Low Error Probability Research Articles

Статистики вищих порядків в задачах виявлення сигналів на фоні корельованих негаусових завад

Signal detection in noise is critical in telecommunications, navigation and radar systems, image processing and biomedical research, where noise often deviates from a normal distribution, and sample values exhibit statistical dependence. Traditional methods for analyzing and designing such systems face significant limitations, including algorithmic and computational complexity, severely restricting their practical application. An effective approach to developing signal detection systems involves using moment and cumulant descriptions of random variables, simplifying the design of detection systems for signals with various probability density functions. The authors propose a novel approach based on one-dimensional (1D) and two-dimensional (2D) moment-cumulant models to describe correlated non-Gaussian processes. This approach enables the modification of the moment-based criterion for statistical hypothesis testing and synthesising polynomial stochastic decision rules for detecting signals in correlated non-Gaussian noise. The study demonstrates that the nonlinear processing of sample values and the application of higher-order statistics to describe the investigated processes account for the structure of non-Gaussian noise and its statistical dependencies. This reduces the error probabilities of the synthesized decision rules compared to traditional Gaussian models. The study aims to enhance the efficiency of signal detection systems under additive interaction with correlated non-Gaussian noise by developing new moment-cumulant models of the investigated processes, modifying the moment-based criterion for hypothesis testing, and designing polynomial decision rules. The study's practical significance lies in creating simple-to-implement algorithms with high accuracy that achieve lower error probabilities in decision rules compared to existing methods.

High-Performance Garbage Collection Scheme with Low Data Transfer Overhead for NoC-Based SSDC

Solid-state drives (SSDs) have become the preferred storage solution for performance-critical applications due to their high speed, durability, and energy efficiency. However, the inherent characteristics of NAND flash memory, such as block-level erasure and data fragmentation, necessitate frequent garbage collection (GC) operations to reclaim storage space. These operations, while essential, introduce significant performance overhead, particularly in modern SSD controllers (SSDCs) that utilize network-on-chip (NoC) architectures. In such architectures, GC requires substantial data transfer over interconnects for error correction, leading to increased latency and reduced throughput. This paper presents a novel GC scheme designed to minimize latency in NoC-based SSDCs. Unlike conventional methods that unconditionally transfer data for error correction, the proposed approach selectively determines the data transfer path based on the presence of errors. By leveraging the low error probability of NAND flash memory, this scheme avoids unnecessary data traversal across the interconnect, significantly reducing GC overhead. A hardware implementation using task queues ensures efficient parallelism without disrupting other operations. The experimental results demonstrate that the proposed scheme improves SSD performance across various real-world workloads, achieving up to a 26.9% reduction in average latency and a 50.0% reduction in peak latency compared to traditional GC methods. These findings highlight the potential of optimizing data traversal paths in NoC architectures, providing a scalable solution for enhancing SSD performance for diverse applications.

The effectiveness of a mobile mammography units in breast cancer screening

Breast cancer remains the leading cause of death in the female population. Screening studies are conducted in order to detect the disease at an early stage. At the same time, domestic authors emphasize the lack of alternatives to preventive mammography in breast cancer screening and the prospects of using mobile mammographs for early diagnosis of breast cancer and precancerous breast diseases.Aim. To evaluat the results of the use of mobile mammography units for active and early detection of breast cancer.Materials and methods. The research uses methods of content analysis, information and analytical materials of Russian and foreign researchers, and forms of federal statistical observation. Statistical research methods were used to analyze the data obtained.Results. Regression analysis with a very low probability of error (p = 0.001) showed that the larger the total volume of mammographic studies with preventive purposes in the subjects of the Russian Federation, the higher the proportion of cases of active detection of breast cancer, although this relationship is rather weak. Taking into account the obtained value of R2 (0.192), only 19.2% of the increase in the proportion of breast cancer cases detected actively can be explained by an increase in the volume of preventive mammograms. The correlation analysis showed that the volumes of preventive mammograms performed using mobile devices in the subjects of the Russian Federation were not associated with either the proportion of breast cancer cases detected actively or with the proportion of breast cancer cases detected in the early stages. At the same time, it is worth noting that the obtained value of p ≥ 0.5 indicates a statistically insignificant result of evaluating the relationship between variables, the interpretation of which should be performed with caution.Conclusion. Despite the high cost of mobile mammography units, the increase in their number in the subjects of the Russian Federation and the growth in their activities, the contribution of these diagnostic devices to solving the problem of early and active diagnosis of breast cancer remains uncertain. More promising are personalized approaches to the timely detection of cancers, including breast cancer, based on an assessment of the individual risk of their occurrence.

A robust temporal map of speech monitoring from planning to articulation

Speakers continuously monitor their own speech to optimize fluent production, but the precise timing and underlying variables influencing speech monitoring remain insufficiently understood. Through two EEG experiments, this study aimed to provide a comprehensive temporal map of monitoring processes ranging from speech planning to articulation.In both experiments, participants were primed to switch the consonant onsets of target word pairs read aloud, eliciting speech errors of either lexical or articulatory-phonetic (AP) origin. Experiment I used pairs of the same stimuli words, creating lexical or non-lexical errors when switching initial consonants, with the degree of shared AP features not fully balanced but considered in the analysis. Experiment II followed a similar methodology but used different words in pairs for the lexical and non-lexical conditions, fully orthogonalizing the number of shared AP features.As error probability is higher in trials primed to result in lexical versus non-lexical errors and AP-close compared to AP-distant errors, more monitoring is required for these conditions. Similarly, error trials require more monitoring compared to correct trials. We used high versus low error probability on correct trials and errors versus correct trials as indices of monitoring.Across both experiments, we observed that on correct trials, lexical error probability effects were present during initial stages of speech planning, while AP error probability effects emerged during speech motor preparation. In contrast, error trials showed differences from correct utterances in both early and late speech motor preparation and during articulation. These findings suggest that (a) response conflict on ultimately correct trials does not persist during articulation; (b) the timecourse of response conflict is restricted to the time window during which a given linguistic level is task-relevant (early on for response appropriateness-related variables and later for articulation-relevant variables); and (c) monitoring during the response is primarily triggered by pre-response monitoring failure. These results support that monitoring in language production is temporally distributed and rely on multiple mechanisms.

Robust Optical Physical Unclonable Function Based on Total Internal Reflection for Portable Authentication.

Physical unclonable functions (PUFs) utilize uncontrollable manufacturing randomness to yield cryptographic primitives. Currently, the fabrication of the most generally employed optical PUFs mainly depends on fluorescent, Raman, or plasmonic materials, which suffer inherent robustness issues. Herein, we construct an optical PUF with high environmental stability via total internal reflection (TIR-PUF) perturbed by randomly distributed polymer microspheres. The response image is transformed into encoded keys via an iterative binning procedure. The concentration of the polymer solution is optimized to debias the bit nonuniformity and maximize encoding capacity. The constructed TIR-PUF shows significantly high encoding capacity (2370) and markedly low total authentication error probability (1.614 × 10-23). The intra-Hamming distance is as low as 0.068, indicating the excellent readout reliability of TIR-PUF. The environmental stability of TIR-PUF has demonstrated promising results under a range of challenging conditions such as ultrasonic washing, high temperature, ultraviolet irradiation, and severe chemical environments. Moreover, the challenge-response pairs of our TIR-PUFs are demonstrated on an authentication system with low-power dissipation, lightweight components, and wireless imaging capture, rendering the possibility of portable authentication for practical applications.

Evolving photonic authentication with sustainable luminescent smart e‐tags

AbstactCounterfeiting remains a significant threat, causing economic and safety concerns. Addressing this, authentication technologies have gained traction. With the rise of the Internet of Things, authentication is crucial. Photonic Physical Unclonable Functions (PUFs) offer unique identifiers. We present low‐cost and sustainable e‐tags that may be printed virtually on any surface for authentication due to the bespoke texturization of sustainable inks of surface‐modified carbon dots. A single e‐tag provides randomized phosphorescence (or afterglow) patterns, which provide multiple layers of safety by exploiting different patterning, excitation energies, and temporal characteristics. A comprehensive case study employing photonic challenge‐response pairs, involving a sample size of up to 29 emission spectra in combination with 102 photographs taken with a smartphone, displays a low authentication probability of error (<10−11), which supports the potential of our combined approach toward the development of more robust photonic PUF systems.

Compact and efficient KEMs over NTRU lattices

The NTRU lattice is a promising candidate to construct practical cryptosystems, in particular key encapsulation mechanism (KEM), resistant to quantum computing attacks. Nevertheless, there are still some inherent obstacles to NTRU-based KEM schemes when considering integrated performance, taking security, bandwidth, error probability, and computational efficiency as a whole, that is as good as and even better than their {R,M}LWE-based counterparts. In this work, we address the challenges by presenting a new family of NTRU-based KEM schemes, denoted as CTRU and CNTR. By bridging low-dimensional lattice codes and high-dimensional NTRU-lattice-based cryptography with careful design and analysis, to the best of our knowledge, CTRU and CNTR are the first NTRU-based KEM schemes featuring scalable ciphertext compression via only one single ciphertext polynomial, and are the first that can outperform {R,M}LWE-based KEM schemes in terms of integrated performance. For instance, when compared to Kyber, the only KEM scheme currently standardized by NIST, our recommended parameter set CNTR-768 exhibits approximately 12% smaller ciphertext size, when its security is strengthened by (8,7) bits for classical and quantum security respectively, with a significantly lower error probability (2−230 for CNTR-768 vs. 2−164 for Kyber-768). In terms of the state-of-the-art AVX2 implementation of Kyber-768, CNTR-768,achieves a speedup of 2.7X in KeyGen, 3.3X in Encaps, and 1.6X in Decaps, respectively. When compared to the NIST Round 3 finalist NTRU-HRSS, CNTR-768,features 15% smaller ciphertext size, coupled with an improvement of (55,49) bits for classical and quantum security respectively. As for the AVX2 implementation, CNTR-768,outperforms NTRU-HRSS by 26X in KeyGen, 3.0X in Encaps, and 2.2X in Decaps, respectively. Along the way, we develop new techniques for more accurate error probability analysis, and a unified number theoretic transform (NTT) implementation for multiple parameter sets, which may be of independent interest.

Определение предела Шеннона с использованием особых свойств прикладной системы

The aim of the paper is to preserve the fundamental nature of the Shannon limit and constraints in the mathematical theory of communication and to find a compromise solution for changing the boundary of the signal-to-noise ratio, at which the probability of errors is minimized, by using the special properties of the applied system, i.e., the modulator and demodulator. In this case, the "ideality" of the applied system according to the Shannon definition is preserved and supplemented by special properties obtained in our time, from the application of the Lyapunov characteristic function and the action of the stochastic resonance effect discovered at the end of the 20th century. Applied system one and applied system two are considered using the results of previous research and the special properties of the systems are identified. The applied systems are adapted to demodulators of signals with varying Lyapunov characteristic function. A new bound on the fundamental Shannon limit is computed. Taking into account the properties of the system, it is proposed to correct the Shannon limit to the value of −17.7 dB when transmitting messages with low error probability.

Discriminating mixed qubit states with collective measurements

It is a central fact in quantum mechanics that non-orthogonal states cannot be distinguished perfectly. In general, the optimal measurement for distinguishing such states is a collective measurement. However, to the best our knowledge, collective measurements have not been used to enhance quantum state discrimination to date. One of the main reasons for this is the fact that, in the usual state discrimination setting with equal prior probabilities, at least three copies of a quantum state are required to be measured collectively to outperform separable measurements. This is very challenging experimentally. In this work, by considering unequal prior probabilities, we propose and experimentally demonstrate a protocol for distinguishing two copies of single qubit states using collective measurements which achieves a lower probability of error than can be achieved by any non-entangling measurement. Additionally, we implemented collective measurements on three and four copies of the unknown state and found they performed poorly.

Optimized receiver design for entanglement-assisted communication using BPSK.

The use of pre-shared entanglement in entanglement-assisted communication offers a superior alternative to classical communication, especially in the photon-starved regime and highly noisy environments. In this paper, we analyze the performance of several low-complexity receivers that use optical parametric amplifiers. The simulations demonstrate that receivers employing an entanglement-assisted scheme with phase-shift-keying modulation can outperform classical capacities. We present a 2x2 optical hybrid receiver for entanglement-assisted communication and show that it has a roughly 10% lower error probability compared to previously proposed optical parametric amplifier-based receivers for more than 10 modes. However, the capacity of the optical parametric amplifier-based receiver exceeds the Holevo capacity and the capacities of the optical phase conjugate receiver and 2x2 optical hybrid receiver in the case of a single mode. The numerical findings indicate that surpassing the Holevo and Homodyne capacities does not require a large number of signal-idler modes. Furthermore, we find that using unequal priors for BPSK provides roughly three times the information rate advantage over equal priors.

Cortico-Cerebellar Monitoring of Speech Sequence Production.

In a functional magnetic resonance imaging study, we examined speech error monitoring in a cortico-cerebellar network for two contrasts: (a) correct trials with high versus low articulatory error probability and (b) overtly committed errors versus correct trials. Engagement of the cognitive cerebellar region Crus I in both contrasts suggests that this region is involved in overarching performance monitoring. The activation of cerebellar motor regions (superior medial cerebellum, lobules VI and VIII) indicates the additional presence of a sensorimotor driven implementation of control. The combined pattern of pre-supplementary motor area (active across contrasts) and anterior cingulate cortex (only active in the contrast involving overt errors) activations suggests sensorimotor driven feedback monitoring in the medial frontal cortex, making use of proprioception and auditory feedback through overt errors. Differential temporal and parietal cortex activation across contrasts indicates involvement beyond sensorimotor driven feedback in line with speech production models that link these regions to auditory target processing and internal modeling-like mechanisms. These results highlight the presence of multiple, possibly hierarchically interdependent, mechanisms that support the optimizing of speech production.

An ensemble learning approach for resampling forgery detection using Markov process

Resampling is an extremely well-known technique performed for Image forgery detection. It includes the changes in the content of a picture in terms of rotation, stretching/zooming, and shrinking, to frame a forged picture that is a localized forgery in comparison to the original picture. With the wrong intention, resampling forgery has been increased day by day, and its negative impact has been increased in criminology, law enforcement, forensics, research etc. Accordingly, the interest in the algorithm of image resampling forgery detection is significantly developed in image forensics. In this paper, a novel image resampling forgery detection technique has been proposed. In the proposed technique, two types of Markov feature with spatial and Discrete Cosine Transform domains have been extracted to recognize the resampling operation. The spatial domain gives the information for the distribution of the pixels and DCT gives the edge information. Further, these Markov features are consolidated. Due to high dimensionality hard thresholding technique is used for reducing the dimensionality. Then, these Markov features are applied to the set of models of different classifiers. With the utilization of classifiers, weighted majority voting values have been calculated during the ensemble classification. Unlike the other techniques, these weighted voting boundaries have been consequently balanced during the training process until the best accuracy has been obtained. However, it is very difficult to get best accuracy so for getting best accuracy this research needs to do lots of iterations and trained the dataset. For the comparative study very few research has been found for this resampling forgery technique with different interpolation techniques and classifier. Still, comparison has been done with some latest research work. The comparative analysis shows that the proposed ensemble learning-based algorithm provides the best outcomes with the accuracy of 99.12% for bicubic, 98.89% for bilinear, and 98.23% for lanczos3 kernel with considerably less complexity and high speed in comparison to prior techniques which are using single support vector machine for classification. Moreover, the proposed algorithm also detects a very low probability of error of 0.44% and detects the type of interpolation kernel, size of the forgery, and the type of resampling, whether it is up sampling and down sampling, using Graphical User Interface which has not been detected previously with multiple forgery detection.

Maximal (v, k, 2, 1) Optical Orthogonal Codes with k = 6 and 7 and Small Lengths

Optical orthogonal codes (OOCs) are used in optical code division multiple access systems to allow a large number of users to communicate simultaneously with a low error probability. The number of simultaneous users is at most as big as the number of codewords of such a code. We consider (v,k,2,1)-OOCs, namely OOCs with length v, weight k, auto-correlation 2, and cross-correlation 1. An upper bound B0(v,k,2,1) on the maximal number of codewords of such an OOC was derived in 1995. The number of codes that meet this bound, however, is very small. For k≤5, the (v,k,2,1)-OOCs have already been thoroughly studied by many authors, and new upper bounds were derived for (v,4,2,1) in 2011, and for (v,5,2,1) in 2012. In the present paper, we determine constructively the maximal size of (v,6,2,1)- and (v,7,2,1)-OOCs for v≤165 and v≤153, respectively. Using the types of the possible codewords, we calculate an upper bound B1(v,k,2,1)≤B0(v,k,2,1) on the code size of (v,6,2,1)- and (v,7,2,1)-OOCs for each length v≤720 and v≤340, respectively.

Quantum-enhanced receiver for quadrature phase shift keying using conjugated homodyne detection

Coherent detection (homodyne detection/heterodyne detection) and single photon detection are commonly applied in classical communication and quantum communication/receiver respectively. In this paper, we firstly propose a quantum-enhanced receiver based on conjugated homodyne detection, which is consisted of two classical balanced homodyne detectors, for discrimination among quadrature-phase-modulated weak coherent states. Our detection part works as a photon counter in every single-shot measurement during the process of detection and feedback, which could help our quantum-enhanced receiver surpass the standard quantum limit when an optimized detection threshold τ from 0.05 to 0.3 is selected. Moreover, our proposed quantum-enhanced receiver with conjugated homodyne detectors can obtain the same low error probability as conventional quantum-enhanced receivers (with its superconducting nanowire single photon detectors’ detection efficiency 60% for pulse separation M=8 and 70% for M=12 under the same adaptive feedback countermeasures.) Meanwhile, our scheme of the quantum-enhanced receiver gains its incomparable advantages on room-temperature working condition and low cost due to the application of conjugated homodyne detectors. As far as we concerned, this is the first time to explore the performance of the quantum-enhanced receiver with commercial homodyne detectors. And the analysis in this paper may pave the way to reduce the cost of the quantum-enhanced receiver and make it more adaptable for long-distance optical communication systems.

Nurse-Task Matching Decision Support System Based on FSPC-HEART Method to Prevent Human Errors for Sustainable Healthcare

To increase the levels of sustainability of service quality as well as to ensure satisfaction and assurance of patients in the health sector, minimizing the probability of making mistakes nurses is of great importance. The extent of this probability is considerably affected by task types, physical conditions of the working environment, workload, and working conditions. Moreover, the physical and mental characteristics of nurses also have a colossal influence on this probability. It is also possible to increase the sustainability of health services by matching nurses appropriately to a specific task according to related risk levels, and by balancing their workload accordingly. This study proposes FSPC-HEART method in that purpose, as a new type of human error reduction and assessment technique (HEART) application based on fuzzy step-wise weight assessment ratio analysis and principal component analysis methods. Unlike the methods in the literature, this new method offers a person-specific proactive error prevention approach. With FSPC-HEART, the probability of each nurse to make a mistake, that is, the human error probability (HEP) values are calculated separately for each task. Also, the combined effect of physical and mental workload factors for each employee was taken into account. In the proposed method, the effect of the subjective judgments of the decision-makers on the objectively obtained HEP values was tried to be reduced. The developed nurse-task matching decision support system enables the FSPC-HEART method to be easily used by decision-makers, and to assign employees to tasks with low error probabilities.

Throughput and error probability improvement in downlink communication based on MIMO‐NOMA using reconfigurable intelligent surface (RIS)

SummaryThe user far from the base station (BS) can decode the signal via direct transmission or a relay network, but it cannot achieve higher throughput because of its high error probability. The MIMO‐NOMA‐Relay networks are replaced by MIMO‐NOMA‐RIS to achieve higher throughput in the proposal. The MIMO‐NOMA‐RIS sends a superimposed signal to the receiver using a multiple RIS beam scanning algorithm. They can improve the strength of the signal as well as improve the throughput and reduce the probability of error. As more reflecting surface area increases, the strength of the beam‐forming signal also increases, which means that the user could receive more signal strength than the direct transmission, amplify and forward (A&F) relay network. In multi‐user case, use the max–min power control algorithm (MMPCA) to allocate optimum power to a weaker channel strength user and achieve an optimum result. Method 1 used a single user with RIS to find the system equations. In both single‐user and multi‐user scenarios, we use MIMO‐NOMA‐RIS. Our proposed method's complexity is low because of its simplicity. In our proposed method, the MMPCA is used for optimum power allocation, and the resulting non‐convex function is converted into a convex one by using successive convex approximations. The result section shows that the RIS achieved the highest throughput and the lowest probability of error.

A Framework for Reliability Analysis of Combinational Circuits Using Approximate Bayesian Inference

A commonly used approach to compute the error rate at the primary outputs (POs) of a circuit is to compare the fault-free and faulty copies of the circuit using XOR gates. This model results in poor accuracies with nonsampling-based methods for reliability estimation. An alternative is to use a single copy of the circuit with a four-valued representation for each net corresponding to the correct and incorrect signals. One problem in this formulation is the accurate propagation of associated probabilities. We use the framework of Bayesian inference (BI) to address this issue. We derive the conditional probability distribution (CPD) corresponding to the four-valued signals and find the output error rate using various approximate BI techniques. With our formulation, we demonstrate that the output error rate scales with the gate error probabilities. It is guaranteed to be zero when the gate error probability is zero, provided approximate BI algorithms based on sum-product belief propagation (BP) are used. Although inaccuracies increase at very low gate error probabilities, it is able to capture the relative reliability of outputs with respect to each other. We also propose a new method for finding the overall circuit error rate as the partition function for a fixed state of POs. This method provides a significant improvement in accuracy when compared with the existing method using OR gates.

A Novel Two-Parametric ISI-Free Pulse Based on Inverse Hyperbolic Functions

In this letter, a new two-parametric pulse that outperforms state-of-the-art pulses with the best performance in terms of BER is proposed, which is studied in terms of frequency and time domain characteristics. Specifically, the pulse is based on the inverse-hyperbolic functions acsch and asech which are used for the first time for an ISI-free pulse, and its design depends only on the roll-off factor and the timing jitter parameter (i.e., two-parametric design). Finally, the proposed pulse is shown to outperform most of the well-known pulses reported in the literature, since it presents lower error probability, smaller maximum distortion and wider eye-diagram.

Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput. The fine-k-nearest neighbor algorithm (FKNN), employed in this paper, estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands. The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules, validating the effectiveness of the proposed scheme. Furthermore, the global decision made at the fusion center (FC) based on the modified sensing samples, results low energy consumption, higher throughput, and improved detection with low error probabilities.

Research and Application of Health Code Recognition Based on Paddle OCR under the Background of Epidemic Prevention and Control

Normalization of epidemic prevention and control,in order to solve the problems of low efficiency and high error probability of manual review of health code and trip card pictures by epidemic prevention personnel, an automatic health code recognition method based on paddeocr is proposed. This method uses the open source paddleocr technology for image recognition, and uses the target text location algorithm to output the required text. Practice shows that this method has good recognition effect and high accuracy. Applying this method to the campus epidemic prevention and control system can greatly improve the audit efficiency.