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Secure and usable user authentication is the first line of defense against cyber attacks on smart end-user devices. Advanced hacking techniques pose severe threats to the traditional authentication systems based on the password/PIN/fingerprint. We propose MagAuth, a secure and usable two-factor authentication scheme with commercial off-the-shelf (COTS) wrist wearables with magnetic strap bands to enhance the security and usability of password-based authentication for mobile touchscreen devices. In MagAuth, a user enrolls a self-chosen unlock pattern or touch gesture into his touchscreen device by performing it with the same hand the magnetic wrist wearable is on. The chosen unlock pattern or touch gesture serves as the first authentication factor, and the user's behavioral features manifested in the magnetic field changes during his finger movement correspond to the second factor. The user can unlock his touchscreen device only when both authentication factors can be validated. Comprehensive user experiments confirm the high security and usability of MagAuth. In particular, MagAuth achieves an average true-positive rate up to 96.3% and a false-positive rate no larger than 8.4%. Moreover, we show that MagAuth is highly resilient to various attacks.

Acoustic fingerprinting aims to identify a mobile device based on its internal microphone(s) and speaker(s) which are unique due to manufacturing imperfection. This paper seeks a thorough understanding of the (in)security of exploring acoustic fingerprints for achieving distributed mobile authentication. Our contributions are threefold. First, we present a new acoustic fingerprint-emulation attack and demonstrate that it is a common vulnerability of acoustic mobile authentication systems. Second, we propose a dynamic challenge-response defense to secure acoustic mobile authentication systems against the acoustic fingerprint-emulation attack. Finally, we thoroughly investigate existing acoustic fingerprinting schemes and identify the best option for accurate, secure, and deployable acoustic mobile authentication systems.

The recent development of tough tissue adhesives has stimulated intense interests among material scientists and medical doctors. However, these adhesives have seldom been tested in clinically demanding surgeries. Here we demonstrate adhesive anastomosis in organ transplantation. Anastomosis is commonly conducted by dense sutures and takes a long time, during which all the vessels are occluded. Prolonged occlusion may damage organs and even cause death. We formulate a tough, biocompatible, bioabsorbable adhesive that can sustain tissue tension and pressurized flow. We expose the endothelial surface of vessels onto a gasket, press two endothelial surfaces to the adhesive using a pair of magnetic rings, and reopen the bloodstream immediately. The time for adhesive anastomosis is shortened compared to the time for sutured anastomosis. We have achieved adhesive anastomosis of a great vein in transplanting the liver of a pig. After the surgery, the adhesive is absorbed, the vein heals, and the pig lives for over one month.

Wireless networks of different technologies may interfere with each other when they are deployed at proximity. Such cross-technology interference (CTI) has become prevalent with the surge of IoT devices. In this paper, we exploit CTI in coexisting WiFi-Zigbee networks and propose DeepJam, a new stealthy jamming strategy, to jam Zigbee traffic. DeepJam relies on deep learning techniques to capture the temporal pattern of the past wireless traffic and predict the future wireless traffic. By only jamming the victim’s transmissions that are not disrupted by CTI, DeepJam can significantly reduce the victim’s throughput with far fewer jamming signals and is thus much more stealthy than conventional jamming strategies. Detailed evaluations show that DeepJam can converge within 10 sec and achieve the jamming-efficiency gains of up to 742% and 285% over conventional random and reactive jamming strategies, respectively, in practical scenarios. We also propose a simple yet effective countermeasure against DeepJam.

Social data outsourcing is an emerging paradigm for effective and efficient access to the social data. In such a system, a third-party Social Data Provider (SDP) purchases social network datasets from Online Social Network (OSN) operators and then resells them to data consumers who can be any individuals or entities desiring social data through query interfaces. The SDP cannot be fully trusted and may return forged or incomplete query results to data consumers for various reasons, e.g., in favor of the businesses willing to pay. In this paper, we initiate the study on verifiable query processing over outsourced social graph whereby a data consumer can verify both the integrity and completeness of any query result returned by an untrusted SDP. We propose three schemes for single-attribute queries and another scheme for multi-attribute queries over outsourced social data. The four schemes all require the OSN provider to generate some cryptographic auxiliary information, based on which the SDP can construct a verification object to allow the data consumer to verify the integrity and completeness of the query result. They, however, differ in how the auxiliary information is generated and how the verification object is constructed and verified. Detailed analysis and extensive experiments using a real Twitter dataset confirm the efficacy and efficiency of the proposed schemes.

A USB4000 spectrometer was installed in the converter mouth to collect spectral information of the flame in theprocess of steelmaking in real time, Gaussian fitting algorithm and wavelet analysis a...

Video venue category prediction has been drawing more attention in the multimedia community for various applications such as personalized location recommendation and video verification. Most of existing works resort to the information from either multiple modalities or other platforms for strengthening video representations. However, noisy acoustic information, sparse textual descriptions and incompatible cross-platform data could limit the performance gain and reduce the universality of the model. Therefore, we focus on discriminative visual feature extraction from videos by introducing a hybrid-attention structure. Particularly, we propose a novel Global-Local Attention Module (GLAM), which can be inserted to neural networks to generate enhanced visual features from video content. In GLAM, the Global Attention (GA) is used to catch contextual scene-oriented information via assigning channels with various weights while the Local Attention (LA) is employed to learn salient object-oriented features via allocating different weights for spatial regions. Moreover, GLAM can be extended to ones with multiple GAs and LAs for further visual enhancement. These two types of features respectively captured by GAs and LAs are integrated via convolution layers, and then delivered into convolutional Long Short-Term Memory (convLSTM) to generate spatial-temporal representations, constituting the content stream. In addition, video motions are explored to learn long-term movement variations, which also contributes to video venue prediction. The content and motion stream constitute our proposed Hybrid-Attention Enhanced Two-Stream Fusion Network (HA-TSFN). HA-TSFN finally merges the features from two streams for comprehensive representations. Extensive experiments demonstrate that our method achieves the state-of-the-art performance in the large-scale dataset Vine. The visualization also shows that the proposed GLAM can capture complementary scene-oriented and object-oriented visual features from videos. Our code is available at: https://github.com/zhangyanchao1014/HA-TSFN .

The fusion quality of infrared and visible image is very important for subsequent human understanding of image information and target processing. The fusion quality of the existing infrared and visible image fusion methods still has room for improvement in terms of image contrast, sharpness and richness of detailed information. To obtain better fusion performance, an infrared and visible image fusion algorithm based on latent low-rank representation (LatLRR) nested with rolling guided image filtering (RGIF) is proposed that is a novel solution that integrates two-level decomposition and three-layer fusion. First, infrared and visible images are decomposed using LatLRR to obtain the low-rank sublayers, saliency sublayers, and sparse noise sublayers. Then, RGIF is used to perform further multiscale decomposition of the low-rank sublayers to extract multiple detail layers, which are fused using convolutional neural network (CNN)-based fusion rules to obtain the detail-enhanced layer. Next, an algorithm based on improved visual saliency mapping with weighted guided image filtering (IVSM-GIF) is used to fuse the low-rank sublayers, and an algorithm for adaptive weighting of regional energy features based on Laplacian pyramid decomposition is used to fuse the saliency sublayers. Finally, the fused low-rank sublayer, saliency sublayer, and detail-enhanced layer are used to reconstruct the final image. The experimental results show that the proposed method outperforms other state-of-the-art fusion methods in terms of visual quality and objective evaluation, achieving the highest average values in six objective evaluation metrics.

Although the traditional image fusion method can obtain rich image results, obvious artificial noise and artifacts are often present in the resulting image. Fusion algorithms based on neural networks can avoid the shortcomings of traditional methods, but they are more complex and less flexible. In this study, we proposed a fusion method using the deep residual neural network ResNet152, which can not only effectively suppress artificial noise but also preserve the edge details of the image and improve the efficiency of the neural network. The proposed method is characterized by a multiscale transformation of an infrared image and visible light image in the optimized nonsubsampled contourlet transformation domain, and the deep residual neural network ResNet152 is used to extract the deep features of the low-pass component to guide the fusion of the low-pass component. The bandpass component is fused by taking the modulus maximum. This method can fully retain the global features and structural information of the source image in the result image. Compared to existing fusion methods using public test image sets, the experimental results show that on a subjective level, the fusion method creates sharper depth edges and fewer noise artifacts than traditional fusion methods. From an objective perspective, the average value for the results of the evaluation function is greater than that of other fusion methods.

Aim: Determination of bone age is an important method to skeletal maturity and growth potential. This paper proposes a determination bone age via X-ray image recognition to obtain a better identification effect by comparison with state-of-the-art techniques. Methods : The proposed approach comprises two steps: the feature extraction and classification method. The feature extraction utilizes depth neural network to study the features of X-ray image, and the Local Binary Patterns (LBP) features and Glutamate cysteine ligase modifier subunit (GCLM) features in the image are extracted. Then, the classification method base on support vector machine is used to classify the features. Results: The experimental results show that the average absolute error of bone age assessment model based on multi-dimensional data feature fusion is 0.455, which is superior to the traditional method and support vector machine method. Because the model is based on feature extraction of deep neural network, it shows that the feature extraction method based on deep neural network can extract feature information better than traditional image analysis method. Conclusion: Compared with the traditional feature extraction method, the feature extraction based on deep convolution neural network has better performance in the bone age regression model. Combining population and gender information, the accuracy of bone age prediction based on image can be further improved.

Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.

In this article, based on the fact that the converter mouth flame is the comprehensive external appearance of the physical and chemical reactions in the converter during the steelmaking process, the continuous spectrum information of the converter mouth flame was obtained by USB4000 spectrometer. In the framework of the Internet of Things, a bidirectional recursive multiscale convolution depth neural network algorithm can take into account the characteristics of frequency domain structure and time domain dynamic sequence. It is applied to the deep-learning of the converter mouth flame spectrum information. The dynamic prediction model of carbon content and temperature value in molten steel at the later stage of steelmaking is constructed. The static control system and dynamic prediction model of automatic steelmaking are intelligently fused to realize one-key steelmaking control. The results show that the average hit rate of carbon content, temperature, and carbon temperature at the end of steelmaking is 94.78%, 98.41%, and 93.43%, which makes the end-point control of steelmaking more stable and the blowing rate less than 1%.

The Internet of Things (IoT) networks are finding massive applications in mission-critical contexts. A group key is needed to encrypt and authenticate broadcast/multicast messages commonly seen in large-scale wireless networks. In this paper, we propose DroneKey, a novel drone-aided PHY-based Group-Key Generation (GKG) scheme for large-scale IoT networks. In DroneKey, a drone is dispatched to fly along random 3D trajectories and keep broadcasting standard wireless signals to refresh the group keys in the whole network. Every IoT device receives the broadcast signals from which to extract the Channel State Information (CSI) stream which captures the dynamic variations of the individual wireless channel between the IoT device and the drone. DroneKey explores a deep-learning approach to extract the hidden correlation among the CSI streams to establish a common group key. We thoroughly evaluate DroneKey with a prototype in both indoor and outdoor environments. We show that DroneKey can achieve a high key-generation rate of 89.5 bit/sec for 10 devices in contrast to 40 bit/sec in the state-of-art prior work. In addition, DroneKey is much more scalable and can support 100 devices in contrast to 10 nodes in the state-of-art prior work with comparable key-generate rates.

In a foster cow system, one or more calves are reared by a foster cow. With regard to the growing rejection towards the early separation of cow and calf after calving, such alternative rearing methods with prolonged cow–calf contact are coming more into focus. Nevertheless, there is a lack of recent studies investigating the effects of suckling multiple calves on the udder health of a foster cow. The present study aimed to initially describe the status and development of the udder condition and the microbial colonisation during the foster period. Thereby, the objectives were to determine whether suckling multiple calves was associated with an increase in intramammary infections (IMI) and a deterioration of udder constitution. A total of 99 Holstein Friesian foster cows from a large organic dairy farm in eastern Germany were included in this pilot study. Each foster cow was suckled by 3–4 calves and not additionally milked. Quarter milk samples were taken for cytomicrobiological examination at the beginning and end of the approximately 3.5 months suckling period. At the same time, the udders of 91 of these foster cows were clinically examined by visual assessment and manual palpation. In both the first and second examination (E1 and E2, respectively), Corynebacterium ssp. and non-aureus staphylococci (NaS) constituted the most frequently detected pathogen species in the 385 examined udder quarters. Classifying the quarters into mastitis categories was significantly different between the two examinations (p = 0.001). The number of quarters considered as healthy increased from E1 to E2 by 8.5%. At the same time, the number of quarters with mastitis decreased by 2.6%, the number of quarters with non-specific mastitis by 11.4% and the number of quarters with a latent infection increased by 5.4%. In total, 69.4% of the 121 infected udder quarters in E1 were bacteriologically cured in E2. A cytological cure was achieved in 28.1% of the 385 quarters. Both the bacteriological and cytological cure rate were spontaneous. A new infection occurred in 23.1% of the 385 examined udder quarters and was mainly attributable to infections with Corynebacterium spp. and NaS. During the suckling period, mainly new infections occurred and amounted to 73% in relation to presumed persistent infections (18.9%). The new infections were mainly attributable to infections with Corynebacterium spp. and NaS. Pasteurella spp. were also found in high percentages. A total of 10.1% of the quarters showed an increase in somatic cell count (SCC) above the threshold of 100,000 cells/mL from E1 to E2, indicating a new subclinical mastitis. Regarding udder condition, significant changes during the foster period were an increase in udder skin and teat lesions, a decrease in udder oedema, an increased amount of udder quarters with a consistency considered as normal and an increase in the number of udders showing no sign of udder cleft dermatitis. Moreover, teat length, circumference, and diameter increased significantly (p &lt, 0.05). In conclusion, there is a possibility that suckling multiple calves improves udder health in terms of mastitis but leads to an increased pathogen transmission at the same time, resulting in an increase in latent infections. Additionally, pathogens as Pasteurella spp., which are usually located in the upper respiratory system, could be detected in the mammary gland. The changes concerning the udder condition were manifold. However, it has not yet been conclusively clarified whether all these changes are exclusively due to calf suckling. Further investigations with a non-suckled control group are required.

Objective We aimed to determine the association between serum receptor activator of nuclear factor-kappa B ligand (sRANKL) levels and ankylosing spondylitis (AS) in Chinese patients. Methods The PubMed, Cochrane Library, Embase, Chinese Biomedical Database, Web of Science, China National Knowledge Infrastructure, VIP, and Wan Fang databases were searched for studies conducted before October 1, 2020, without language restrictions. STATA version 12.0 and Revman version 5.3 were used to analyze the data. The standard mean differences (SMDs) and corresponding 95% confidence intervals (95% CIs) were calculated. Results Twelve clinical case–control studies, including 585 patients with AS and 423 healthy controls, were included. The combined SMD for sRANKL suggested that the sRANKL level was significantly higher in Chinese patients with AS than in healthy controls (SMD: 3.27, 95% CI 2.11–4.43, P P P 4 than those of patients in other subgroups. In terms of duration, patients with AS for > 8 years had higher sRANKL levels than health controls. Other subgroup analyses were conducted by region, language, source of control, age, and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). In these subgroups, the sRANKL levels were significantly higher in the patients with AS than in healthy controls. The BASFI and BASDAI were sources of heterogeneity. Conclusions The sRANKL levels are higher in Chinese patients with AS, especially among those from South China. sRANKL levels may be positively correlated with the pathogenesis of AS among Chinese patients.

Funding Information: ACKNOWLEDGEMENT The authors would like to thank the anonymous reviewers for their constructive comments and helpful advice. This work was partially supported by National Natural Science Foundation of China under Grant 62072351, Academy of Finland under Grant 308087 and Grant 335262, US National Science Foundation through grants CNS-1933069, CNS-1824355, CNS-1651954 (CAREER), CNS-1718078 and CNS-1933047. Publisher Copyright: © 2021 IEEE. As cloud computing reshapes the global IT industry, an increasing number of business owners have outsourced their datasets to third-party cloud service providers (CSP), which in turn answer data queries from end users on their behalf. A well known security challenge in data outsourcing is that the CSP cannot be fully trusted, which may return inauthentic or unsound query results for various reasons. This paper considers top-k selection queries, an important type of queries widely used in practice. In a top-k selection query, a user specifies a scoring function and asks for the k objects with the highest scores. Despite several recent efforts, existing solutions can only support a limited range of scoring functions with explicit forms known in advance. This paper presents three novel schemes that allow a user to verify the integrity and soundness of any top-k selection query result returned by an untrusted CSP. The first two schemes support monotone scoring functions, and the third scheme supports scoring functions comprised of both monotonically non-decreasing and non-increasing subscoring functions. Detailed simulation studies using a real dataset confirm the efficacy and efficiency of the proposed schemes and their significant advantages over prior solutions.

Wireless home alarm systems are being widely deployed, but their security has not been well studied. Existing attacks on wireless home alarm systems exploit the vulnerabilities of networking protocols while neglecting the problems arising from the physical component of IoT devices. In this paper, we present new event-eliminating and event-spoofing attacks on commercial wireless home alarm systems by interfering with the reed switch in almost all COTS alarm sensors. In both attacks, the external adversary uses his own magnet to control the state of the reed switch in order to either eliminate legitimate alarms or spoof false alarms. We also present a new battery-depletion attack with programmable electromagnets to deplete the alarm sensor’s battery quickly and stealthily in hours which is expected to last a few years. The efficacy of our attacks is confirmed by detailed experiments on a representative Ring alarm system.

This paper presents a handheld micro-fluorescence spectrometer with an integrated ultraviolet light-emitting diode. A small transmission grating was used as a dispersive element to disperse light. The spectrum was detected by a CCD array. The instrument can be connected to a PC or a smartphone through a USB cable for data processing and spectrum display. The spectrometer is 30 × 30 × 100 mm3 only. The spectrometer with a wavelength range from 380 to 750 nm had a spectral resolution of 3 nm. The fluorescence spectra from edible oil, chlorophyll, and paper were measured by the spectrometer we designed. This spectrometer has the advantages of compact structure, small size, light weight, fast detection speed, convenient use, and low cost.

Poly-γ-glutamic acid (γ-PGA) is a promising, environmentally friendly, non-toxic, biodegradable, and super water-absorbable polymer. Using γ-PGA in agriculture is of great significance to promote crop growth, save irrigation water, and cope with water scarcity problems. The objectives of this study were to investigate the effects of application of different γ-PGA amounts on soil water storage, cotton growth, water use efficiency (WUE), cotton yield, and fiber quality under plastic mulched drip irrigation conditions. A 2-year potted experiment in a cotton field were conducted in the sandy soil of southern Xinjiang, China. Five different amounts of γ-PGA consisted of 0 kg ha−1 (P0), 20 kg ha−1 (P20), 40 kg ha−1 (P40), 80 kg ha−1 (P80), and 160 kg ha−1 (P160) were applied to these potted plots. All treatments were arranged in a randomized block designed with three replicates. The result showed that using γ-PGA significantly increased soil water storage and soil profile water content in the 0–40 cm soil layers compared to P0. γ-PGA significantly increased cotton stem diameter, boll numbers per plant, plant height, and leaf area index compared to the P0. Furthermore, cotton yield was significantly increased as well as WUE and fiber length; however, no influence was found on the fiber quality of cotton. Quadratic relationships were found between the different amount of γ-PGA applied and WUE and between the different applied amounts of γ-PGA and seed cotton yield. High WUE (7.48–8.19 kg ha−1 mm−1) and cotton yield (4376–4682.5 kg ha−1) were obtained under the P80 treatment. The study suggested that γ-PGA improved soil water-holding capacity, promoted cotton growth, and enhanced WUE and cotton yield. Therefore, these results recommend using γ-PGA at a dose of 80 kg ha−1 for dealing with drought conditions in the sandy soil of southern Xinjiang, China.

Barnyard grass (Echinochloa crusgalli) and weedy rice (Oryza sativa f. spontanea) are two common and troublesome weed species in rice (Oryza sativa L.) crop. They cause significant yield loss in rice production while it is difficult to differentiate them for site-specific weed management. In this paper, we aimed to develop a classification model with important spectral features to recognize these two weeds and rice based on hyperspectral imaging techniques. There were 287 plant leaf samples in total which were scanned by the hyperspectral imaging systems within the spectral range from 380 nm to 1080 nm. After obtaining hyperspectral images, we first developed an algorithmic pipeline to automatically extract spectral features from line scan hyperspectral images. Then the raw spectral features were subjected to wavelet transformation for noise reduction. Random forests and support vector machine models were developed with the optimal hyperparameters to compare their performances in the test set. Moreover, feature selection was explored through successive projection algorithm (SPA). It is shown that the weighted support vector machine with 6 spectral features selected by SPA can achieve 100%, 100%, and 92% recognition rates for barnyard grass, weedy rice and rice, respectively. Furthermore, the selected 6 wavelengths (415 nm, 561 nm, 687 nm, 705 nm, 735 nm, 1007 nm) have the potential to design a customized optical sensor for these two weeds and rice discrimination in practice.

In this paper, a streak tube laser lidar(STIL) based on cathode gating (CG) method is introduced,range gating of laser echo is realized by triggering the photocathode voltage of the streak tube. A Nd:YAG pulse laser is used as the light source, with a wavelength of 532 nm and a maximum repetition rate of 10 Hz,the raw image was collected by CCD with a resolution of 640 × 480. Imaging experiments of a target with a stand-off distance of 2 km are carried out by using the developed STIL system, the intensifier gating (IG) method and the CG method were used respectively. The experimental results show that, compared with IG mode, the CG mode with the same parameters has the advantages of strong detection ability and high signal-to-noise ratio(SNR). It is proved that CG mode is more suitable for the range gating of the STIL, and the SNR of the raw image is raised from 4.2 dB in non-gating mode to 16.8 dB in CG mode which effectively improves the image definition.

Dynamic spectrum access (DSA) is the key to solving worldwide spectrum shortage. The open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate users. Secondary-user authentication is thus critical to ensure the proper operations of DSA systems. This paper presents SpecGuard, the first crowdsourced spectrum misuse detection framework for DSA systems. In SpecGuard, a transmitter is required to embed a spectrum permit into its physical-layer signals, which can be decoded and verified by ubiquitous mobile users. We propose three novel schemes for embedding and detecting a spectrum permit at the physical layer. The first scheme relies on a higher transmission power to embed the spectrum permit. To alleviate the assumptions on the additional transmission power, the second scheme is proposed with a limited negative impact on the normal data transmission. The third scheme takes a different approach by adopting a novel constellation design and exploiting the trust between the transmitter and the receiver. Crowdsourced spectrum misuse detection eliminates the need for the deployment of dedicated sensors and thus greatly reduces the deployment and maintenance cost. Detailed theoretical analyses, MATLAB simulations, and USRP experiments confirm that our schemes can achieve correct, low-intrusive, and fast spectrum misuse detection.

Mobile payment apps are seeing explosive usage worldwide. This paper focuses on Venmo, a very popular mobile person-to-person payment service owned by Paypal. Venmo allows money transfers between users with a mandatory transaction note. More than half of transaction records in Venmo are public information. In this paper, we propose a multilayer location inference (MLLI) technique to infer user locations from public transaction records in Venmo. MLLI explores two observations. First, many Venmo transaction notes contain implicit location cues. Second, the types and temporal patterns of user transactions have strong ties to their location closeness. With a large dataset of 2.12M users and 20.23M Venmo transaction records, we show that MLLI can identify the top-1, top-3, and top-5 possible locations for a Venmo user with accuracy up to 50%, 80%, and 90%, respectively. Our results highlight the danger of sharing transaction notes on Venmo or similar mobile payment apps.

In-plane uniformity of narrow-gap semiconductor $\mathrm{In}\mathrm{As}$/$\mathrm{Ga}\mathrm{Sb}$ type-II superlattice (T2SL) wafer is a crucial yet hard-to-evaluate prerequisite for high-performance long-wavelength infrared optoelectronic device applications of, e.g., focal-plane-array (FPA) photodetectors. In this work, we report a modulated photoluminescence-mapping (PL-mapping) study of $\mathrm{In}\mathrm{As}$/$\mathrm{Ga}\mathrm{Sb}$ T2SL in long-wavelength infrared range with a spatial resolution of a typical FPA-pixel scale. Spatial distributions are analyzed of PL-peak energy, linewidth, and integral intensity, which indicate a high in-plane uniformity of effective band gap but a considerable fluctuation of radiative recombination. The in-plane distributions of effective carrier lifetime and Shockley-Read-Hall defect concentration are evaluated, with the aid of a model that takes into account the pumping power dependence of the PL integral intensity. The results reveal a considerable in-plane nonuniformity of the optoelectronic response that may restrict the performance of the derivative FPA photodetector, and indicate the modulated PL mapping of a good pathway particularly for uniformity analysis of long-wavelength infrared FPA semiconductors.

Crack is a common type of metal indication defects, which brings great hidden dangers to safety of hoisting machinery in use. Automatic metal crack detection methods could be practical in less expensive and high efficiency. In this paper, an encoder-decoder convolutional neural network is proposed, called GFU-Net, which can automatically predict pixel-level crack segmentation by end-to-end method. GFU-Net introduces the guide transformer module on U-Net’s base to strengthen the fusion between corresponding features and applies the Deeply-Supervised Net (DSN), which places features of each convolutional stage under the integrated straight supervision. The experiments show that our work outperforms all other models we test in this article and detects metal cracks from low-contrast images effectively and explicitly.

ObjectiveWe evaluated the association between monocyte chemotactic protein-1 (MCP-1) and osteoarthritis.MethodsWe searched PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), VIP (Chinese database), and Wan Fang (Chinese database) (before May 10, 2020), with no language limitations. STATA version 12.0 and Revman version 5.3 were used for data analysis. The standard mean difference (SMD) and corresponding 95% confidence intervals (95% CIs) were calculated. Nine clinical studies, including 376 patients with osteoarthritis and 306 healthy controls, were evaluated.ResultsThe combined SMDs of MCP-1 expression levels suggested that MCP-1 expression was significantly higher in patients with osteoarthritis than healthy controls (SMD = 1.97, 95% CI = 0.66–3.28,p= 0.003). Moreover, subgroup analysis implied that osteoarthritis patients from both Asians and mixed populations had higher MCP-1 expression levels than controls, whereas Caucasians did not (p> 0.05). Serum MCP-1 levels (SMD = 2.83, 95% CI = 1.07–4.6,p< 0.00001) were significantly higher in patients with osteoarthritis than in controls; however, this difference was not significant in synovial fluid and cartilage tissue. Subgroup analysis for ethnicity showed that MCP-1 levels were significantly higher in Chinese, Dutch, and Brazilian patients with osteoarthritis than in control groups, although significant differences were not observed for American and Italian subgroups.ConclusionsOur meta-analysis demonstrated that MCP-1 expression levels were higher in patients with osteoarthritis than in healthy controls and that MCP-1 may play important roles in the progression of osteoarthritis. Serum MCP-1 levels may serve as a potential biomarker for the diagnosis of osteoarthritis.

Internet-of-things (IoT) devices are sharing the radio frequency band (e.g., 2.4 GHz ISM band). The exponentially increasing number of IoT devices introduces potential security issues at the gateway in IoT networks. In this paper, we introduce a set of new attacks through concealed jamming - an adversary pretends to be (or compromises) a legitimate WiFi device, then sends out WiFi packets to prevent ZigBee devices' communication or collide with ZigBee's packets. By doing this, concealed jamming has the potential to severely delay the reception of ZigBee packets that may contain important information (e.g., critical health data from wearables, fire alarms, and intrusion alarms). To defend against these attacks, we designed a novel ZigBee data extraction technique that can recover ZigBee data from the ZigBee packets that were collided with WiFi packets. We extensively evaluated our design in different real-world settings. The results show that ZigBee devices (protected by our proposed methods) achieve similar performance as those that are not under the concealed jamming attack. Moreover, compared with unprotected devices, their throughput is more than 15 times higher than the unprotected one that is under concealed jamming attacks.

Data outsourcing is a promising technical paradigm to facilitate cost-effective real-time data storage, processing, and dissemination. In such a system, a data owner proactively pushes a stream of data records to a third-party cloud server for storage, which in turn processes various types of queries from end users on the data owner’s behalf. This paper considers outsourced multi-version key-value stores that have gained increasing popularity in recent years, where a critical security challenge is to ensure that the cloud server returns both authentic and fresh data in response to end users’ queries. Despite several recent attempts on authenticating data freshness in outsourced key-value stores, they either incur excessively high communication cost or can only offer very limited real-time guarantee. To fill this gap, this paper introduces KV-Fresh, a novel freshness authentication scheme for outsourced key-value stores that offers strong real-time guarantee. KV-Fresh is designed based on a novel data structure, Linked Key Span Merkle Hash Tree, which enables highly efficient freshness proof by embedding chaining relationship among records generated at different time. Detailed simulation studies using a synthetic dataset generated from real data confirm the efficacy and efficiency of KV-Fresh.

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user's tapping rhythm. In addition to verifying the RFID card's identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user's secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero., To appear at IEEE INFOCOM 2020

To reduce the negative effects of mastitis in dairy heifers in early lactation on the future milking performance, the aim of this study was to define the time-related period of intramammary infections and to relate this to risk factors at heifer and quarter level for intramammary infections and subclinical mastitis. In total, 279 German Holstein Frisian heifers in three farms in Northern and Eastern Germany were included in this study. Quarter milk samples for cytomicrobiological examination were collected 3 &plusmn, 1 days after calving and 17 &plusmn, 3 days after calving, and risk factors at heifer and quarter level associated with intramammary infections and clinical mastitis were recorded during the trial period. Data were analyzed using logistic regression procedures and odds ratios were calculated. Calving at older ages increased the odds of intramammary infections with non-aureus staphylococci (NAS) and coryneforms 17 &plusmn, 3 days after calving compared to heifers calving at a younger age. Detaching of milking cups during milking is a risk factor for new infections between day 3 &plusmn, 1 and 17 &plusmn, 3 postpartum. The milk yield after calving is associated with a decrease in intramammary infections with environmental pathogens and clinical mastitis. A high milk yield assists the development of udder edema with an increased risk of intramammary infections with NAS and coryneforms. An increased somatic cell count (SCC) after calving increased the odds of intramammary infections with contagious pathogens 17 &plusmn, 3 days postpartum. The early lactation has an important influence on udder health and intramammary infections postpartum in dairy heifers. Udder quarters eliminated pathogens during early lactation by 6.9% for cases in this study. New infections manifest themselves up until 17 &plusmn, 3 days postpartum, especially with Corynebacterium spp. and NAS. In total, 82.9% of the infected quarters showed new infections with another pathogen species 17 &plusmn, 3 days postpartum than 3 &plusmn, 1 days postpartum. In conclusion, the early lactation has an important influence on udder health and intramammary infections postpartum in heifers with the possibility that udder quarters eliminate pathogens, but also the danger that new infections manifest themselves. Thus, the prevention of new infections by minimizing the associated risk factors is of great importance.

Rats are suitable animal models in which to study the effects of gastric bypass surgery. However, construction of gastrojejunal anastomosis in the rat is technically demanding and is associated with high rate of postoperative complications. The aim of this study was to explore the feasibility and efficacy of the magnetic compression technique (MCT) in side-to-side gastrojejunal anastomosis in rats. Thirty male rats underwent gastrojejunal anastomosis using one of three techniques: hand-sewn, magnetic compression using cuboid magnets, and magnetic compression using magnetic rings. The mean anastomosis time using the magnetic compression technique was significantly less than that of the hand-sewn technique (3.6 ± 0.96 and 6.50 ± 1.58 vs. 14.40 ± 2.37 minutes,). The survival rate was highest in animals treated with magnetic compression using cuboid magnets (100%), followed by animals treated with magnetic compression using magnetic rings (90%) and then hand sewing (70%). The mean burst pressure did not differ significantly between the magnetic compression and hand-sewn anastomoses. Anastomoses constructed by magnetic compression were smoother and flatter than hand-sewn anastomoses. The results showed that MCT is a simple and feasible method for gastrojejunal anastomosis in the rat.

WiFi and Bluetooth Low Energy (BLE) are widely used in Internet of Things (IoT) devices. Since WiFi and BLE work within the overlapped ISM 2.4 GHz band, they will interfere with each other. Existing approaches have demonstrated their effectiveness in mitigating the interference. However, further performance improvement has been hampered by the design goal of exclusive communication of WiFi or BLE, which only allows one WiFi or BLE device to transmit packets at any specific time slot on the overlapped channel within the communication range. In this paper, we explore a new communication method, called Parallel Inclusive Communication (PIC), which leverages the unique modulation schemes of WiFi and BLE for parallel inclusive bi-directional transmission of both WiFi and BLE data at the same time within the overlapped channel. In this communication system, the PIC gateway is designed upon the IEEE 802.11g and 802.15.1 frameworks while the WiFi and BLE clients are commercial off-the-shelf devices. PIC achieves similar data rates for these parallel WiFi and BLE communications as if WiFi and BLE are communicating separately. PIC's system architecture naturally fits at the edge of the Internet, which is an optimal site for concurrently collecting (or disseminating) data from (or to) an exponentially increasing number of IoT devices that are using WiFi or BLE. We conducted extensive evaluations under four real-world scenarios. Results show that compared with existing approaches, PIC can significantly i) increase the packet reception ratios by 183%; ii) reduce the round-trip delay time by 590 times and energy consumption by 50.5 times; and iii) improve the throughput under WiFi and BLE coexistence scenarios.

Dynamic spectrum access is promising for mitigating worldwide wireless spectrum shortage. Crowdsourced spectrum sensing (CSS) refers to recruiting ubiquitous mobile users to perform real-time spectrum sensing at specified locations and has great potential in mitigating the drawbacks of current spectrum database operations. Without strong incentives and location privacy protection in place, however, mobile users will be reluctant to act as mobile crowdsourcing workers for spectrum-sensing tasks. In this paper, we first formulate participant selection in CSS systems as a reverse auction problem, in which each participant’s true cost for spectrum sensing is closely tied to his current location. Then, we demonstrate how the location privacy of CSS participants can be easily breached under the framework. Finally, we present PriCSS, a novel framework for a CSS service provider to select CSS participants in a differentially privacy-preserving manner. In this framework, we propose PriCSS− and PriCSS+, two different schemes under distinct design objectives and assumptions. PriCSS− is an approximately truthful scheme that achieves differential location privacy and an approximate minimum payment, while PriCSS+ is a truthful scheme that achieves differential location privacy and an approximate minimum social cost. The detailed theoretical analysis and simulation studies are performed to demonstrate the efficacy of both schemes.

Delta doped GaSbBi quantum wells (QWs) grown by molecular beam epitaxy was investigated to extend light emission wavelength at room temperature with the Bi content of 7.0%. The delta-doped GaSbBi QWs transition energy shifts up to 47.0 meV with increasing the Te dopant concentration from 0 to 4.56 × 10 12 cm −2 , resulting in maximum light emission of 2.42 μm, without obvious degradation of optical quality. The temperature coefficient of the band-gap for the delta-doped QW is only 0.099 meV/K compared with 0.265 meV/K from the undoped GaSbBi reference QW.

Aerial spraying using UAV has gained great interest worldwide. UAV spraying can overcome crop height limits and is unlikely to crust soil or damage crop plants. More experiments concerning spraying are needed, such as spraying method tests, spraying droplet analysis, variable rating spraying tests, etc. These experiments either take a long time for real flights or have an unrecognized danger to UAV in real flights. To address this problem, an indoor spraying platform, which consists of X-Y direction movement and wind field generation was developed to simulate UAV aerial spraying. The maximum moving speed in the horizontal direction was 3.5 m/s and 0.25 m/s in the vertical direction. The horizontal moving distance was 11 m and the vertical moving distance was 0.5 m. The platform consists of 4 parts: upper machine software, central controller, X-Y moving part and far end spraying structure. CAN bus was used for communication between the central control board and far end controller. An experiment was carried out to test how the platform performs with different moving speeds and different wind strength. The wind strength test shows that wind forces the deposit down to the ground when droplets were equally affected by 2 wind forces. The result shows that the platform can meet the requirement of UAV aerial spraying.

This paper introduces a streak tube imaging lidar (STIL) used for real-time 3D range imaging. Based on the LabVIEW platform, this system realized system control, image acquisition, data processing and image display through PC. Long distance target imaging experiments have been carried out with our self-designed system. The frame rate of raw image processing is up to 100 Hz, and the real-time display of the intensity image and range image is realized. For a specific target with a distance of 2000 m away, the experimental results show that our system has realized spatial resolution up to 0.1 m and the range resolution up to 0.18 m, which can be further improved. Compared with the traditional STIL system using FPGA, DSP and other hardware control and processing methods, our system has the advantages of short design cycle, low cost and high flexibility.

A femtosecond pulsed laser was used to perform a processing study on carbon-fiber-reinforced silicon carbide (C/SiC) composites. The effect of femtosecond laser diffraction on the processed morphology was studied by simulation and experiments. Using the diffraction of femtosecond laser, a regular multileveled concentric ring structure was fabricated on the surface of C/SiC composites, and controllable morphology and size could be achieved by changing the processing parameters such as amount of defocus, power, and number of pulses. The results indicate that the spot energy distribution generated by the femtosecond laser diffraction is more uniform than Gaussian beam; therefore, femtosecond laser diffraction can be applied to surface polishing of C/SiC composites.

In this paper, we proposed an adjacent frame difference (AFD) method to reconstruct 3-dimension (3D) images for range-gated flash imaging Lidars. In this novel method, the range information of the target is obtained by thresholding the intensity difference between two adjacent frames from the intensified charge-coupled device (ICCD), which is triggered by the range-gate signals with step delays. To verify the feasibility of this method, a target building at a distance of 2.4 km was measured by our self-developed range-gated flash imaging Lidar based on AFD method. The real-time 3D images of the target were reconstructed at the front and back edges of the range gate, respectively. The difference of the range information from the front and back edges can be used to denoise the target image and improve image clarity. Compare to the trapezoid-range-intensity profile method and the triangular-range-intensity profile method, this method needn’t strict correlation between the range-gate and laser pulse widths, and has many advantages such as fast imaging speed, large imaging range, and simple data processing. The theoretical analysis and experimental results indicate the range-gated flash imaging Lidar using AFD method has the high practicality and wide applications.

Based on the finite-time thermodynamics, the optimized round-trip efficiencies of pumped thermal electricity storage (PTES) and pumped cryogenic electricity storage (PCES) are derived under different optimization criteria, which include maximum power optimization, efficiency power optimization, ecological optimization, and unified trade-off optimization. The effect of charging and discharging time ratio on the optimized round-trip efficiency (ORTE) of PTES and PCES systems is investigated. The upper and lower bounds of ORTE under different optimization criteria are reached when the charging and discharging time ratio tend, respectively, to infinity or zero. The optimized performance of PTES and PCES systems under different optimization criteria is compared, and it is found that the ecological optimization and unified trade-off optimization of PTES and PCES systems can give more superior performance.

Coherent control of self-contained quantum systems offers the possibility to fabricate smallest thermal transistors. The steady coherence created by the delocalization of electronic excited states arouses nonlinear heat transports in non-equilibrium environment. Applying this result to a three-level quantum system, we show that quantum coherence gives rise to negative differential thermal resistances, making the thermal transistor suitable for thermal amplification. The results demonstrate that quantum coherence facilitates efficient thermal signal processing and can open a new field in the application of quantum thermal management devices.

A new model of the thermal amplifier consisting of three electronic reservoirs connected by two energy filters and an electronic conductor with negligible resistance is proposed. The thermal amplifier may have two different connective methods and can be regarded as the equivalent system composed of an energy selective electron (ESE) heat engine and an ESE heat pump. With the help of the theory of the ESE devices operating between two electronic reservoirs, the coefficient of performance and rate of heat-pumping of the thermal amplifier are directly derived. The effects of the center energy levels and the half width (or the half width at the half maximum) of two energy filters on the performance of the thermal amplifier are discussed. The maximum coefficient of performance and rate of heat-pumping are calculated. The optimally operating regions of the thermal amplifier are determined. Moreover, the optimum performance characteristics of the thermal amplifier with two configurations of energy filters are compared.

Device-to-device (D2D) communications are emerging due to the explosive growth of smartphones and tablets. Given the possible presence of attackers, a fundamental challenge in secure D2D communications is to develop sound mobile authentication techniques whereby mobile users can select the most trustworthy D2D communication partners from possibly many candidates. This paper tackles this open challenge and proposes spatiotemporal matching as a promising enabler for secure D2D communications. Spatiotemporal matching is built upon the location-aware capability of D2D devices. In particular, a mobile user could very easily maintain his spatiotemporal profile recording his continuous whereabouts in time, and the level of his spatiotemporal profile matching that of the other user can be translated into the level of trust they two can have in each other. Since spatiotemporal profiles contain very sensitive personal information, privacy-preserving spatiotemporal matching is needed to ensure that as little information as possible about the spatiotemporal profile of either matching participant is disclosed beyond the matching result. Toward this end, we propose two novel privacy-preserving spatiotemporal matching protocols, which are thoroughly analyzed and evaluated through detailed simulation studies driven by experimental data.

A model of the graphene-based thermionic solar cell (TSC) consisting of a concentrator, an absorber, and a thermionic emission device configured with graphene-based cathode is proposed, where the radiation and reflection losses from the absorber to the environment, the thermal radiation between the cathode and the anode electrodes, and the heat losses from the anode to the environment are considered. The performance characteristics of the TSC are analyzed by numerical calculations. It is found that the maximum efficiency can reach 21% when the area ratio is 0.24 and the voltage output is 2.01 V. In addition, the maximum efficiencies of the TSC under different concentrations and the optimal values of some key parameters are determined, and consequently, the corresponding optimally operating conditions are obtained. The results obtained here may provide guidance for the appropriate selection of electrode materials and the optimum design of practical TSC devices.

Indoor positioning systems (IPSes) can enable many location-based services in large indoor venues where GPS signals are unavailable or unreliable. Among the most viable types of IPSes, RSS-IPSes rely on ubiquitous smartphones and indoor WiFi infrastructures and explore distinguishable received signal strength (RSS) measurements at different indoor locations as their location fingerprints. RSS-IPSes are unfortunately vulnerable to physical-layer RSS attacks that cannot be thwarted by conventional cryptographic techniques. Existing defenses against RSS attacks are all subject to an inherent tradeoff between indoor positioning accuracy and attack resilience. This paper presents the design and evaluation of MV-IPS, a novel RSS-IPS based on weighted multi-voting, which does not suffer from this tradeoff. In MV-IPS, every WiFi access point (AP) that receives a user's RSS measurement gives a weighted vote for every reference location, and the reference location that receives the highest accumulative votes from all APs is output as the user's most likely position. Trace-driven simulation studies based on real RSS measurements demonstrate that MV-IPS can achieve much higher positioning accuracy than prior solutions no matter whether RSS attacks are present.

We propose WristUnlock, a novel technique that uses a wrist wearable to unlock a smartphone in a secure and usable fashion. WristUnlock explores both the physical proximity and secure Bluetooth connection between the smartphone and wrist wearable. There are two modes in WristUnlock with different security and usability features. In the WristRaise mode, the user raises his smartphone in his natural way with the same arm carrying the wrist wearable; the smartphone gets unlocked if the acceleration data on the smartphone and wrist wearable satisfy an anticipated relationship specific to the user himself. In the WristTouch mode, the wrist wearable sends a random number to the smartphone through both the Bluetooth channel and a touch-based physical channel; the smartphone gets unlocked if the numbers received from both channels are equal. We thoroughly analyze the security of WristUnlock and confirm its high efficacy through detailed experiments.

Rationale: The old Monteggia fracture is an uncommon lesion pattern in adult, which may lead to the potential complications such as recurrent dislocation of the radial head, heterotopic ossification of the elbow, nerve palsy, malunion of the ulna, and residual forearm deformity. However, the secondary distal radioulnar joint (DRUJ) dislocation is rarely reported in the similar lesion. Here we present a unique reoperation of old Monteggia fracture combined with secondary DRUJ disruption after the initial operation failure. Patient concerns: A 38-year-old male presented to our hospital outpatient office complaining of left elbow stiffness and ongoing wrist dysfunction with a history of injury to the left forearm caused by a forklift accident that occurred 5 months previously. Diagnosis and interventions: At the local hospital, the patient underwent successively fasciotomy and decompression, ulnar open reduction, and internal fixation due to osteofascial compartment syndrome and a misdiagnosed ulnar fracture. Upon examination, the secondary dorsal dislocation of the DRUJ was obvious both clinically and radiographically. We performed a revision surgery called ulnar osteotomy, radioulnar ligament repair, and temporary fixation of the DRUJ with a Kirschner wire. Outcomes: The patient received a satisfactory result without observed redislocation and relapse according to the 1-year follow-up. Lessons: Considering the notoriously poor outcomes, the importance of early recognition and accurate treatment should be emphasized repeatedly in similar lesions. Paying close and continuous attention to the clinical and radiographic examinations of the elbow and wrist joint is necessary to avoid misdiagnosis and missed diagnosis.

The axicon is a rotationally symmetrical optical element along the optical axis of the focal line, including concave shaft cone and convex shaft cone. It is widely used in laser beam shaping, laser drilling, optical detection, laser resonator and lithography illumination due to the property of long focal length and narrow transverse width. The axicon has only one optical axis, and the curvature radius of each point of the surface is different in the direction of the optical axis, that is, the line is a straight line, but the points of the adjacent bus are really circular torus, and the traditional equipment is difficult to polish. A new process suitable for axicon processing is proposed, including forming, traditional polishing, Atmospheric Pressure Plasma Processing for surface correction, flexible asphalt discs for smoothing. With it, the convergence of the polishing process is significantly improved, and the PV is decreases from 27λ to 3λ. There are two main factors that affect processing efficiency: temperature and gas flow rate. An analytical method is selected to estimate the complex interaction between the temperature field analysis and the polishing efficiency during the polishing process, and it is possible to achieve efficient and determined process.

The overturning of the Internet Privacy Rules by the Federal Communications Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. With third-party trackers embedded on Web pages, this new rule has put user privacy under more risk. The need arises for users on their own to protect their Web browsing history from any potential adversaries. Although some available solutions such as Tor, VPN, and HTTPS can help users conceal their online activities, their use can also significantly hamper personalized online services, i.e., degraded utility. In this paper, we design an effective Web browsing history anonymization scheme, PBooster, aiming to protect users' privacy while retaining the utility of their Web browsing history. The proposed model pollutes users' Web browsing history by automatically inferring how many and what links should be added to the history while addressing the utility-privacy trade-off challenge. We conduct experiments to validate the quality of the manipulated Web browsing history and examine the robustness of the proposed approach for user privacy protection., Comment: This paper is accepted in the 12th ACM International Conference on Web Search and Data Mining (WSDM-2019)

A new method for rapid water quality assessment was introduced and a micro laser induced fluorescence spectrometer was used to measure five kinds of water. The results show that the method could evaluate water quality well. © 2019 The Author(s)