Camera-based facial-emotion recognition (FER) suffers from poor lighting, occlusion, and privacy exposure, whereas mmWave-only solutions lack the spatial detail required for fine-grained affect analysis. To close this gap, we present SwinLSTM-EmoRec (Shifted Window Transformer + Long Short-Term Memory Emotion Recognition). This non-contact dual-modal framework fuses micro-Doppler signatures captured by a TI IWR1443 mmWave radar with RGB imagery while treating radar as the primary, identity-obscured source and adaptively limiting reliance on RGB. Privacy is preserved because the cross-attention gate down-weights or bypasses RGB when illumination is poor or when potential identity exposure is detected, leaving decisions dominated by illumination-invariant radar dynamics. A shifted-window Swin Transformer extracts spatial facial cues, an LSTM models temporal radar dynamics, and a lightweight cross-attention layer aligns the two streams, boosting F1 by up to 4% over early, late, and self-attention baselines. On a 50-participant interactive-gaming dataset recorded under varied lighting and distances of 0.5–2 m, the system achieves 98.5% accuracy (F1 ≈ 0.98). It maintains 33.9 ms end-to-end latency on a 15 W Jetson Xavier NX edge device. Performance remains > 92% at 2 m, demonstrating robust, privacy-preserving FER robust, privacy-aware emotion sensing suitable for smart-home, tele-health, and e-sports IoT applications.

Reliable, privacy-preserving emotion sensing is essential for next-generation IoT applications; however, vision or audio-only pipelines often break down when faces are masked, environments are noisy, or multiple speakers coexist. We present AuraVox, a contactless system that couples millimetre-wave lip micro-Doppler with speech acoustics and fuses them through AuraNet, a bespoke cross-modal transformer. The radar first localizes each talker via MUSIC-based direction-of-arrival estimation and Bartlett beamforming, then captures high-resolution Doppler signatures of lip motion; in parallel, prosodic and spectral speech cues are extracted from a lapel microphone. AuraNet holistically attends to these heterogeneous streams and produces a unified representation for emotion classification. Evaluated on a 30-subject bilingual (English/Mandarin) corpus that includes mask-wearing, multi-speaker overlap, and 30–90 cm ranges, AuraVox attains 96% macro-F1, outperforming radar-only and audio-only baselines by up to eight percentage points. End-to-end latency is 12.8 ms per frame on a 10 W Jetson Xavier NX, meeting real-time constraints for edge deployment. By unifying beamformed lip kinematics with speech cues through AuraNet, AuraVox delivers the first multi-speaker, cross-language, mask-resilient emotion recognizer that runs on commodity hardware. Representative use cases include stress-aware in-cabin driver assistance, hospital check-in triage, and mood-adaptive smart-home interfaces.

In recent years, more and more people choose to work out at home or in the office to improve their physique and build muscle. However, the lack of professional guidance makes it difficult for many fitness practitioners to achieve optimal results. Consequently, research on non-contact fitness monitoring using wireless signals has gained attention. Existing studies primarily focus on isolated exercises, while compound exercises, which involve multiple isolated exercises, remain underexplored. This combination introduces new challenges for fitness recognition and monitoring. To this end, we propose M-Fitness, a millimetre-wave radar-based fitness assistant system capable of recognizing and monitoring both isolated and compound exercises. First, we capture fine-grained motion features and design image enhancement algorithms to generate intuitive motion images. Next, we design a novel motion segmentation method for fitness actions. We further formulate compound exercise recognition as a sequential task and develop customized deep learning models that allow users to incorporate new compound exercises. Finally, we perform a comprehensive fitness assessment based on the FITT principle. Experiments on a dataset of over 5,000 movement samples from 18 volunteers demonstrate that M-Fitness achieves 96.5% accuracy for isolated exercise recognition and 92.7% accuracy for compound exercise recognition, exhibiting strong adaptability to diverse environments.

Maintaining focus is essential for carrying out tasks accurately and efficiently, but it can be difficult to do so in settings that are full of distractions and continuous change in daily life, the workplace, and education. Traditional assessment methods such as self-reporting, eye tracking, or camera-based observation, are often intrusive, subjective, or limited by privacy concerns. To address these limitations, this study proposes a novel radar-based framework for continuous estimation of human attention during fine-grained hand–object interaction tasks using frequency-modulated continuous-wave (FMCW) millimeter-wave radar. We capture fine-grained Doppler–time motion patterns. The considered activities included pouring water, stacking cups, and writing, representing different motion types (translational, repetitive, and fine-motor) collected under focused and distracted conditions. A multi-input Deep regression network is introduced, which combines handcrafted behavioral descriptors with temporal Doppler–time feature embeddings using a 1D–CNN– BiLSTM–Attention fusion pipeline. This network simultaneously encodes motion smoothness, energy dynamics, and temporal regularity to derive attention scores on a 0-100 scale. Extensive experiments show the model’s capability to generalize across different subjects and activities, achieving an overallRMSEof ≈2.21 andR2of 0.9959. Ablation analysis validates the needs of both handcrafted features and multi-head-attention fusion for performance. signal-to-noise ratios below 15 dB, noise tests revealed a performance decline of less than 5%. Changes in attention impact behavioral patterns was gained through correlation analysis. Our approach offers a privacy-preserving and contactless alternative to existing methods, making it ideal for use in classroom monitoring, workplace productivity assessment, and cognitive health evaluation.

Accurate authentication of paper-based materials is crucial for secure document verification, counterfeit currency detection, and intelligent packaging across industries such as finance, logistics, and security. Differentiating between subtle variations, such as glossy, recycled, coated, or counterfeit paper, remains a significant challenge due to minimal material-level differences. We introduce PSense, the first millimeter-wave MIMO radar-based sensing framework for paper material analysis, offering a fully contactless solution that relies solely on reflected signals, eliminating the need for transmission-based or dual-sided setups. To characterize intrinsic paper properties, we propose a novel material-sensitive feature called the Energy Reflection Ratio (ERR), which encodes key physical interactions including surface reflectivity, internal attenuation, and dispersion, uniquely capturing the electromagnetic signature of each paper type. We further present Trans-PapNet, a dual-model transfer learning framework designed to enhance classification robustness and generalization. One model extracts spatial and spectral patterns from radar signal scalograms using ResNet-50, while the other processes ERR-based physical features. These complementary representations are fused via a shared attention mechanism, enabling effective domain adaptation across paper types. Our method achieves an overall classification accuracy of 97.85%, with strong performance in three knowledge-based transfer scenarios (94.8%, 93.0%, and 90.0%) and under both seen and unseen material conditions (95.3%). These results underscore PSense's robustness and scalability, paving the way for real-world deployment in mobile, secure, and intelligent paper-based authentication systems.

Sitting posture is closely related to our health. Poor sitting posture can cause various diseases and jeopardize our health. Among the current methods for detecting sitting posture, computer vision solutions suffer from privacy leakage and wearable sensor solutions suffer from inconvenience and cost of wearing. In this study, we introduce 3D-Sitpose, which leverages millimeter-wave radar to detect human sitting posture. 3D-Sitpose utilizes wireless signal transmission for non-contact detection, ensuring privacy protection and cost reduction. Firstly, we analyze the impact of variations in human sitting posture on millimeter-wave radar signals, and design sophisticated signal processing methods to refine the collected radar data, yielding clearer point cloud information for volunteers in various sitting postures. Secondly, we develop a two-channel neural network to extract fine-grained features related to volunteers from the point cloud data. Finally, we obtain coordinates for 25 human skeletal points. 3D-Sitpose can instruct users to maintain correct sitting posture based on a set of six key angles. We recruit 20 volunteers from our institute to conduct comprehensive evaluations of 3D-Sitpose. Experiments are conducted in two indoor environments to estimate sitting posture. The results reveal the mean Euclidean distance error for all skeletal point locations is 6.65 cm. This demonstrates that our method is able to estimate various sitting changes in volunteers.

Traditional RF-based liquid identification methods generally rely on a single characteristic, such as refractive index or permittivity, and often assume prior container knowledge, limiting their versatility. These approaches also face challenges in scenarios involving gradual state changes in the liquid. We propose LiqState, a contactless framework for fine-grained liquid identification and continuous state monitoring, capable of operating without prior container information. To mitigate container effects, we developed a LiqState reflection model that analyzes frequency-dependent changes, leveraging the diverse permittivity profiles of liquids across the mmWave frequency range. Our approach introduces a novel feature extraction method, VRCP, which captures four distinct physical and chemical properties for robust identification and state monitoring. Using LiqNet, a service-oriented and customized deep learning model, LiqState achieves an average classification accuracy of 97.3% across diverse conditions, accurately distinguishing 12 liquid types. Additionally, case studies highlight LiqState’s capability to monitor complex processes, such as milk fermentation (RMSE: 0.251) and fruit juice ripening (RMSE: 0.162), and differentiate between similar liquids with minimal alcohol concentration variations.

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users’ active participation, making it impractical to monitor blood alcohol content (BAC) during driving. This article introduces BACFuse, a device-free, contactless, and noninvasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multimodal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse’s performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with an MAE of 2.13 mg/dl, showing promise for future in-car driving management paradigms.

Wireless sensing offers a promising approach for non-destructive and contactless identification of the moisture content in fruits. Traditional methods assess fruit quality based on external features, such as color, shape, size, and texture. However, fruits often appear perfect externally while being rotten inside. Thus, accurately measuring internal conditions is crucial. This paper introduces mmFruit, a non-destructive and ubiquitous system that employs mmWave signals for precise and robust moisture level sensing in thin and thick pericarp fruits. We propose a novel dual incidence moisture estimation model for regular moisture monitoring to achieve high granularity and eliminate fruit type and size dependency. Additionally, we leverage unique reflection responses across different mmWave frequencies to provide discriminative information about fruit moisture levels. Our comprehensive theoretical model demonstrates how fruits’ refractive index, attenuation factor, and elasticity can be estimated by eliminating fruit type dependency. We developed an electric field distribution model utilizing two receiving antennas to address the challenge of varying fruit sizes through a differential approach, aiming to improve overall robustness. mmFruit integrates a customized Spatial-invariant network (SpI-Net) to eliminate interference from different frequencies and locations, ensuring stable moisture monitoring regardless of target displacement. Extensive experiments were conducted over a month in varied environments on seven types of fruits with thin and thick pericarps (apple, pear, peach, mango, orange, dragon fruit, and watermelon). The results demonstrate that mmFruit achieves a commendable RMSE of 0.276 in moisture estimation. It accurately distinguishes fruits with minor moisture level differences (0% to 7%) with 93.6% accuracy and higher moisture differences (45% to 65%) with over 95.1% accuracy, even in scenarios involving diverse displacements and rotations.

Early detection of myocardial infarction (MI) is essential for alleviating symptoms and improving daily activity performance. Researchers typically employ continuous segments of heartbeat signals (20–30 seconds), such as ECG signals, for MI detection, as MI often induces changes in heartbeat patterns. Current MI detection methods, like wearable sensors, may induce discomfort from prolonged wear, and Radio Frequency (RF) based approaches might fail to extract fine-grained heartbeat signals during vigorous movement. This article presents a reliable and motion-robust MI detection method based on RF signals. By developing a series of advanced signal processing algorithms, MI-Ra can capture fine-grained heartbeat signals during various daily activities. Our design is inspired by the fact that RF reflections caused by heartbeat signals are mixed with other motion-induced reflections in a nonlinear manner. We utilize the Taylor series expansion method to extract the linear component of these mixed non-linear signals and propose a novel Generative Adversarial Networks (GAN) method, named IQ-TransGAN, to separate the heartbeat signal. To enhance MI detection reliability, MI-Ra employs a multi-periodicity modeling method to extract refined signal representations from recovered heartbeat signals. We have recruited 50 volunteers with MI from Zhongnan Hospital of Wuhan, China, and 50 volunteers without MI, for comprehensive evaluations. The results demonstrate that MI-Ra achieves an average MI detection accuracy of 95.2% when user is quasi-stationary. Even during user non-stationary conditions, MI-Ra maintains an average detection accuracy of 90.5%. MI-Ra shows promise in paving way for smart home healthcare.

Tracking the menstrual cycle (MC) is essential for women to manage their health and schedule, especially for those with irregular MC. Existing MC tracking methods either rely on length of previous cycles (e.g., calendar noting) or require additional devices to collect more information (e.g., basal temperature), which are not able to realize both accuracy and convenience. Inspired by the medical studies that gait patterns will be affected by MC, we design a smartphone-based application named MC-Tracking, which monitors MC based on the Inertial Measurement Unit (IMU) signals. By identifying the walking activity based on the acceleration and angular velocity signals, we train an attention-based prediction model that can be generalized to new users with meta learning. 40 volunteers participate in an extensive experiment for more than 3 months, in which more than 2.4 TB of time-series data is collected to evaluate the performance of MC-tracking. It is verified that MC-tracing can predict the onset of MC seven days in advance with an average error of 0.56 days. We also demonstrate that the prediction accuracy is robust to the age, emotion, biological clock and smartphone brand.

Abstract: Target material sensing in non-invasive and ubiquitous contexts plays an important role in various applications. Recently, a few wireless sensing systems have been proposed for material identification. In this article, we introduce mm-CUR, A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles using a Millimeter-Wave Sensor. This system eliminates the need for individual note inspection and pinpoints the location of counterfeit notes within the bundle. We use Frequency Modulated Continuous Wave (FMCW) radar sensors to classify different counterfeit currency bundles on a tabletop setup. To extract informative features for currency detection from FMCW signals, we construct a Radio Frequency Snapshot (RFS) and build signal scalogram representations that capture the distinct patterns of currency received from different currency bundles. We refine the RFS by eliminating multi-path interference, and noise cancellation and apply high pass filters for mitigating the smearing effect with the continuous wavelet transform (CWT). To broaden the usage of mm-CUR, we built a transferable learning model that yields robust detection results in different scenarios. The classification results demonstrated that the proposed counterfeit currency detection system can detect counterfeit notes in 100-note bundles with an accuracy greater than 93%. Compared to the standard CNN and DNN methods, the proposed mm-CUR model showed superior performance in distinguishing each bundle data, even for a limited-size dataset.

Alzheimer’s disease (AD) is an insidious and progressive neurodegenerative disease, and the annual relevant social cost for AD patients can reach about $1 trillion worldwide. Therefore, early diagnosis and treatment of AD play a vital role in slowing disease progression. However, existing detection methods for cognitive impairment cannot consistently screen the stage of AD. To tackle this challenge, we propose an AD detection system, Ubi-AD, which combines the features of multiple biomarkers to realize passive and accurate AD detection. Unlike existing work, Ubi-AD can passively recognize the AD digital biomarkers during daily smartwatch usage without interfering with the user. At the user end, Ubi-AD first extracts the non-speech sounds (pause words, such as em, ah), which contain no privacy-sensitive content. Then, Ubi-AD recognizes the user’s walking activity, dining activity, and sleep activity from daily activities. Ubi-AD analyzes these data from smartwatch and predicts the AD stages using a multi-modal fusion neural network at the cloud end. We evaluate our model on a collected dataset from 45 volunteers. As a result, Ubi-AD can reach a detection accuracy of 93.4%, which means that Ubi-AD can provide multiple effective biomarkers for ubiquitous and passive detection in daily life.

Asthma is a common respiratory disease in modern society. However, people are rarely aware of the symptoms of asthma because the early stage of asthma is similar to that of the common cold (e.g., wheeze, cough, and shortness of breath). To tackle this challenge, we propose an asthma detection system, Ubi-Asthma, based on the smartwatch. Ubi-Asthma combines breathing signals and guttural sound (e.g., cough sound and throat-clearing sound) signals to realize passive and accurate asthma detection without interrupting the user. Not only can Ubi-Asthma extract breathing signals from the user even if the user is walking but also recognize guttural sound signals when the user is engaged in voice communication without being disturbed by noise. The features of breathing and guttural sound are combined to improve the accuracy of asthma detection. We have implemented a fully functional prototype using an off-the-shelf smartwatch. Fifty volunteers participate in an extensive experiment of up to 150 h to train Ubi-Asthma. As a result, Ubi-Asthma can reach a detection accuracy of 98.4%, which is higher than breath- or guttural-based asthma detection systems. Ubi-Asthma is expected to provide a potential solution for smart-home applications in the future.

Blood pressure (BP) is an essential vital sign related to many severe diseases, such as heart failure, kidney failure. Frequent BP detection can provide doctors more information to treat the disease. However, conventional at-home BP detection devices require completely blocking blood flow, which can lead to discomfort and disruption of normal activity when users want to perform frequent assessments. So a convenient solution should reduce the trouble of detecting BP in the daily life. In this work, we have designed and evaluated a smartphone-based BP detection application named WIB . WIB utilizes the smartphone’s acoustic sensors to obtain the chest motion caused by the heart beating and the smartphone’s camera to capture the pulse at the fingertip. We have recruited 30 volunteers who come from the author’s institution, to carry out comprehensive evaluations of WIB . We perform BP perturbation experiments to obtain different blood pressure data from these volunteers. The experiment results show that the average of Pearson correlation coefficient across all volunteers of the blood pressure estimation is 0.42–0.74 (α =0.6, σ =0.12), the average of RMSE across all volunteers is 4.2–8.8 mmHg (α =5.8, σ =1.8).

Fatigue is believed to be the leading factor for traffic accidents (e.g., fatigue driving) and health problems (e.g., heart disease and diabetes). However, fatigue-related risks are difficult to quantify because there is no efficient and reliable fatigue detection method comparable to blood alcohol testing for drunk drivers. Conventional fatigue detection methods either require wiring of sensors (e.g., EEG and ECG) that are inconvenient or leverage video camera systems that are lighting sensitive and may leak privacy. We present Ubi-Fatigue, a comfortable and contactless fatigue monitor system using wireless signals. Ubi-Fatigue combines both vital signs and facial features to achieve reliable fatigue detection. A series of novel signal recovery algorithms is developed to extract the heartbeat signal and the eye blink signal from the same raw signal captured by the single-antenna radar. We have implemented a fully functional prototype of Ubi-Fatigue using off-the-shelf radar. Twenty volunteers are involved in extensive experiments for a total duration of 480 h with more than 60 h of collected time-series data. The results demonstrate that Fatigue-Radio can reach a detection accuracy of 81.4%, which is higher than ECG- or visual-based fatigue detection systems, and is approximated to the ECG + visual-based fatigue detection system. Ubi-Fatigue is expected to provide a potential solution for smart home applications in the coming days.

Myocardial infarction (MI) is the myocardial necrosis caused by persistent ischemia and hypoxia of coronary arteries. People do not realize that they are suffering from MI until they have a heart attack. Early MI detection plays a vital role in symptom relief and improvement in the performance of daily activities. However, conventional MI detection methods require expensive and inconvenient medical tests, e.g., intrusive blood tests or wear electrocardiogram (ECG) sensors, which can only be performed in medical institutions. In this paper, we introduce a contactless and non-intrusive MI detection method based on wireless sensing that monitors abnormalities in heartbeats. Specifically, we present Health-Radio, a radar-based system towards early MI detection. Health-Radio extracts heart rate variability (HRV) from the RF signals reflected from users. In particular, with our carefully designed signal processing algorithms, Health-Radio is able to not only obtain heartbeat signals when the user is stationary, but also tolerate interference when the user is performing certain activities, e.g., eating, reading and browsing the Internet. We have recruited 30 MI patients from the Central Hospital of Wuhan, China, and 30 healthy university students to conduct comprehensive evaluations of the performance of Health-Radio. The experiment results show that Health-Radio can achieve a median MI detection accuracy of 81.2 percent when the users are stationary, which is comparable to ECG-based MI detection. Even when the users are not stationary, Health-Radio can still achieve a median detection accuracy of 66.5 percent. Health-Radio is promising in providing a new paradigm for smart-home healthcare in the future.

Blockchain-based cryptocurrencies are severely limited in transaction throughput and latency. A promising solution to this issue is a payment channel, which allows trust-free payments between two peers without exhausting the resources of the blockchain. A linked payment channel network (PCN) enables payments between two peers through a series of intermediate nodes that forward and charge for the payments. However, most of existing proposals only use the shortest path as the path of the transaction, which causes the frequently reused channels to be exhausted quickly. In addition, most of existing PCNs are almost only designed for payments between two parties, which leads to limited application scenarios. When multiple payments use the same intermediate channel, the two-party PCNs cannot achieve simultaneous payments. In this paper, we propose a multi-party payment channel (MPC) network, a payment channel proposal that supports multiple payments using the same intermediate channel simultaneously, thereby greatly expanding the application scenarios of payment channels. In addition, our channel selection and transaction conversion strategies can also increase the success rate of transactions. We implement MPC network in the simulated blockchain network and lightning network based on Truffle, and a large number of experiments verify the effectiveness of our solution.

Blockchain-based cryptocurrencies are severely limited in transaction throughput and latency due to the need to seek consensus among all peers of the network. A promising solution to this issue is payment channels, which allow unlimited numbers of atomic and trust-free payments between two peers without exhausting the resources of the blockchain. A linked payment channel network enables payments between two peers without direct channels through a series of intermediate nodes that forward and charge for the transactions. However, the charging strategies of intermediate nodes vary with different payment channel networks. Existing works do not yet have a complete routing algorithm to provide the most economical path for users in a multi-charge payment channel network. In this work, we propose MPCN-RP, a general routing protocol for payment channel networks with multiple charges. Our extensive experimental results on both simulated and real payment channel networks show that MPCN-RP significantly outperforms the baseline algorithms in terms of time and fees.

As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we presentDetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver’s psychomotor coordination through steering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants,DetectDUIachieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of$2\sim 5mg/dl$. These promising results provide a highly encouraging case for continued development.

Following the correct stroke order while writing Chinese characters composed of strokes plays an important role in handwriting efficiency and quality, especially for early education. Most existing systems use image processing techniques for character and stroke order recognition, which is sensitive to lighting conditions. In this paper, we present the design, implementation and evaluation of SmartSO, which utilizes the inertial sensors of an off-the-shelf smartwatch for Chinese character and stroke order recognition. SmartSo first identifies the Chinese character written by the user, based on which SmartSo decides whether the stroke order is written correctly to help improve users' writing behavior. The biggest challenge for stroke order recognition is that some Chinese characters have repeated strokes (strokes of the same type), e.g., with two same horizontal strokes, and it is challenging to differentiate the writing order of such strokes given only the detected stroke composition (number and type of strokes). To mitigate this problem, we further analyze the hand movement between two adjacent strokes (referred to as direction motion) and propose a novel algorithm to recognize stroke order based on direction motion information. Finally, we build a fully functional prototype of SmartSO, and extensive experiments confirm its effectiveness and robustness.

Most existing systems use portable devices or image processing techniques for handwritten Chinese character recognition (HCCR), which are unable to detect character when writing on a paper or sensitive to lighting conditions. In this article, we present the design, implementation, and evaluation of a smartwatch-based HCCR system, called SmartHandwriting. To segment each Chinese character, we further analyze the hand movement between the handwriting gesture and the wrist movement gesture and propose a novel algorithm to distinguish the two types of gestures. Due to too many Chinese characters for classification, we utilize the data augmentation method for avoiding overfitting. Then, we build the HCCR model using the deep convolutional neural network (DCNN) method. The recognition accuracy of the Chinese characters is 96.0%, and extensive experiments confirm its effectiveness and robustness. Moreover, we also explore adverse factors that affect the recognition performance, which can be avoided in the future.

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, we present the design, implementation and evaluation of SmartWriting, which utilizes the smartwatch for pen-holding gesture recognition for both Chinese and English writing. SmartWriting can automatically identify whether the user is writing Chinese or English, based on which two classifiers are built to detect pen-holding gestures by strokes in Chinese characters or English letters. In particular, we propose to leverage the combined signal of one vertical stroke and one horizontal stroke for efficient pen-holding gesture identification in Chinese writing, and we are able to infer pen-holding gesture according to any letter in English writing. We build a fully functional prototype of SmartWriting, and extensive experiments confirm its effectiveness and robustness. The detection accuracy is 9. % and 9 % for Chinese and English respectively. SmartWriting provides a natural, convenient and inexpensive way to improve users' writing habits.

With the development of cloud storage, data owners no longer physically possess their data and thus how to ensure the integrity of their outsourced data becomes a challenging task. Several protocols have been proposed to audit cloud storage, all of which rely mainly on data block tags to check data integrity. However, their block tag constructions employ cryptographic operations, which makes them computationally complex. In this paper, we investigate a secure cloud storage protocol based on the classic discrete logarithm problem. Our protocol generates data block tags with only basic algebraic operations, which brings substantial computation savings compared with previous work. We also strictly prove that the proposed protocol is secure under a definition which captures the real-world uses of cloud storage. In order to fit more application scenarios, we extend the proposed protocol to support data dynamics by employing an index vector and third-party public auditing by using a random masking number, both of which are efficient and provably secure. At last, theoretical analysis and experimental evaluation are provided to validate the superiority of the proposed protocol.

With the increasing development of cloud computing, how to securely outsource prohibitively expensive computation to unfaithful clouds has caught more and more attention. Large-scale systems of linear matrix equations (LME) are com-monly deployed in scientific and engineering fields, which is a computationally complex task. Thus, it is necessary to design a protocol for practically outsourcing large-scale systems of LME to a malicious cloud. For this purpose, we propose a protocol called OutLME in this paper. In OutLME, we adopt a special permutation technique for the client to transform the original lin-ear matrix equation into a randomized one and decrypt the result returned from cloud into the right one of original problem. As to robust cheating resistance, we propose an effective verification algorithm by utilizing the algebraic property of matrix-vector operations. In addition, both the chosen permutation technique and result verification mechanism incur close-to-zero additional cost on both the cloud and the client, so our proposed protocol is efficient. In the end, the theoretical analysis and the experimental evaluation are provided to demonstrate the validity of OutLME.

In this paper, we propose a null space-based precoding scheme for secondary transmission in a cognitive radio multiple-input multiple-output (CR-MIMO) network under the assumption that time-division-duplex is employed by the primary transmission. First, the secondary transmitter periodically senses the transmitted signals from the primary users and estimates the corresponding covariance matrix. Then, subspace techniques are utilized to estimate the noise subspace of this covariance matrix, and the dimension of the noise subspace is estimated using information theory criteria (AIC or MDL). Finally, the obtained null space is used as the precoding matrix for the secondary transmission, which effectively avoids the interference induced by the secondary transmission at the primary users. Moreover, the achievable capacity of the secondary MIMO channel by the proposed scheme is derived. Simulations are performed to show the efficacy of the proposed scheme.