Miracle-dz/UniFAR-FTUs huggingface dataset:
CATHODOLUMINESCENCE MAPPING OF EPITAXIAL LATERAL OVERGROWTH IN GALLIUM NITRIDE

The dislocation arrangements in gallium nitride (GaN) films prepared by lateral epitaxial overgrowth (LEO) have been studied by cathodoluminescence mapping and transmission electron microscopy. A very low density of electrically active defects (<10−6 cm−2) in the laterally overgrown material is observed. Individual electrically active defects have been observed that propagate laterally from the line of stripe coalescence into the overgrown material. Additionally, by mapping wavelength-resolved luminescence in an InGaN quantum well grown on top of the overgrown material, these defects are shown to be limited to the underlying material and do not propagate normal to the surface, as in other GaN films. In the seed region, threading dislocation image widths are seen to be nearly identical in the quantum well and the underlying GaN, indicating a comparable upper limit (∼200 nm) for minority carrier diffusion length in InGaN and GaN. Additionally, it is shown that, through processing variation, these lateral de.
Robust estimation of the process standard deviation for control charts

Control charts are tools used to detect aberrant behavior in manufacturing processes. The x chart plots subsample averages as a function of time, and the R chart plots subsample ranges. Both of these charts rely on an estimate of the standard deviation of the process when it is operating correctly. The estimate has traditionally been based on the average range of 2040 subgroups, but this will produce an estimate that is biased high when outliers are present. One standard solution is to construct a range chart for the original subgroups and estimate the standard deviation only from those subgroups within the control limits, repeating the procedure as necessary. Proposals have also recently been made to use a trimmed mean of the subsample ranges with a fixed percentage of trimming, as well as the trimmed mean of the subsample interquartile ranges. This article presents a new approach to robust estimation of the process standard deviation. The procedure first centers each subsample on its own median and then.
Sparse Partial Pivoting in Time Proportional to Arithmetic Operations

Existing sparse partial pivoting algorithms can spend asymptomatically more time manipulating data structures than doing arithmetic, although they are tuned to be efficient on many large problems. We present an algorithm to factor sparse matrices by Gaussian elimination with partial privoting in time proportional to the number of arithmetic operations. Implementing this algorithm requires only simple data structures and gives a code that is competitive with, and often faster than, existing sparse codes. The key idea is a new triangular solver that uses depth-first search and topological ordering to take advantage of sparsity in the right-hand side.
Potential clinical benefits of warfarin in end‐stage cancers: A retrospective analysis

Coagulopathy and thromboembolism are common comorbidities in cancer, and anticoagulants, such as warfarin, are needed in specific situations. This study aimed to determine the clinical relevance of prothrombin time (PT) monitoring and the clinical usefulness of warfarin in patients with malignancy.
LTE-Based Low-Cost and Low-Power Soil Moisture Sensing

Soil moisture sensing is a basic function required by applications like precision irrigation. Recently, RF based soil moisture sensing solutions [10, 43] have been proposed, which, however, can hardly support large scale deployment in challenging outdoor environments, since they must have dedicated signal emitters and also require power supply for either the signal emitters (WiFi or RFID reader) or both the transceivers (WiFi AP and client). LTE signal provides a unique opportunity for soil moisture sensing as the ubiquitously deployed base stations are naturally always-on signal emitters, eliminating the need for deploying extra hardware. In this paper, we implement a low-cost LTE based soil moisture sensor using commercial off-the-shelf hardware. We also realize duty-cycled soil sensing by automatically self-calibrating the phase offset after powering on the devices, significantly reducing the overall power consumption of the sensor. Extensive experiments show that our low-cost sensor ($55) achieves a high accuracy (3.15%) which is comparable to high-end soil moisture sensors ($850), wide coverage (2.4 km from the base station) and low power consumption (lasting 16 months using batteries).
Indoor Place Prediction on Smart Phones

High-accuracy and low-latency indoor place prediction for mobile users is crucial to enable applications for assisted living, emergency services, smart homes, and augmented reality. Previous studies on indoor place prediction use complex infrastructure with multiple visual/wireless anchors or multiple wireless access points. These localization techniques are difficult to deploy, may negatively impact user privacy through location tracking, and their data collection is not suitable for personalized place prediction. To solve these challenges, this paper proposes GoPlaces, a novel app that fuses inertial sensor data with WiFi-RTT estimated distances to predict the future indoor places visited by a user. GoPlaces does not require any infrastructure, except for one cheap off-the-shelf WiFi access point that supports ranging with RTT. In addition, it enables personalized place naming and prediction through its on-the-phone data collection and protects users' location privacy because user's data never leaves the phone. GoPlaces uses an attention-based bidirectional long short-term memory model to detect user's current trajectory, which is then used together with historical information stored in a prediction tree to infer user's future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. The experimental results demonstrate prediction accuracy as high as 92%, low latency, and low resource consumption on the phones.
Multicolor generation using silicon nanodisk absorber

A multicolored matrix that spans the visible range was demonstrated by using silicon nanodisk arrays. A nanostructured silicon substrate, which featured periodic silicon nanodisk arrays of various diameters, inter-nanodisk distances, and heights, was fabricated using electron-beam lithography and reactive ion etching. These silicon nanodisks were able to support HE1m leaky modes, which depended on the diameter of the nanodisks, resulting in wavelength-dependent reflection spectra. The resonant wavelength redshifted linearly with the increasing nanodisk diameter. The output color lay in the visible range and was observed to be tunable when varying the diameter, interdistance, and height. The results of finite-difference time-domain simulations exhibited close agreement with the observed optical properties of the periodic silicon nanodisk arrays.
Low-Latency Visible Light Backscatter Networking with RetroMUMIMO

Visible Light Backscatter Communication (VLBC) presents an emerging ultra-low-power IoT connectivity solution with high spatial-spectral efficiency and intrinsic human-perceivable privacy advantages. However, research progress on enhanced data rate and sophisticated device coordination of state-of-the-art VLBC systems still cannot meet the low-latency requirement (sub-second level for an IoT network) for massive connections. In this paper, we present the design, implementation and evaluation of RetroMUMIMO, the first visible light backscatter network that enables physical-layer concurrency to minimize network latency. We propose a pulse feature extraction scheme to enable concurrent transmissions with the unexplored pulse diversity of tags. We design a low-latency demodulation algorithm assisted by such features to demodulate the concurrent symbols efficiently and effectively. Based on the physical-layer concurrency, we further design a MAC layer with contention-based transmission and minimal protocol overhead for low-latency networking. Our evaluation shows that the prototype system achieves up to 8 concurrent VLBC uplinks with real-time demodulation and up to 92.0% latency reduction compared with the state-of-the-art VLBC systems.
LLDPC: A Low-Density Parity-Check Coding Scheme for LoRa Networks

Low-density parity-check (LDPC) codes have been widely used for Forward Error Correction (FEC) in wireless networks because they can approach the capacity of wireless links with lightweight encoding complexity. Although LoRa networks have been developed for many applications, they still adopt simple FEC codes, i.e., Hamming codes, which provide limited FEC capacity, causing unreliable data transmissions and high energy consumption of LoRa nodes. To close this gap, this paper develops LLDPC, which realizes LDPC coding in LoRa networks. Three challenges are addressed. 1) LoRa employs Chirp Spread Spectrum (CSS) modulation, which only provides hard demodulation results without soft information. However, LDPC requires the Log-Likelihood Ratio (LLR) of each received bit for decoding. We develop an LLR extractor for LoRa CSS. 2) Some erroneous bits may have high LLRs (i.e., wrongly confident in their correctness), significantly affecting the LDPC decoding efficiency. We use symbol-level information to fine-tune the LLRs of some bits to improve the LDPC decoding efficiency. 3) Soft Belief Propagation (SBP) is typically used as the LDPC decoding algorithm. It involves heavy iterative computation, resulting in a long decoding latency, which prevents the gateway from sending timely an acknowledgment. We take advantage of recent advances in graph neural networks for fast belief propagation in LDPC decoding. Extensive simulations on a large-scale synthetic dataset and in-filed experiments reveal that LLDPC can extend the lifetime of the default LoRa by 86.7% and reduce the decoding latency of the SBP algorithm by 58.09×.
Implementing Data Exfiltration Defense in Situ: A Survey of Countermeasures and Human Involvement

In this article we consider the problem of defending against increasing data exfiltration threats in the domain of cybersecurity. We review existing work on exfiltration threats and corresponding countermeasures. We consider current problems and challenges that need to be addressed to provide a qualitatively better level of protection against data exfiltration. After considering the magnitude of the data exfiltration threat, we outline the objectives of this article and the scope of the review. We then provide an extensive discussion of present methods of defending against data exfiltration. We note that current methodologies for defending against data exfiltration do not connect well with domain experts, both as sources of knowledge and as partners in decision-making. However, human interventions continue to be required in cybersecurity. Thus, cybersecurity applications are necessarily socio-technical systems that cannot be safely and efficiently operated without considering relevant human factor issues. We conclude with a call for approaches that can more effectively integrate human expertise into defense against data exfiltration.
Testbed Hardware Design to Collect Data for Underground PVC Water Pipe Crack Detection: Challenges and Solutions

A premature crack is a significant indicator for early failure detection for underground polyvinyl chloride (PVC) water pipes. Using an array of strain gauges mounted on a pipe surface to monitor the strain of adjacent areas of a premature crack is a novel technology that has not been explored. To reduce the risks and up-front investments, we need a testbed to investigate, verify, and validate the innovative technology. However, establishing such a pipe monitoring testbed that covers completely realistic underground situations is challenging. The main reason lies in three main challenges: (i) mimicking the natural changes of water flowing in pipes; (ii) identifying the proper placement of strain gauges and detectable crack sizes; (iii) simulating the crucial underground conditions such as temperature and external stress. To this end, in this paper, we present a testbed to get more insights into the effects of different crack types on the pressure-strain characteristic in realistic conditions. In particular, we use pressure meters and water pumps to control the water flow (for challenge (i)); deploy various strain gauges types and induce cracks with different sizes (for challenge (ii)); fill the pipe with water at various temperatures and underground-like external stress (for challenge (iii)); Analyzing experimental results reveals useful hints for designing a realistic testbed, including but not limited to, the required distance among strain gauges, the influence of temperature and pipe axial stress. The dataset and analytic results of this work would provide more insights into how to design a realistic testbed for underground PVC water pipe crack detection.
Attention-Based Deep Bayesian Counting For AI-Augmented Agriculture

Object counting in images has been studied extensively, in particular using deep network models recently. The existing counting models typically output the point estimates of the object counts in given images. However, none of these can provide reliable uncertainty quantification of the derived count estimates, which is critical for consequent decision making when adopting these counting models in real-world applications. In this paper, we propose a novel deep counting model in a Bayesian framework. With the designed Bayesian attention module and Bayesian counting loss function, our deep Bayesian counting model not only improves the accuracy of count estimates with varying object and background appearance; but also enables their uncertainty quantification. We specifically focus on plant counting, which plays important roles in AI-augmented agriculture, for example crop yield estimates and farm management. Our ablation studies and experiments with the real-world agriculture data in the Global Wheat dataset have demonstrated that our deep Bayesian counting model obtains high count estimation accuracy as well as reliable uncertainty quantification. In addition, with the integrated Bayesian attention modules, it may help improve the interpretability of the derived count estimates, especially when the distribution of the interested plants in images is heterogeneous.
C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on prior Knowledge

CO2 concentration data with high resolution in large venues is highly required during indoor sport events for in-time environment adjustment to guarantee the athlete performances and audience experience. However, the limited battery energy of the wireless sensors cannot support high data resolution and long time coverage simultaneously. Besides, there also lacks effective embedded methods to clean anomaly data caused by the human and environmental factors probably occurring in large venues. Thus, in this paper, we propose C-RIDGE, a low-power sensing system for high resolution CO2 data collection in large venues. Based on prior knowledge, firstly, an adaptive sampling rate adjustment policy is developed for lower energy consumption to extend the time coverage of data. Secondly, CO2 physical property (CPP) aided data cleaning algorithm is designed to improve data quality as well, using Pearson Correlation Coefficient (PCC) and standard deviation with sliding windows. C-RIDGE has been deployed in one venue during a world-class event. The experiments and collected data have shown the system power consumption can be reduced by 36.1%, with measurement error less than 10.2%. The outliers and anomaly trends can also be detected and calibrated effectively via CPP algorithm. The dataset is available at https://doi.org/10.5281/zenodo.7160830.
Accuth: Anti-Spoofing Voice Authentication via Accelerometer

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which makes these systems vulnerable to various acoustic attacks and suffer high-security risks. In this work, we present Accuth, a novel authentication system that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks. Accuth captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks. Accuth is an effective complement to existing authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments, Accuth achieves over 90% identification accuracy among 15 human participants and an average equal error rate (EER) of 3.02% for spoofing attack detection.
Smart Objects: Impact Localization Powered by TinyML

Growing momentum in embedded systems and the wide use of sensors in everyday life, have motivated significantly, novel research in Internet of Things (IoT) systems and on-device Machine Learning (TinyML) processing. However, limitations in the energy stock and the computational capabilities of resource-scarce devices prevent the implementation of complex ML algorithms in IoT devices, which typically have limited computing power, small memory, and generate large amounts of data. This paper, aims to research and exploit the TinyML emerging technology for embedding intelligence in low-power devices, towards next generation IoT paradigm and smart sensing, in the context of SHM. In particular, the purpose is to provide integrated SHM functionality in plastic structures and thus make them "conscious" and self-explanatory (smart objects), by being able to localize any occurring impacts on the structure. We implement and benchmark Random Forest and Shallow Neural Network models on Arduino NANO 33 BLE, using an experimental dataset of piezoelectric sensor measurements concerning impact events in a thin plastic plate. The classification and model footprint results, 98.71% - 8KB and 95.35% - 12KB of accuracy and flash memory size for each model respectively, are very promising and constitute a solid baseline for motivating our concept.
Non-Acoustic Speech Sensing System Based on Flexible Piezoelectric

Speech is one of the most important biological signals to complement human-human and human-computer interaction. Traditional speech datasets were collected by air microphones, but using these datasets in noisy environments such as factories is practically challenging. Therefore, speech recognition in noisy environments poses higher requirements. The non-acoustic speech dataset plays a significant role in robust speech recognition under high background noise. Existing datasets suffered from dull sound, low intelligibility and poor recognition accuracy due to hardware and computer technology limitations. This paper presents a non-acoustic speech sensing system based on flexible piezoelectric. The system collected vibration signals from the jaws of six males and five females, and the corpus contained ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and capable of achieving 93.7% recognition accuracy by calculation. With the aforementioned benefits, this dataset is an essential tool for studying human-computer interaction in high-noise environments, analyzing human acoustic properties, and aiding medical rehabilitation.
Bracelet+: Harvesting the Leaked RF Energy in VLC with Wearable Bracelet Antenna

Visible Light Communication (VLC) is widely considered a promising technology for the coming 6G networks. Recent studies show that a VLC transmitter not only emits visible light signals but also leaks RF signals during the transmission. In this work, we devote effort to harvesting the free leaked RF energy from VLC transmissions. We observe that the surrounding objects could help a coil antenna harvest significantly more RF energy. Based on this observation, we propose our system Bracelet+, which involves the human body in the harvesting system to increase the harvested power. After careful analysis of the influence of the human body on the harvested power, we prototype the coil antenna as a bracelet that achieves both high harvested power and convenience for wearing. The average power of the RF energy harvested by our design is 10× larger than that of the conventional coil antenna, without causing any interference to the communication of VLC systems. The harvested power can reach up to micro-watts in our tested scenarios. Such a micro-watt level of harvested energy has the potential to power up ultra-low-power sensors such as temperature sensors and glucose sensors.
KITE: Exploring the Practical Threat from Acoustic Transduction Attacks on Inertial Sensors

In cyber-physical systems, inertial sensors are the basis for identifying motion states and making actuation decisions. However, extensive studies have proved the vulnerability of those sensors under acoustic transduction attacks, which leverage malicious acoustics to trigger sensor measurement errors. Unfortunately, the threat from such attacks is not assessed properly because of the incomplete investigation on the attack's potential, especially towards multiple-degree-of-freedom systems, e.g., drones. To thoroughly explore the threat of acoustic transduction attacks, we revisit the attack model and design a new yet practical acoustic modulation-based attack, named KITE. Such an attack enables stable and controllable injections, even under frequency offset based distortions that limit the effect of prior attacking approaches. KITE exploits the potential threat of transduction attacks without the need of strengthening attackers' abilities. Furthermore, we extend the attack surface to multiple-degree-of-freedom systems, which are more widely deployed but ignored by prior work. Our study also covers the scenario of attacking moving targets. By revealing the practical threat from acoustic transduction attacks, we appeal for both the attention to their harm and necessary countermeasures.
Solving the WiFi Sensing Dilemma in Reality Leveraging Conformal Prediction

With the wide deployment of smart environments and IoT devices, WiFi sensing has demonstrated its great convenience and contactless sensing capabilities in supporting a broad array of applications. However, designing a ubiquitous WiFi sensing system for heterogeneous scenarios in practice is still a big dilemma as the system performs poorly when the testing data is significantly different from the training data caused by domain variations. To address this dilemma, existing studies involve extra efforts to develop new features or even to retrain the original model under environmental variations. However, none of them can resolve the dilemma completely. In this work, we conduct a comprehensive study on the domain variation problem to make WiFi sensing robust and accurate in reality. Our definition of domains is comprehensive and includes environments, surrounding settings, user differences, user's facing directions, user's positions relative to WiFi sensors, and user participating time frames. Our innovation is to achieve reliable WiFi sensing across all the domains based on the conformal prediction framework. Our approach quantifies the conformity (i.e., similarity) between the testing WiFi samples and the training samples, then labels the testing samples with the most probable class(es). We develop a novel cross-domain transformal prediction scheme based on the multivariate kernel density estimation to effectively assess and learn the conformity of each domain in the training data. To meet various application-specific requirements, we further develop two approaches to fuse the knowledge of conformity derived from the training domains to perform predictions. Extensive experiments with both self-collected and public datasets show that our framework can improve prediction accuracies from 30% to 74% improvements in three most representative WiFi-based applications across six types of domain variations.