National Standards Institute Research Articles

Robust Permittivity Reconstruction With the Transmission and Reflection Method at mm-Wave Frequencies

The permittivity reconstruction with the transmission and reflection method usually requires time-gating to mitigate distorted measurement data. State-of-the-art approaches reduce the available bandwidth as they introduce ringing. Available frequency extension approaches to avoid a bandwidth reduction are not based on a physically meaningful estimate. In this work, we propose a method that takes advantage of the information already given by the measurement to calculate a reasonable frequency extension. The inverse problem of the actual permittivity reconstruction is most commonly approached by the Baker-Jarvis algorithm from the National Institute of Standards and Technology (NIST) which considers the magnitude of one single complex number. It is calculated from the difference between measurement and model of the scattering matrix. In this article, we demonstrate the superiority of matching all four scattering parameters individually instead. This results in particular in a wider range of initial start parameters with proper convergence to the desired solution and more reliable reconstruction results for optimizations with unknown sample thickness. The benefits of the novel reconstruction techniques are demonstrated and evaluated in detail on simulation and measurement data in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$</tex-math> </inline-formula> band.

Spectrum Analysis for Fully Connected Neural Networks.

This article studies the meaning of parameters of fully connected neural networks with single hidden layer from the perspective of spectrum. Under the constraints of numerical range, the corresponding relationship between parameters and the spectrum of network function can be established by the Fourier series coefficients of the activation function, which is truncated and periodically extended. This work is substantiated on the Mixed National Institute of Standards and Technology (MNIST) handwritten dataset and two illustrative examples with certain spectra. The simulations complete the conversion between spectrum and parameters with high precision and give the significance of hidden nodes to the spectrum of network function. Some algorithms derived from these properties, such as the parameter initialization method using spectrum and the pruning method by sorting amplification weights, are also presented to introduce how spectrum analysis affects neural network decision-making. Thus, spectrum analysis has great potential in network interpretation.

Statistical analysis of the key scheduling of the new lightweight block cipher

This research paper is aimed at studying the generation of round keys (GRK) of the lightweight block cipher (LBC), which provides an optimal balance between security, performance, and minimal costs in internet of things (IoT). For comparative analysis, the GRK of the well-known PRESENT algorithm was studied. A number of studies have been carried out to assess the cryptographic strength of encryption algorithms, however, less attention has been paid to the assessment of the reliability of GRK algorithms, which can lead to a possible weakening of a cryptosystem. A trusted GRK should issue random and independent round keys regardless of the secret key. The experiments were carried out with secret keys of low and high density, as well as random numbers. The obtained results show that the GRK of the LBC algorithm generates random round keys that successfully pass tests of the National Institute of Standards and Technology (NIST) for randomness.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor.

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.

In vitro anti-Trypanosoma cruzi activity of methanolic extract of Bidens pilosa and identification of active compounds by gas chromatography-mass spectrometry analysis.

Chagas disease, caused by Trypanosoma cruzi parasite, is a significant but neglected tropical public health issue in Latin America due to the diversity of its genotypes and pathogenic profiles. This complexity is compounded by the adverse effects of current treatments, underscoring the need for new therapeutic options that employ medicinal plant extracts without negative side effects. Our research aimed to evaluate the trypanocidal activity of Bidens pilosa fractions against epimastigote and trypomastigote stages of T. cruzi, specifically targeting the Brener and Nuevo León strains-the latter isolated from Triatoma gerstaeckeri in General Terán, Nuevo León, México. We processed the plant's aerial parts (stems, leaves, and flowers) to obtain a methanolic extract (Bp-mOH) and fractions with varying solvent polarities. These preparations inhibited more than 90% of growth at concentrations as low as 800 μg/ml for both parasite stages. The median lethal concentration (LC50) values for the Bp-mOH extract and its fractions were below 500 μg/ml. Tests for cytotoxicity using Artemia salina and Vero cells and hemolytic activity assays for the extract and its fractions yielded negative results. The methanol fraction (BPFC3MOH1) exhibited superior inhibitory activity. Its functional groups, identified as phenols, enols, alkaloids, carbohydrates, and proteins, include compounds such as 2-hydroxy-3-methylbenzaldehyde (50.9%), pentadecyl prop-2-enoate (22.1%), and linalool (15.4%). Eight compounds were identified, with a match confirmed by the National Institute of Standards and Technology (NIST-MS) software through mass spectrometry analysis.

Emulation of Pavlovian conditioning and pattern recognition through fully connected neural networks using Holmium oxide (Ho2O3) based synaptic RRAM device.

In this manuscript, we report on the paramagnetic Ho2O3 -based synaptic resistive random-access memory (RRAM) device for the implementation of neuronal functionalities such as long-term potentiation (LTP), long-term depression (LTD) and spike timing dependent plasticity (STDP) respectively. The plasticity of the artificial synapse is also studied by varying pulse amplitude, pulse width, and pulse interval. In addition, we could classify handwritten Modified National Institute of Standards and Technology (MNIST) data set using a fully connected neural network (FCN). The device-based FCN records a high classification accuracy of 93.47% which is comparable to the software-based test accuracy of 97.97%. This indicates the highly optimized behavior of our synaptic device for hardware neuromorphic applications. Successful emulation of Pavlovian classical conditioning for associative learning of the biological brain is achieved. We believe that the present device consists the potential to utilize in neuromorphic applications.

Nanowire-Enhanced Fully Transparent and Flexible Indium Gallium Zinc Oxide Transistors with Chitosan Hydrogel Gate Dielectric: A Pathway to Improved Synaptic Properties.

In this study, a transparent and flexible synaptic transistor was fabricated based on a random-network nanowire (NW) channel made of indium gallium zinc oxide. This device employs a biocompatible chitosan-based hydrogel as an electrolytic gate dielectric. The NW structure, with its high surface-to-volume ratio, facilitated a more effective modulation of the channel conductance induced by protonic-ion polarization. A comparative analysis of the synaptic properties of NW- and film-type devices revealed the distinctive features of the NW-type configuration. In particular, the NW-type synaptic transistors exhibited a significantly larger hysteresis window under identical gate-bias conditions. Notably, these transistors demonstrated enhanced paired-pulse facilitation properties, synaptic weight modulation, and transition from short- to long-term memory. The NW-type devices displayed gradual potentiation and depression of the channel conductance and thus achieved a broader dynamic range, improved linearity, and reduced power consumption compared with their film-type counterparts. Remarkably, the NW-type synaptic transistors exhibited impressive recognition accuracy outcomes in Modified National Institute of Standards and Technology pattern-recognition simulations. This characteristic enhances the efficiency of practical artificial intelligence (AI) processes. Consequently, the proposed NW-type synaptic transistor is expected to emerge as a superior candidate for use in high-efficiency artificial neural network systems, thus making it a promising technology for next-generation AI semiconductor applications.

Kernel Mapping Methods of Convolutional Neural Network in 3D NAND Flash Architecture

A flash memory is a non-volatile memory that has a large memory window, high cell density, and reliable switching characteristics and can be used as a synaptic device in a neuromorphic system based on 3D NAND flash architecture. We fabricated a TiN/Al2O3/Si3N4/SiO2/Si stack-based Flash memory device with a polysilicon channel. The input/output signals and output values are binarized for accurate vector-matrix multiplication operations in the hardware. In addition, we propose two kernel mapping methods for convolutional neural networks (CNN) in the neuromorphic system. The VMM operations of two mapping schemes are verified through SPICE simulation. Finally, the off-chip learning in the CNN structure is performed using the Modified National Institute of Standards and Technology (MNIST) dataset. We compared the two schemes in terms of various parameters and determined the advantages and disadvantages of each.

A Programmable Crypto-Processor for National Institute of Standards and Technology Post-Quantum Cryptography Standardization Based on the RISC-V Architecture

The advancement of quantum computing threatens the security of conventional public-key cryptosystems. Post-quantum cryptography (PQC) was introduced to ensure data confidentiality in communication channels, and various algorithms are being developed. The National Institute of Standards and Technology (NIST) has initiated PQC standardization, and the selected algorithms for standardization and round 4 candidates were announced in 2022. Due to the large memory footprint and highly repetitive operations, there have been numerous attempts to accelerate PQC on both hardware and software. This paper introduces the RISC-V instruction set extension for NIST PQC standard algorithms and round 4 candidates. The proposed programmable crypto-processor can support a wide range of PQC algorithms with the extended RISC-V instruction set and demonstrates significant reductions in code size, the number of executed instructions, and execution cycle counts of target operations in PQC algorithms of up to 79%, 92%, and 87%, respectively, compared to RV64IM with optimization level 3 (-O3) in the GNU toolchain.

Determination of persistent organochlorine pollutants in human serum by combining liquid-liquid extraction and solid-phase extraction with gas chromatography-tandem mass spectrometry

Persistent organochlorine pollutants (POCPs) are a group of “forever chemicals” that are ubiquitous in the environment and pose adverse health effects to humans. However, the lack of sensitive and widely applicable analytical method for internal POCPs exposure assessment puts limits on the researches of health risk evaluations. Therefore, we developed a method for the measurement of 43 POCPs in human serum using gas chromatography-tandem mass spectrometry (GC-MS/MS). Serum samples were treated with methanol and acid to precipitate protein and remove lipids, respectively. 13C-labeled analogue was spiked and liquid-liquid extraction (LLE) was performed to extract target analytes by hexane and methyl tert-butyl ether. The extract was purified by solid-phase extraction (SPE) and eluted with dichloromethane. The eluate was concentrated under nitrogen stream and reconstituted with toluene. Samples were analyzed by GC-MS/MS operated in electron impact (EI) and multiple reaction monitoring (MRM) mode. Calibration curves of all target analytes showed favorable linearity within the concentration range of 0.008 to 10 μg/L for different POCPs, with regression coefficients above 0.991. The method detection limits and limits of quantification ranged from 0.005 to 0.10 μg/L and 0.017 to 0.34 μg/L, respectively. The recoveries and precision of all analytes at 3 different spike levels were in the range of 65.3 % to 121.8 % and 1.8 % to 13.9 %, respectively. The matrix effects (ME) ranged from 74.9 % to 125.6 %. The results of measuring quality control samples from the National Institute of Standards and Technology (NIST) were with relative deviations of less than 30 %. With an optimized sample preparation procedure, the proposed method has successfully achieved in improving extraction efficiency and reducing ME. It was applied in the simultaneous determination of POCPs in a small volume of human serum in China National Human Biomonitoring.

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights.

Comprehensive Analysis of Risk Factors for Bronchopulmonary Dysplasia in Preterm Infants in Taiwan: A Four-Year Study.

Bronchopulmonary dysplasia (BPD) is a major respiratory condition mainly affecting premature infants. Although its occurrence is global, risk factors may differ regionally. This study, involving 3111 infants with birth weight ≤ 1500 gm or gestational age (GA) < 30 weeks, aimed to identify risk factors for BPD and BPD/mortality in Taiwan using data from the Taiwan Neonatal Network. The BPD criteria were based on the National Institute of Child Health and Human Development standards. Average GA was 27.5 weeks, with 23.7% classified as small for GA (SGA). Multivariate analysis highlighted low GA, low birth weight, and other perinatal factors as significant risk indicators for BPD. For moderate-to-severe BPD, additional risk factors included male gender and SGA, endotracheal intubation (ETT) or cardiopulmonary cerebral resuscitation (CPCR) in initial resuscitation. In the moderate-to-severe BPD/death group, SGA and ETT or CPCR in initial resuscitation remained the only additional risk factors. The study pinpoints male gender, SGA and ETT or CPCR as key risk factors for moderate-to-severe BPD/death in low-birth-weight infants in Taiwan, offering a basis for focused interventions and further research.

Laser-induced breakdown spectroscopy for rapid and accurate qualitative analysis of elemental composition in fertilizers

Through elemental analysis, laser-induced breakdown spectroscopy (LIBS), a quick and effective analytical method, has enormous potential to advance agriculture. In this context, our study intends to better the fertilizer sector by utilizing LIBS’s capabilities. The main objective is to make it possible to rapidly and accurately analyze the elements in frequently used fertilizers such as nitrophos (NP), diammonium phosphate (DAP), and single super phosphate (SSP). We identify all elements contained in the fertilizers using qualitative and semi-quantitative analysis utilizing a single LIBS shot by noting their matching spectral lines and peak positions. This identification is validated more thoroughly by matching peak positions to the National Institute of Standards and Technology (NIST) database. Phosphorus (P), potassium (K), iron (Fe), calcium (Ca), nickel (Ni), and aluminum (Al) are effectively recognized due to their distinct spectral fingerprints. Our findings highlight the vital need to precisely characterize fertilizer content in order to maximize agricultural yield. Furthermore, our approach presents an efficient means for rapid and precise analysis of fertilizers, offering the advantage of minimal sample requirements.

Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network.

Quantum secure direct communication (QSDC) offers a practical way to realize a quantum network which can transmit information securely and reliably. Practical quantum networks are hindered by the unavailability of quantum relays. To overcome this limitation, a proposal has been made to transmit the messages encrypted with classical cryptography, such as post-quantum algorithms, between intermediate nodes of the network, where encrypted messages in quantum states are read out in classical bits, and sent to the next node using QSDC. In this paper, we report a real-time demonstration of a computationally secure relay for a quantum secure direct communication network. We have chosen CRYSTALS-KYBER which has been standardized by the National Institute of Standards and Technology to encrypt the messages for transmission of the QSDC system. The quantum bit error rate of the relay system is typically below the security threshold. Our relay can support a QSDC communication rate of 2.5 kb/s within a 4 ms time delay. The experimental demonstration shows the feasibility of constructing a large-scale quantum network in the near future.

Unified analytical formulation regarding thermodynamic properties subject to gaseous metal hydroxides

Analytical formulations concerning the Gibbs free energy for simple substances and compounds show important applications in dealing with many issues, but acquiring such a formulation with few adjustable parameters remains a challenging goal. Here, we report a unified analytical formulation regarding the Gibbs free energy subject to gaseous metal hydroxides MOH for (M=Al, Be, Cs, K, Li, Mg, Na, Sr, and Ca). It depends only on experimental data of atomic masses and six molecular constants except for an adjustable dimensionless pure number, while the temperature-dependent Gibbs free energy polynomial correlation provided in the National Institute of Standards and Technology (NIST) Chemistry WebBook has eight adjustable coefficients, which take different values in different temperature ranges for each species. The mean relative errors between the predicted Gibbs free energy values subject to nine metal hydroxides and the NIST data are within the range of 0.012 % to 0.078 %, and the predicted results of entropy, enthalpy and isobaric heat capacity are also in good agreement with the NIST data. The proposed unified model possesses the characteristics of high prediction accuracy and strong flexibility.

A novel pseudo-random number generator based on multivariable optimization for image-cryptographic applications

Pseudo-random number generators (PRNGs) play an important role to ensure the security and confidentiality of image cryptographic algorithms. Their primary function is to generate a sequence of numbers that possesses unpredictability and randomness, which is crucial for the algorithms to work effectively and provide the desired level of security. However, traditional PRNGs frequently encounter limitations like insufficient randomness, predictability, and vulnerability to cryptanalysis attacks. To overcome these limitations, we propose a novel method namely an elliptic curve genetic algorithm (ECGA) for the construction of an image-dependent pseudo-random number generator (IDPRNG) that merges elliptic curves (ECs) and a genetic algorithm (GA). The ECGA consists of two primary stages. First, we generate an EC-based initial sequence of random numbers using pixels of a plain-image and parameters of an EC that depart from traditional methods of population initialization. In our proposed approach, the image itself serves as the seed for the initial population in the genetic algorithm optimization, taking into account the image-dependent nature of cryptographic applications. This allows the PRNG to adapt its behavior to the unique characteristics of the input image, leading to enhanced security and improved resistance against differential attacks. Furthermore, the use of a good initial population reduces the number of generations required by a genetic algorithm which results in decreased computational cost. In the second stage, we use well-known operations of a genetic algorithm to optimize the generated sequence by maximizing a multi-variable fitness function that is based on both the information entropy and the period of the PRNG. By combining elliptic curves and genetic algorithms, we enhance the randomness and security of the ECGA. To evaluate the effectiveness and security of our generator, we conducted comprehensive experiments using various benchmark images and applied several standard tests, including the National Institute of Standards and Technology (NIST) test suite. We then compared the results with the state-of-the-art PRNGs. The experimental results demonstrate that the ECGA outperforms the state-of-the-art PRNGs in terms of uniformity, randomness, and cryptographic strength.

Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect.

For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.

Performance and Applicability of Post-Quantum Digital Signature Algorithms in Resource-Constrained Environments

The continuous development of quantum computing necessitates the development of quantum-resistant cryptographic algorithms. In response to this demand, the National Institute of Standards and Technology selected standardized algorithms including Crystals-Dilithium, Falcon, and Sphincs+ for digital signatures. This paper provides a comparative evaluation of these algorithms across key metrics. The results indicate varying strengths and weaknesses for each algorithm, underscoring the importance of context-specific deployments. Our findings indicate that Dilithium offers advantages in low-power scenarios, Falcon excels in signature verification speed, and Sphincs+ provides robust security at the cost of computational efficiency. These results underscore the importance of context-specific deployments in specific and resource-constrained technological applications, like IoT, smart cards, blockchain, and vehicle-to-vehicle communication.

Analysis of Atom against quantum attacks

AbstractA significant amount of study is being done to review the security promises made for the various ciphers now in use as a result of the development of quantum computing technology. A general attack against symmetric key cryptography primitives that can reduce search costs to the square root is Grover's search algorithm. To implement Grover's algorithm, it is necessary that the target cipher be implemented as a quantum circuit. Despite being relatively new, this area of study has received significant attention from the research community. The authors have estimated the cost of Grover's key search attack against the stream cipher Atom, for the first time, under circuit depth restrictions defined in National Institute of Standards and Technology (NIST) PQC standardisation process. The authors implement the quantum circuit of Atom in QISKIT, (open‐source software development kit for working with quantum computers running on IBM Quantum Experience). The results are also compared with other existing literature on LFSR‐based stream ciphers, such as Grain‐v1, Grain‐128‐AEAD, and Lizard. The authors also find that, to the best of their knowledge, in the existing literature on estimating the cost of Grover's attack on symmetric ciphers, Atom is the only 128‐bit key cipher that meets the threshold of ≈2170 set by NIST for quantum security of 128‐bit key ciphers. The authors also analyse the security of Atom against quantum TMDTO attacks.

Quantification of Unsupported Thin-Film X-ray Spectra Using Bulk Standards.

A quantification model which uses standard X-ray spectra collected from bulk materials to determine the composition and mass thickness of single-layer and multilayer unsupported thin films is presented. The multivariate model can be iteratively solved for single layers in which each element produces at least one visible characteristic X-ray line. The model can be extended to multilayer thin films in which each element is associated with only one layer. The model may sometimes be solved when an element is present in multiple layers if additional information is added in the form of independent k-ratios or model assumptions. While the algorithm is suitable for any measured k-ratios, it is particularly well suited to energy-dispersive X-ray spectrometry where the bulk standard spectra can be used to deconvolve peak interferences in the thin-film spectra. The algorithm has been implemented and made available in the Open Source application National Institute of Standards and Technology DTSA-II. We present experimental data and Monte Carlo simulations supporting the quantification model.