Error Detection Method Research Articles

An On-Chip Error Detection Method to Reduce the Post-Silicon Debug Time

Debug time has become a major issue in post silicon debug because of the increasingly complicated nature of circuit design. However, reducing debug time is a major challenge because of the limited size of the trace buffer used to observe internal signals in the circuit. This study proposes an on-chip error detection method to overcome this challenge. The on-chip process detects the error-suspect window using the pre-calculated golden data stored in the trace buffer. This allows the selective compaction and capture of the debug data in the trace buffer during the error-containing interval. As a result, reducing the number of debug sessions significantly reduces the total debug time. The experimental results on various debug cases show significant reductions in total debug time compared to previous work.

On Probability of Detection Lossless Concurrent Error Detection Based on Implications

In recent years, a new concurrent error detection method by using invariant relationships inside a circuit, called implications, has been proposed. Algorithms have also been developed to reduce the total number of required implications so as to minimize the incurred area overhead due to implication checking logic. This implication reduction process, however, would result in degradation on the probability of error detection ( ${P}_{{\mathrm{ detection}}}$ ) of the method. In this paper, we analyze the impact of this issue mathematically together with illustration by a real case study. Our analytical results show that just one percent degradation on ${P}_{{\mathrm{ detection}}}$ would result in millions more errors being undetected per second and thereby significant loss on reliability of the target circuit. To address this issue, we develop a new implication reduction algorithm that guarantees no loss on ${P}_{{\mathrm{ detection}}}$ . In our algorithm, the detectability of errors for each candidate implication is carefully evaluated. The evaluation results are then utilized to select the most efficient candidates for detecting all the detectable errors. We also analyze the computation and memory complexity of the proposed algorithm. The experimental results on 28 representative benchmark circuits from ISCAS’85, ISCAS’89, and ITC’99 show that the implication reduction rate of our method (92.59%) is close to that of the previous work (95.8%). Only a small number of additional implications need to be selected to guarantee no loss on ${P}_{{\mathrm{ detection}}}$ .

LZ78 압축 데이터의 구조적 패턴에 기반한 새로운 오류 검출 알고리즘

본 논문에서는 LZ78 알고리즘으로 압축된 데이터의 오류 검출 알고리즘을 제안하였다. 기존의 비트 오류를 검출하는 방법들은 송신 단에서 패리티(parity) 비트를 추가하여 전송한 후, 수신 단에서 값이 '1'인 비트의 개수를 이용하여 오류를 검출하는 방법을 사용하였다. 이러한 기존의 방법들은 오류 검출을 위하여 추가적인 비트를 사용하는데 이는 데이터의 크기를 줄이는 것을 목적으로 하는 압축 데이터에 대해서는 적합하지 않은 방법이다. 따라서 본 논문에서는 LZ78 알고리즘 기반의 압축 데이터에서 추가적인 비트를 사용하지 않고 알고리즘의 구조적인 특성을 이용하여 오류를 검출하는 방법을 제안하였다. 실험을 통하여 제안하는 알고리즘의 오류 검출율에 대한 효율이 기존의 오류 검출 알고리즘에 비해 약 1.3 배 높은 효율을 가지는 것을 보였다. In this paper, we propose a novel error detection algorithm for LZ78-compressed data. The conventional error detection method adds a certain number of parity bits in transmission, and the receiver checks the number of bits representing '1' to detect the errors. These conventional methods use additional bits resulting in increased redundancy in the compressed data which results in reduced effectiveness of the final compressed data. In this paper, we propose error detection algorithm using the structural properties of LZ78 compression without using additional bits in the compressed data. The simulation results show that the error detection ratio of the proposed algorithm is about 1.3 times better for error detection than conventional algorithms.

Can we trust Big Data? Applying philosophy of science to software

We address some of the epistemological challenges highlighted by the Critical Data Studies literature by reference to some of the key debates in the philosophy of science concerning computational modeling and simulation. We provide a brief overview of these debates focusing particularly on what Paul Humphreys calls epistemic opacity. We argue that debates in Critical Data Studies and philosophy of science have neglected the problem of error management and error detection. This is an especially important feature of the epistemology of Big Data. In “Error” section we explain the main characteristics of error detection and correction along with the relationship between error and path complexity in software. In this section we provide an overview of conventional statistical methods for error detection and review their limitations when faced with the high degree of conditionality inherent to modern software systems.

Investigation of a real-time EPID-based patient dose monitoring safety system using site-specific control limits.

PurposeThe aim of this study is to investigate the performance and limitations of a real-time transit electronic portal imaging device (EPID) dosimetry system for error detection during dynamic intensity modulated radiation therapy (IMRT) treatment delivery. Sites studied are prostate, head and neck (HN), and rectal cancer treatments.MethodsThe system compares measured cumulative transit EPID image frames with predicted cumulative image frames in real-time during treatment using a χ comparison with 4 %, 4 mm criteria. The treatment site-specific thresholds (prostate, HN and rectum IMRT) were determined using initial data collected from 137 patients (274 measured treatment fractions) and a statistical process control methodology. These thresholds were then applied to data from 15 selected patients including 5 prostate, 5 HN, and 5 rectum IMRT treatments for system evaluation and classification of error sources.ResultsClinical demonstration of real-time transit EPID dosimetry in IMRT was presented. For error simulation, the system could detect gross errors (i.e. wrong patient, wrong plan, wrong gantry angle) immediately after EPID stabilisation; 2 seconds after the start of treatment. The average rate of error detection was 7.0 % (prostate = 5.6 %, HN= 8.7 % and rectum = 6.7 %). The detected errors were classified as either clinical in origin (e.g. patient anatomical changes), or non-clinical in origin (e.g. detection system errors). Classified errors were 3.2 % clinical and 3.9 % non-clinical.ConclusionAn EPID-based real-time error detection method for treatment verification during dynamic IMRT has been developed and tested for its performance and limitations. The system is able to detect gross errors in real-time, however improvement in system robustness is required to reduce the non-clinical sources of error detection.

A hybrid ontology-based information extraction system

Information Extraction is the process of automatically obtaining knowledge from plain text. Because of the ambiguity of written natural language, Information Extraction is a difficult task. Ontology-based Information Extraction (OBIE) reduces this complexity by including contextual information in the form of a domain ontology. The ontology provides guidance to the extraction process by providing concepts and relationships about the domain. However, OBIE systems have not been widely adopted because of the difficulties in deployment and maintenance. The Ontology-based Components for Information Extraction (OBCIE) architecture has been proposed as a form to encourage the adoption of OBIE by promoting reusability through modularity. In this paper, we propose two orthogonal extensions to OBCIE that allow the construction of hybrid OBIE systems with higher extraction accuracy and a new functionality. The first extension utilizes OBCIE modularity to integrate different types of implementation into one extraction system, producing a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. The second extension is a novel ontology-based error detection mechanism. Following a heuristic approach, we can identify sentences that are logically inconsistent with the domain ontology. Because the implementation strategy for the extraction of a concept is independent of the functionality of the extraction, we can design a hybrid OBIE system with concepts utilizing different implementation strategies for extracting correct or incorrect sentences. Our evaluation shows that, in the implementation extension, our proposed method is more accurate in terms of correctness and completeness of the extraction. Moreover, our error detection method can identify incorrect statements with a high accuracy.

Disparity map enhancement in pixel based stereo matching method using distance transform

With the great success in three-dimensional (3D) movies, a lot of 3D content have been generated. Depth information is one of the important elements in 3D content generation. Stereo matching methods obtain depth information using the characteristic of binocular disparity. These methods find corresponding points between two images which have different viewpoints to calculate the disparity value. However, these methods have difficulties computing accurate disparity values in the textureless region. Smeared pixels near the edge region also make difficult for the stereo matching. In this paper, we propose a pixel based cost computation for the cross-scale stereo matching using the distance transform to improve these problems. In addition, the disparity error detection and correction methods are also proposed as a post-processing step. As a result, we obtain the enhanced disparity map which is robust to the textureless region and the edge region.

MO-FG-202-07: Real-Time EPID-Based Detection Metric For VMAT Delivery Errors

Purpose: To create and test an accurate EPID-frame-based VMAT QA metric to detect gross dose errors in real-time and to provide information about the source of error. Methods: A Swiss cheese model was created for an EPID-based real-time QA process. The system compares a treatmentplan- based reference set of EPID images with images acquired over each 2° gantry angle interval. The metric utilizes a sequence of independent consecutively executed error detection Methods: a masking technique that verifies infield radiation delivery and ensures no out-of-field radiation; output normalization checks at two different stages; global image alignment to quantify rotation, scaling and translation; standard gamma evaluation (3%, 3 mm) and pixel intensity deviation checks including and excluding high dose gradient regions. Tolerances for each test were determined. For algorithm testing, twelve different types of errors were selected to modify the original plan. Corresponding predictions for each test case were generated, which included measurement-based noise. Each test case was run multiple times (with different noise per run) to assess the ability to detect introduced errors. Results: Averaged over five test runs, 99.1% of all plan variations that resulted in patient dose errors were detected within 2° and 100% within 4° (∼1% of patient dose delivery). Including cases that led to slightly modified but clinically equivalent plans, 91.5% were detected by the system within 2°. Based on the type of method that detected the error, determination of error sources was achieved. Conclusion: An EPID-based during-treatment error detection system for VMAT deliveries was successfully designed and tested. The system utilizes a sequence of methods to identify and prevent gross treatment delivery errors. The system was inspected for robustness with realistic noise variations, demonstrating that it has the potential to detect a large majority of errors in real-time and indicate the error source. J. V. Siebers receives funding support from Varian Medical Systems.

Design and implementation of an error detection and correction method compatible with MODBUS-RTU by means of systematic codes

The design and implementation of an error detection and correction scheme in a MODBUS-RTU network based on the REED-SOLOMON code is presented. The transmission of the parity bits is made respecting the parameters of the MODBUS protocol, so the bus can be used by common devices without the error detection/correction scheme. The error detection–correction is performed by means of specialized repeating devices in the receiver and transmitter. This method respects the parameters of the MODBUS-RTU protocol, so the bus enhanced with retransmitter devices can be used along with common devices. Finally, the implementation of an experimental network is described and the practical results obtained are given.

An effective toolkit for the interpolation and gross error detection of GPS time series

As data gaps and gross errors inevitably exist in GPS time series, robust detection and interpolation procedures are needed to obtain a uniform time series for various geospatial studies and applications. The use of traditional methods for this purpose is usually based on some improper assumptions, which are not derived based on the real properties of the data. Moreover, different interpolation methods may need to be investigated for interpolation for various gaps with different types and amount of missing data. These make the interpolation for missing data not easy. To address the issue of mentioned above, in this study, a data-analysis method named singular spectrum analysis (SSA) for missing data is assessed for reconstructing a reliable model from unevenly sampled time series without the need for any a priori knowledge of the time series data. In this method, the interpolation and detection of gross errors are carried out in one go along with the reliable reconstructed model. Both simulation data and real GPS data testing results showed that this was an efficient method for interpolation and gross error detection.

ADEPT, a dynamic next generation sequencing data error-detection program with trimming.

BackgroundIllumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery.ResultsIn this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the true positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads.ConclusionsADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0967-z) contains supplementary material, which is available to authorized users.

가변 프레임 구조를 지원하는 국경 감시/정찰용 통신 단말 및 프레임 구조 가변 방법

This paper is about the implementation of the OFDM-based border guard/reconnaissance communication terminal systems. We have implemented Real-time Dynamic DL/UL symbol rate control function using the DL-MAP message, and proposed error detection method caused by malfunctioning and timing optimization method. The proposed scheme detects the variable rate symbol decoding timing without increasing additional physical layer logic, and also provides a wide variety of DL/UL data transfer rate. Furthermore, the proposed scheme applies to the current border guard/reconnaissance equipment and confirms a operation performance through field tests and demonstration at home and abroad.

Matrix Code Based Error Correction for LUT Based Cyclic Redundancy Check

Communication is one of the vast and rapidly growing fields of engineering. Increasing the efficiency of communication by overcoming the external electromagnetic sources and noise is a challenging task. Various error detection and correction methods are introduced to reduce the loss of data while transmission. This paper proposes a novel method which use Cyclic Redundancy Check(CRC) for the error detection and the generated CRC error is corrected by using Hybrid Matrix Code(HMC) and they are coded in verilog HDL and simulated using Xilinx ISE Design suite 14.2. This proposed method can provide maximum error detection and correction capability with reduction in delay.

Computational Technique for Detecting Errors in Network-Level Pavement Condition Data

Errors in pavement condition data can affect not only the assessment of current and predicted performance of the network but also the quality of pavement maintenance and rehabilitation plans. The large size of pavement condition databases makes manual error detection methods difficult to implement. This paper provides a new computational technique for detecting errors in network-level pavement condition data sets. The technique integrates conventional statistical methods and heuristics. The statistical methods are used to identify outliers in uniform performance families, and the heuristics are used for delineating potential errors from extreme, yet valid, behavior within these outliers. The new technique was validated with field data from Texas on pavement cracking. Of 9,166 audited pavement sections, 681 sections having potentially erroneous cracking data were detected. These potential errors are randomly distributed across the Texas roadway network and are not related to a specific geographic region, district, or climatic zone. Pavement engineers can use the presented technique to identify potential errors in large pavement condition data sets and to design more effective programs that ensure the quality of these data.

Adaptive Error Detection Method for P300-based Spelling Using Riemannian Geometry

Brain-Computer Interface (BCI) systems have be-come one of the valuable research area of ML (Machine Learning) and AI based techniques have brought significant change in traditional diagnostic systems of medical diagnosis. Specially; Electroencephalogram (EEG), which is measured electrical ac-tivity of the brain and ionic current in neurons is result of these activities. A brain-computer interface (BCI) system uses these EEG signals to facilitate humans in different ways. P300 signal is one of the most important and vastly studied EEG phenomenon that has been studied in Brain Computer Interface domain. For instance, P300 signal can be used in BCI to translate the subject’s intention from mere thoughts using brain waves into actual commands, which can eventually be used to control different electro mechanical devices and artificial human body parts. Since low Signal-to-Noise-Ratio (SNR) in P300 is one of the major challenge because concurrently ongoing heterogeneous activities and artifacts of brain creates lots of challenges for doctors to understand the human intentions. In order to address above stated challenge this research proposes a system so called Adaptive Error Detection method for P300-Based Spelling using Riemannian Geometry, the system comprises of three main steps, in first step raw signal is cleaned by preprocessing. In second step most relevant features are extracted using xDAWN spatial filtering along with covariance matrices for handling high dimensional data and in final step elastic net classification algorithm is applied after converting from Riemannian manifold to Euclidean space using tangent space mapping. Results obtained by proposed method are comparable to state-of-the-art methods, as they decrease time drastically; as results suggest six times decrease in time and perform better during the inter-session and inter-subject variability.

Development of an Integrated Micro-Environmental Monitoring System for Construction Sites

This paper demonstrates the design and development of an Integrated micro-Environmental Monitoring System (IEMS), which aims to provide an effective platform for micro-environmental monitoring in construction sites. The system mainly comprises three parts: sensing device, server platform and Android application. The monitoring sensing device is equipped with an Arduino microprocessor, wireless communication modules and environmental sensors including humidity, temperature, dust intensity, UV radiation, noise index sensors. In addition, a correction algorithm and a random error detection method were developed and integrated in the processing engine running at the background server platform. Multiple sets of monitoring sensing devices were deployed in a construction site for evaluation. This system is the first ever developed and used system in construction sites to monitor micro-environmental conditions for improving safety and health assessment. This system can also be modified for other applications (e.g. in bus terminal, shopping mall, factory, as well as in household apartment).

Error Detection in 2-bit and 4-bit Multiplier using Parity Predictor Circuit in QCA

Error detection is the detection of errors caused by noise or other impairments during the transmission of signal from transmitter to receiver. Logic design errors may occur during simulation and synthesis due to increase in the complexity of CMOS and VLSI circuits. Error detection method can be either systematic or non-systematic. In systematic method, the transmitter sends the original data unit, and a fixed number of check bits or Parity data is been attached to it, which are derived from the same input data unit. In this work, we describe a method of error detection in 4-bit multiplier with parity predictor circuit in QCA tool. 4-bit multiplier is used as a logic in which we detect error according to its input data. The outputs of logic used and the parity predictor are then compared using comparator. If the values do not match, error has occurred. The technique we used is Concurrent error detection using parity predictor circuit.

Low cost design of microprocessor EDAC circuit

An optimization method of error detection and correction (EDAC) circuit design is proposed. The method involves selecting or constructing EDAC codes of low cost hardware, associated with operation scheduling implementation based on 2-input XOR gates structure, and two actions for reducing hardware cells, which can reduce the delay penalties and area costs of the EDAC circuit effectively. The 32-bit EDAC circuit hardware implementation is selected to make a prototype, based on the 180 nm process. The delay penalties and area costs of the EDAC circuit are evaluated. Results show that the time penalty and area cost of the EDAC circuitries are affected with different parity-check matrices and different hardware implementation for the EDAC codes with the same capability of correction and detection code. This method can be used as a guide for low-cost radiation-hardened microprocessor EDAC circuit design and for more advanced technologies.

An automatic 3D CAD model errors detection method of aircraft structural part for NC machining

Abstract Feature-based NC machining, which requires high quality of 3D CAD model, is widely used in machining aircraft structural part. However, there has been little research on how to automatically detect the CAD model errors. As a result, the user has to manually check the errors with great effort before NC programming. This paper proposes an automatic CAD model errors detection approach for aircraft structural part. First, the base faces are identified based on the reference directions corresponding to machining coordinate systems. Then, the CAD models are partitioned into multiple local regions based on the base faces. Finally, the CAD model error types are evaluated based on the heuristic rules. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach. Highlights The 3D CAD model of an aircraft structural part is partitioned into multiple local regions in terms of the base faces. The fillet association level graph is proposed to represent the local region for exploring the CAD model errors. An automatic CAD model error detection method based on heuristics rule is proposed.

Method for identifying errors in chemical process development and design base on accidents knowledge

It has been claimed that the high accident rate in the chemical process industry is due to poor dissemination of accident knowledge that affects directly the level of learning from accidents. In response to this situation, this paper utilized past accident knowledge as a basis to develop a safety oriented design tool whereby the accident information were directly disseminated into plant design. The method was developed based on our previous accident analysis of design error in which the common design errors were ranked in accordance to their frequency and its origins during normal plant design project. Based on the design error ranking and its origin at a specific design phases, a method for design error detection is proposed. The method is expected to be able to identify the possible design error and its causes throughout chemical process development and design. The main objective is to trigger safe design thinking at the specific design phases so that appropriate action for risk reduction could be timely implemented. The Bhopal and BP Texas tragedies are used as case studies to test and verify the method. The proposed method can detect up to 74% of design errors.