Original Template Research Articles

Privacy-Preserving Fingerprint Authentication Using D-H Key Exchange and Secret Sharing

Biometric based remote authentication has been widely deployed. However, there exist security and privacy issues to be addressed since biometric data includes sensitive information. To alleviate these concerns, we design a privacy-preserving fingerprint authentication technique based on Diffie-Hellman (D-H) key exchange and secret sharing. We employ secret sharing scheme to securely distribute fragments of critical private information around a distributed network or group, which softens the burden of the template storage center (TSC) and the users. To ensure the security of template data, the user’s original fingerprint template is stored in ciphertext format in TSC. Furthermore, the D-H key exchange protocol allows TSC and the user to encrypt the fingerprint template in each query using a random one-time key, so as to protect the user’s data privacy. Security analysis indicates that our scheme enjoys indistinguishability against chosen-plaintext attacks and user anonymity. Through experimental analysis, we demonstrate that our scheme can provide secure and accurate remote fingerprint authentication.

Cancelable ECG biometric based on combination of deep transfer learning with DNA and amino acid approaches for human authentication

Recently, electrocardiogram (ECG) signals have received a high level of attention as a physiological signal in the field of biometrics. It has presented great possibilities for its strength against counterfeit. However, the ECG feature templates are irreplaceable, and a compromised template implies a permanent loss of identity. Therefore, several studies have been introduced biometric template protection techniques such as cancelable techniques to protect the original template in case it is stolen or lost. In this research, a cancelable ECG approach is proposed to protect the ECG feature template for human authentication. In our system, we first employed some image processing techniques for preprocessing the input ECG signals. Then, a deep transfer learning approach is employed to extract the deep ECG features. Later, the proposed cancelable approach based on DNA and amino acid is applied to protect the deep feature templates. Lastly, a Support Vector Machine (SVM) is employed for authentication. Extensive experiments on two commonly used datasets coupled with comprehensive theoretical analysis demonstrate the highest accuracy of the proposed system and the strong resilience of the system to various security and privacy attacks. Results show that the proposed cancelable method meets all requirements of cancelable biometrics such as irreversibility, revocability, and unlinkability.

Template assisted synthesis of porous termite nest-like manganese cobalt phosphide as binder-free electrode for supercapacitors

The development of metal-organic frameworks (MOFs) opens up a new pathway of original or sacrificial template for synthesizing unique morphologies to serve as novel supercapacitor electrode materials. Herein, we describe a synthesis of termite nest-like nanostructured manganese cobalt phosphide on carbon fiber cloth (MCP/CC) by simple wet chemical method and followed by phosphorization. The fabricated MOF-derived binder-free MCP/CC electrode unveils a maximum areal capacitance (Ca) of 338 mF cm−2 at a current density of 2 mA cm−2 in 6 M KOH electrolyte. Additionally, a hybrid supercapacitor (HSC) was assembled using MCP/CC as a positive electrode and N, O, S enriched activated carbon coated CC (AC@CC) as a negative electrode, which delivered a maximum specific capacitance of 48 F g−1 at a wide potential window of 0–1.7 V. Moreover, HSC showed an excellent specific energy and power of 19.18 Wh kg−1 and 9996 W kg−1 with superior capacitance retention (94%) even after 10,000 cycles. Further, the device exhibited negligible self-discharge and have good stability even after 50 h of charge holding test. These results indicate that the MCP/CC nanostructure is a favorable electrode material for highly efficient energy storage applications.

Managing Power Dependence in Diversity Work at San Francisco Bay Area High-Growth Firms (2016-2020)

We examine how diversity-promoters manage their high dependence on their firms for employment while pushing effectively for innovative rather than template-driven diversity practices. Building theory inductively from multiple case studies, we tracked the evolution of diversity practices within six technology firms across four years in the San Francisco Bay Area. Through privileged access to line executives, Black and Hispanic/Latino employee activists, and several incumbents of the diversity program coordinator position within firms, we learned how diversity programs began and how they changed over time. Firms with very similar structures (diversity program managers and employee affinity groups) and similar diversity ideology had different experiences of one change outcome: whether their diversity practices remained stuck in their original templates or were customized to meet new needs. We contribute to studies of power dependence inside organizations by clarifying the tradeoff between insider knowledge and power dependence. Additionally, we add a strategizing perspective to power dependence, underscoring how diversity advocates use three behaviors to overcome resource blockages and effectively manage their power dependence. Finally, we contribute a novel perspective on the activist-employer relationship -- i.e., emphasizing internal coalitions (not just individual change agents) and activists as customizers of practices (not just diffusers of practice templates). We conclude with implications for power as a control that can be borrowed from a power-seekers perspective (not only delegated first by a power-holder), autonomy as a power distance issue achieved by neglect (not always ascribed or achieved status), and innovation as a customization process within institutional constraints.

UAV based accurate displacement monitoring through automatic filtering out its camera's translations and rotations

In this paper, three translations and three rotations of the unmanned aerial vehicle (UAV) were derived through the suggested Normalized Cross Correlation (NCC) based template matching computer vision method and their effect on the monitored structural displacement is analyzed. The NCC-based template matching is used to obtain the vibration of UAVs by tracing far-field background points at the recorded images. The images captured at any general orientations are rotated back to their original positions which could detect errors in the monitored displacement induced by the rotation angles. A fast NCC-based template matching method was also proposed in this paper, which could accelerate the original NCC-based template matching method significantly and reaches the same level accuracy as that of the original NCC-based template matching method. In order to verify the concept, a series of experiments were performed on a Multipurpose Testing System (MTS) machine with amplitudes of 10 mm, 5 mm, and 2.5 mm and frequencies of 2 Hz, 1 Hz and 0.5 Hz, respectively, at the same time a UAV and a fixed camera were used to record the motions of the MTS piston. The derived displacements through the UAV and the fixed camera are compared with the true motions of the MTS machine. Excellent precision and consistence were obtained for the UAV monitored displacement, the MTS piston motion, and the fixed camera derived displacement. Comparing to the case of the fixed camera, scales and rotation angles of the UAV are critical and largely affect the accuracy of monitored displacements. A field experiment was conducted to test the proposed method in a windy environment. Excellent agreement was found between the monitored displacement obtained by the UAV and that derived from the fixed camera.

Towards Facial Biometrics for ID Document Validation in Mobile Devices

Various modern security systems follow a tendency to simplify the usage of the existing biometric recognition solutions and embed them into ubiquitous portable devices. In this work, we continue the investigation and development of our method for securing identification documents. The original facial biometric template, which is extracted from the trusted frontal face image, is stored on the identification document in a secured personalized machine-readable code. Such document is protected from face photo manipulation and may be validated with an offline mobile application. We apply automatic methods of compressing the developed face descriptors to make the biometric validation system more suitable for mobile applications. As an additional contribution, we introduce several print-capture datasets that may be used for training and evaluating similar systems for mobile identification and travel documents validation.

Utilization of heat-mapping tools to match a resident staffing template to emergency department arrival patterns.

Academic emergency departments (ED) rely on a steady flow of patients to provide residents with good clinical training. Understanding institutional volume patterns allows training directors to create a schedule that maximizes learning opportunities while also adequately staffing the ED. Our primary objective of this study was to utilize heat-mapping software to optimize resident staffing in an academic ED. Heat-mapping tools within Microsoft Excel were utilized to overlay ED patient arrival patterns on top of the potential patients per hour based on published productivity data for trainees and historical averages for advanced practice providers at our institution. Time frames for under- and overstaffing were identified and color-coded. This analysis informed a revised schedule template and the same heat-mapping process was used to determine the appropriateness of the revised staffing template. The heat map for the original schedule template revealed understaffing in the morning and overstaffing the rest of the day. Informed by these findings, schedule adjustments were made. There was no net increase in the number of resident or advanced practice provider coverage hours. Prior to implementation, the ED was understaffed by 5% or more during 18.4% of operating hours. Following changes to the staffing template, only 5.9% of operating hours were understaffed (p<0.001). Furthermore, significant understaffing (20% or more) decreased from 16.6% to 3.1% (p<0.001). Novel use of heat-mapping software has the potential to successfully match ED patient arrival patterns to an optimal resident staffing template. Future directions include incorporation of variable resident productivity to account for fatigue as the shift progresses.

Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion

Cancellable biometrics is the art of generating distorted or encrypted templates of original biometric templates. The evolution of cancellable biometrics is attributed to the advanced hacking technologies that can capture the original stored biometrics from databases. One of the solutions for this problem is to store cancellable biometric templates in the database rather than the original ones. This paper presents a cancellable face recognition scheme that is based on face image encryption with Fractional-Order (FO) Lorenz chaotic system. The basic idea is to generate user-specific random keys to be XORed with the red, green, and blue components of color face images. These keys are generated from the fractional-order Lorenz chaotic system. In addition, some post-processing is implemented on the encrypted color components of the face images with rotation and transposition of matrices. Finally, a wavelet fusion process is applied on these encrypted and processed face image components. The reason behind the utilization of wavelet fusion is to generate a single cancellable template for each color face image. Furthermore, wavelet fusion after post-processing of encrypted color components leads to a better degree of diffusion in the encrypted face templates. Actually, the encryption with the proposed algorithm is not full encryption, but it is appropriate for cancellable biometric applications. Moreover, the proposed scheme is secure due to the power of fractional-order Lorenz chaotic system that is very sensitive to initial conditions selected by the user. In addition, the post-processing incorporated with wavelet fusion is a non-invertible process. The validation of the proposed scheme is performed with experiments on FERET, LFW, and ORL databases. Evaluation metrics including Equal Error Rate (EER), Area under the Receiver Operating Characteristic (AROC) curve are utilized in the proposed scheme. Numerical values reveal EER levels close to zero and AROC values of 100% at low and mild noise levels.

Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images.

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

Unimodal‐Bio‐GAN: Keyless biometric salting scheme based on generative adversarial network

Cancellable biometrics enabled us to develop robust authentication systems by replacing the storage of the original biometric template with another secured version. A technique called biometric salting uses a parameter (key) and an invertible function to transform the human biometrics features into a secured format that can be protected and stored securely in a biometric database system. The salting key plays a main role in the success of this transformation, which makes it robust or vulnerable to many security attacks. One of the main challenges that faces biometrics' researchers currently is how to design and protect such a salting key considering two basic measures: security and recognition accuracy. In this article, we propose unimodal-Bio-GAN, a reliable keyless biometric salting technique based on standard generative adversarial network (GAN). In unimodal-Bio-GAN, a random permuted version of the human biometric data is implicitly considered as a salting key and required only during the enrolment stage, which increases the system reliability to overcome different security attacks. The experimental results of unimodal-Bio-GAN using the CASIA Iris-V3-Internal database outperform the previous methods and its security efficiency is analysed using different attack types.

Design of a hybrid approach using a revocable technique and steganographic text color coding technique for fingerprint template protection

The most crucial matter to be taken care of while deploying biometric systems is the security of the biometric template. Several algorithms that do not store the original template have been formulated in recent years to protect biometric templates from various attacks. Most of the existing algorithms offer a trade-off between matching performance and biometric template security. Moreover, it is believed that no single template protection scheme is capable of satisfying security, diversity, revocability and performance at the same time. Present research work proposes a two-step hybrid template protection scheme, where first step is the creation of a transformed template from the original template, and the second step is conceal the value of transformed template by applying the text steganography.The effectiveness of the proposed method is well established by thorough experimental analysis, which illustrates that the recognition performance is well maintained as the original template executes.

Long short-term memory improved Siamese network for robust target tracking

Visual target tracking is an important function in real-time video monitoring application, whose performance determines the implementation of many advanced tasks. At present, Siamese-network trackers based on template matching show great potential. It has the advantage of balance between accuracy and speed, due to the pre-trained convolutional network to extract deep features for target representation and off-line tracking of each frame. During tracking, however, the target template feature is only obtained from the first frame of the video in the existing algorithms. The tracking performance is completely depending on the framework of template matching, resulting in the independence of frames and ignoring the feature of inter-frame connection of video sequence. Therefore, the existing algorithms do not perform well in the face of large deformation and severe occlusion. We propose a long short-term memory (LSTM) improved Siamese network (LSiam) model, which takes advantages of both time-domain regression capability of the LSTM and the balanced ability in tracking accuracy and speed of Siamese network. It focus on the temporal and spatial correlation information between video sequences to improve the traditional Siamese-network trackers with an LSTM prediction module. In addition, an improved template updating module is constructed to combine the original template with the changed appearance. The proposed model is verified in two types of difficult scenarios: deformation challenge and occlusion challenge. Experimental results show that our proposed approach can get better performance in terms of tracking accuracy.

Recent advances in applications of fragment/dummy molecularly imprinted polymers

分子印迹聚合物(MIPs)是通过模拟酶与底物或抗原抗体特异性结合原理而制备的高分子聚合物,以其结构预定性、识别特异性、制备简便、成本低、耐受性强等优点而被广泛用于样品前处理、传感分析、生物医药、环境/食品分析等多个领域。目前已发展多种策略用于MIPs制备,达到简化制备过程或提高聚合物性能等目的,极大拓宽了MIPs的应用范围。对各种先进印迹策略及其组合使用的探索已成为MIPs制备的研究热点之一。其中,片段印迹策略和虚拟模板印迹策略备受青睐。片段印迹策略是选择目标分子中含有特定官能团的一部分(片段结构)作为模板进行印迹,通过对片段的识别达到对整个分子的识别,能够克服某些目标物不易获得或体积较大不适合作为模板的问题,为印迹易失活、易传染的目标物及整体印迹困难的大分子提供可行的方法。虚拟模板印迹策略是选用与目标物特异性结构相似或相同的其他物质代替目标物作为模板制备MIPs,可在很大程度上解决模板不易获得或较昂贵等问题,以及避免模板可能泄漏对结果造成的影响,尤其适用于目标物造价高、具有感染性、易燃易爆、易降解等不适合作为模板分子的情况。该文选取了最近4年发表在ACS、Elsevier、RSC等数据库约20篇相关文献,综述了片段/虚拟MIPs(FMIPs/DMIPs)的应用新进展。首先,针对蛋白质和微生物检测以及哺乳动物细胞印迹,介绍了FMIPs在生物医药领域的应用,另外介绍了FMIPs在食品分析领域的研究进展。随后,介绍了DMIPs在样品前处理和传感分析领域的应用。在样品前处理中,DMIPs主要作为固相萃取吸附剂进行装柱固相萃取、分散固相萃取、磁固相萃取、基质固相分散萃取等,或作为分子印迹膜材料,用于选择性萃取和富集分离样品中的目标分析物。在传感分析领域,DMIPs主要作为传感器的传感和转导元件,提高化学发光或荧光检测等方法的灵敏度和准确度。最后,对片段印迹和虚拟模板印迹策略的优缺点、区别与联系进行了总结,并展望了这两种策略的发展与应用前景。

Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of 2.0243×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , FAR (False Acceptance Rate) of 4.8843×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , and FRR (False Rejection Rate) of 2.2693×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .

On the Practicality of Local Ranking-Based Cancelable Iris Recognition

Practical cancelable biometrics (CB) schemes should satisfy the requirements of revocability, non-invertibility, and non-linkability without deteriorating the matching accuracy of the underlying biometric recognition system. In order to bridge the gap between theory and practice, it is important to prove that new CB schemes can achieve a balance between the conflicting goals of security and matching accuracy. This paper investigates the security and accuracy trade-off of a recently proposed local ranking-based cancelable biometrics (LRCB) scheme for protecting iris-codes. First, the irreversibility of the LRCB is revisited and a new attack for reversing the LRCB transform is proposed. The proposed attack utilizes the distribution of order statistics for discrete random variables to reverse the protected rank values and obtain a close approximation of the original iris template. This ranking-inversion attack is then utilized to realize authentication, record multiplicity, and correlation attacks against the LRCB transform. Our theoretical analysis shows that the proposed reversibility attack can recover more than 95% of the original iris-code bits and the proposed correlation attack can correctly correlate two templates 100% of the time for the parameter setting that retain the recognition accuracy of the underlying iris recognition system. The validity of the proposed attacks is verified using the same iris dataset adopted by the authors of the LRCB scheme. Experimental results support our theoretical findings and demonstrate that the security properties of irreversibility and non-linkability can only be fulfilled at the expense of significant and impractical degradation of the matching accuracy.

Surgical Safety Checklist: Polychromatic or Achromatic Design.

The Surgical Safety Checklist (SSC) has been created based on the recommendations of the WHO and obligatorily introduced worldwide. SSC is used to increase the patient's safety and reduce complications while in the hospital, especially in the perioperative period. The original SSC template was of a multicolor polychromatic design. However, an achromatic black-and-white or gray-gray design on plain printer paper appears often used in clinical practice. This review aims to assess the level of SSC use in the polychromatic versus achromatic versions and the pros and cons of using either in practice. We used the Google browser for the identification and collection of SSC graphic images available as of June 2021 using the following search commands: "surgical safety checklist WHO" or "surgical safety checklist" or "SSC WHO." The commands were repeated in 103 languages representing the five continents with the back answers provided in 41 languages. The successive top 10 thematically relevant images or fewer if not available in the cases of some foreign languages were considered for analysis, providing a mean of 5 ±2 images per language. The numbers of achromatic and polychromatic two-color or multicolor images were calculated. The number of images corresponding to the respective color designs ranged as follows: 0-6 (27.6%), 0-9 (41.6%), and 0-6 (27.6%) We conclude that polychromatic imaging of SSC documents predominates in practical use. The polychromatic SSC design catches the doctor's eye, which likely increases the effectiveness of completing the document.

Reducing noise and stutter in short tandem repeat loci with unique molecular identifiers

Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs). Traditional MPS approaches may simply lead to the observation of alleles that are consistent with the hypotheses of stutter, while with UMIs stutter products bioinformatically may be re-associated with their parental alleles and subsequently removed. Herein, a bioinformatics pipeline named strumi is described that is designed for the analysis of STRs that are tagged with UMIs. Unlike other tools, strumi is an alignment-free machine learning driven algorithm that clusters individual MPS reads into UMI families, infers consensus super-reads that represent each family and provides an estimate the resulting haplotype’s accuracy. Super-reads, in turn, approximate independent measurements not of the PCR products, but of the original template molecules, both in terms of quantity and sequence identity. Provisional assessments show that naïve threshold-based approaches generate super-reads that are accurate (∼97 % haplotype accuracy, compared to ∼78 % when UMIs are not used), and the application of a more nuanced machine learning approach increases the accuracy to ∼99.5 % depending on the level of certainty desired. With these features, UMIs may greatly simplify probabilistic genotyping systems and reduce uncertainty. However, the ability to interpret alleles at trace levels also permits the interpretation, characterization and quantification of contamination as well as somatic variation (including somatic stutter), which may present newfound challenges.

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

Effective template update mechanism in visual tracking with background clutter

Today, artificial intelligence is everywhere in people’s daily lives. Visual tracking, which is used to identify and continuously track specific targets, is an important research domain in the study of artificial intelligence. However, current visual tracking methods are not accurate enough for object tracking with background clutter, which can easily lead to tracking failures. Therefore, in this paper, in order to solve the problem of tracking failure in clutter background, we propose a template update mechanism to improve the accuracy of visual tracking. First, an original template is saved when the background clutter is detected. During background clutter, we use both the original template and the current template at the location estimated by the optical flow and choose better one. Next, the original template is reused after the background clutter is ended. Finally, the proposed mechanism is used both in the KCF and BACF algorithm to verify the effectiveness of the mechanism. With experiments on the OTB2015 dataset, results show that the proposed mechanism has improved accuracy and success rate of the two baseline algorithms. Meanwhile, in state-of-the-art algorithms, the algorithm using the proposed mechanism also has excellent tracking performance. In addition, this method also has strong tracking robustness and adaptation capability to sequential learning for video data.

Organic-free direct crystallization of t-LaVO4:Eu nanocrystals with favorable luminescence for LED lighting and optical thermometry

Tetragonal structured LaVO4 (t-LaVO4) is superior to its thermodynamically stable monoclinic polymorph for luminescence, but is difficult to synthesize without using chelating molecules/ions. With Ln2(OH)4SO4·2H2O layered hydroxide as an original template for phase conversion, well-crystallized t-(La0.95Eu0.05)VO4 nanocrystals (∼40–80nm) of favorable dispersion and size uniformity were directly produced via hydrothermal reaction at 200°C for 24h without using any organic additive. Luminescence analysis showed that the as-made nanocrystals have an absolute quantum yield of ∼38.2%, fluorescence lifetime of ∼1.52ms and color coordinates of around (0.33, 0.65) under 304nm UV excitation at room temperature (RT), and the 620nm main emission can retain as high as ∼66% of its RT intensity at 150°C. The activation energies for thermal quenching were analyzed to be ∼0.321, 0.286 and 0.291eV for the 537nm (5D1→7F1 transition), 593nm (5D0→7F1) and 620nm (5D0→7F2) emissions of Eu3+, respectively. Application of the nanocrystals in LED lighting found electric-field enhanced 5D0→7F4 emission of Eu3+ (∼697nm) and increasing brightness but relatively stable color correlated temperature (∼3270–3400K) at a higher driving current (30–100mA) for the lamp. The nanocrystals were also demonstrated to be well capable of temperature sensing with the I537/I593 fluorescence intensity ratio, whose maximum absolute sensitivity, maximum relative sensitivity and minimum temperature resolution are ∼0.27%K−1, 2.01%K−1, and 0.015K at 298K, respectively.