Jun Luo

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Verified

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• PhD (EPFL)
• Professor (Associate) at Nanyang Technological University

The intention of leveraging Radio-Frequency (RF) resources for diverse sensing purposes has grown increasingly keen, thanks to the ever-expanding deployment of IoT devices using RF communications to maintain connectivity. Whereas the original idea was to integrate sensing and communications (a.k.a. ISAC) for individual IoT devices, most proposals i...

Having been studied for more than a decade, Wi-Fi human sensing still faces a major challenge in the presence of multiple persons, simply because the limited bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically separate multiple subjects. Existing solutions mostly avoid this challenge by switching to radars with GHz bandw...

The limited bandwidth of Wi-Fi severely confines the granularity (especially in differentiating multiple subjects) of Wi-Fi sensing, posing a significant challenge for its wide adoption. Though utilizing multiple channels to expand the effective bandwidth sounds plausible, continuous spectrum stitching towards ultra-wideband (UWB) is far from pract...

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high through-put and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text beamforming feedback information (BFI): though similar risks have been exposed using emulation platform...

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep sem...

Wi-Fi human sensing, boosted by latest progress in both system innovation and deep analytics, has demonstrated ever-increasing resolution of users' activities. Nonetheless, it may become a spy on users' private activities such as password entry or intimate social interactions. Existing countermeasures include signal obfuscation and adversarial pert...

Recently, federated learning (FL) has been considered a promising and well-suited technique for edge computing applications, such as intelligent traffic control, autonomous driving, and mobile crowdsensing. However, since each edge device may perform individual-specific tasks, they often have heterogeneous data distributions that impact the perform...

Reusing Wi-Fi communication packets for sensing purpose has been regarded as one of the most cost-effective ways to realize integrated sensing and communication (ISAC) on commodity Wi-Fi. However, the channel state information (CSI) measured from these packets can be heavily compromised by modern Wi-Fi beamforming protocols tailored primarily to ma...

Enabling multi-person differentiation is crucial for the wide adoption of Wi-Fi sensing, so it is imperative for Wi-Fi sensing to gain GHz-level bandwidth and thus to achieve sufficient spatial resolution. Whereas stitching wide bandwidth leveraging continuous channel samples appears to be plausible, it is inefficient (if not impossible) in both ti...

While 3D head reconstruction is widely used for modeling, existing neural reconstruction approaches rely on high- resolution multi-view images, posing notable privacy issues. Individuals are particularly sensitive to facial features, and facial image leakage can lead to many malicious activities, such as unauthorized tracking and deepfake. In contr...

As a main approach towards touch-free human-computer interaction, hand gesture recognition (HGR) has long been a research focus for both academia and industry. Meanwhile, visible light communication (VLC) has become increasingly popular with VLC-ready commercial products (e.g., Philips lamps) available on the market. These facts provoke us to ask:...

Wireless indoor localization has been a pivotal area of research over the last two decades, becoming a cornerstone for numerous sensing applications. However, conventional wireless localization methods rely on channel state information to perform blind modelling and estimation of a limited set of localization parameters. This oversimplification neg...

The wide adoption of Large Language Models (LLMs) has attracted significant attention from \textit{jailbreak} attacks, where adversarial prompts crafted through optimization or manual design exploit LLMs to generate malicious content. However, optimization-based attacks have limited efficiency and transferability, while manual designs are either ea...

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicle s (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability...

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we propose Romeo as the fir...

Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we propose m $^{2}$ Vision , which is the...

Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera...

Ultra-high precision motion sensing leveraging computer vision (CV) is a key technology in many high-precision AR/VR applications such as precise industrial manufacture and image-guided surgery, yet conventional CV can be challenged by moiré-based sensing mechanism, thanks to moiré pattern's high sensitivity to six degrees of freedom (6-DoF) pose c...

Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera...

The medial axis, a lower-dimensional shape descriptor, plays an important role in the field of digital geometry processing. Despite its importance, robust computation of the medial axis transform from diverse inputs, especially point clouds with defects, remains a significant challenge. In this paper, we tackle the challenge by proposing a new impl...

Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs). Using the channel state information (CSI) across multiple frequency bands, RIS-aided multi-band ISAC systems can potentially track users’ positions with high precision. Though tracking...

Password inference attacks by covert wireless side-channels jeopardize information safety, even for people with high security awareness and vigilance against snoopers. Yet, with limited spatial resolution , existing attacks cannot accurately infer password input on QWERTY keyboards in distance, creating psychological safety in using laptops publicl...

Whereas Wi-Fi communications have been exploited for sensing purpose for over a decade, the bistatic or multistatic nature of Wi-Fi still poses multiple challenges, hampering real-life deployment of integrated sensing and communication (ISAC) within Wi-Fi framework. In this paper, we aim to re-design Wi-Fi so that monostatic sensing (mimicking rad...

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep sem...

The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches such as keystroke inference (KI). However, the use of channel state information (CSI) in existing attacks is highly questionable due to its signal instability and hardness to acquire. Moreover, such Wi-Fi-based attacks are confined to only one victim because...

Recent years, point-of-sale (POS) terminals are no longer limited to wired connections, with many relying on Wi-Fi for data transmission. Although Wi-Fi offers the convenience of wireless connectivity, it introduces significant security vulnerabilities. This work presents a non-intrusive method for eavesdropping POS passwords via Wi-Fi sensing, nam...

Wi-Fi sensing leveraging plain-text beamforming feedback information (BFI) in multiple-input-multipleoutput (MIMO) systems attracts increasing attention. However, due to the implicit relationship between BFI and the channel state information (CSI), quantifying the sensing capability of BFI poses a challenge in building efficient BFI-based sensing a...

Real-time deepfake, a type of generative AI, is capable of "creating" non-existing contents (e.g., swapping one's face with another) in a video. It has been, very unfortunately, misused to produce deepfake videos (during web conferences, video calls, and identity authentication) for malicious purposes, including financial scams and political misinf...

Peak detection is useful in a wide range of applications. To achieve this task, conventional approaches [including dedicated application specific integrated circuit-based designs] often demand analog readout chains and compulsory add-ons [e.g., additional analogy-to-digital converter (ADC) for peak sampling], rendering them neither compact nor flex...

Monocular depth estimation (MDE) plays a crucial role in modern autonomous driving (AD) by facilitating 3-D scene understanding and interaction. While vulnerabilities in deep neural networks (e.g., adversarial perturbations) have been exploited to compromise MDE, existing attacks face challenges in target accessibility and stealthiness. To address...

Submodular optimization has been identified as a powerful tool for many data mining applications, where a representative subset of moderate size needs to be extracted from a large-scale dataset. In scenarios where data points possess sensitive attributes such as age, gender, or race, it becomes imperative to integrate fairness measures into submodu...

Deep neural networks (DNNs) as one of the key enabling technologies have been widely used in Industrial Artificial Intelligence (IAI). However, recent research has revealed that they are quite vulnerable to adversarial attacks, arousing serious concerns about DNNs' robustness in many IAI-driven applications, such as industrial video analysis tasks....

Biometrics has been increasingly integrated into wearables for enhanced data security in recent years. Meanwhile, wearable popularity offers a unique chance to capture novel biometrics via embedded sensors. In this paper, we study new intracorporal biometrics combining the uniqueness of heart motion, bone conduction, and body asymmetry. Specificall...

Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eaves-droppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractic...

In recent times, gait recognition, a type of biometric identification, has been widely used for area access control and smart homes. It improves convenience, privacy, and personalized experiences. Contemporary academic inquiry centers on privacy-preserving wireless sensing solutions as substitutes for computer vision. Yet, prevailing strategies hea...

Blood pressure (BP) measurement is significant to the assessment of many dangerous health conditions. Apart from invasively inserting catheters into arteries, non-invasive approaches typically rely on wearing devices on specific skin areas with consistent pressure. However, this can be uncomfortable and unsuitable for certain individuals, and the a...

Wi-Fi signals are commonly used for conventional communication, yet they can also realize low-cost and non-invasive human sensing. However, Wi-Fi sensing in Multi-person scenarios is still a challenging problem. In this paper, we propose M^2-Fi to achieve multi-person respiration monitoring using a handheld device. M^2-Fi leverages Wi-Fi BFI (beamf...

The FPGA-based Time to Digital Converter (TDC) has been notoriously troubled by its nonlinearity problems. To address it, conventional approaches often demand redundant resources or calibration efforts. In this paper, we propose Twin-Pop to tackle this issue in a resource-efficient manner, by largely reducing chain resources. The key technique is...

Aerial acoustic communication in a peer-to-peer manner attracts much attention recently thanks to its ubiquitous device support. These benefits have led Google's nearby platform to adopt this communication paradigm to establish copresence between nearby devices. However, state-of-the-art research cannot reliably work under dynamic channels and long...

Transformer winding turn-to-turn fault is the prominent cause of transformer total failure, so detecting the winding fault in real time to stop the failure development in advance is imperative. However, existing techniques entailing periodic offline inspections fail to continuously monitor transformer winding states while causing extra costs due to...

Hand pose estimation (HPE), which aims to identify and recover the keypoints of a hand, is essential to many potential applications. Conventional computer vision (CV) methods extract visible features from images or videos captured by cameras. However, they are heavily affected by low image contrast, fail to work under occluded scenarios, and inevit...

User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short of implementability thanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices...

Door lock is regarded as a critical line of defending the privacy and security of personal areas. However, for inner doors in environments like factories, existing locking mechanisms can be poor in user-friendliness and high in cost. For instance, mechanical locks require carrying keys that inevitably compromise user experiences, while smart locks...

Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. R...

Whereas adversarial training can be useful against specific adversarial perturbations, they have also proven ineffective in generalizing towards attacks deviating from those used for training. However, we observe that this ineffectiveness is intrinsically connected to domain adaptability, another crucial issue in deep learning for which adversarial...

The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches, yet existing attacks relying on Wi-Fi CSI (channel state information) demand hacking Wi-Fi hardware to obtain desired CSIs. Since such hacking has proven prohibitively hard due to compact hardware, its feasibility in keeping up with fast-developing Wi-Fi techno...

Continuous respiration monitoring is significant for real-life healthcare applications, but realizing it is extremely hard as wearable sensors are cumbersome and contact-free sensors largely fail to tolerate user movements. Meanwhile, tracking users indoors mostly demands user-held devices, while device-free localization can barely tell what and wh...

Heart disease has now become a very common and impactful disease, which can actually be easily avoided if treatment is intervened at an early stage. Thus, daily monitoring of heart health has become increasingly important. Existing mobile heart monitoring systems are mainly based on seismocardiography (SCG) or photoplethysmography (PPG). However, t...

Hand Pose Estimation (HPE) is crucial to many applications , but conventional cameras-based CM-HPE methods are completely subject to Line-of-Sight (LoS), as cameras cannot capture occluded objects. In this paper, we propose to exploit Radio-Frequency-Vision (RF-vision) capable of bypassing obstacles for achieving occluded HPE, and we introduce OCHI...

Object detection with on-board sensors (e.g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities. While crowdsensing may potentially exploit these sensors (of huge quantity) to derive more comprehensive knowledge, federated learning (FL) appears to be the necessary tool to...

Recently, passive behavioral biometrics (e.g., gesture or footstep) acquired from wireless networks or mobile services have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple u...

The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches, yet existing attacks relying on Wi-Fi CSI (channel state information) demand hacking Wi-Fi hardware to obtain desired CSIs. Since such hacking has proven prohibitively hard due to compact hardware, its feasibility in keeping up with fast-developing Wi-Fi techno...

Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractica...

Having been studied for more than a decade, Wi-Fi human sensing still faces a major challenge in the presence of multiple persons, simply because the limited bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically separate multiple subjects. Existing solutions mostly avoid this challenge by switching to radars with GHz bandw...

Hand Pose Estimation (HPE) is crucial to many applications, but conventional cameras-based CM-HPE methods are completely subject to Line-of-Sight (LoS), as cameras cannot capture occluded objects. In this paper, we propose to exploit Radio-Frequency-Vision (RF-vision) capable of bypassing obstacles for achieving occluded HPE, and we introduce OCHID...

In recent years, particular attention has been devoted to earable acoustic sensing due to its numerous applications. However, the lack of a common platform for accessing raw audio samples has forced researchers/developers to pay great efforts to the trifles of prototyping often irrelevant to the core sensing functions. Meanwhile, the growing popula...

Metamaterial-based reconfigurable holographic surfaces (RHSs) have been proposed as novel cost-efficient antenna arrays, which are promising for improving the positioning and communication performance of integrated sensing and communications (ISAC) systems. However, due to the high frequency selectivity of the metamaterial elements, RHSs face chall...

Vibration sensing is crucial to human life and work, as vibrations indicate the status of their respective sources (e.g., heartbeat to human health condition). Given the inconvenience of contact sensing, both academia and industry have been intensively exploring contact-free vibration sensing, with several major developments leveraging radio-freque...

Biometrics has been increasingly integrated into wearable devices to enhance data privacy and security in recent years. Meanwhile, the popularity of wearables in turn creates a unique opportunity for capturing novel biometrics leveraging various embedded sensing modalities. In this paper, we study a new intracorporal biometrics combining the unique...

In many practical scenarios of signal extraction from a nonlinear mixture, only one (signal) source is intended to be extracted. However, modern methods involving Blind Source Separation are inefficient for this task since they are designed to recover all sources in the mixture. In this paper, we propose supervised Variational Component Decoder (sV...

Metamaterial-based reconfigurable holographic surfaces (RHSs) have been proposed as novel cost-efficient antenna arrays, which are promising for improving the positioning and communication performance of integrated sensing and communications (ISAC) systems. However, due to the high frequency selectivity of the metamaterial elements, RHSs face chall...

Acoustic sensing has attracted significant attention recently, thanks to the pervasive availability of device support. However, adopting consumer-grade devices (e.g., smartphones) to deploy acoustic sensing applications faces the challenge of device/OS heterogeneity. Researchers have to pay tremendous efforts in tackling platform-dependent details...

Though measuring ambient temperature is often deemed as an easy job, collecting large-scale temperature readings in real-time is still a formidable task. The recent boom of network-ready (mobile) devices and the subsequent mobile crowdsourcing applications do offer an opportunity to accomplish this task, yet equipping commodity devices with ambient...

Object detection with on-board sensors (e.g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities. While crowdsensing may potentially exploit these sensors (of huge quantity) to derive more comprehensive knowledge, \textit{federated learning} (FL) appears to be the necessar...

Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting Radio-Frequency (RF) signals arise as a promising alternative for HAR. Most of the latest...

In this paper we study the problem of passive human localization using an infrared (IR) thermal imaging camera which detects IR radiation emitted by human without carry-on devices and thereby generates a heat map of human body. Rather than directly using the heat map, we propose to exploit temperature of human body and design a lightweight approach...

We study the problem of cross-modality person re-identification (ReID) and tracking with dual visible-infrared (VI) cameras, while most exsiting efforts on tracking-by-detection have been paid on single-modality visible ReID which are inapplicable for poor-light environments. The major difficulties for cross-modaltiy (e.g., visible-infrared) ReID s...

Recent years have witnessed a growing interest in contact-free respiration monitoring leveraging radio-frequency (RF) technologies. However, the proposed solutions mostly consider single-person scenarios , whereas a few multi-person monitoring proposals simply apply blind source separation to handle inter-person interference, without drawing a clea...

Optical camera communication (OCC) enabled by LED and embedded cameras has attracted extensive attention, thanks to its rich spectrum availability and ready deployability. However, the close interactions between OCC and the indoor spaces have created two major challenges. On one hand, the stripe pattern incurred by OCC may greatly damage the accura...

The intention of leveraging Radio-Frequency (RF) resources for diverse sensing purposes has grown increasingly keen, thanks to the ever-expanding deployment of IoT devices using RF communications to maintain connectivity. To this end, we propose ISACoT as the framework for enabling Integrated Sensing and Communication (ISAC) over IoT devices. ISACo...

Aerial acoustic communication attracts substantial attention for its simplicity, cost-effectiveness, and power-efficiency. Unfortunately, the preferred inaudible transmission has to strike a balance between the transmission rate and communication range, when the Bit-Error-Rate (BER) is under a certain threshold. Additionally, the performance of pre...

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users' inner feelings, causing erroneous user preference estimation...

Contact-free vital-signs monitoring enabled by radio frequency (RF) sensing is gaining increasing attention, thanks to its non-intrusiveness, noise-resistance, and low cost. Whereas most of these systems only perform respiration monitoring or retrieve heart rate, few can recover fine-grained heartbeat waveform. The major reason is that, though both...

Proliferation of smart environments entails the need for real-time and ubiquitous human-machine interactions through, mostly likely, hand/arm motions. Though a few recent efforts attempt to track hand/arm motions in real-time with COTS devices, they either obtain a rather low accuracy or have to rely on a carefully designed infrastructure and some...

We propose a robust, anisotropic normal estimation method for both point clouds and meshes using a low rank matrix approximation algorithm. First, we compute a local feature descriptor for each point and find similar, non-local neighbors that we organize into a matrix. We then show that a low rank matrix approximation algorithm can robustly estimat...

Both voice communication and automatic speech verification (ASV) over smart devices are vulnerable to voice impersonation (VI) attack, which is often launched via imitating a target's voice characteristics to deceive human auditory sense or fool the ASV system. Researchers have designed a number of defense schemes yet without consideration of unive...

With the proliferation of Internet-of-Things (IoT) devices, acoustic sensing attracts significant attention in recent years. It exploits acoustic transceivers such as microphones and speakers beyond their primary functions, namely recording and playing, to enable novel applications and new user experiences. In this paper, we present the first syste...

Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we d...

Vital signs are crucial indicators for human health, and researchers are studying contact-free alternatives to existing wearable vital signs sensors. Unfortunately, most of these designs demand a subject human body to be relatively static, rendering them very inconvenient to adopt in practice where body movements occur frequently. In particular, ra...

Radio frequency (RF) technologies have achieved a great success in data communication. In recent years, pervasive RF signals are further exploited for sensing; RF sensing has since attracted attentions from both academia and industry. Existing developments mainly employ commodity Wi-Fi hardware or rely on sophisticated SDR platforms. While promisin...

Whereas adversarial training can be useful against specific adversarial perturbations, they have also proven ineffective in generalizing towards attacks deviating from those used for training. However, we observe that this ineffectiveness is intrinsically connected to domain adaptability, another crucial issue in deep learning for which adversarial...

Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies. Therefore, researchers have been constantly searching for contact-free alternatives. Nonetheless, existing contact-free designs mostly require human subjects to r...

Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies. Therefore, researchers have been constantly searching for contact-free alternatives. Nonetheless, existing contact-free designs mostly require human subjects to r...

Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications. However, device-free (or contactless) sensing is often more sensitive to environment changes than device-based (or wearable) sensing. Also, RF datasets strictly require on-line labeling during collection, starkly different fr...

Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we d...

Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting RF signals arise as a promising alternative for HAR. Most of the latest device-free appro...

In recent years, radio frequency (RF) sensing has gained increasing popularity due to its pervasiveness, low cost, non-intrusiveness, and privacy preservation. However, realizing the promises of RF sensing is highly nontrivial, given typical challenges such as multipath and interference. One potential solution leverages deep learning to build direc...

Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern. Such issues may vary from fatigue, asthma, stroke, to even heart attack, yet they can be adequately indicated by vital signs and abnormal activities. Therefore, in-vehicle vital sign monitoring can help us predict and he...

Indoor localization is crucial to enable context-aware applications, but existing solutions mostly require a user to carry a device, so as to actively sense location-discriminating signals. However, many applications do not prefer user involvement due to, e.g., the cumbersome of carrying a device. Therefore, solutions that track user locations pass...

Deemed as a practical approach to realize Visible Light Communication on commercial-off-the-shelf devices, the Optical Camera Communication (OCC) is attracting increasing attention, thanks to its readiness to be built purely upon ubiquitous LED illuminating infrastructure and handy smartphones. However, limited by the low sampling ability of the bu...