verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a tradeoff optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multiagent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500 ms during the verification phase, meeting practical requirements.

To meet the stringent demanding low latency and high throughput of cloud datacenter applications, recent receiver-driven transport protocols transmit only one packet once receiving each credit packet from the receiver to achieve ultra-low queueing delay. However, the round-trip time variation and the highly dynamic background traffic significantly deteriorate the performance of receiver-driven transport protocols, resulting in under-utilized bandwidth. This paper designs a simple yet effective solution called RPO, which retains the advantages of receiver-driven transmission while efficiently utilizing the available bandwidth. Specifically, RPO rationally uses low-priority opportunistic packets to ensure high network utilization without increasing the queueing delay of high-priority normal packets. Furthermore, to tackle the queueing buildup due to line-rate transmission in the first RTT, we design a selective dropping mechanism called SDM to help the majority of small flows complete within only one RTT by prioritizing the first-RTT bursty packets over the packets triggered by grants. We implement RPO in Linux hosts with DPDK. The experimental results show that RPO significantly improves the network utilization by up to 35% over the state-of-the-art schemes, without introducing additional queueing delay. Moreover, RPO integrated with SDM reduces the AFCT of small flows by up to 45% compared with RPO integrated with Aeolus.

Cross-technology communication (CTC) facilitates seamless interaction between different wireless technologies. Most existing methods use reverse engineering to derive the required transmission payload, generating a waveform that the target device can successfully demodulate. However, traditional approaches have certain limitations, including reliance on specific reverse engineering algorithms or the need for manual parameter tuning to reduce emulation distortion. In this work, we present NNCTC, a framework for achieving physical layer cross-technology communication through explainable neural networks, incorporating relevant knowledge from the wireless communication physical layer into the neural network models. We first convert the various signal processing components within the CTC process into neural network models, then build a training framework for the CTC encoder-decoder structure to achieve CTC. NNCTC significantly reduces the complexity of CTC by automatically deriving CTC payloads through training. We demonstrate how NNCTC implements CTC in WiFi systems using OFDM and CCK modulation. On WiFi systems using OFDM modulation, NNCTC outperforms the WEBee and WIDE designs in terms of error performance, achieving an average packet reception ratio (PRR) of 92.3% and an average symbol error rate (SER) as low as 1.3%. In WiFi systems using OFDM modulation, the highest PRR can reach up to 99%.

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To address these challenges, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, consisting of two tailored modules. Moreover, the effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework in both energy saving and reconstruction accuracy.

Blockchain has been considered as a critical solution to handle the privacy and security concerns for data sharing in vehicular networks. However, deploying vehicular blockchain onboard vehicles is constrained by the sophisticated communication environments of vehicular networks, the restricted resources of vehicles, and self-interested property of vehicles. In this paper, a Cellular Vehicle-to-Everything (C-V2X) based vehicular blockchain sharding framework is presented in vehicular edge computing. To motivate vehicles to assist in validating block data in vehicular shard, a contract-based incentive mechanism is presented to efficiently solve the joint moral hazard and adverse selection problem. Considering packet sensing ratio, half-duplex effect, and successful sensing probability, a dual PC5/Uu interface based block consensus delay model is formulated during the consensus process. To achieve two objectives of capability-discrimination and effort-motivation, we aim at enhancing the saved delay utility of blockchain service requester (BSR) while ensuring complex conditions of vehicles. Simulation outcomes demonstrate that the proposed mechanism successfully fulfills capability-discrimination and effort-motivation, and offers a 52% and 7% increase in BSR’s utility compared to the linear pricing scheme and uniform scheme, respectively.

Cross-technology communication (CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technologies and the necessity for intricate algorithms to mitigate distortion. In this work, we present NNCTC, a Neural-Network-based Cross-Technology Communication framework inspired by the adaptability of trainable neural models in wireless communications. By converting signal processing components within the CTC pipeline into neural models, the NNCTC is designed for end-to-end training without requiring labeled data. This enables the NNCTC system to autonomously derive the optimal CTC payload, which significantly eases the development complexity and showcases the scalability potential for various CTC links. Particularly, we construct a CTC system from Wi-Fi to ZigBee. The NNCTC system outperforms the well-recognized WEBee and WIDE design in error performance, achieving an average packet reception rate (PRR) of 92.3% and an average symbol error rate (SER) as low as 1.3%.

Cross-technology communication (CTC) enables seamless interactions between diverse wireless technologies. Most existing work is based on reversing the transmission path to identify the appropriate payload to generate the waveform that the target devices can recognize. However, this method suffers from many limitations, including dependency on specific technologies and the necessity for intricate algorithms to mitigate distortion. To address these challenges, we present NNCTC, a Neural-Network-based Cross-Technology Communication framework which can achieve reliable and interpretable Cross-Technology Communication through a training process with an example of WiFi (OFDM and CCK) to both known and unknown modulation schemes.

The collective communication has become the bottleneck of large-scale distributed deep learning due to the huge volume of gradients aggregated during the training process. Despite much recent progress in reducing traffic volume by compressing the stochastic gradients inside each training worker, how to share the inter-worker data redundancy to alleviate communication overhead has remained elusive. In this paper, we reveal that most gradients have a great similarity with close value among training workers. From this hypothesis, we propose a Similarity-guided Gradient Compression framework named SGC which skips aggregating the similar gradients among each worker which utilizes local one rather than average value to save communication expenses. Each worker utilizes local SGC firstly quantifies the similarity of gradients among workers, and then elaborately adjusts the aggregation frequency of similar gradients without hurting DNN model accuracy. Meanwhile, we theoretically analyze the convergency accuracy of SGC. The comprehensive evaluation demonstrates that SGC outperforms the state-of-the-art schemes by up to 47% in convergence time.

Millimeter-wave (mmWave) radar is emerging as a promising sensor for various human sensing tasks. Deep learning is frequently applied in radar-based applications, which typically require extensive data collection and labeling. In this demo, we present a low-cost hardware setup and a cross-platform software pipeline that automatically captures radar data of human activities, labels ground truth, and tests inference models in real time. The effectiveness of the testbed is demonstrated through real-time human pose estimation.

Tracking the human arm is essential for a variety of applications, including medical rehabilitation, sports analysis, and human-computer interaction. Current vision-based and wearable sensor-based approaches either struggle with occlusion, poor lighting conditions, and privacy concerns or result in intrusive user experiences. This paper introduces mmHAT, a novel 3D arm trajectory tracking method using mmWave radar. mmHAT proposes an end-to-end neural network design to address two major challenges: the lack of arm semantic information and dynamic variations in the mmWave point clouds. Firstly, mmHAT incorporates a multi-task joint learning framework, where the primary task is 3D arm tracking and the auxiliary task is gesture recognition. This aims to leverage the auxiliary task to guide the network in developing a deeper understanding of the user's arm movements. Secondly, for dynamic mmWave point clouds, mmHAT incorporates a new spatial-temporal feature encoder that aggregates the features of the arms point cloud from a global perspective. We collect ~320K frames of daily arm activity data for experimental validation. The results show that mmHAT achieves an average joint location error of 1.67 cm and angle estimation error of 4.23° for arm joints (i.e., elbow, wrist), while delivering excellent performance with only 2.67 ms latency.

Matrix multiplication (MM) is pivotal in fields from deep learning to scientific computing, driving the quest for improved computational efficiency. Accelerating MM encompasses strategies like complexity reduction, parallel and distributed computing, hardware acceleration, and approximate computing techniques, namely AMM algorithms. Amidst growing concerns over the resource demands of large language models (LLMs), AMM has garnered renewed focus. However, understanding the nuances that govern AMM’s effectiveness remains incomplete. This study delves into AMM by examining algorithmic strategies, operational specifics, dataset characteristics, and their application in real-world tasks. Through comprehensive testing across diverse datasets and scenarios, we analyze how these factors affect AMM’s performance, uncovering that the selection of AMM approaches significantly influences the balance between efficiency and accuracy, with factors like memory access playing a pivotal role. Additionally, dataset attributes are shown to be vital for the success of AMM in applications. Our results advocate for tailored algorithmic approaches and careful strategy selection to enhance AMM’s effectiveness. To aid in the practical application and ongoing research of AMM, we introduce LibAMM —a toolkit offering a wide range of AMM algorithms, benchmarks, and tools for experiment management. LibAMM aims to facilitate research and application in AMM, guiding future developments towards more adaptive and context-aware computational solutions.

This paper presents Mission, the first-of-this-kind cross-modal reidentification (ReID) design for mmWave Radar and RGB cameras. Given a person of interest detected by Radar in camera-restricted scenarios, Mission can identify the image of the person from cameras that are ubiquitously deployed in camera-allowed areas. We envision that cross Vison-RF ReID can significantly enrich mmWave human sensing with a wide spectrum of applications in security surveillance, tracking, and personalized services. Technically, we introduce a novel method for cross-modal similarity estimation that exploits inherent synergies between fine-grained 2D images and coarse-grained 3D Radar point clouds to effectively overcome their modal discrepancy. Through extensive experiments, we demonstrated that our proposed system can achieve 85% top-1 accuracy and 90% top-5 accuracy among 58 volunteers.

Precise polyp segmentation is vitally essential for detection and diagnosis of early colorectal cancer. Recent advances in artificial intelligence have brought infinite possibilities for this task. However, polyps usually vary greatly in shape and size and contain ambiguous boundary, bringing tough challenges to precise segmentation. In this letter, we introduce a novel Boundary Refinement Network (BRNet) for polyp segmentation. To be specific, we first introduce a boundary generation module (BGM) to generate boundary map by fusing both low-level spatial details and high-level concepts. Then, we utilize the boundary-guided refinement module to refine the polyp-aware features at each layer with the help of boundary cues from the BGM and the prediction from the adjacent high layer. Through top-down deep supervision, our BRNet can localize the polyp regions accurately with clear boundary. Extensive experiments are carried out on five datasets, and the results indicate the effectiveness of our BRNet over seven recently reported methods.

The early lesions of Hashimoto's thyroiditis are inconspicuous, and the ultrasonic features of these early lesions are indistinguishable from other thyroid diseases. This paper proposes a Hashimoto Thyroiditis ultrasound image classification model HT-RCM which consists of a Residual Full Convolution Transformer (Res-FCT) model and a Residual Channel Attention Module (Res-CAM). To collect the low-order information caused by hypoechoic signals accurately, the residual connection is injected between FCTs to form Res-FCT which helps HT-RCM superimpose the low-order input information and high-order output information together. Res-FCT can make HT-RCM focus more on hypoechoic information while avoiding gradient dispersion. The initial feature map is inserted into Res-FCT again through a down-sampling component, which further helps HT-RCM exact multi-level original semantic information in the ultrasound image. Res-CAM is constructed by implementing a residual connection between a channel attention module and a convolution layer. Res-CAM can effectively increase the weights of the lesion channels while suppressing the weights of the noise channels, which makes HT-RCM focus more on the lesion regions. The experimental results on our collected dataset show that HT-RCM outperforms the mainstream models and obtains state-of-the-art performance in HT ultrasound image classification.

Distinguishing Hashimoto's thyroiditis (HT) lesions from ordinary thyroid tissues is difficult with ultrasound images. Challenges in achieving high performance of HT ultrasound image classification include the low resolution, blurred features and large area of irrelevant noise. To address these problems, we propose a Feature-level Boosting Ensemble Network (FBENet) for HT ultrasound image classification. Specifically, to capture the features of suspicious HT lesions efficiently, an Ensemble Feature Boosting Module (EFBM) is introduced into the feature-level ensemble to boost the blurred features. Then, the spatial attention mechanism is adopted in backbone models to improve the feature focusing performance and representation ability. Furthermore, feature-level ensemble technique is employed in the training process to achieve more comprehensive feature representation ability. Experimentally, FBENet was trained on 6,503 HT ultrasound images, and tested on 1,626 HT ultrasound images with 82.92% accuracy and 89.24% AUC on average.

A physical-layer modulator is crucial for IoT gateways, but current solutions face issues like limited extensibility and platform-specificity due to soldered chipsets for specific technologies or diverse software toolkits for software radios. With the rapid expansion of the Internet of Things (IoT), such limitations are hard to ignore as the demand for versatile wireless technologies has increased. This paper introduces a novel approach using neural networks as an abstraction layer for these modulators in IoT gateways, termed NN-defined modulators. This method overcomes the challenges of extensibility and portability across different hardware platforms. The NN-defined modulator employs a model-driven approach based on mathematical principles, resulting in a lightweight, hardware-acceleration-friendly structure. These modulators are containerized with necessary runtime, facilitating agile deployment on varied platforms. We tested NN-defined modulators on platforms like Nvidia Jetson Nano and Raspberry Pi, showing they perform comparably to traditional modulators while offering efficiency improvements. The implementation is memory-efficient and adds minimal latency. Additionally, we demonstrate real-world applications of our NN-defined modulators in generating ZigBee and WiFi packets, compatible with standard TI CC2650 (ZigBee) and Intel AX201 (WiFi NIC) devices.

Single-image dehazing is a challenging task that requires both local details and global distribution. Existing methods face challenges in color imbalance and inconsistent details when predicting a haze-free image, because of their limitations in generalization from a specific setting (physics-based methods), capturing global information (CNN-based methods) and capturing detailed local information ( ViT-based methods). In response to these challenges, we propose a balanced low-cost hybrid network called LHNetV2 based on LHNetV1. The key insight of LHNetV2 is the effective fusion of different features, and a series of novel approaches is proposed to increase the running speed of the original LHNetV1. Firstly, building upon the Feature-aware Information Fusion method, we preserve the original Physical Embedding and Architecture Aggregation components in LHNetV1. Next, to overcome the speed bottleneck of LHNetV1, we enhance the calculation method of attention in the ViT sub-network and streamline the cross-stage interaction strategy in the CNN main-network. Finally, we introduce a dynamic adversarial loss function to bolster both the training stability and performance of LHNetV2. The experiments are extensively conducted on mainstream datasets, and the results demonstrate that LHNetV2 achieves the best balance between the performance and the running speed in single-image dehazing. The code is available at https://github.com/SHYuanBest/LHNet.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.

In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8%, respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.

Enabling path programmability is an essential feature of Software-Defined Networking (SDN). During controller failures in Software-Defined Wide Area Networks (SD-WANs), a resilient design should maintain path programmability for offline flows, which were controlled by the failed controllers. Existing solutions can only partially recover the path programmability rooted in two problems: 1) the implicit preferable recovering flows with long paths and 2) the sub-optimal remapping strategy in the coarse-grained switch level. In this paper, we propose ProgrammabilityGuardian to recover the path programmability of offline flows while maintaining low communication overhead. These goals are achieved through the fine-grained flow-level mappings enabled by existing SDN techniques. ProgrammabilityGuardian configures the flow-controller mappings to recover offline flows with a similar path programmability, maximize the total programmability of the offline flows, and minimize the total communication overhead for controlling these recovered flows. Simulation results of different controller failure scenarios under two different topologies show that ProgrammabilityGuardian recovers offline flows with a balanced path programmability, improves the total programmability of the recovered flows up to 68% and 70%, and reduces the communication overhead by 96% and 99%, compared with the baseline algorithm.

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies.

Defects in software may result in system crashes, sluggish performance, or even deadlock, leading to the depletion of valuable resources. Implementing defect prediction can assist quality assurance teams in identifying potential software issues and rationalizing the allocation of testing resources, thereby decreasing the elimination of resources and enhancing software sustainability. Researchers have recently incorporated deep learning into defect prediction, extracting structural-semantic features from codes' abstract syntax trees (ASTs). However, inappropriate node granularity in ASTs may adversely impact the effectiveness of the extracted features. In addition, converting AST nodes into integer vectors may lead to the loss of structure information, resulting in poor model predictive capability. This paper proposes a tree-based encoding method with hybrid granularity for defect prediction to address these challenges. Specifically, five granularity selection schemes are extended to generate various ASTs from codes. Subsequently, a tree-based continuous bag-of-words model is utilized to map nodes of ASTs into numeric vector representations that conform to the tree-like structure of codes. The matrices converted from ASTs are then fed into a convolutional neural network to extract program features automatically. Experiments involving 24 versions of open-source projects demonstrate that our method can improve the effectiveness of extracted features in defect prediction tasks.

In existing federated learning (FL), the base station (BS) coordinates devices to collaboratively train a shared model by avoiding the transmission of raw data. To achieve communication-efficient model uploading, over-the-air computation (AirComp) is often employed to aggregate model parameters. However, in conventional AirComp assisted FL, the BS’s abundant computation resources are underutilized due to its non-involvement in model training. Meanwhile, transmission failures resulting from fluctuating wireless channels impair the quality of model aggregation. In this paper, we propose a retransmission-based semi-federated learning (SemiFL) framework, wherein devices upload model parameters and public privacy-free data for enabling a hybrid implementation of FL and centralized learning (CL). In our new framework, the BS leverages its abundant computation resources to aid CL model training, which mitigates the resource wastage while alleviating local computational burden of devices. Moreover, the proposed new retransmission mechanism effectively overcomes detrimental transmission failures resulting from the fluctuating quasi-static channel, aiming to guarantee improved learning performance of SemiFL. Successful transmission probabilities of both retransmission-based AirComp and retransmission-based digital communication are provided in closed forms. To attain deep insights, we derive an optimality gap to capture the convergence behavior of retransmission-based SemiFL. Then, we formulate a non-convex long-term problem to minimize a weighted sum of overall latency and energy consumption by jointly optimizing communication, computation, and learning parameters. Extensive experimental results show that our retransmission-based SemiFL obtains 21.9%, 30.5%, and 44.1% accuracy gains on three datasets, while efficaciously reducing latency and energy consumption compared to benchmarks. Meanwhile, our scheme enhances learning performance on the fluctuating quasi-static channel compared to state-of-the-art schemes.

In unsupervised domain adaptation (UDA), learning of domain invariant representation is easily dominated by labeled source domain data, while the data distribution characteristic of target domain is often ignored. Although there were some studies reported on extraction of the inter-class relation of samples and reduce the classifier mistakes in the target domain, however, they still failed to solve the problem on shared knowledge distillation when domains were cross-transferring. In this paper, we propose a two-stage maximizing cross-domain invariant features adaptation method, designing a collaborative learning model based on divergence congruence among source and target domain representation correlations. In the first stage, we use generative adversarial network to extract the common features from unpaired images, and the positive migration of classifiers is created between two domain spaces. Secondly, in the mixing domain label space, and correlation efficiency is obtained, upon which uncertainty weight mechanism is used to strengthen the features to stand out from cross-domain prediction and suppress the negative migration in the meantime. With cross-domain knowledge, few-shot learning in the target domain can further promote the training of adaptive information, enhancing a collaborative effect across two domains. In the UDA scenario, two baseline datasets Image CLEF-DA and Office-home are subjected to domain adaptation experiments. Compared with the state-of-the-art algorithms, the average recognition accuracy reaches up to nearly 2% increase.

In this paper, we propose a semi-federated learning (SemiFL) framework to ameliorate the performance of conventional federated learning. The base station and devices are coordinated to collaboratively train a shared model. However, due to the rapidly fluctuating channels and irrationally assigned local learning workloads, SemiFL encounters excessive latency. To overcome the challenges, we propose a retransmission-based over-the-air computation mechanism to facilitate model aggregation and data mixing over quasi-static channels. The closed-form probability of successful aggregation is derived, while the communication latency is modeled based on the Pascal distribution. Further, we establish an optimality gap to characterize the convergence performance of SemiFL, wherein the minimum number of iterations for attaining a specific local target accuracy is identified. Next, a joint resource allocation and local target accuracy assignment problem is formulated to minimize the latency of each round, subject to the decay rate, central processing unit (CPU) frequency, and transmit power. To address this non-convex problem, we develop an algorithm using the closed-form solutions for the normalizing factors and CPU frequencies. Simulation results on two real-world datasets confirm the superiority of SemiFL over benchmarks in terms of latency and learning performance.

A digital modulator plays a crucial role in converting symbols into signals in an IoT gateway. However, the ever-increasing modulation schemes pose practical challenges, such as flexibility for different schemes and portability with different hardware platforms. To address these challenges, we propose a new approach that employs a neural network as an abstraction layer for physical layer modulators, called the NN-defined modulator. We will demonstrate that the NN-defined modulator functions like traditional modulators and offers high portability and efficiency with example communication to ZigBee and WiFi.

Single image dehazing is a challenging task that requires both local detail and global distribution, and can be applied to various scenarios. However, physics-based dehazing algorithms perform well only in specific settings, while CNN-based algorithms struggle with capturing global information, and ViT-based approaches suffer from inadequate representation of local details. The shortcomings of the above three types of methods lead to issues such as imbalanced colors and incoherent details in the predicted haze-free image. To address these challenges, we propose a new Low-cost Hybrid Network called LHNet. The key insight of LHNet is the effective hybrid of different features, which can achieve better information fusion in the form of feature awareness at the cost of few parameters. This fusion approach narrows the gap between different features and enables LHNet to autonomously choose the fusion granularity to maximize the utilization of prior, local and global information. Extensive experiments are performed on the mainstream dehazing datasets, and the results show that LHNet achieves state-of-the-art performance in single image dehazing. By adopting our fusion approach, a better dehazing effect can be achieved than with other dehazing algorithms with more parameters, even when only CNN and ViT are used. The code is available at https://github.com/SHYuanBest/LHNet.

Code clone detection technology aims to automatically detect code similarity and help developers identify and reduce code duplication.While code syntax analysis-based methods are commonly used for clone detection, they may not capture semantic information due to bypassing the analysis of code text.To address this issue, this paper proposes a new method called visualization representation learning for code clone detection (VRL4CCD).This method converts source code fragments into grayscale images to preserve textual information and then utilizes VGG16 and a self-attention mechanism to extract features related to code semantic similarity.A siamese neural network is used to learn the similarity pattern between code features.Experimental results on the Big Clone Bench and Google Code Jam datasets demonstrate that VRL4CCD outperforms current clone detection methods regarding precision, recall, and F1-score, indicating the effectiveness of code visualization technology in clone detection tasks.

3D human pose plays a critical role in human behavior understanding and has many applications (e.g., VR/AR). Conventional pose estimations deploy sensors as fixed infrastructure, which significantly restrains the mobility of the user. Inspired by the emerging head-mounted devices (e.g., VR/AR glasses) and the recent advance in low-cost mmWave radar, we present mmEgo, the first egocentric human pose estimation design using a head-mounted mmWave radar, which offers ubiquitous pose tracking with high mobility, robustness to complex environments, and privacy preservation. To tackle the unique challenges of radar sensing from the egocentric perspective (e.g., random radar motion and the scarcity of information on the lower body), we propose several technical designs, including root-relative radar motion tracking for radar motion decoupling and a two-stage pose estimator that incorporates human kinematics priors. Extensive experiments and case studies show that our method can reduce the joint localization error by 44.2% and potentially enable a wide spectrum of applications.

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the intermittently connected vehicular networks, fulfilling the 6G vision of semantic and green communications. Specifically, we introduce a task-driven knowledge graph (KG)-assisted semantic communication system, and model the KG into a weighted directed graph from the viewpoint of transmission. Meanwhile, we identify three predictable parameters about the individual vehicles to perform the following anticipatory analysis. Firstly, to facilitate semantic extraction, we derive the closed-form expression of the achievable throughput within the delay requirement. Then, for the extracted semantic representation, we formulate the mutually coupled problems of semantic unit assignment and predictive relay selection as a combinatorial optimization problem, to jointly optimize the energy efficiency and semantic transmission reliability. To find a favorable solution within limited time, we proposed a low-complexity algorithm based on Markov approximation. The promising performance gains of the PreCMTS are demonstrated by the simulations with realistic vehicle traces generated by the SUMO traffic simulator.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy calledVALIDin 364 Chinese cities.VALIDis pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12-2021/01),VALIDconsists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers’ behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants’ smartphones under their consent as a virtual infrastructure to design, build, deploy, and operateVALIDfrom in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers’ behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

loT devices have evolved from providing remote connection to being an essential component of the Metaverse. The integration of loT and vision technologies has been incubating emerging applications such as vision-enhanced device tracking and remote education/medicine/maintenance. Despite the exciting vision, practical challenges include coordinate transformation, angle estimation, target mapping, and personal error. Instead of proposing yet-another localization approach, we propose a novel vision-enhanced device tracking system, called VisBLE. VisBLE takes advantage of the new localization capability introduced in BLE 5.1 and advances in vision technologies for high accuracy, robust, and intuitive BLE device tracking. There are two novel technical mechanisms: i) a rotation-based wireless localization mechanism that accurately and robustly locates the BLE transmitter in the camera coordinate and ii) a homography-based matching mechanism that identifies target BLE devices with high accuracy on the camera screen. We prototype VisBLE and deploy it on the smartphone (i.e., Nexus 5X) and development board (i.e., CC26X2 + BOOSTXL-AOA). Our results show that VisBLE outperforms the state of the art in both angular accuracy and position accuracy.

Autonomous driving requires vehicles to achieve fine detection of objects in the surrounding environment, especially living pedestrians. Nevertheless, in real world road environments there are living pedestrians and roadside portrait billboards. Existing vision-based object detection technologies fail to ac-curately distinguish living pedestrians from human figures. As an important sensor of autonomous driving system, mmWave radar has extra help to detect living pedestrians. In this paper, we extract the radar cross section (RCS) of the object from the low-cost mmWave radar signal as a distinguishing feature between living pedestrian and portrait billboard. Based on this observation, we propose a feature fusion network of mmWave radar and computer vision based on attention mechanism, and detect living pedestrians from fusion features. We implement the design with commodity mmWave radar IWR6843ISK-ODS and RGB camera Logitech Pro C920. The evaluation results show that our method effectively detects living pedestrians with an mAP of 97.7% and outperforms existing studies.

The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy rate in downstream tasks is increased by 0.65% on average after using BERTBooster on CAGBERT.

To solve the problem of query restriction in supply-chain financial blockchain system, this paper proposes a privacy budget management and noise reusing method in multichain blockchain environment based on Hyperledger multichannel technology and community clustering algorithm. A historical record book is established to manage the privacy budget according to historical query types, and a differential privacy protection algorithm based on noise reusing is used to generate and reusing noise. In the experiments, we tested the privacy budget loss, data utility and system performance based on the business data-set from chemical supply chain. The experimental results show that the blockchain system with multichain structure based on community clustering algorithm reduces the system data storage space and decreases request processing time effectively. The privacy budget loss of the proposed method is about 1/3 of that of the pure Gaussian mechanism method. After 100 queries, the total amount of noise is about 8% less than that of pure Gaussian mechanism. Besides that, the noise reusing algorithm reduces the loss of the privacy budget and breaks through the query limitation caused by privacy budget wasting.

The real-world complex networks, such as biological, transportation, biomedical, web, and social networks, are usually dynamic and change over time. The communities which reflect the substructures hidden in the networks usually overlap each other, and detecting overlapping communities in the dynamic complex networks is a challenging task. Prior researchers have applied multiobjective optimization method to the detection of dynamic overlapping communities and achieved some excellent results. However, in terms of multiobjective processing, the prior studies all adopt the decomposition method based on weight parameters, and different weight parameters or different parameter values can easily affect the community detection results which further results in the uneven distribution of the detected results in the target space. To solve the above problems, a hybrid algorithm, that is, Collaborative Particle Swarm multiobjective Optimization-based Dynamic Overlapping Community Detection (CPSO-DOCD) algorithm is proposed in this paper. First, to improve the diversity of particles, the encoding/decoding of the particle and the cross inheritance and the variation of particle are redefined first based on label propagation. In each network snapshot, multiple particle swarms are initialized based on Community Overlap Propagation Algorithm (COPRA) to generate particles with uniform distribution. Multiple different objective functions are optimized using multiple particle swarms respectively to avoid the incorrect selection of weight parameters. In addition, a reference-point-based is adopted in the particle selecting stage to solve the uneven distribution of detected results in the target space. Second, a node label entropy-based particle swarm algorithm is proposed to improve the accuracy of community detection of current network snapshots. Finally, when one snapshot switches to another over time, a migration strategy based on COPRA local-search and clique generation is utilized to adjust the prior community detection results, which enables the former results can be adapted to the new network snapshots. The experiments are implemented based on four dynamic networks which are Cit-HepPh, Cit-HepTh, Emailed-EU-core-temporal, and CollegeMsg. The hypervolume value of the overlapping community detection result obtained by CPSO-DOCD is 0.5%–2% higher than MDOA, MCMOEA, SLPAD, and iLCD. Furthermore, CPSO-DOCD also performed better than MDOA, MCMOEA, SLPAD, and iLCD on C-metric values, and CPSO-DOCD can approach approximately to the Pareto frontier.

Aiming at the problems of secondary pollution and resource waste caused by inaccurate input of coal and ammonia in coal-fired power plant, an optimal controlling method of combustion and denitration coordinated operation based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper. First, the environmental model is constructed by the Stacking algorithm to predict the NOx emission concentration of the combustion and denitration system, which provides environmental state feedback for the optimal controlling model. Second, the optimization controlling model is constructed based on the DDPG algorithm within the standard limitation of denitration efficiency and NOx emission concentration. This model takes the minimization of comprehensive cost as its optimization objective to realize the optimal control of controllable variables in the cooperative operation process of combustion and denitration. The experimental results of real operational data from 1000 MW boiler unit in a power plant locating in south China show that the optimization results of coordinated operation for the combustion and denitration system are better than single-stage optimization results. In addition, the total cost is reduced by 1%–3% on average compared with before optimization.

Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time-domain spectroscopy technology based on deep learning can be used for nondestructive detection of various hazardous materials by recognizing the fingerprint spectrum of substances. However, due to the high cost of collecting spectral data, training samples are not easy to obtain and scarce for classification models, which leads to poor training effectiveness and low accuracy of classification. To address this problem, a fully connected layer-based auxiliary classifier generative adversarial network (FC-ACGAN) data augmentation method is proposed in this paper, we realized the generator and discriminator with fully connected layers to fit original data distribution better and generate data with higher quality. First, THz time-domain spectral data from seven flammable liquids were augmented using Mixup and FC-ACGAN, and then we fed the generated data set and expanded data set into Residual Network (ResNet), convolutional neural network, fully convolutional network, and multilayer perceptron for training. It is demonstrated that our method can solve the overfitting of models because of insufficient data. Compared with direct training on original data set, the accuracy of models using augmented data set improved by 5.1325% on average, which is 3.15% higher than that using Mixup. Furthermore, we experimented on expanded data set with ResNet long short-term memory for classification, the final accuracy reaches 99.42% on average, which is 1.09% higher than that using the original data set.

Cross-modal retrieval has attracted great attention due to the increasing demand for tremendous amounts of multimodal data in recent years. These retrievals could either be text-to-image or image-to-text. To address the problem of inappropriate information included between images and texts, we propose two cross-modal recovery techniques established on a dual-branch neural network defined on a common subspace and the hashing learning method. First, a cross-modal recovery technique established on a multilabel information deep ranking model (MIDRM) is provided. In this method, we introduce a triplet-loss function into the dual-branch neural network model. This function takes advantage of the semantic information of the bimodal components, focusing on not only the similarities between similar images and text features but also the distances between dissimilar images and texts. Second, we establish a new cross-modal hashing technique said to be the deep regularized hashing constraint (DRHC). In this method, the regularized function is used to replace the binary constraint, and the discrete value is constrained to a certain numerical range so that the network can achieve end-to-end training. Overall, the time complexity is greatly improved, and the occupied storage space is also greatly reduced. Different experiments on our proposed MIDRM and DRHC models demonstrate their superior performance to those of the state-of-the-art methods on two widely used data sets. The experimental results show that our approach also increases the mean average precision of cross-modal recovery.

Aiming at the problems of complex structure, high components coupling, and difficultly monitoring of the whole health status with the industrial robot, a metric learning-based whole health indicator model is proposed. First, according to the more obvious degradation characteristics of industrial robots during accelerated operation, the accelerated signal is segmented and then the time-domain features are extracted. Second, the long-term and short-term memory (LSTM) network combined with the multihead attention is used to construct the network model, and the metric learning method is adopted to learn the similarity measurement method of the industrial robot monitoring data. Finally, the similarity measure method got from metric learning is used to construct the whole health indicator, which describes the whole degradation trend of the industrial robot. The experiments are based on the real accelerated aging data set from industrial robots. The results show that the proposed model can effectively construct the whole health indicator for industrial robots. The average trend of the proposed model reaches 0.9769. The average monotonicity reaches 0.5666, which is 0.1748, 0.1577, and 0.1492 higher than the similarity measurement method based on Euclidean distance, Markov distance, and LSTM.

To solve the problems such as low accuracy and low retrieval performance in feature extraction of environmental sound data from Internet consumer finance scenario, a novel method for environmental sound filtering and feature extraction (VF-EFENet) is proposed. First, the Conv-TasNet speech separation model is clipped and migrated to filter foreground voice. Second, an environmental sound feature extraction model is established based on the improved VGGish, and pretraining weight is used to improve the feature extraction accuracy. Finally, metric learning is used to optimize the distance function to improve retrieval accuracy. Metric learning can make the same kind of audio feature space cohesive and the different types of audio feature space away. The experiments are implemented based on AISHELL-1 and ESC-50 data sets to test voice filter performance, average classification accuracy and average retrieval accuracy. The experimental results show that VF-EFENet can effectively filter the voice in mixed audio and the SI-SNR reaches 12.51 db. When sampling rate is 8 kHz, the average classification accuracy is improved by 8.3% after voice filtering using VF-EFENet. When Top30 samples are retrieved, the average retrieval accuracy of VF-EFENet is 7.37% higher than that of ESResNetAttention.

Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM-TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real-time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM-RNN, HMM-LSTM, and HMM-GRU.

Since the new crown epidemic, mask-wearing has become a new normal in people's work and life. The inspection mechanism for mask-wearing at the entrance and exit of public places is seriously insufficient. The phenomenon of “pick-up on entry” has led to the severe formalization of mask-wearing inspection. Manual detection of mask-wearing in an open and dynamic crowded environment is unrealistic, which is not only time-consuming and labor-intensive but also cannot achieve early warning throughout the entire process. In response to this problem, this paper proposes a real-time recognition and early warning method for mask-wearing in an open, dynamic, complex environment based on improved YOLOv5 R6.1. First, replacing the first Conv structure of the backbone network in the YOLOv5 R6.1 model with an improved Stem structure to minimize the computational overhead while improving the performance. Then by normalizing the data, the random erasure data expansion technique is used to enhance the antiocclusion robustness of the algorithm. Finally, according to the mask-wearing specification in the training data set, optimizing and adjusting the anchor box parameters of the YOLOv5 R6.1 model to improve the model's ability to recognize small targets. The experiments are based on open data sets, and the results show that the mean precision (mAP), precision, and recall of this method reach 92.9%, 94.1%, and 88.5% on average, and the average frames per second (FPS) reaches 117. Moreover, the mAP and FPS are improved by an average of 6.5% and 474% compared with algorithms based on RetinaNet, Attention-Retina, Single Shot multibox Detector, Fast-RCNN, YOLOv4, and YOLOv5.

In this paper, a non-negative matrix factorization feature expansion (NMFFE) approach was proposed to overcome the feature-sparsity issue when expanding features of short-text. First, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Second, we utilized the Dual regularization non-negative matrix tri-factorization (DNMTF) algorithm to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short-text to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89%, and 1.79% on three data sets: Web snippets, Twitter sports, and AGnews, respectively compared with the Word2Vec algorithm and Char-CNN algorithm. It indicated that the NMFFE algorithm was better than the BOW algorithm and the Char-CNN algorithm in terms of classification accuracy and algorithm robustness.

Providing resilient network control is a critical concern for deploying Software-Defined Networking (SDN) into Wide-Area Networks (WANs). For performance reasons, a Software-Defined WAN is divided into multiple domains controlled by multiple controllers with a logically centralized view. Under controller failures, we need to remap the control of offline switches from failed controllers to other active controllers. Existing solutions have three limitations: (1) the least flow programmability (e.g., the ability to change paths of flows) cannot be maintained; (2) active controllers could be overloaded, interrupting their normal operations; (3) network performance could be degraded because of the increasing controller-switch communication overhead. In this paper, we propose RetroFlow+ to recover the flow programmability and achieve low communication overhead during controller failures. By intelligently configuring a set of selected offline switches working under the legacy routing mode and several active controllers releasing a few control resources, RetroFlow+ enables active controllers to use the minimum control resource to sustain the flow programmability. RetroFlow+ also smartly transfers the control of offline switches with the SDN routing mode to active controllers to minimize the communication overhead from these offline switches to the active controllers. Simulation results show that RetroFlow+ realizes low communication overhead, recovers all offline flows under one and two controller failures, and improves the flow recovery percentage up to 70% under three controller failures, compared with the state-of-the-art solution.

Modern datacenter applications bring fundamental challenges to transport protocols as they simultaneously require low latency and high throughput. Recent receiver-driven trans-port protocols transmit only one data packet once receiving each grant or credit packet from the receiver to achieve ultra-low queueing delay and zero packet loss. However, the round-trip time variation and the highly dynamic background traffic significantly deteriorate the performance of receiver-driven transport protocols, resulting in under-utilized bandwidth. This paper designs a simple yet effective solution called RPO that retains the advantages of receiver-driven transmission while efficiently utilizing the available bandwidth. Specifically, RPO rationally uses low-priority opportunistic packets to ensure high network utilization without increasing the queueing delay of high-priority normal packets. In addition, since RPO only uses Explicit Congestion Notification (ECN) marking function and priority queues, RPO is ready to deploy on switches. We implement RPO in Linux hosts with DPDK. Our small-scale testbed experiments and large-scale simulations show that RPO significantly improves the network utilization by up to 35% under high workload over the state-of-the-art receiver-driven transmission schemes, without introducing additional queueing delay.

Despite the popularity of Bluetooth low energy (BLE) location-based services (LBS) in Internet of things applications, large-scale BLE LBS are extremely challenging due to the expenses of deploying and maintaining BLE beacons. To alleviate this issue, this work presents WiBeacon, which repurposes ubiquitously deployed WiFi access points (AP) into virtual BLE beacons via only moderate software upgrades. Specifically, a WiBeacon-enabled AP can broadcast elaborately designed WiFi packets that could be recognized as iBeacon-compatible location identifiers by unmodified mobile BLE devices. This offers fast deployment of BLE LBS with zero additional hardware costs and low maintenance burdens. WiBeacon is carefully integrated with native WiFi services, retaining transparency to WiFi clients. We implement WiBeacon on commodity WiFi APs (with various chipsets such as Qualcomm, Broadcom, and MediaTek) and extensively evaluate it across various scenarios, including a real commercial application for courier check-ins. During the two-week pilot study, WiBeacon provides reliable services, i.e., as robust as conventional BLE beacons, for 697 users with 150 types of smartphones.

The large-scale Bluetooth low energy (BLE) location-based services (LBS) are challenging due to the requirement of additional Bluetooth beacons, which inevitably incur tremendous hardware and maintenance cost. To alleviate this issue, this work presents WiBeacon which repurposes ubiquitously deployed WiFi access points into virtual beacons via cross-technology communication (CTC). WiBeacon only requires moderate software updates in APs, thus enabling fast deployment with zero additional hardware and also low maintenance cost via the remote Internet access.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy called VALID in 364 Chinese cities. VALID is pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12- 2021/01), VALID consists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers' behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants' smartphones under their consent as a virtual infrastructure to design, build, deploy, and operate VALID from in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers' behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

Summary In this paper, we present the study of interactional arrangements that support the collaboration of headquarters (HQ), field responders, and a computational planning agent in a time‐critical task setting created by a mixed‐reality game. Interactional arrangements define the extent to which control is distributed between the collaborative parties. We provide 2 field trials, one to study an “on‐the‐loop” arrangement in which HQ monitors and intervenes in agent instructions to field players on demand and the other, to study a version that places HQ more tightly “in‐the‐loop.” The studies provide an understanding of the sociotechnical collaboration between players and the agent in these interactional arrangements by conducting interaction analysis of video recordings and game log data. The first field trial focuses on the collaboration of field responders with the planning agent. Findings highlight how players negotiate the agent guidance within the social interaction of the collocated teams. The second field trial focuses on the collaboration between the automated planning agent and the HQ. We find that the human coordinator and the agent can successfully work together in most cases, with human coordinators inspecting and “correcting” the agent‐proposed plans. Through this field trial‐driven development process, we generalise interaction design implications of automated planning agents around the themes of supporting common ground and mixed‐initiative planning.

The state-of-the-art datacenter load balancing designs commonly optimize bisection bandwidth with homogeneous switching granularity. Their performances surprisingly degrade under mixed traffic containing both short and long flows. Specifically, the short flows suffer from long-tailed delay, while the throughputs of long flows also degrade dramatically due to low link utilization and packet reordering. To solve these problems, we design a traffic-aware load balancing (TLB) scheme to adaptively adjust the switching granularity of long flows according to the load strength of short ones. Under the heavy load of short flows, the long flows use large switching granularity to help short ones obtain more opportunities in choosing short queues to complete quickly. On the contrary, the long flows reroute flexibly with small switching granularity to achieve high throughput. Furthermore, under extremely bursty scenario, we utilize the packet slicing scheme for long flows to release bandwidth for short ones. The experimental results of NS2 simulation and testbed implementation show that TLB significantly reduces the average flow completion time of short flows by 16%-67% over the state-of-the-art load balancers and achieves the high throughput for long flows. Moreover, for extreme bursty case, at the acceptable throughput degradation of long flows, TLB with packet slicing reduces the deadline missing ratio of bursty short flows by up to 80%.

Overcrowded wireless devices in unlicensed bands compete for spectrum access, generating excessive cross-technology interference (CTI), which has become a major source of performance degradation especially for low-power IoT (e.g., ZigBee) networks. This paper presents a new forward error correction (FEC) mechanism to alleviate CTI, named SafetyNet. Designed for ZigBee, SafetyNet is inspired by the observation that ZigBee is overly robust for environment noises, but insufficiently protected from high-power CTI. By effectively embedding correction code bits into the PHY layer SafetyNet significantly enhances CTI robustness without compromising noise resilience. SafetyNet additionally offers a set of unique features including (i) transparency, making it compatible with millions of readily-deployed ZigBee devices and (ii) zero additional cost on energy and spectrum, as it does not increase the frame length. Such features not only differentiate SafetyNet from known FEC techniques (e.g., Hamming and Reed-Solomon), but also uniquely position it to be critically beneficial for today's crowded wireless environment. Our extensive evaluation on physical testbeds shows that SafetyNet significantly improves ZigBee's CTI robustness under a wide range of networking settings, where it corrects 55% of the corrupted packets.

Wireless technologies are increasingly diversified to serve various Internet-of-things applications. Yet, our mobile devices (e.g., smartphones) are manufactured with limited types of wireless radio, making it challenging to access the data in the heterogeneous IoT devices. To address this fundamental problem, this work proposes XFi, which enables mobile devices to use commodity WiFi radio to directly and simultaneously collect data from diverse heterogeneous IoT devices. Our critical insight is that when an IoT frame collides with an ongoing WiFi transmission, its IoT data is captured by WiFi receiver and retained even after the demodulation procedures in WiFi hardware. Motivated by this observation, XFi proposes a general approach to obtain IoT data by analyzing the decoded WiFi payload. The method is fully compatible with existing commodity WiFi hardware and generally applicable to various IoT protocols. We implement XFi on commodity devices (e.g., RTL8812au, CC2650, and SX1280). Our comprehensive evaluation demonstrates that XFi can collect data from 8 IoT devices in parallel with over 97% accuracy, offering reliable cross-technology data collection.

Client-side adaptive bitrate (ABR) algorithms based on deep reinforcement learning (RL) can continuously improve its adaptability to network conditions. However, most existing methods adopt fixed reward functions to train the ABR policy, which leads the results being not consistent with user-perceived quality of experience (QoE) in a long duration under various network conditions. In order to optimize the QoE, this paper proposes a novel ABR algorithm considering user preference based on short trajectory segments. The user-specific preference feedback, which is selected by the user from a pair of short track segments in advance, is collected and applied to define the training goal of RL. Specifically, we train a deep neural network to define the RL reward and integrate it with A3C-based ABR algorithm. The experiment results show that the accuracy of the proposed reward model outperforms most existing fixed reward functions by 13.6% in user preference prediction, and the optimized ABR algorithm improves QoE by 16.4% on average.

Enabling path programmability is an essential feature of Software-Defined Networking (SDN). During controller failures in Software-Defined Wide Area Networks (SD-WANs), a resilient design should maintain path programmability for offline flows, which were controlled by the failed controllers. Existing solutions can only partially recover the path programmability rooted in two problems: (1) the implicit preferable recovering flows with long paths and (2) the sub-optimal remapping strategy in the coarse-grained switch level. In this paper, we propose Programmability Guardian to improve the path programmability of offline flows while maintaining low communication overhead. These goals are achieved through the fine-grained flow-level mappings enabled by existing SDN techniques. Programmability Guardian configures the flow-controller mappings to recover offline flows with a similar path programmability, maximize the total programmability of the offline flows, and minimize the total communication overhead for controlling these recovered flows. Simulation results of different controller failure scenarios show that Programmability Guardian recovers all offline flows with a balanced path programmability, improves the total programmability of the recovered flows up to 68%, and reduces the communication overhead up to 83%, compared with the baseline algorithm.

In the data center network, a packet-level load balancer such as random packet spraying (RPS) achieves high throughput by spraying data packets to all transmission paths, which easily suffers from the packet out-of-order problem under network asymmetry. While state-of-the-art network coding schemes can mitigate the issue, too many encoded redundant packets introduced by the network coding will cause extra traffic overhead, larger queueing delay and even TCP time out. In this paper, we propose OPportunistic Encoded Redundant (OPER), a middle-layer design upon existing coding schemes to mitigate the curse of redundant packets. Specifically, OPER uses opportunistic redundant packets which are replaceable by the data packets in the switches under heavy congestion. OPER is implemented as a shim layer between TCP and IP layers at end-hosts and a loadable plugin at switches, leaving existing TCP/IP protocols unmodified. The testbed and NS2 experiments show that, OPER reduces the average flow completion time by up to 71% compared with the state-of-the-art multipath coding schemes.

Providing resilient network control is a critical concern for deploying Software-Defined Networking (SDN) into Wide-Area Networks (WANs). For performance reasons, a Software-Defined WAN is divided into multiple domains controlled by multiple controllers with a logically centralized view. Under controller failures, we need to remap the control of offline switches from failed controllers to other active controllers. Existing solutions could either overload active controllers to interrupt their normal operations or degrade network performance because of increasing the controller-switch communication overhead. In this paper, we propose RetroFlow to achieve low communication overhead without interrupting the normal processing of active controllers during controller failures. By intelligently configuring a set of selected offline switches working under the legacy routing mode, RetroFlow relieves the active controllers from controlling the selected offline switches while maintaining the flow programmability (e.g., the ability to change paths of flows) of SDN. RetroFlow also smartly transfers the control of offline switches with the SDN routing mode to active controllers to minimize the communication overhead from these offline switches to the active controllers. Simulation results show that compared with the baseline algorithm, RetroFlow can reduce the communication overhead up to 52.6% during a moderate controller failure by recovering 100% flows from offline switches and can reduce the communication overhead up to 61.2% during a serious controller failure by setting to recover 90% of flows from offline switches.

Conventional gateway solutions are limited in satisfying the demand for ubiquitous connections among heterogeneous wireless devices, e.g., wide-area and personal-area network devices, due to the deployment complexity, high cost, and the incurred extra traffic. Recent advances propose the physical layer cross-technology communication to address these issues. However, existing CTC techniques commonly emulate the target waveform in the frequency domain (FDE). Despite their success, these FDE based techniques inherently suffer from high quantization errors and are insufficient for IoT applications that require high communication reliability.

The cross-technology communication (CTC) is a promising technique proposed recently to bridge heterogeneous wireless technologies in the ISM bands. Existing solutions use only the coarse-grained packet-level information for CTC modulation, suffering from a low throughput (e.g., 10 b/s). Our approach, called BlueBee, explores the dense PHY-layer information for CTC by emulating legitimate ZigBee frames with the Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency for its only modifying the payload of Bluetooth frames, requiring neither hardware nor firmware changes at either the Bluetooth sender or the ZigBee receiver. Our implementation on both USRP and commodity devices shows that BlueBee can achieve standard ZigBee bit rate of 250 kb/s at more than 99% accuracy, which is over 10000 x faster than the state-of-the-art packet-level CTC technologies.

Cross-technology Communication (CTC) is a key technique to explore the full capacity of heterogeneous wireless. The latest CTC designs explore the PHY-layer to reach the standards' maximum rate, but leaving a critical gap to practicality -- existing PHY-layer CTCs are commonly transmitter-side techniques requiring a high-end transmitter (with a high degree of freedom in signal manipulation) to emulate the receiver signal closely. This inherently limits the reverse direction (low-end to high-end) communication. We present XBee, a unique receiver-side CTC that fills in the gap and makes a critical step towards achieving CTC bidirectionality. XBee is demonstrated as a ZigBee to BLE communication, where the key innovation lies in the unique mechanism of cross-technology decoding, or cross-decoding in short, which interprets a ZigBee frame only by carefully observing the bit patterns obtained at the BLE receiver. Technically, XBee counterintuitively explores the sampling offset to overcome the intrinsic challenge due to BLE's narrower bandwidth (1MHz) than ZigBee (2MHz). Extensive implementation and evaluation on USRP and commodity devices reach 250 kbps under 85% reliability, a 15,000x improvement over state-of-the-art ZigBee to BLE communication, and comparable with the latest PHY-layer CTCs to achieve CTC bidirectionality.

WiFi-fingerprint localization is recognized as a promising indoor localization technique. However, it suffers from high implementation overhead such as heavy initial training and fingerprint map maintenance overtime. In this paper, we present the design, implementation, and evaluation of AP-Sequence. It is a fingerprint-based localization system that achieves extremely low overhead in fingerprint map construction and maintenance. AP-Sequence achieves this by treating a scan from any reference locations as an input to adjust a large portion of the fingerprint map. The power of AP-Sequence comes from dynamic region partitioning mechanism generating a fingerprint based on relative RSS values. AP-Sequence offers several advantages over existing methods with respect to robustness against environment noises, ability to handle dynamic power control, and mobile device heterogeneity. We have implemented AP-Sequence on an Android platform. Experiment results with over one month of evaluation demonstrate that our design achieves an average localization accuracy of 4-7.6 m over an extended time period with low-overhead in fingerprint map construction and maintenance.

Cross-technology communication (CTC) techniques are introduced in recent literatures to explore the opportunities of collaboration between heterogeneous wireless technologies, such as WiFi and ZigBee. Their applications include context-aware services and global channel coordination. However, state-of-the-art CTC schemes either suffer from channel inefficiency, low throughput, or disruption to existing networks. This paper presents the CTC via data packets (DCTC), which takes advantage of abundant existing data packets to construct recognizable energy patterns. DCTC features (i) a significant enhancement in CTC throughput while (ii) keeping transparent to upper layer protocols and applications. Our design also features advanced functions including multiplexing to support concurrent transmissions of multiple DCTC senders and adaptive rate control according to the traffic volume. Testbed implementations across WiFi and ZigBee platforms demonstrate reliable bidirectional communication of over 95% in accuracy while achieving throughput 2.3x of the state of the art. Meanwhile, experiment results show that DCTC has little and bounded impact on the delay and throughput of original data traffic.

Recent research on CTC (cross-technology communication) demonstrates the viability of direct coordination among heterogeneous devices (e.g., WiFi and ZigBee) with incompatible physical layers. Although encouraging, current solutions suffer from either severe inefficiency in channel utilization or low throughput using limited beacons. To address these limitations, this paper presents C-Morse, which leverages all traffic (such as through data packets, beacons and other control frames) to achieve a high cross-technology communication throughput. The key idea of C-Morse is to slightly perturb the transmission timing of existing WiFi packets to construct recognizable radio energy patterns without introducing noticeable delays to upper layers. At the receiver side, ZigBee captures such patterns by sensing the RSSI value, and then decodes the transmitted symbols. C-Morse also introduces a novel timing-based multiplexing technique to allow the coexistence of multiple C-Morse access points and reject other interference, showing a reliable symbol delivery ratio. As a result, C-Morse achieves a free side-channel, whose CTC throughput is as much as 9 χ of the present state of the art, while maintaining the through traffic within a negligible delay that goes unnoticed by applications and end-users.

Cross-Technology Communication is an emerging research direction providing a promising solution to the coexistence problem of heterogeneous wireless technologies in the ISM bands. However, existing works use only the coarse-grained packet-level information for cross-technology modulation, suffering from a low throughput (e.g., 10bps). Our approach, called BlueBee, aims at achieving much higher CTC throughput thus extends CTC applications. We pro- poses a new direction by emulating legitimate ZigBee frames using a Bluetooth Low Energy (BLE) radio. Uniquely, BlueBee achieves dual-standard compliance (i.e., BLE and ZigBee) and transparency by selecting only the payload of Bluetooth frames, requiring neither hardware nor firmware changes at the BLE senders and ZigBee receivers. Our implementation on commodity device testbeds shows that BlueBee can achieve a more than 99% accuracy and a through- put 10,000x faster than the state-of-the-art CTC reported so far. In addition, we show a demo of using BlueBee on a smartphone to control several smart light bulbs a ached with ZigBee radio.

Cross-Technology Communication is an emerging research direction providing a promising solution to the wireless coexistence problem in the ISM bands. However, the state-of-the-art CTC designs have intrinsic limitations in the throughput due to their use of coarse-grained packet-level information. In contrast, we propose to exploit the fine-grained signal modulation information via a technique called PHY-layer emulation to boost CTC throughput. We can embed a legitimate packet of a target technology, e.g., ZigBee, within the payload of a source technology, e.g., WiFi or Bluetooth Low Energy (BLE). At the mean time, we require no modification at the hardware or firmware at either sender or receiver. We can achieve 8,000x throughput from WiFi to ZigBee and 10,000x throughput from BLE to ZigBee compared to the state of the art. We also have a demo showcasing how our designs can be implemented on off-the-shelf smartphones for smart light bulbs control.

Cross-Technology Communication is a promising solution proposed recently to the coexistence problem of heterogeneous wireless technologies in the ISM bands. The existing works use only the coarse-grained packet-level information for cross-technology modulation, suffering from a low throughput (e.g., 10bps). Our approach, called BlueBee, proposes a new direction by emulating legitimate ZigBee frames using a Bluetooth radio. Uniquely, BlueBee achieves dual-standard compliance and transparency by selecting only the payload of Bluetooth frames, requiring neither hardware nor firmware changes at the Bluetooth senders and ZigBee receivers. Our implementation on both USRP and commodity devices shows that BlueBee can achieve a more than 99% accuracy and a throughput 10,000x faster than the state-of-the-art CTC reported so far.

Recent studies on CTC (cross-technology communication) have demonstrated the possibility of building direct communication between heterogeneous wireless communication technologies, such as WiFi and ZigBee. However, current solutions suffer from significant spectrum usage or limited throughput. To address the issues, this paper presents Transparent Cross-technology Communication (TCTC), a novel CTC technique that establishes a side channel by exploiting today's abundant wireless traffic. The key idea of TCTC is to embed messages in the timings of on-going legacy traffic (e.g., HTTP packets in WiFi), with small and bounded impact on upper-layer applications. Feasibility and effectiveness of our design has been validated via test-bed implementations and experiments on bidirectional communication between WiFi and ZigBee platforms.

Human physical activity recognition based on wearable sensors has applications relevant to our daily life such as healthcare. How to achieve high recognition accuracy with low computational cost is an important issue in the ubiquitous computing. Rather than exploring handcrafted features from time-series sensor signals, we assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it outperforms state-of-the-arts in terms of recognition accuracy and computational cost.

Radio Frequency IDentification (RFID) systems have rich applications in daily life. A crucial problem in RFID systems is to estimate the cardinality of RFID tags. Most exiting probabilistic RFID cardinality estimation algorithms utilize a certain pattern hidden in the response vector formed by the tag responses to make estimators. In this paper, we argue that finding the patterns is actually not necessary! In this paper, we present a novel approach to RFID cardinality estimation by making the full use of the whole response vector. We characterize the critical relationship between the number of RFID tags and the specific distribution of the bits in the response vector. In this paper, we consider two kinds of responses, i.e., empty / non-empty responses and empty / singleton / collision responses. To cater for RFID systems with a very large number of tags, we also generalize our estimation approach to handle geometrically distributed response vectors. Both rigid theoretical analysis and extensive simulations have been conducted and the conclusive results demonstrate that our approach is more accurate than existing state-of-the-art RFID estimation approaches.

Efficient identification of tags has been an essential operation for Radio Frequency IDentification (RFID) systems. In this paper, we consider the crucial problem of collecting all tags in a large-scale system through a handheld RFID reader. The reader has to move around due to the limited communication range of tags. We focus on the minimization of power consumption of the reader given the constraint on its movement distance. Two challenges must be addressed. First, the communication range of a tag is dependent on the reader. There is an intrinsic tradeoff between power saving and movement distance. Second, the number of sites at which the reader can collect tags can be numerous and the problem complexity is extremely high. We theoretically prove that the problem of minimizing the energy consumption of the reader is NP Complete (NPC). To solve the problem, we first analytically reveal that the time needed for reading a given number of tags is linearly proportional to the number of tags only. With this insight, we next propose an approach called ePath by constructing an energy-efficient candidate path and then incrementally pruning the path when the tag locations are given. We further relax the assumption on tag locations by extending ePath to exploit the tag distribution density knowledge only. Extensive simulations have been performed, and results show that our approach significantly reduces the power consumption of the reader comparing to an existing approach.

In order to reduce untrustworthy between cloud users and the underlying cloud storage platform, a novel cloud security storage solution is proposed based on autonomous data storage, management, and access control. The roles of users are re-evaluated, and the knowledge provided by the users is incorporated into the cloud storage model. Both the superiority of the public cloud in large scale data storage and the advantages of the private cloud in privacy preserving can be obtained. The main advantages of our approach include avoiding the superposition of complex security policies and overcoming the mistrust between the users and the platform. Furthermore, our security storage service can be easily integrated into the cooperative cloud computing environment. A prototype system is developed, and a use case is also presented.

A hot and primary problem in RFID system is the estimation of RFID tag cardinality and many algorithms have been proposed for estimating RFID cardinality. However, we find that there is a serious problem that has been widely neglected by some of previous works of RFID cardinality estimation. They incorrectly assume that states of different time slots in the same frame are independent of each other. The consequence of such incorrect assumption would lead to low estimation accuracy. To demonstrate how we correct this mistake, we propose an accurate and fast estimating algorithm called AFR based on the pattern of the number of consecutive empty slots before the first non-empty slot. We theoretically derive the expected number and the variance of consecutive empty slots before the first non-empty slot as a function of the number of tags. Based on such theoretical derivation, we determine the best estimation of the number of tags by only looking at the numbers of consecutive empty slots before the first non-empty slot. Simulation results demonstrate that our algorithm makes more accurate estimation of RFID tag cardinality than an existing state-of-the-art algorithm while using shorter estimation time.

The Network Service Interface (NSI) has been created as a result of collaborative development of network and application engineers primarily associated with the Research and Education (R&E) community. The NSI allows workflow systems not only to check available service points for a workflow engine to schedule executions, but also to reserve and provide network connections among those service points. The Open Flow technology provides programmability on the network Flow and allows software to define dynamically behaviour of the network. These new features offer data intensive applications new opportunities to optimize the mapping between data Flow patterns and the infrastructure yielding better system level quality. However, they also require the computing support systems effectively capture not only the characteristics of the application workflow but also the controllability of the underlying network. In this paper we discussed the extension of our previous system called Network QoS Planner (NEWQoSPlanner) and investigated how reservation based connection services can be enhanced by dynamic network Flow control. We also discusse how NEWQoSPlanner invokes network services to achieve connection reservation and provisioning, and includes Open Flow to realize dynamic Flow optimization for data intensive workflows.

UNLABELLED: WebGLORE is a free web service that enables privacy-preserving construction of a global logistic regression model from distributed datasets that are sensitive. It only transfers aggregated local statistics (from participants) through Hypertext Transfer Protocol Secure to a trusted server, where the global model is synthesized. WebGLORE seamlessly integrates AJAX, JAVA Applet/Servlet and PHP technologies to provide an easy-to-use web service for biomedical researchers to break down policy barriers during information exchange. AVAILABILITY AND IMPLEMENTATION: http://dbmi-engine.ucsd.edu/webglore3/. WebGLORE can be used under the terms of GNU general public license as published by the Free Software Foundation.