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Open-source pulse sequence programs offer an accessible and transparent approach to sequence development and deployment. However, a common framework for testing, documenting, and sharing open-source sequences is still needed to ensure sequence usability and repeatability. We propose and demonstrate such a framework by implementing two sequences, Inversion Recovery Spin Echo (IRSE) and Turbo Spin Echo (TSE), with PyPulseq, and testing them on a commercial 3 T scanner. We used the ACR and ISMRM/NIST phantoms for qualitative imaging and T1/T2 mapping, respectively. The qualitative sequences show good agreement with vendor-provided counterparts (mean Structural Similarity Index Measure (SSIM) = 0.810 for IRSE and 0.826 for TSE). Both sequences passed five out of the seven standard ACR tests, performing at similar levels to vendor counterparts. Compared to reference values, the coefficient of determination R2 was 0.9946 for IRSE T1 mapping and 0.9331 for TSE T2 mapping. All sequences passed the scanner safety check for a 70 kg, 175 cm subject. The framework was demonstrated by packaging the sequences and sharing them on GitHub with data and documentation on the file generation, acquisition, reconstruction, and post-processing steps. The same sequences were tested at a second site using a 1.5 T scanner with the information shared. PDF templates for both sequence developers and users were created and filled.

Companion diagnostic immunohistochemistry (IHC) tests are developed and performed without incorporating the tools and principles of laboratory metrology. Basic analytic assay parameters such as lower limit of detection (LOD) and dynamic range are unknown to both assay developers and end users. We solved this problem by developing completely new tools for IHC-calibrators with units of measure traceable to National Institute of Standards & Technology (NIST) Standard Reference Material (SRM) 1934. In this study, we demonstrate the clinical impact and opportunity for incorporating these changes into PD-L1 testing. Forty-one laboratories in North America and Europe were surveyed with newly-developed PD-L1 calibrators. The survey sampled a broad representation of commercial and laboratory-developed tests (LDTs). Using the PD-L1 calibrators, we quantified analytic test parameters that were previously only inferred indirectly after large clinical studies. The data show that the four FDA-cleared PD-L1 assays represent three different levels of analytic sensitivity. The new analytic sensitivity data explain why some patients' tissue samples were positive by one assay and negative by another. The outcome depends on the assay's lower LOD. Also, why previous attempts to harmonize certain PD-L1 assays were unsuccessful; the assays' dynamic ranges were too disparate and did not overlap. PD-L1 assay calibration also clarifies the exact performance characteristics of LDTs relative to FDA-cleared commercial assays. Some LDTs' analytic response curves are indistinguishable from their predicate FDA-cleared assay. IHC assay calibration represents an important transition for companion diagnostic testing. The new tools will improve patient treatment stratification, test harmonization, and foster accuracy as tests transition from clinical trials to broad clinical use.

Purpose Accurate radiation dose is required to ensure reproducibility in establishing the radiobiological effect in biological systems among institutions. The dose should be the most precise and accurate parameter of the entire process. The goal is a system to provide uniform radiation dose verification among institutions that is traceable to the National Institute of Standards and Technology (NIST) through an Accredited Dosimetry Calibration Laboratory. Methods and Materials Radiobiological beams are not NIST traceable but can be approximated based on the radiograph's half value layer. Phantoms have been developed containing detectors to measure the dose from total body irradiation of mice and others. Ionization chambers calibrated to NIST-traceable beams are the best detectors for precise and accurate dose determinations. However, thermoluminescent dosimeters have been mostly used for this application for comparison between institutions. Results A comparison of thermoluminescent dosimeters results among surveyed institutions showed a large variation in delivered dose. The range of radiograph doses that were measured deviated from the standard dose by 12% to 42%. The results have an uncertainty of 2.5% at 1 standard deviation. The surveyed radionuclide irradiators demonstrated a dose range variation of 1.6% to 13.5% from target dose. There is less variation among high energy (linacs) because a calibrated ionization chamber is generally used by personnel (eg, medical physicist) and the output is determined for radiation therapy applications as well. Conclusions Radiobiological dosimetry is lacking with respect to its precision and accuracy. The accuracy of radiograph calibrations for radiobiology can be estimated to be approximately 5%, because there are no NIST-traceable beams. However, among institutions, the variations can be up to 42%. Intercomparisons between institutions is important to have a clear understanding of the transference of dose between given studies.

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.

This article presents the construction and consumption of hyperjerk system having chaotic nature with the commensurate ordered fractional derivative on rapidly digitalized emerging technologies. The system analyzed analytically using Lipschitz condition and numerically with the help of predictor corrector approaches. Originality of constructed system is confirmed using log error plots which gives satisfactory small values on finite time scale. The dynamical performances of the complex system are termed in multiple phase planes by the mean of their significant nature. Stability and bifurcation analysis around the fractional derivative is also studies for the various parameter of system to check the visibility of chaotic solution. The system is also designed in analog circuit simulator with the aid of operational amplifier, anti-parallel semiconductor diodes for validation of the system. With the help of random number generators (RNGs), binary array generated from the hyperjerk system and plugged in to the NIST 800–22 test suite to measure the randomness. The array having high randomness is used as strong cypher key for the cryptographic execution straightforwardly in both direction encryption and decryption.

Speaker recognition continues to grow as a research challenge in the field with expanded application in commercial, forensic, educational and general speech technology interfaces. However, challenges remain, especially for naturalistic audio streams including recordings with mismatch between train and test data (i.e., when train or system development data and enrollment/test data or application data are collected from different sources). Mismatch conditions (Hansen and Hasan, 2015) can be divided into two categories, extrinsic (channel, noise, etc.) and intrinsic (duration, language, and speaker traits including stress, emotion, Lombard effect, vocal effort, accent). Here, we investigate speaker recognition for the domain mismatch problem (intrinsic mismatch) especially for those challenges introduced by NIST (National Institute of Standards and Technology) SRE (speaker recognition evaluation) in 2016 and 2018. The challenges introduced in NIST SRE-16 and SRE-18 include language mismatch between train (used for the development of the system) and enrollment/test (used at the application phase). Here, we develop three alternative speaker embedding systems; i-vector, t-vector (an improved triplet loss solution), and x-vector. In addition, a number of unsupervised and supervised (using pseudo labels) methods are also studied for domain mismatch compensation, especially applied at the back-end level. These include adapted PLDA, adapted discriminant analysis, as well as score normalization and calibration methods using unlabeled in-domain data. We propose new variations to discriminant analysis with support vectors (SVDA) as well. These results confirm that SVDA can measurably improve speaker recognition performance for SRE-16 and SRE-18 tasks respectively by +15% and +8% in terms of min-Cprimary; and for EER the gains are +14% and +16% respectively, using i-vector speaker embeddings as the baseline. These advancements offer promising steps toward addressing speaker recognition in naturalistic audio streams.

Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response.ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms.Overall, repeatability CV of ADC was2.40%, reproducibility CV was3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p0.05) but increased error in accuracy (p0.001). Repeatability and reproducibility of R2* was4.5% and7.3% respectively in the system phantom across all scanners.Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.

With respect to the procedures of test suite construction and statistical tests’ utilization, it is important to find out whether tests from a particular suite are statistically independent. A new method for verification of statistical tests’ independence presented in this paper has some advantages in comparison with previously proposed methods. It has a rigorous mathematical substantiation, and may be applied to an arbitrary suite of tests and to an arbitrary random or pseudorandom number generator. It also requires a smaller number of sequences from the generator, as compared to previously known methods. We also present results of application of this method to some test suites, such as NIST, and to another test suite proposed by authors, which consists of six simple tests (we called it “Six Tests Suite”). It is essential that the tests from NIST appear to be dependent, so that suite is redundant and may be reduced, resulting in shorter testing time interval without loss of quality. In contrast to NIST, the tests from Six Tests Suite turn out to be independent in the sense of the proposed method.

In this paper, two different photoplethysmogram (PPG) based cryptographic key generation approaches are proposed. The goal is to improve the security of Body Area Networks (BAN) through robust key generation where keys are generated in a plug-n-play manner without requiring key pre-distribution solutions. The Inter-beat Interval (IBI) feature of PPG underlays both of the proposed approaches. The first approach is realized by using Galois linear feedback shift register and consecutive IBI sequences. An IBI is measured from two consecutive peak points of an individual’s PPG signal where they are the tallest and most conspicuous peaks in a PPG signal. The second approach is realized by utilizing the Advanced Encryption Standard (AES) algorithm and IBI as the seed generator for the AES algorithm. The efficiency of the proposed approaches is evaluated using real PPG signals obtained from the PPG-DaLiA dataset of University of California, Irvine. The security analyses of the generated keys are carried out in terms of randomness, distinctiveness, and temporal variance as well as using the NIST benchmark. The analyses show that our key generation approaches provide a better randomness level in comparison to existing approaches relying only on singleton IBI sequences. In addition, our calculations reveal that the average Hamming Distance (HD) between the cryptographic keys generated from PPG signal of two different subjects using the IBI, IBI-Galois, and IBI-AES approaches are: 47.56%, 48.34%, and 49.67%, respectively. Also, our experiments show that the average HD between the cryptographic keys which are generated via PPG signal of the same subject at different time intervals are 47.18%, 48.73%, and 49.51%, respectively.

Small and medium-sized enterprises (SMEs) are among the least mature and most vulnerable in terms of their cybersecurity risk and resilience. In this article, we describe a methodology developed using the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework (CSF) as a starting point. The NIST CSF does not meet all the needs of the SME IT leader, but it offers a solid foundation for a useful evaluation and recommendation methodology. We propose an SME cybersecurity evaluation tool (CET) that consists of a 35-question online survey to be completed by IT leaders to self-rate their maturity within the five NIST framework categories: identify, protect, detect, respond, and recover. We outline this approach to cybersecurity risk management before discussing its effectiveness and implications for practitioners.

The performance of speaker verification depends on the overlap region of the decision scores of true and imposter trials. Motivated by the fact that the overlap region can be reduced by maximizing the between-class distance while minimizing the within-class variance of the trials, we present in this paper two cosine metric learning (CML) back-end algorithms. The first one, named m-CML, aims to enlarge the between-class distance with a regularization term to control the within-class variance. The second one, named v-CML, attempts to reduce the within-class variance with a regularization term to prevent the between-class distance from getting smaller. The regularization terms in the CML methods can be initialized by a traditional channel compensation method, e.g., the linear discriminant analysis. These two algorithms are combined with front-end processing for speaker verification. To validate their effectiveness, m-CML is combined with an i-vector front-end since it is good at enlarging the between-class distance of Gaussian score distributions while v-CML is combined with a d-vector or x-vector front-end as it is able to reduce the within-class variance of heavy-tailed score distributions significantly. Experimental results on the NIST and SITW speaker recognition evaluation corpora show that the proposed algorithms outperform their initialization channel compensation methods, and are competitive to the probabilistic linear discriminant analysis back-end in terms of performance. For comparison, we also applied the m-CML and v-CML methods to the i-vector and x-vector front-ends.

In response to the environmental impact of mercury and the neuro-toxic risk associated with trophic transfer of methyl mercury to humans, the reduction of mercury emissions from coal-fired electric utilities, a significant anthropogenic source, is now receiving global attention. To address impending regulations for mercury reduction, there is a need to establish a mercury emissions measurement framework, which has demonstrated accuracy and traceability to national and international standards. To support this effort, the National Institute of Standards and Technology (NIST) has been working jointly with the United States Environmental Protection Agency (EPA) to develop a traceability system which links the output of elemental mercury calibration instrumentation sited at the power utilities, to the SI (International System of Units). This system is based on a reference (NIST Prime) elemental mercury generator standard, which is used by NIST to certify elemental mercury generators from the instrument vendors. These in turn are used by the instrument vendors to provide NIST traceable calibration for the instrumentation supplied to each electric utility. The NIST Prime generator is certified using a primary analytical method for the determination of mercury using sorbent trap analysis with isotope dilution cold-vapor inductively coupled plasma mass spectrometry (ID-CV-ICP-MS).

In recent years many authors have explored the use of biological signals for security issues. In the context of cardiac signals, the use of Inter-Pulse Interval (IPI) values as a source of entropy is one of the most widely used solutions in the literature. To date, there is a broad consensus that the four least significant bits of each IPI are highly entropic and can be used, for instance, in the generation of a cryptographic key. In this article, we demonstrate that the choice of the IPI bits used to date may not be the most correct (e.g., the combination of bits 2638 are much better that the common assumed 5678). To come to our conclusions, we have done a rigorous and in-depth study, analyzing cardiac signals from more than 160,000 files from 19 databases of the Physionet public repository and basing our analysis on the NIST 800-90B recommendation.

Matthew Eisaman is an assistant professor in the Electrical & Computer Engineering Department at Stony Brook University. From 2008 to 2011, Eisaman was an applied physicist at Xerox PARC. He received an AB in physics from Princeton in 2000 and a PhD in physics from Harvard in 2006, and he was an NRC postdoc at NIST from 2006 to 2008. He holds 17 patents and has coauthored 29 papers with over 3,700 citations. Eisaman invented indirect ocean capture in 2010–2011 while at PARC. From 2014 to 2016, Eisaman led Project Foghorn at X (formerly Google X), which used IOC to make carbon-neutral fuel from seawater.

In the present work extensive stopping power calculations were performed, implementing general–purpose Monte Carlo codes (GEANT4, MCNP6.1, FLUKA, PHITS) and dedicated stopping power compilations (such as SRIM2013, PSTAR/ ASTAR from NIST and ATIMA), having protons, alpha particles, 12C, 16O and 56Fe ions as beam particles, in the energy range between ~1 and ~1000 MeV/u, impinging on a variety of thin or infinitely thick, pure, single–element targets, such as aluminum, iron, copper and silicon, which are typically used as shielding materials or components in complex devices, in order to investigate all the occurring differences and similarities in the obtained results. Water was the only compound target to be examined, since it is commonly used as a close substitute for biological tissue. In all the simulations the process of multiple scattering was ignored or disabled. The obtained results are presented in graphical form, along with all the available experimental data in the studied energy range and the occurring similarities and discrepancies are discussed and analyzed.

Recently, deep learning and character recognition have drawn the attention of many researchers. The deep neural networks have state-of-the-art performance in solving many classification and recognition problems. The Optical Character Recognition (OCR) takes an optical image of character as input and produces the corresponding character as output. It has a wide range of applications including traffic surveillance, robotics, digitization of printed articles, etc. The OCR can be implemented by using Convolutional Neural Network (CNN), which is a popular deep neural network architecture. The traditional CNN classifiers are capable of learning the important 2D features present in the images and classify them, the classification is performed by using soft-max layer. In this article, we have presented OCR by combining CNN and Error Correcting Output Code (ECOC) classifier. The CNN is used for feature extraction and the ECOC is used for classification. In order to find suitable CNN for extracting features, which can be used in combination with ECOC classifier for recognition of handwritten characters accurately, several popular CNN classifiers have been explored. The CNN-ECOC are trained and validated by using NIST handwritten character image dataset. The simulation result shows that CNN-ECOC gives higher accuracy as compared to the traditional CNN classifier.

This paper explores the behavior of different normalization techniques viz. cepstral mean normalization, cepstral variance normalization, cepstral mean subtraction. cepstral mean and variance normalization, wiener filter, and spectral subtraction in noisy conditions. The performance parameters viz. EER (Equal Error Rate) and DCF (Detection Cost Function) has been calculated using NIST 2003 SRE and Aurora 2 with the help of various normalization techniques considered in this paper for different noisy backgrounds at 0, 5 and 10 dB signal-to-noise ratio. The experimental results obtained from these techniques reveal that cepstral mean normalization (CVN) normalization method is found to be better when compared to other normalization techniques used in this paper.

In this work, we classify all templates according to their periods and for each template we evaluate the exact probabilities using generating functions. Afterwards, we propose a new family of statistical randomness tests, that is periodic template tests, for a collection of binary sequences. We apply these tests to the outputs of AES, SHA-3, SHA-2 family, SHA-1 and MD5 and the binary expansion of π and 2 , and biased non-random data to test the power of new tests. Moreover, we give the probabilities for all templates for the overlapping template matching test in the NIST test suite. Afterwards, we analyse the power of templates and compare the periodic template tests with NIST overlapping template test.

The National Electrostatics Corporation (NEC) 250 kV single-stage AMS (YS-AMS) was installed in March 2013 at the Laboratory for Accelerator Mass Spectrometry (LAMS), Atmosphere and Ocean Research Institute, The University of Tokyo. This is the first single stage AMS system installed in Japan. The system is equipped with a 40 solid sample ion source (MC-SNICS-II), sequential injection system at low energy mass spectrometry side, open air 250 kV high energy deck including helium gas stripper which acts as a molecular dissociation, analyzing magnet, electrostatic analyzer, sequential post-accelerator deflector, and final detector. The performance tests with 11 reference materials distributed via the International Atomic Energy Agency (IAEA), National Institute of Standards and Technology (NIST), and National Institute of Advanced Industrial Science and Technology, Japan (AIST) were in very good agreement with consensus values. Thus our routine 14C measurements have been started since August 2013 and maintaining high performance. We have measured about 8000 unknown samples during this period. Typical 12C− currents are 27 μA at low energy Faraday cup, the transmission in the accelerator is about 42%, and precision of 14C/12C and 13C/12C is better than 0.2%. Geological, archaeological, oceanographic as well as a large number of biological samples have been measured efficiently using the current system.

This paper introduces design and FPGA implementation of sound encryption system based on a fractional-order chaotic system. Also, it presents the FPGA implementation of Tang, Yalcin, and Ozoǧuz fractional order chaotic systems. The Grunwald-Letnikov (GL) definition is used to generalize the investigated systems into the fractional-order domain. Also, the variation of parameters for each system is investigated against the window size of the GL definition. Xilinx ISE 14.5 is used to simulate the proposed design. Also, some hardware reduction techniques are applied to decrease hardware utilization and increase throughput. Moreover, Yalcin system is employed as a chaotic generator in a speech encryption algorithm. Security analysis techniques are presented to show the robustness of the proposed algorithm. They include basic perceptual and statistical aspects, the NIST tests, key space analysis, mean square error (MSE) and key sensitivity. Statistical analyses including correlation, histogram, spectrogram, and entropy analysis are presented. The system is implemented on Artix-7 FPGA with one clock latency. Finally, experimental results are illustrated.

This paper introduces a novel Logarithmic Chaotic Map (LCM) that is inspired by the well known quadratic map. The chaotic dynamic of the proposed map LCM is thoroughly investigated and its chaotic properties are found to be superior in comparison with the existing maps in the literature. In addition, a new encryption algorithm is proposed that is based on LCM. The chaotic output generated from LCM passed through all 15 National Institute of Standards and Technology (NIST) statistical tests thus confirming its randomness. The proposed encryption algorithm is best suited for encrypting numeric sensor data values preferably for IoT applications and has been found to be highly resilient to security attacks.

This paper introduces a Weighted-Correlation Principal Component Analysis (WCR-PCA) for efficient transformation of speech features in speaker recognition. A Recurrent Neural Network (RNN) technique is also introduced to perform the weighted PCA. The weights are taken as the log-likelihood values from a fitted Single Gaussian-Background Model (SG-BM). For speech features, we show that there are large differences between feature variances which makes covariance based PCA less optimal. A comparative study of the performance of speaker recognition is presented using weighted and unweighted correlation and covariance based PCA. Extensions to improve the extraction of MFCC and LPCC features of speech are also proposed. These are Odd Even filter banks MFCC (OE-MFCC) and Multitaper-Fitted LPCC. The methodologies are evaluated for the i-vector speaker recognition system. A subset of the 2010 NIST speaker recognition evaluation set is used in the performance testing in addition to evaluations on the VoxCeleb1 dataset. A relative improvement of 44% in terms of EER is found in the system performance using the NIST data and 18% using the VoxCeleb1 dataset.

Linear and mass attenuation coefficients from the X-ray and gamma ray in transmission Computed Tomography – CT are measured and evaluated by a dual energy ratio. By X-ray CT interaction the Hounsfield attenuation ratio is calculated for the main soil components in simulated samples. An experimental set up with different soil components to make up the soil phantom was prepared for scanning in both CT modalities. This procedure provides a simulation of soil sampling in particulate media whose density is close to the natural soil density. Discrete energy in a single beam gamma ray CT allows a precise comparison with mass attenuation coefficient from reference NIST XCOM database values. In an X-Ray CT model XT H 225 from Nikon Metrology, the soil phantom was evaluated in continuous spectrum for mass attenuation coefficient measurements. A detailed soil structure analysis was carried out by means of imaging processing following X-ray transmission interaction with the soil phantom. Measurements of the soil particulate media density and radiometric attenuation coefficients found a 1.5% maximal error when compared to NIST XCOM database values. The results suggest that combining gamma and X rays CT makes feasible a precise methodology for soil structure investigation, and therefore, it allows producing sharper, more informative images, a process that can be availed for new materials technology.

Statistical tests of pseudorandom number generators (PRNGs) are applicable to any type of random number generators and are indispensable for evaluation. While several practical packages for statistical tests of randomness exist, they may suffer from a lack of reliability: for some tests, the amount of approximation error can be deemed significant. Reducing this error by finding a better approximation is necessary, but it generally requires an enormous amount of effort. In this paper, we introduce an experimental method for revealing defects in statistical tests by using a three-level test proposed by Okutomi and Nakamura. In particular, we investigate the NIST test suite and the test batteries in TestU01, which are widely used statistical packages. Furthermore, we show the efficiency of several modifications for some tests.

The Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) will be used to develop and evaluate advanced control systems for commercial building heating, ventilation, and air conditioning (HVAC) applications. Advanced control techniques include model predictive control (MPC) and distributed optimization, both of which can utilize machine learning. This study is a first step in the development of more advanced techniques. MPC is used to generate an optimal schedule for the operation of a cooling plant consisting of ice-on-coil thermal energy storage and two chillers with different maximum capacities. The schedule is generated offline and then used in the IBAL. The performance of the actual system is used to validate the simulation. The simulation is then used to conduct studies relating to the effect of the time horizon and electrical tariff on the optimal schedule. The results demonstrate that an agent-based control architecture is effective in defining optimal control of thermal energy storage systems.

As a potential replacement for the National Bureau Standards Reactor (NBSR) at the U.S. National Institute of Standards and Technology (NIST), a conceptual design of a new reactor with a horizontally-split core has recently been studied using low-enriched uranium (LEU) silicide dispersion (U3Si2/Al) fuel. In this paper, the neutronics calculations of the proposed NIST reactor with other two low-enriched U-Mo fuels (U-10Mo monolithic fuel and U-7Mo/Al dispersion fuel) were performed, and the results were compared to that of the U3Si2/Al fuel, with the objective of identifying the best fuel candidate for the reactor cycle length and maximum cold neutron production. To make consistent comparisons, fuel inventories for multi-cycle equilibrium cores were produced for each fuel based on a 30 d reactor cycle at 20 MW thermal power. With its very high uranium density, the potential to load more uranium in the core with U-10Mo monolithic fuel was explored with test cases using an alternate fuel management scheme, a higher power level (30 MW), or a longer cycle (45 d). The research results indicate similar neutronics performance characteristics of the three LEU fuel options in the proposed NIST reactor with the same power level. However, the ability to load more fuel in the reactor with the U-10Mo option allows additional flexibility in the reactor design and could lead to other optimizations that maximize cold neutron production.

To assess the accuracy and capabilities of BARC, for standardization of 63Ni before participating in larger scale trial exercise to implement and test the methods for the extension of the SIR to β emitters, a bilateral intercomparison was organised with National Metrology Institute of Japan (NMIJ), Japan. Standardization of 63Ni was carried out and the results were compared with those obtained from NMIJ to assess the accuracy and capabilities of the laboratories. The CIEMAT/NIST efficiency tracing technique based on 3H standard was used for measurement of 63Ni activity concentration at BARC, India whereas Triple to Double Coincidence Ratio (TDCR) method was used at NMIJ, Japan. The procedures adopted for the standardization of 63Ni by CIEMAT/NIST method at BARC and TDCR method at NMIJ are presented. The percentage deviation in activity concentration of 63Ni between BARC, India and NMIJ, Japan is 0.27%. To evaluate the performance of techniques used at both the laboratories, En score (k = 2) and degrees of equivalence was calculated. The En score of −0.12 and degrees of equivalence −0.06 kBq g−1 clearly indicates that the activity concentration of 63Ni measured at BARC, India and NMIJ, Japan are in excellent agreement and comparable within uncertainty limits and demonstrates the degrees of equivalence of the standards maintained at BARC, India and NMIJ Japan.

This paper reports on the design, realization and characterization of a True Random Number Generator (TRNG) that operates using as seeds of entropy, the jitter and the metastability introduced by primitives of a Field Programmable Gate Arrays (FPGA) board. In particular, the TRNG architecture has been implemented on a Xilinx Ultrascale XCKU040 FPGA board. Generally, the implementations on FPGA of fully-digital TRNGs make use of ring oscillators employing a large number of Look-Up-Table (LUT) blocks. Differently from this approach, this paper demonstrates that a reliable FPGA-based TRNG architecture can be realized mainly employing only a single PLL and three on-board primitives together with other few basic logic elements (i.e., 8 D-type Flip-Flop, 17 LUT and 2 Counters) used only for the initial overall system synchronization and post-processing operations. In this way, the proposed solution largely reduces the employed number of the FPGA Configurable Logic Blocks (CLB), the circuitry complexity and the overall power consumption without affecting the achievable output bit rate so resulting suitable for full-custom VLSI implementations. The random and statistical properties of the generated 100 Mbps output bitstreams have been validated by passing all the National Institute of Standards and Technology (NIST) tests as well as the Anderson-Darling and the Kolmogorov-Smirnov tests so demonstrating that the proposed TRNG architecture can be suitably employed in security/cybersecurity network systems as well as, once integrated, in Internet-of-Things (IoT) and Industrial-Internet-of-Things (IIoT) applications.

The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures., Comment: Accepted for publication in Digital Signal Processing: A Review Journal

This paper is to investigate the effects of valve parameters on the performance of a trans-critical refrigeration compressor numerically. A numerical model has been implemented in Matlab based on mass balance, first thermodynamic law, NIST REFPROP database for gas properties and thermodynamic relationships. Good agreement is indicated by comparing numerical results with our previous measured data. The impact of key parameters such as: valve lift, spring stiffness, valve plate mass and valve port area are particularly evaluated on variation of valve displacement, valve speed, in-cylinder pressure and in-cylinder mass. Results demonstrate that: with lift increasing valve plate will not remain open to the limiter for a while but bounce back to seat after hitting the limiter; the valve plate mass has significant influence on both the opening motion and the closing motion of valves.

Low-power consumption and low-voltage operation are critical enabling features for Internet of Things (IoT) devices that are powered from energy harvesting. This paper presents a chaos-based true random number generator (TRNG) that can operate at an ultra-low voltage (ULV) and be integrated into energy-constrained IoT devices for secure communications. Folded Bernoulli maps are adopted for random number generation. Switched-capacitor chaotic circuits utilize bulk-driven amplifiers to mitigate gate leakage issue, two-stage comparators to increase voltage headroom, and low-complexity calibration schemes to ensure robustness. This system covers a wide range of process and temperature variations while consuming 142 nW from a 0.4 V supply at a bit rate of 10 kb/s. The generated bits pass National Institute of Standards and Technology (NIST) Pub-800.22 randomness tests successfully.

Achieving information security in the Internet of things (IoT) by means of physical layer key generation is an open research area. In the key generation, the secret key is generated using the physical (PHY) layer characteristics of the communication channel as a source of randomness. IoT networks are characterized as noisy & heterogeneous networks with lower transmission power. In this paper, we propose a wireless secret key generation approach by pre-processing the received signal strength indicator (RSSI) of beacons exchanged between Alice and Bob using moving window averaging (MWA). Our proposed approach has significant improvement in performance, at lower SNR range. The size of the moving window is selected on the basis of improvement in the standard deviation of the RSSI pattern. It is also suggested to quantize the pre-processed RSS samples through Lloyd–Max based quantizer to minimize quantization error, which further improves bit disagreement (BDR). It is clear from results that the proposed approach has considerable improvement in bit disagreement rate (BDR) at low SNR by applying the selected window size. The performance of the proposed approach is also evaluated by performing randomness testing of the generated keys using National Institute of Standards and Technology (NIST) test set for randomness.

Terrestrial Laser Scanners (TLSs) are used in a variety of large scale scanning applications such as reverse engineering, assembly of aircraft or ships and surveying. Contrast targets are used with such instruments for enabling scene registration or to establish a scale when used on a scale bar. Currently, the algorithms to calculate the centers of contrast targets (CCT) are either proprietary to the original equipment manufacturers (OEMs) or not precise and accurate. Some of these algorithms may also operate only on OEM's proprietary data format. To overcome these limitations, a novel algorithm was developed at the National Institute of Standards and Technology (NIST) to calculate the center of contrast targets. Several targets in various orientations were scanned by one TLS and their centers were calculated by both the NIST algorithm and one OEM software. The results show that the NIST algorithm is robust, addresses many data quality issues and has better precision than the OEM software in most cases.

Ionic liquids (ILs) are a significant class of chemicals with applications in solar cells, sensors, capacitors, batteries, plasticizers and thermal fluids. These compounds have attracted wide attention due to their low vapor pressure, tunable viscosity, non-flammability, wide liquid region phase diagrams and substantial chemical and thermal stability. Moreover, ILs structures can be easily modified leading to highly tunable physicochemical properties, which widen the application of these compounds. Heat capacity of ILs is an essential property for heat transfer evaluation as well as the estimation of widely used thermodynamic properties. Establishing a generalized and accurate model for predicting the heat capacity of ILs is valuable for their further development. In this manuscript, a hybrid group method of data handling (GMDH) was employed to establish a model estimating the ILs heat capacities. The database employed is an all-inclusive source of data taken from NIST standard, which includes the heat capacities of 56 ILs as a function of temperature and four structural parameters. About 80% of the database was assigned for building the model, and the remainder was used for evaluating the model performance. Statistical parameters and graphical techniques revealed that the model developed in this study is very accurate, with an R2 value of 0.982 and an average absolute percent relative error (AAPRE) of 1.84%. Moreover, the sensitivity analysis showed that the chemical structure of the cation has the highest impact on the heat capacity of ILs.

Glasses in the series, 49B2O3-25SiO2-(26-x)Bi2O3-xBaO (with x between 2 and 8 wt%) are considered for radiation shielding applications based on the gamma-ray half-value layer (HVL) and mass neutron removal cross-section. Theoretical data for the calculation of the HVL is obtained from the NIST XCOM software. The calculated HVL shows good correspondence to the experimentally determined values. The mass neutron removal cross-section and the half-value layer of the glass system are optimized using the genetic algorithm. The genetic algorithm is used alongside regression analysis to determine the relative concentration of each oxide needed to obtain the best radiation shielding parameter values. The use of numerical methods is employed to ascertain simple functions that can be used instead of traditional analytical functions in modeling the behavior of glasses. The auto-Zeff software is used to generate a plot of the effective atomic number over a spectrum of photon energies, while spectroscopic techniques are used to examine the structure and optical properties to determine what advantages the glasses have over concrete or water. This approach had fast convergence due to increased numerical stability. The optimal solution is equal to barite concrete in gamma-ray shielding and limonite concrete in neutron shielding.

This paper presents an FPGA Pseudo Random Number Generator (PRNG) that is based on the Lorenz and Lu chaotic systems. These two systems are used to generate four different 3D chaotic attractors. One attractor is generated from Lorenz while the other three attractors are generated from Lu. The output attractor of the proposed PRNG can be reconfigured during real time operation using an efficient hardwired shifting and multiplexing scheme. Furthermore, in order to exploit the proposed reconfiguration feature, the proposed PRNG has been embedded in an FPGA cascaded encryption processor that ciphers the input data from one up to four times successively. In each ciphering operation the PRNG is set to a new configuration and is initialized according to a part of the encryption key. The size of the encryption key can be varied according to the number of required ciphering operations. The proposed PRNG has been realized using VHDL, synthesized on Xilinx using the FPGA device XC5VLX50T, and analyzed using MATLAB and the NIST statistical suite. The proposed PRNG has utilized only 1.4% from the FPGA’s slices, achieved an operating frequency up to 78 MHz, and successfully passed all the NIST statistical tests.

We combine Total Variability algorithm with Deep Learning theory to complete the language recognition task. The Total Variability algorithm can compensate for the influence of differences in channels and speakers among various languages, while deep learning methods have a stronger ability of nonlinear modeling compared with traditional statistical models. In this paper, I-vector feature is extracted using Total Variability algorithm, and model training is established using fully connected neural network. Meanwhile, the dropout strategy is also used to suppress overfitting. The experimental results show that the new system outperforms the baseline system on the NIST LRE 2007 corpus.

Risk management is a practical step in handling risk scenarios in an organization, including in the field of information security. There are many techniques used to carry out information security risk assessments. One of them is a combination technique using ISO 27005 and NIST SP 800-30 revision 1. Previous research proved that the combination technique could be implemented in a non-profit organization (government). However, the detailed risk assessment steps are not explained clearly yet. Thus, raising the question of whether this new approach can be utilized in a common organization or not (not only non-profit but also profit organization). This research focuses on information security risk assessment by implementing the combination technique in a profit organization using semi-quantitative methods. The result, the combination technique can be used in common organizations both profit and non-profit with clear step by step translation.

INTRODUCTION: Immunoassays and liquid chromatography-tandem mass spectrometry assays are commonly employed in clinical laboratories for measurement of total testosterone in serum. Results obtained from either of these methodologies compare poorly due to differences in calibration and/or inadvertent detection of interfering substances by the immunoassays. Standardization efforts are underway, but recent studies indicate that accuracy remains an issue. METHODS: This study compares the results from four independently developed and validated LC-MS/MS assays for total testosterone. The calibration for each assay was verified using National Institute of Standards and Technology Standard Reference Material 971. RESULTS: Initially, one of the four assays had a mean percent difference of +11.44%, compared to the All Method Mean, but following re-verification of all five non-zero calibrator concentrations with the NIST SRM 971, the mean percent difference decreased to −4.88%. Subsequently, the agreement between all four assays showed a mean bias of

Iodine environmental measurement programs are in need of new materials with certified 129I activity. Frequently 129I measurements are validated in the literature using the standard material IAEA-375, Chernobyl soil, which is the only soil/sediment material with a recommended 129I activity. IAEA-375 has not been available for purchase since 2010. This study is an extension of previous work at INL to include four additional standard materials that are commercially available (NIST materials: RM 8704, Buffalo River sediment, SRM 2710a, Montana I soil, and IAEA materials: SL-1, lake sediment, IAEA-385, Irish Sea sediment). These materials have certified or recommended activities for a variety of radionuclides but not for 129I. This paper reports a comparison of accelerator mass spectrometry (AMS) and thermal ionization mass spectrometry (TIMS) data for 129I activity, as well as the 129/127I ratios for these standards to assist in identifying a suitable alternative for IAEA-375. Two independent chemical separation and mass spectrometric analysis techniques have been applied in an effort to corroborate the data. Both methods were validated via analyses of IAEA-375 for 129I and show good agreement with the recommended activity of 1.7 × 10−3 Bq kg−1 for 129I; (1.6 × 10−3 Bq kg−1 by AMS and 1.8 × 10−3 Bq kg−1 by TIMS) with both sets of results within the 95% confidence interval of the recommended value.

The cold neutron guide of the Prompt Gamma Activation Analysis (PGAA) instrument (Revay et al. 2015) at the Heinz Maier-Leibnitz Zentrum (MLZ) provides an excellent opportunity for neutron depth profiling (NDP) due to its high collimated neutron flux. We present the concept, layout and first measurements with the new N4DP facility set-up at this beamline. A special focus is put on background analysis and instrument characterization. We present measurements of a NIST reference sample and of boron-containing float glasses which are used for neutron optical waveguides. We give an outlook concerning the ongoing development of position-resolved NDP using silicon microstrip detectors.

Spike-timing Dependent Plasticity (STDP) is a learning mechanism that can capture causal relationships between events. STDP is considered a foundational element of memory and learning in biological neural networks. Previous research efforts endeavored to understand the functionality of STDP's learning window in spiking neural networks (SNNs). In this study, we investigate the interaction among different encoding/decoding schemes, STDP learning windows and normalization rules for the SNN classifier, trained and tested on MNIST, NIST and ETH80-Contour datasets. The results show that when no normalization rules are applied, classical STDP typically achieves the best performance. Additionally, first-spike decoding classifiers require much less decoding time than a spike count decoding classifier. Thirdly, when no normalization rule is applied, the classifier accuracy decreases as the encoding duration increases from 10ms to 34ms using count decoding scheme. Finally, normalization of output weights is shown to improve the performance of a first-spike decoding classifier, which reveals the importance of weight normalization to SNN.

Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector based systems have become the standard in speaker verification applications, but they are less effective with short utterances. In this paper, we first compare two state-of-the-art universal background model training methods for i-vector modeling using full-length and short utterance evaluation tasks. The two methods are Gaussian mixture model (GMM) based and deep neural network (DNN) based methods. The results indicate that the I-vector_DNN system outperforms the I-vector_GMM system under various durations. However, the performances of both systems degrade significantly as the duration of the utterances decreases. To address this issue, we propose two novel nonlinear mapping methods which train DNN models to map the i-vectors extracted from short utterances to their corresponding long-utterance i-vectors. The mapped i-vector can restore missing information and reduce the variance of the original short-utterance i-vectors. The proposed methods both model the joint representation of short and long utterance i-vectors by using autoencoder. Experimental results using the NIST SRE 2010 dataset show that both methods provide significant improvement and result in a max of 28.43% relative improvement in Equal Error Rates from a baseline system, when using deep encoder with residual blocks and adding an additional phoneme vector. When further testing the best-validated models of SRE10 on the Speaker In The Wild dataset, the methods result in a 23.12% improvement on arbitrary-duration (1-5 s) short-utterance conditions., Comment: Submitted to Speech Communication; under final review

Recent works have reported the successful use of sparse representation (SR) over learned dictionary for speaker verification (SV) task. For large variability practical data, the SR based approaches are noted to produce inconsistent sparse coding. In other words, for the true-target trials, the dominant coefficients in the sparse codes of enrollment and test data happen to involve different atoms of the dictionary. This, in turn, enhances the false rejection rate. In this work, we propose a novel yet simple way to address that problem. The key idea is to exploit the sparse coding of enrollment data in finding the representation of the test data. As the proposed constraint affects the false alarm rate, the multi-offset decimation diversity is introduced to address the same. The combined approach has lower computational complexity yet shown to outperform the existing factor analysis based SV approach when evaluated on a large variability NIST 2012 speaker recognition evaluation dataset.

A cycle simulation code with integrated turbine design/optimization algorithm has been developed and utilized for the analysis/optimization of ≈ 10 M W e recompression supercritical carbon dioxide Brayton cycle. Integrated turbine design and optimization code computes the turbine design engaging the meanline design calculations as a function of received inlet conditions from the main cycle simulation code. Moreover embedded turbine code optimizes the turbine geometric by minimized the losses with in the turbine using response surface optimization methodology and returns turbine geometry and performance parameters to main code. In order to capture the swift variation in thermophysical properties of SCO 2 in the supercritical region, cycle design point code and embedded turbine codes are linked with NIST REFPROP program. Integrated turbine design/optimization code was validated using 3D Reynolds-Averaged-Navier-Stokes calculations. Response surface methodology was tested by comparing the 3D computation results for the turbine baseline and optimized designs. Finally recompression supercritical carbon dioxide Brayton cycle was analyzed under various conditions of cycle's minimum temperature, minimum pressure, and pressure ratio and split mass fraction. On the basis of the results it is recommended to use proposed model for more realistic and accurate modeling cycle design point analysis.

One of the major applications of Pseudo-Random Numbers is cryptography. Cryptography is a technique of securing information and communications through the use of codes so that only those people for whom the information is intended can understand it and process it thus preventing unauthorized access to information. Attackers can bypass cryptography, hack into computers that are responsible for data encryption and decryption, and exploit weak implementations, such as the use of default keys. To tackle this problem, logistic chaotic maps are used to generate a Pseudo-Random Binary Sequence (PRBS). The output of the designed PRBS Generator has successfully passed all the performance assessments of the NIST test suite. Hence a highly efficient Cryptographic Pseudo Random Bit Sequence Key is developed which not only has good performance in terms of statistical properties but can also achieve high product throughput if realized by FPGA hardware.

This paper discusses the Impact of Voice Trace for Detecting Suspects in a multi-speaker stream for forensic or legal research (IVTDS). This scientific evidence was found in a crime and is exploited for the task of detecting suspects in this audio stream among all involved. So in this framework of the work, the first objective is to obtain segments of homogeneous speakers, that is to say containing the segments of only one speaker. These segments must be as long as possible with a priori information on the number of speakers present in the audio stream and that these speakers do not speak simultaneously. The second objective is to study the impact of scientific evidence on the detection of suspects in this multi-speaker stream assuming that no prior knowledge is available on the suspects. For this, the work takes place in three main phases: the parametrization of the multi-speaker flow, the speaker changes are segmented and detected as valid or otherwise canceled, and finally the voice traces are exploited and compared to the models GMM (Gaussian Mixture Models) and the global model UBM (Universal Background Model) by selecting only the best scores. Applying this approach to data generated from the NIST 2005 telephone-type database. The results of the experiments carried out demonstrate the effectiveness of this approach of detecting suspects using voice traces in solving certain forensic problems.

Cyber Security is a global challenge and experts are working hard to address the security cropping up day by day. The delineation of the keywords for the emerging area is important to enlighten the emerging technology’s research path. In this paper, an overview of redefining the keywords for cyber security from a bibliometrics perspective has been provided. For the construction of keywords, a number of sources have been consulted. The collected keywords cleansed according to the definition provided by the National Institute of Standards and Technology (NIST) and the correlated keywords have been clustered manually. As a result, a set of keywords has been identified and justification for the same has been provided. These keywords can be used to download the related bibliographic records for the emerging discipline Cyber Security from the indexing databases.

Interlaboratory studies are a type of collaborative exercise in which many laboratories are presented with the same set of data to interpret, and the results they produce are examined to get a "big picture" view of the effectiveness and accuracy of analytical protocols used across participating laboratories. In 2005 and again in 2013, the Applied Genetics Group of the National Institute of Standards and Technology (NIST) conducted interlaboratory studies involving DNA mixture interpretation. In the 2005 NIST MIX05 study, 69 laboratories interpreted data in the form of electropherograms of two-person DNA mixtures representing four different mock sexual assault cases with different contributor ratios. In the 2013 NIST MIX13 study,108 laboratories interpreted electropherogram data for five different case scenarios involving two, three, or four contributors, with some of the contributors potentially related. This paper describes the design of these studies, the variations observed among laboratory results, and lessons learned.

The duration of speech segments can significantly impact the performance of text-independent speaker verification systems. In real world applications which require high accuracy on short utterances, the performance of i-vector speaker verification framework degrades significantly considering that i-vectors extracted from short utterances are less reliable (i.e., uncertainty is higher) than those extracted from long utterances. Therefore, to handle duration variability properly, a more realistic approach seems to be required. This study is an extension to our recently proposed nearest neighbor probabilistic linear discriminant analysis (NN-PLDA) which estimates the parameters of PLDA in i-vector speaker verification framework using a nonparametric form rather than maximum likelihood estimation (MLE) obtained by an EM algorithm, and has been shown to provide superior performance. In NN-PLDA, the between-speaker covariance matrix that represents global information about the speaker variability is replaced with a local estimation computed on a nearest neighbor basis for each target speaker. Compared to their parametric counterparts, the nonparametric between- and within-speaker scatter matrices can better exploit the discriminant information in training data and are more adapted to sample distributions. In this paper, we provide further analysis on the proposed nonparametrically trained PLDA as well as introduce a duration variability modeling technique in the estimation of the within-speaker scatter matrix as to compensate for the effect of limited speech data. We evaluate our approach using core–10sec and 10sec–10sec telephone trial conditions of NIST 2010 SRE as well as on the truncated test utterances in extended core condition with duration less than 10 s. We also present the results obtained by the successful incorporation of NN-PLDA on the recent NIST 2016 speaker recognition evaluation. In all experiments, considerable performance improvement is obtained with the proposed technique compared to a generatively trained PLDA model.