Error Rate Reduction Research Articles

Process Variation Based Electrical Model of STT-Assisted VCMA-MTJ and Its Application in NV-FA

Fast switching speed, low power consumption, and good stability are some of the important properties of spin transfer torque assisted voltage controlled magnetic anisotropy magnetic tunnel junction (STT-assisted VCMA-MTJ) which makes the non-volatile full adder (NV-FA) based on it attractive for Internet of Things. However, the effects of process variations on the performances of STT-assisted VCMA-MTJ and NV-FA will be more and more obvious with the downscaling of STT-assisted VCMA-MTJ and the improvement of chip integration. In this paper, a more accurate electrical model of STT-assisted VCMA-MTJ is established on the basis of the magnetization dynamics and the process variations in film growth process and etching process. In particular, the write voltage is reduced to 0.7 V as the film thickness is reduced to 0.9 nm. The effects of free layer thickness variation (γtf) and oxide layer thickness variation (γtox) on the state switching as well as the effect of tunnel magnetoresistance ratio variation (β) on the sensing margin (SM) are studied in detail. Considering that the above process variations follow Gaussian distribution, Monte Carlo simulation is used to study the effects of the process variations on the writing and output operations of NV-FA. The result shows that the state of STT-assisted VCMA-MTJ can be switched under -0.3%≤γtf≤6% or -23%≤γtox≤0.2%. SM is reduced by 16.0% with β increases from 0 to 30%. The error rates of writing ‘0’ in the NV-FA can be reduced by increasing Vb1 or increasing positive Vb2. The error rates of writing ‘1’ can be reduced by increasing Vb1 or decreasing negative Vb2. The reduction of the output error rates can be realized effectively by increasing the driving voltage (Vdd).

Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones.

This paper focuses on the hardware components of smartphones, namely, the use of displays and cameras in mobile devices as transmitters and receivers to establish a near-field multiple-input-multiple-output (MIMO) visible light communication (VLC) system. Based on the relationship between the grayscale values of transmitted and received signals, the physical channel responses are detected and approximated with a high-order regression to obtain the channel gain. With the constraint of bit numbers in the MIMO VLC system, an integer-type water-filling scheme was designed for bit allocation to improve transmission efficiency. The physical examinations show that bit error rate (BER) reduction can be 26.4% with Gaussian noise of 30 dB and detected channel gain compared with the equal bit allocation. The optimization of the simulation was confirmed with the bit assignments in real cases.

Impact of implementing the aseptic compounding management system, Medcura, on internal error rates within an oncology pharmacy aseptic unit: a mixed methods evaluation

BackgroundAs cancer survivorship improves, pressure on oncology services to provide safe, timely treatments increases. Traditional manual compounding processes are error prone, putting patients at risk. Additionally, errors have a detrimental...

Neural Network-Based Histogram Transpose and Naor Arnold Reversible Data Hiding for Data Security

Data security concerns safeguarding the data from unauthorized users. Numerous research works have been conducted using Reversible Data Hiding (RDH) technique, but not been found to be fooproof. In this paper, a novel reversible data hiding method, based on an Artificial Neural Network, called Histogram Transpose and Naor Pseduorandom Arnold (HT-NPA) data security system for handling health care reports and securely transferring patient information, is proposed to evaluate the embedding capacity, improve the visual quality of the recovered image and ensure security via duplex scramble. This entire process is performed by means of an Artificial Neural Network (i.e. multilayer perceptron). The experimental results reveal that the HT-NPA performs better with 29%, 16%, 19%, and 9% improvement in embedding rate, PSNR, SSIM, entropy, and NPCR, and 26% reduction of bit error rate, respectively for retrieved medical images compared to existing works.

Explicit attacks on the Bennett-Brassard 1984 protocol with partially distinguishable photons

Distinguishability of photons in non-operational degrees of freedom compromises unconditional security of quantum key distribution since an eavesdropper can improve attack strategies by exploiting this distinguishability. However, the optimal eavesdropping strategies in the presence of light source side channels are not known. Here we provide several explicit attack strategies on the BB84 protocol with partially distinguishable photons. In particular, we consider the phase-covariant cloning attack, which is optimal in the absence of side channels, and show that there are even better strategies in the presence of side channels. The improved strategies exploit a measurement of the side channel state followed by an attack on the signal photon. Our results explicitly demonstrate reduction of the critical error rate, and set an upper bound on the practical secret key rate.

Jacobians of single-scattering optical properties of super-spheroids computed using neural networks.

In atmospheric aerosol remote sensing and data assimilation studies, the Jacobians of the optical properties of non-spherical aerosol particles are required. Specifically, the partial derivatives of the extinction efficiency factor, single-scattering albedo, asymmetry factor, and scattering matrix should be obtained with respect to microphysical parameters, such as complex refractive indices, shape parameters and size parameters. When a look-up table (LUT) of optical properties of particles is available, the Jacobians traditionally can be calculated using the finite difference method (FDM), but the accuracy of the process depends on the resolution of microphysical parameters. In this paper, a deep learning scheme was proposed for computing Jacobians of the optical properties of super-spheroids, which is a flexible model of non-spherical atmospheric particles. Using the neural networks (NN), the error of the Jacobians in the FDM can be reduced by more than 60%, and the error reduction rate of the Jacobians of the scattering matrix elements can be more than 90%. We also tested the efficiency of the NN for computing the Jacobians. The computation takes 30 seconds for one million samples on a host with an NVIDIA GeForce RTX 3070 GPU. The accuracy and efficiency of the present NN scheme proves it is promising for applications in remote sensing and data assimilation studies.

An intrusion identification and prevention for cloud computing: From the perspective of deep learning

As the Internet industry has connected the globe using advancements in computer network from LAN to Cloud Infrastructure, Fashions and Developments in Internet of Things (IoT), Cloud Infrastructures, E-Commerce, Banking and Healthcare are allowing unimaginable access to devastating attacks.Intrusion detection is a critical component of information security, and the essential technique is the ability to properly recognise diverse network assaults. In this article, the methodology of an intrusion detection scheme utilising deep learning technique which is based on Fuzzy Min Max Neural Networks-Based Intrusion Detection System (FMMNN-IDS) has been proposed. The fuzzy min-max learning algorithm, an expansion-contraction method that can learn nonlinear class boundaries in a single pass through the data and gives the capacity to incorporate new and improve current classes without retraining, is used to identify the min-max points.Furthermore, the article investigated the model's performance in terms of binary and multiclass classification, as well as the count of learning rate and neurons affect the model's performance. The performance of FMMNN-IDS is compared with state-of-art approaches determined in the literatures. The FMMNN-IDS model increases accuracy rate of intrusion detection, achieves better intrusion detection rate and reduced error rate.

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

Abstract Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real‐time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change.

(Retracted) Research on computer network security evaluation based on image recognition and neural network

In today’s social development, the level of informatization has been greatly improved. Computer networks are now used in all aspects of society, especially with the popularity of mobile phones and computers. Basically, a mobile phone is a computer that stores all the information of a person, so it is indispensable for carrying out research on computer network security evaluation. The purpose of this article is to examine the growing state of information security threats in computer networks. Neural network technology, which is considered secure, has high flexibility, fast access to information, strong logic, and low failure. Toward creating a low-cost network topology, we used a low-cost network risk assessment method to assess the security of computer networks based on neural networks, and we evaluated existing risks that are appropriate for future computer security research. The results of the study show that neural networks using the Bayesian network method have a significant impact on network security. Considering the problems that can occur with information security risks, computer security systems based on neural networks have a 30% reduction in information error rate compared with traditional methods, and computer network systems based on neural networks have better learning performance to safeguard network security. Improving the security of information requires further research and evaluation of computer network security based on neural networks.

Deep Learning based DFWF Model for Audio Spoofing Attack Detection

One of the biggest threats in the speaker verification system is that of fake audio attacks. Over the years several detection approaches have been introduced that were designed to provide efficient and spoof-proof data-specific scenarios. However, the speaker verification system is still exposed to fake audio threats. Hence to address this issue, several authors have proposed methodologies to retrain and finetune the input data. The drawback with retraining and fine-tuning is that retraining requires high computation resources and time while fine-tuning results in degradation of performance. Moreover, in certain situations, the previous data becomes unavailable and cannot be accessed immediately. In this paper, we have proposed a solution that detects fake without continual-learning based methods and fake detection without forgetting in order to develop a new model which is capable of detecting spoofing attacks in an incremental fashion. In order to retain original model memory, knowledge distillation loss is introduced. In several scenarios, the distribution of genuine voice is said to be very consistent. In several scenarios, there is consistency in distribution of genuine voice hence a similarity loss is embedded additionally to perform a positive sample alignment. The output of the proposed work indicates an error rate reduction of up to 80% as observed and recorded.

Shaping of converter interference for error rate reduction in PLC based smart metering systems

Smart metering systems often utilize Power Line Communication (PLC) for bidirectional data exchange between smart energy meters and data concentrators. Electromagnetic interference generated by power electronic converters have been found as a main cause of communication errors in PLC data transmission. In this paper novel, modified pseudo-random pulse width modulation technique was developed to significantly reduce PLC frame error rate caused by interference generated by power electronic converters. Moreover, cyber–physical system has been presented as a case study providing high flexibility in controlling the switching rate of change of the modulation. As a general contribution, in the paper has been proposed the concept of electromagnetic interference noise shaping to broaden the possibility of preventing and mitigating non-compatibility scenarios between power electronic converters and telecommunications devices. The experimental results might be useful for system integrators involved in assurance of electromagnetic compatibility of power electronic interfaces and PLC based smart metering systems.

An intelligent deep convolutional network based COVID-19 detection from chest X-rays

Coronavirus disease-2019 (COVID-19) seems to be a fast spreading contagious illness that affects both humans and animals. This catastrophic deadly virus has an impact on people's daily lives, their wellbeing, and a nation's economy. According to a clinical research of COVID-19 affected patients, these individuals have been most commonly infected with a lung illness after coming into touch with the virus. A chest X-ray (also known as radiography) or a chest CT scan seems to be more efficient imaging techniques for detecting lung issues. Nonetheless, when compared to a chest CT, a significant chest X-ray remains a less expensive procedure. Thus, in this research, a novel Deep convolution neural network algorithm is presented to detect the COVID-19 from X-ray image. Moreover, to enhance diagnostics sensitivity and reduce error rate, a hybrid Two-step-AS clustering approach with Ensemble Bootstrap aggregating training and Multiple NN methods used. In addition, TSEBANN model has been employed to explore the qualification procedure effects. The proposed algorithm was trained before and after classification while compared to traditional Convolutional Neural Network (CNN). After, the process of pre-processing and feature extraction, the CNN strategy was adopted as an identification approach to categorize the information depending on Chest X-ray recognition. These examples were then classified using the CNN classification technique. The testing was conducted on the COVID-19 X-ray dataset, and the cross-validation approach was used to determine the model’s validity. The result indicated that a CNN system classification has attained an accuracy of 98.062 %.

Region Convolutional Neural Network for Brain Tumor Segmentation.

Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.

English Language Learning Pattern Matching Based on Distributed Reinforcement Learning

The rapid development of a new generation of information technology, the promotion of network technology, and the emergence of complex and diverse requirements for control objects make the structure of language learning models more and more distributed. Distributed learning theory emphasizes the central position of learners in the learning process and the universality of learning scenes. This paper explores the significance and value of various learning modes to improve students’ learning effect. By analyzing the research data and explaining various effective language learning models, this paper aims to establish a theoretical framework of English language learning models and explore more effective language model matching schemes. This paper analyzes the adaptive multiagent, reward function, Markov model, probability function model, etc. and conducts experiments on the basis of the designed model. The linear correlation parameters of the model and the English language pattern matching efficiency are analyzed and judged on several important indicators. Because the algorithm designed in this paper has a good effect on the control of error, the error reduction rate has reached 85.6%.

Electronic prescribing systems in hospitals to improve medication safety: a multimethods research programme

Electronic prescribing systems in hospitals to improve medication safety: a multimethods research programme

On the Performance of Partial RIS Selection vs. Partial Relay Selection for Vehicular Communications

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work.

A Phonetic-Semantic Pre-Training Model for Robust Speech Recognition

Robustness is a long-standing challenge for automatic speech recognition (ASR) as the applied environment of any ASR system faces much noisier speech samples than clean training corpora. However, it is impractical to annotate every types of noisy environments. In this work, we propose a novel phonetic-semantic pre-training (PSP) framework that allows a model to effectively improve the performance of ASR against practical noisy environments via seamlessly integrating pre-training, self-supervised learning, and fine-tuning. In particular, there are three fundamental stages in PSP. First, pre-train the phone-to-word transducer (PWT) to map the generated phone sequence to the target text using only unpaired text data; second, continue training the PWT on more complex data generated from an empirical phone-perturbation heuristic, in additional to self-supervised signals by recovering the tainted phones; and third, fine-tune the resultant PWT with real world speech data. We perform experiments on two real-life datasets collected from industrial scenarios and synthetic noisy datasets, which show that the PSP effectively improves the traditional ASR pipeline with relative character error rate (CER) reductions of 28.63% and 26.38%, respectively, in two real-life datasets. It also demonstrates its robustness against synthetic highly noisy speech datasets.

Rich learning representations for human activity recognition: How to empower deep feature learning for biological time series

Deep learning versus feature engineering has drawn significant attention specifically for applications where expertly crafted features have been used for decades. Human activity recognition is no exception where statistical and motion specific features have shown potential in detecting falls and other daily activities across a wide range of datasets. This paper provides an in-depth study and comparison of two fundamentally different approaches to HAR while introducing a novel way to harness the spectral properties of biological time series in addition to temporal features. A research group at Stanford recently proposed subject agnostic features as state-of-the-art when applied to a large dataset with many participants of different ages. In this paper, we demonstrate that implicit feature learning in the latent spaces of deep learning algorithms can be powerful alternatives to using finely tuned domain-specific features for HAR. In fact, when using a spectrotemporal representation of the raw sensor data in the form of spectrograms, a standard convolutional neural network without any prior conditioning on the features, statistically significantly outperforms the prior state-of-the-art using subject agnostic features in all the different partitions of the dataset with a significant 29.8% reduction in the overall average error rate.

Hyperparameter Optimization on Ensemble Regression Tree for Lip Coloring Simulation

Technology helps us in many activities and keeps growing, so it makes activities more efficient, time-saving, using fewer resources and also information and entertainment are accessible. Machine Learning technology is the fastest-growing field in computer science that is used in many areas such as marketing, healthcare, manufacturing, information security, and transportation. One of the machine learning methods is the Ensemble of Regression Tree (ERT) which has succeeded in detecting facial features on the eyebrows, eyes, nose, and lips. However, utilization ERT method has not been found to detect specific areas such as lips only for gaining optimization. Then this research will be conducted to extract the facial feature annotation dataset from the iBUG 300W dataset with 68 facial features to 20 lip area points. The results of the extraction are reduced error rate, resources saving, lip features still detected and lip coloring simulation was successfully carried out using the configuration of hyperparameter values, tree = 4, regularization = 0.25, cascade = 8, feature pool = 500, oversampling = 40 and translation jitter = 0. From observations also discovered optimization that hard disk resource savings are 69.36%, RAM 30.8%, and CPU 3.8%; reduce the error rate by 0.058%; and increase inference speed by 39%.

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning.

The construction and operation of China's rail transit system have entered a high-speed development stage, and the rapid increase of train speed and mileage has brought greater challenges to the safety and reliability of the rail transit system. Network planning evaluation is the key to the early decision-making of urban rail transit project, which directly determines the success or failure of the whole project. How to scientifically and reasonably evaluate the urban rail transit information resource network planning has become a difficult problem for many urban planners to solve. Therefore, this paper studies the optimization of the communication resource allocation algorithm and the comprehensive evaluation of its application for urban rail transit planning. In this paper, based on CVNN structure, the network prototype is an extension of RVNN structure. In the abstract, its processing unit is composed of a pair of real-number processors that can realize certain operations. HNN is a fully connected recurrent neural network based on the idea of the energy function, which is helpful to understand the calculation mode of HNN, and the research shows that HNN can solve many combinatorial optimization problems. In addition, the combination of neural network and genetic algorithm with simulated annealing mechanism can also bring new directions for research. On the basis of experimental analysis, it can be concluded that in general, the error reduction rate of the optimization scheme designed in this paper can reach 58.6% on average. In practical application, the accuracy of the optimal bit error rate is 52.4%.