A Practical Comparison between Signature Approach and Other Existing Approaches in Error Detection over TCP

Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time.

Biomedical Event Extraction Using a New Error Detection Learning Approach Based on Neural Network

Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.

Exploiting Unlabeled Data for Neural Grammatical Error Detection

Identifying and correcting grammatical errors in the text written by non-native writers have received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVM and convolutional networks with fixed-size context window.

An advanced SEU tolerant latch based on error detection

This paper proposes a latch that can mitigate SEUs via an error detection circuit.The error detection circuit is hardened by a C-element and a stacked PMOS.In the hold state,a particle strikes the latch or the error detection circuit may cause a fault logic state of the circuit.The error detection circuit can detect the upset node in the latch and the fault output will be corrected.The upset node in the error detection circuit can be corrected by the C-element.The power dissipation and propagation delay of the proposed latch are analyzed by HSPICE simulations.The proposed latch consumes about 77.5%less energy and 33.1%less propagation delay than the triple modular redundancy（TMR）latch.Simulation results demonstrate that the proposed latch can mitigate SEU effectively.

Error detection using mode information of macroblocks for video transmission

A new method for error detection using mode information of macroblocks （MBs） is proposed. For decodable inter MBs, inter residues are calculated by adding up absolute values of received residual pixels and intra complexities are estimated by that of motion compensated reference blocks. If inter residues are larger than intra complexities by a predefined quantity, MBs are considered to be erroneous. For decodable intra MBs, the connective smoothness of the current MB with correctly decoded neighboring MBs is tested to find erroneous MBs. Combined with error concealment, the new method improves the quality of reconstructed images by about 0.5-1 dB in peak signal-noise ratio （PSNR）.

A Low Power Error Detection in the Syndrome Calculator Block for Reed-Solomon Codes: RS(204,188)

Reed-Solomon （RS） codes have been widely adopted in many modern communication systems. This paper describes a new method for error detection in the syndrome calculator block of RS decoders. The main feature of this method is to prove that it is possible to compute only a few syndrome coeffi- cients -- less than half-- to detect whether the codeword is correct. The theoretical estimate of the prob- ability that the new algorithm failed is shown to depend on the number of syndrome coefficients computed. The algorithm is tested using the RS（204,188） code with the first four coefficients. With a bit error rate of 1 ~ 104, this method reduces the power consumption by 6% compared to the basic RS（204,188） decoder. The error detection algorithm for the syndrome calculator block does not require modification of the basic hardware implementation of the syndrome coefficients computation. The algorithm significantly reduces the computation complexity of the syndrome calculator block, thus lowering the power needed.

On Error Detection of Intermediate Nodes Offour-Node Network with Small Downstream Links

We investigate the error detection ability of intermedi- ate nodes of zig-zag network with small downstream link. We show that the error detection capability of zig-zag networks in the presence of z malicious edges and 2z-1 limited links from node B to node u. Also, at last we analyze a family of networks, which shows that the upper bound of network is smaller than the bound we expected, indicating that Singleton bound is not the tightest bound in the particular condition of network. According to this result, we design corresponding encode and decode strategy to reach the bound we propose in this paper.

Multi-Level Error Detection and Concealment Algorithm to Improve Speech Quality in GSM Full Rate Speech Codecs

Digital mobile telecommunication systems, such as the global system for mobile （GSM） system, want to further improve speech communication quality without changing the channel encoders and decoders. Speech quality is most affected by residual bit errors in received speech frames. Conventional methods use binary decision strategies for error detection and concealment in frames. This paper presents a multi-level error detection and concealment algorithm for GSM full rate speech codec systems. The algorithm uses multi-source knowledge to detect and conceal speech frame errors at the frame, parameter, and even bit levels. Tests show that most corrupted frames can be appropriately concealed by this algorithm, resulting in MOS gains of more than 50% for real-world data tests.

Error detection based on MB types

This paper proposes a method of error detection based on macroblock （MB） types for video transmission. For decoded inter MBs, the absolute values of received residues are accumulated. At the same time, the intra textural complexity of the current MB is estimated by that of the motion compensated reference block. We compare the inter residue with the intra textural complexity. If the inter residue is larger than the intra textural complexity by a predefined threshold, the MB is considered to be erroneous and errors are concealed. For decoded intra MBs, the connective smoothness of the current MB with neighboring MBs is tested to find erroneous MBs. Simulation results show that the new method can remove those seriously-corrupted MBs efficiently. Combined with error concealment, the new method improves the recovered quality at the decoder by about 0.5--1 dB.

ERROR DETECTION CAPABILITY OF SHORTENED HAMMING CODES AND THEIR DUAL CODES

The undetected error probability and error detection capability of shortened Hamming codes and their dual codes are studied in this paper. We also obtain some interesting properties for the shortened Simplex codes.

Joint optimization algorithm of sensing thresholds and cooperative users in cooperative detection

Cooperative detection is an effective method to improve the spectrum sensing of Cognitive Radio (CR), and its detection performance can be improved through optimization. An optimization algorithm for cooperative detection based on "OR Rule" which can optimize the detection threshold of each user and the number of cooperative users simultaneously is proposed in this paper. The algorithm, which is based on minimizing the error detection probability, adopts partial fusion to improve the detection performance effectively. The simulation results show that the error detection probability of the proposed algorithm is lower than that of the cooperative detection algorithm with the settled threshold, and the better performance can be achieved through choosing fewer users.

Efficient Explanation and Evaluation Methodology Based on Hybrid Feature Dropout

AI-related research is conducted in various ways,but the reliability of AI prediction results is currently insufficient,so expert decisions are indispensable for tasks that require essential decision-making.XAI(eXplainable AI)is studied to improve the reliability of AI.However,each XAI methodology shows different results in the same data set and exact model.This means that XAI results must be given meaning,and a lot of noise value emerges.This paper proposes the HFD(Hybrid Feature Dropout)-based XAI and evaluation methodology.The proposed XAI methodology can mitigate shortcomings,such as incorrect feature weights and impractical feature selection.There are few XAI evaluation methods.This paper proposed four evaluation criteria that can give practical meaning.As a result of verifying with the malware data set(Data Challenge 2019),we confirmed better results than other XAI methodologies in 4 evaluation criteria.Since the efficiency of interpretation is verified with a reasonable XAI evaluation standard,The practicality of the XAI methodology will be improved.In addition,The usefulness of the XAI methodology will be demonstrated to enhance the reliability of AI,and it helps apply AI results to essential tasks that require expert decision-making.

Dynamic portfolio choice with uncertain rare‑events risk in stock and cryptocurrency markets

In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.

Verification of SEU resistance in 65 nm high-performance SRAM with dual DICE interleaving and EDAC mitigation strategies

A dual double interlocked storage cell(DICE)interleaving layout static random-access memory(SRAM)is designed and manufactured based on 65 nm bulk complementary metal oxide semiconductor technology.The single event upset(SEU)cross sections of this memory are obtained via heavy ion irradiation with a linear energy transfer(LET)value ranging from 1.7 to 83.4 MeV/(mg/cm^(2)).Experimental results show that the upset threshold(LETth)of a 4 KB block is approximately 6 MeV/(mg/cm^(2)),which is much better than that of a standard unhardened SRAM with an identical technology node.A 1 KB block has a higher LETth of 25 MeV/(mg/cm^(2))owing to the use of the error detection and correction(EDAC)code.For a Ta ion irradiation test with the highest LET value(83.4 MeV/(mg/cm^(2))),the benefit of the EDAC code is reduced significantly because the multi-bit upset proportion in the SEU is increased remarkably.Compared with normal incident ions,the memory exhibits a higher SEU sensitivity in the tilt angle irradiation test.Moreover,the SEU cross section indicates a significant dependence on the data pattern.When comprehensively considering HSPICE simulation results and the sensitive area distributions of the DICE cell,it is shown that the data pattern dependence is primarily associated with the arrangement of sensitive transistor pairs in the layout.Finally,some suggestions are provided to further improve the radiation resistance of the memory.By implementing a particular design at the layout level,the SEU tolerance of the memory is improved significantly at a low area cost.Therefore,the designed 65 nm SRAM is suitable for electronic systems operating in serious radiation environments.

Multimedia model of atrazine in plant-soil-groundwater system with a fugacity approach

The application of atrazine in China during the last ten years has led to some environmental problems. In this paper, the multimedia model of atrazine in soil-plant-groundwater system at Baiyangdian Lake area in Northern China was established using a fugacity approach, and verified with observed values. The model involved 7 environmental compartments which are air, groundwater, soil, corn roots, corn stem, corn leaf and kernel of corn. The results showed that the relative errors between calculated and observed values have a mean value of 24.7%, the highest value is 48% and the lowest value is 1.4%. All these values indicated that this multimedia model can be used to simulate the environmental fate of atrazine. Both the calculated and observed values of concentrations of atrazine in plant compartments are in the following order: in corn roots > in corn stem > in kernel of corn > in corn leaf, it exhibited a good regularity. The prediction results indicated that concentrations of atrazine in the groundwater and kernel of corn will override the limitation of 3 μg/L and 0.05 mg/kg respectively.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

Fast interactive volume rendering method for adjustable vessel segmentation visualization

Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.

Towards Specifications for Automatic Recognition Software:An Example of a User-Centred Design

This article describes a user-centred method used to design innovative pattern recognition software for technical paper documents. This kind of software can make some errors of interpretation. It will therefore be important that human operators are able to identify and correct these mistakes. The identification of errors is a difficult task because operators need to establish co-reference between the initial document and it interpretation. Moreover, users must be able to checks the interpretation without forgetting any area. This task requires the interface is easy to use. The experiments showed that the sequential display of interpretation is the most effective and that the interruptions by user reduce task duration. Moreover, queries by the system may improve error detection. This paper summarizes the main results of the research conducted in the context of this design for enhance the interface, and describes the specifications to which it gave rise.

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.