Error Detection Mechanisms Research Articles

Implementation and evaluation of 2D SEC-DED forward error correction scheme in wireless sensor networks

Numerous applications in wireless sensor networks (WSNs) require collecting data without loss during transmission, long-term sensing, and long system lifetime. Achieving reliable data transfer and long system lifetime is difficult because, from one hand, the wireless transfer is error prone, and, on the other, sensor node (SN), as battery powered device, is energy limited. By using some power-aware techniques, such as duty-cycling and power-gating, it is possible to reduce the energy consumption at an acceptable level. The requirements for higher level of reliability during wireless data transfer have increased the use of error correcting codes (ECCs). Codes represent an effective means of providing protection against injection of single-/double-/multiple bit errors over noisy communication channel. The two basic mechanisms to recover erroneous packets in any network are Automatic Repeat Request (ARQ), and Forward Error Correction (FEC). As energy consumption is a major issue in concern in WSN, packet retransmission is not an option and FEC would be preferred over ARQ. In this paper, an efficient scheme, based on two-dimensional (rectangular) block ECC code, referred as Two Dimensional Single Error Correction and Double Error Detection (2D SEC-DED), has been developed. By using 2D SEC-DED encoding all single-bit and 99.9% of double−/multiple-bit errors, within transferred packets, are recovered. In this way, the number of retransmissions, when WSN operates in harsh environmental (bit error rate (BER), BER>10−4) is decreased, what means that not only energy saving but also extension of the transmit range (transmission distance between the transmitter and receiver), is achieved. As illustration, for indoor environment (the path loss exponent, also known as propagation constant or space loss factor, α=4) at the target BER of 5·10−4, the proposed encoding scheme is able to improve the transmission distance by about 18m or the received signal strength (RSSI) by about 8.5dBm compared to WSN without error correction (WSN which use Cycle Redundancy Check (CRC) encoding as error detection mechanism).

Improved Lempel-Ziv-Welch’s Error Detection and Correction Scheme using Redundant Residue Number System (RRNS)

The greatest difficulty of compressing data is the assurance of the security, integrity, and accuracy of the data in storage in volatile media or transmission in network communication channels. Various methods have been proposed for dealing with the accuracy and consistency of compressed and encrypted data using error detection and correction mechanisms. The Redundant Residue Number System (RRNS) which is a trait of Residue Number System (RNS) is one of the available methods for detecting and correcting errors which involves the addition of extra moduli called redundant moduli. In this paper, Residue Number System (RNS) is efficiently applied to the Lempel-Ziv-Welch (LZW) compression algorithm resulting in new LZW-RNS compression scheme using the traditional moduli set, and two redundant moduli added resulting in the moduli set {2^n-1,〖 2〗^n,〖 2〗^n+1,〖 2〗^2n-3,〖 2〗^2n+1} for the purposes of error detection and correction. This is done by constraining the data or information within the legitimate range of the dynamic range provided by the non-redundant moduli. Simulation with MatLab shows the efficiency and fault tolerance of the proposed scheme than the traditional LZW compression method and other related known state of the art schemes.

Mechanisms to Provide Fault Tolerance to a Network-on-Chip

The constant reduction in the components size of integrated circuits, as well as higher operating frequencies, increases the vulnerability to internal and external noise sources. These noises can cause a failure in any component, affecting the functioning of the system as a whole. Systems-on-Chip (SoCs) with dozens of cores are based on Networks-on-Chip (NoCs), and require networks that are able to detect and prevent a fault in leading to a system failure and an application malfunction. In this context, this work aims at evaluating solutions to increase the reliability and availability of a NoC, implementing mechanisms for error detection and correction. Spatial and information redundancy techniques were applied in order to protect the network against Single Event Upset (SEU) faults. The applied techniques ensured the correct operation of the network in the presence of faults, with low impact to the performance and with acceptable silicon costs.

Model Checking-based Software-FMEA: Assessment of Fault Tolerance and Error Detection Mechanisms

Failure Mode and Effects Analysis (FMEA) is a systematic technique to explore the possible failure modes of individual components or subsystems and determine their potential effects at the system level. Applications of FMEA are common in case of hardware and communication failures, but analyzing software failures (SW-FMEA) poses a number of challenges. Failures may originate in permanent software faults commonly called bugs, and their effects can be very subtle and hard to predict, due to the complex nature of programs. Therefore, a behavior-based automatic method to analyze the potential effects of different types of bugs is desirable. Such a method could be used to automatically build an FMEA report about the fault effects, or to evaluate different failure mitigation and detection techniques. This paper follows the latter direction, demonstrating the use of a model checking-based automated SW-FMEA approach to evaluate error detection and fault tolerance mechanisms, demonstrated on a case study inspired by safety-critical embedded operating systems.

Convergent? Minds? Some questions about mental evolution.

In investigating convergent minds, we need to be sure that the things we are looking at are both minds and convergent. In determining whether a shared character state represents a convergence between two organisms, we must know the wider distribution and primitive state of that character so that we can map that character and its state transitions onto a phylogenetic tree. When we do this, some apparently primitive shared traits may prove to represent convergent losses of cognitive capacities. To avoid having to talk about the minds of plants and paramecia, we need to go beyond assessments of behaviourally defined cognition to ask questions about mind in the primary sense of the word, defined by the presence of mental events and consciousness. These phenomena depend upon the possession of brains of adequate size and centralized ontogeny and organization. They are probably limited to vertebrates. Recent discoveries suggest that consciousness is adaptively valuable as a late error-detection mechanism in the initiation of action, and point to experimental techniques for assessing its presence or absence in non-human mammals.

Simulation Based Fault Detection and Diagnosis for Additive Manufacturing

Objectives: To develop a mechanism for error detection and diagnosis in additive manufacturing system by analysing generated data for reducing manual efforts. Methods/Analysis: Data generated from the actual run is more and currently this data is analysed manually for error detection and diagnosis. We proposed developed a software tool which will help for sorting, filtering and analysing data for error detection and these data are fed to Bayesian network which help to find the cause of that error and help for error diagnosis. Bayesian network with probability reasoning is used and it requires historical data and experience person for efficient prediction. Findings: This mechanism is efficient compared to manual system. It takes lesser time to sort, filter and analyse the data for error detection. We get the idea about scenario and parameters which causes the error; hence we can limit those parameters and can resolve those issues quickly. As this mechanism helps to set a proper relationship between cause and error, hence it reduces the uncertainty of the system in fault finding. As this is automated process hence saves too much time of error detection and diagnosis, hence this proposal will speed up the fault detection and diagnosis and enable the developer to solve issue quicker and indirectly help to optimize the process and cost. Novelty: Statistical tool used for this is a Bayesian network with Probability Reasoning which has ability to infer and self-learning.

The Cognitive Role of the Globus Pallidus interna; Insights from Disease States

The motor symptoms of both Parkinson’s disease and focal dystonia arise from dysfunction of the basal ganglia, and are improved by pallidotomy or deep brain stimulation of the Globus Pallidus interna (GPi). However, Parkinson’s disease is associated with a greater degree of basal ganglia-dependent learning impairment than dystonia. We attempt to understand this observation in terms of a comparison of the electrophysiology of the output of the basal ganglia between the two conditions. We use the natural experiment offered by Deep Brain Stimulation to compare GPi local field potential responses in subjects with Parkinson’s disease compared to subjects with dystonia performing a forced-choice decision-making task with sensory feedback. In dystonic subjects, we found that auditory feedback was associated with the presence of high gamma oscillations nestled on a negative deflection, morphologically similar to sharp wave ripple complexes described in human rhinal cortex. These were not present in Parkinson’s disease subjects. The temporal properties of the high gamma burst were modified by incorrect trial performance compared to correct trial performance. Both groups exhibited a robust low frequency response to ‘incorrect’ trial performance in dominant GPi but not non-dominant GPi at theta frequency. Our results suggest that cellular processes associated with striatum-dependent memory function may be selectively impaired in Parkinson’s disease even if dopaminergic drugs are administered, but that error detection mechanisms are preserved.

Performance Evaluation of Software-Based Error Detection Mechanisms for Supply Noise Induced Timing Errors

Performance Evaluation of Software-Based Error Detection Mechanisms for Supply Noise Induced Timing Errors

Design and Analysis of Transient Fault Tolerance in SRAM with different NT Techniques

In digital domain applications one of the main important analyses is to check the system performance even if the fault is occurred. This paper describes the fault tolerance technique based on the software approach for SRAM memory unit present in the multi core system using Processor Level Redundancy (PLR). The PLR proceeds with software-centric approach, soft fault tolerance which ensuring a correct software execution. In this approach we applied here only for SRAM available in the processor. This scheme is used at user space level which does not necessitate changes to the original application. In this approach is used to detect the soft errors presented in the memory unit and it will recover from the fault without stop the process of the memory unit. To design the SRAM here we use the different nT technique. The main goal of this approach is which implements fault error detection and fault recovery mechanism to check the performance of the memory unit. This paper presents software based nT for SRAM design and analysis transient fault tolerance using Process Level Redundancy (PLR) is lower when comparable to existed.

DYSLEXIA AND SPELLING IN TWO DIFFERENT ORTHOGRAPHIES (GREEK VS. ENGLISH): A LINGUISTIC ANALYSIS.

In our study spelling skills of 40 dyslexic (mean age 13,2 years) and 40 non dyslexic children (mean age 10,9 years) matched on the basis of their general level of language proficiency and foreign language acquisition, were assessed through a dictation passage spelling test and the composing of picture elicited narratives, in both Greek (L1) and English (L2). Results indicate that spelling performance is affected by the distinct nature of each orthographic system. In both groups, phonological errors were the least frequent type of spelling errors, while the predominance of morphological and etymological errors indicates both groups' persistent difficulties with applying linguistic rules and systematicities. As expected, dyslexics made more errors of all types despite the fact that the two groups' error profiles did not differ qualitatively. Finally, both groups‟ revising and pausing behaviour indicated that spelling has been the main concern for both dyslexic and non dyslexic writers and confirmed the dyslexics' deficient error detection mechanism.

Verilog Implementation of Collision Avoidance System based on FlexRay Protocol

Background/Objective: The objective of this paper is to provide an insight into the FlexRay protocol, as well as build a simple collision avoidance system based on it. FlexRay is a next generation intra-vehicular communication protocol that provides fault-tolerant, flexible, high data rate and deterministic communication between the different electronic units in an automobile, mainly used for advanced automobile control and safety-critical applications. Methods/Statistical Analysis: Here, a simple collision avoidance system for an automobile is built using Verilog HDL, where data is transmitted according to the FlexRay protocol. Separate Verilog modules are designed for an ultrasonic sensor and a motor. Data is transmitted in the FlexRay frame format. Simulation of results are tested in ModelSim tool. Findings: It is observed that under the FlexRay protocol, data is transmitted at a much greater speed. Also, there are better fault tolerance and better error detection mechanisms, all of which are quintessential for a safety-critical application like collision avoidance. It also ensures safe communication between the devices inside an automobile. Applications/Improvements: More advanced automotive applications like automatic steering, temperature regulation, etc., can be implemented in a similar manner based on the protocol. The protocol is mainly used for safety-critical applications owing to its huge cost of implementation.

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.

Safety framework and platform for functions of future automotive E/E systems

This paper proposes a new safety framework and platform for the functions of future electrical/electronic (E/E) systems. The framework aims to cope with the increasing complexity of the E/E systems, and to enhance their flexibility, but retain the safety properties and keep low engineering costs. A domain-specific meta-model is used to specify relevant aspects of the system such as component interface requirements and function descriptions. The meta-model is used in a tool that generates data structures, which are then used to configure the fault-management layer of the run-time environment. The fault-management layer preserves the safety properties of the system at run-time, by facilitating error detection and fault-handling mechanisms, and supporting controlled adaptation. By reusing already developed safety measures for different systems and functions, future development costs for non-functional qualities can be saved.

Dialect Variation Influences the Phonological and Lexical-Semantic Word Processing in Sentences. Electrophysiological Evidence from a Cross-Dialectal Comprehension Study.

This event-related potential (ERP) study examines the influence of dialectal competence differences (merged vs. unmerged dialect group) on cross-dialectal comprehension between Southern German dialects. It focuses on the question as to whether certain dialect phonemes (//, //), which are attributed to different lexemes in two dialect areas (Central Bavarian, Bavarian-Alemannic transition zone) evoke increased neural costs during sentence processing. In this context, the phonological and semantic processing of lexemes is compared in three types of potentially problematic communication settings (misunderstanding, incomprehension, allophonic variation = potential comprehension). For this purpose, an oddball design including whole sentences was combined with a semantic rating task. Listeners from the unmerged Central Bavarian dialect area heard sentences including either native or non-native lexemes from the merged neighboring dialect. These had to be evaluated with regard to their context acceptability. The main difference between the lexemes can be attributed to the fact that they have different meanings in the respective dialect areas or are non-existent in the linguistic competence of the Central Bavarians. The results provide evidence for the fact that non-native lexemes containing the //-diphthong lead to enhanced neural costs during sentence processing. The ERP results show a biphasic pattern (N2b/N400, LPC) for non-existent lexemes (incomprehension) as well as for semantically incongruous lexemes (misunderstanding), reflecting an early error detection mechanism and enhanced costs for semantic integration and evaluation. In contrast, allophonic // deviations show reduced negativities and no LPC, indexing an unproblematic categorization and evaluation process. In the light of these results, an observed change of // to // in the Bavarian-Alemannic transition zone can be interpreted as a facilitation strategy of cross-dialectal comprehension to reduce both misunderstandings as well as neural costs in processing, which might be interpreted as the initial trigger for this particular phoneme change.

The natural defense system and the normative self model

Infectious agents are not the only agressors, and the immune system is not the sole defender of the organism. In an enlarged perspective, the ‘normative self model’ postulates that a ‘natural defense system’ protects man and other complex organisms against the environmental and internal hazards of life, including infections and cancers. It involves multiple error detection and correction mechanisms that confer robustness to the body at all levels of its organization. According to the model, the self relies on a set of physiological norms, and NONself (meaning : Non Obedient to the Norms of the self) is anything ‘off-norms’. The natural defense system comprises a set of ‘civil defenses’ (to which all cells in organs and tissues contribute), and a ‘professional army ‘, made of a smaller set of mobile cells. Mobile and non mobile cells differ in their tuning abilities. Tuning extends the recognition capabilities of NONself by the mobile cells, which increase their defensive function. To prevent them to drift, which would compromise self/NONself discrimination, the more plastic mobile cells need to periodically refer to the more stable non mobile cells to keep within physiological standards.

The natural defense system and the normative self model.

Infectious agents are not the only agressors, and the immune system is not the sole defender of the organism. In an enlarged perspective, the 'normative self model' postulates that a 'natural defense system' protects man and other complex organisms against the environmental and internal hazards of life, including infections and cancers. It involves multiple error detection and correction mechanisms that confer robustness to the body at all levels of its organization. According to the model, the self relies on a set of physiological norms, and NONself (meaning: Non Obedient to the Norms of the self) is anything 'off-norms'. The natural defense system comprises a set of 'civil defenses' (to which all cells in organs and tissues contribute), and a 'professionalarmy', made of a smaller set of mobile cells. Mobile and non mobile cells differ in their tuning abilities. Tuning extends the recognition capabilities of NONself by the mobile cells, which increase their defensive function. To prevent them to drift, which would compromise self/NONself discrimination, the more plastic mobile cells need to periodically refer to the more stable non mobile cells to keep within physiological standards.

Differential neural mechanisms for early and late prediction error detection.

Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error’s extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictive models. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models.

Improvement of the Error-detection Mechanism in Adults with Dyslexia Following Reading Acceleration Training.

The error-detection mechanism aids in preventing error repetition during a given task. Electroencephalography demonstrates that error detection involves two event-related potential components: error-related and correct-response negativities (ERN and CRN, respectively). Dyslexia is characterized by slow, inaccurate reading. In particular, individuals with dyslexia have a less active error-detection mechanism during reading than typical readers. In the current study, we examined whether a reading training programme could improve the ability to recognize words automatically (lexical representations) in adults with dyslexia, thereby resulting in more efficient error detection during reading. Behavioural and electrophysiological measures were obtained using a lexical decision task before and after participants trained with the reading acceleration programme. ERN amplitudes were smaller in individuals with dyslexia than in typical readers before training but increased following training, as did behavioural reading scores. Differences between the pre-training and post-training ERN and CRN components were larger in individuals with dyslexia than in typical readers. Also, the error-detection mechanism as represented by the ERN/CRN complex might serve as a biomarker for dyslexia and be used to evaluate the effectiveness of reading intervention programmes. Copyright © 2016 John Wiley & Sons, Ltd.

Prevalence and pattern of prescription errors in a Nigerian kidney hospital.

To determine (i) the prevalence and pattern of prescription errors in our Centre and, (ii) appraise pharmacists' intervention and correction of identified prescription errors. A descriptive, single blinded cross-sectional study. Kidney Care Centre is a public Specialist hospital. The monthly patient load averages 60 General Out-patient cases and 17.4 in-patients. A total of 31 medical doctors (comprising of 2 Consultant Nephrologists, 15 Medical Officers, 14 House Officers), 40 nurses and 24 ward assistants participated in the study. One pharmacist runs the daily call schedule. Prescribers were blinded to the study. Prescriptions containing only galenicals were excluded. An error detection mechanism was set up to identify and correct prescription errors. Life-threatening prescriptions were discussed with the Quality Assurance Team of the Centre who conveyed such errors to the prescriber without revealing the on-going study. Prevalence of prescription errors, pattern of prescription errors, pharmacist's intervention. A total of 2,660 (75.0%) combined prescription errors were found to have one form of error or the other; illegitimacy 1,388 (52.18%), omission 1,221(45.90%), wrong dose 51(1.92%) and no error of style was detected. Life-threatening errors were low (1.1-2.2%). Errors were found more commonly among junior doctors and non-medical doctors. Only 56 (1.6%) of the errors were detected and corrected during the process of dispensing. Prescription errors related to illegitimacy and omissions were highly prevalent. There is a need to improve on patient-to-healthcare giver ratio. A medication quality assurance unit is needed in our hospitals. No financial support was received by any of the authors for this study.

Compact and On-the-Fly Secure Dynamic Reconfiguration for Volatile FPGAs

The dynamic partial reconfiguration functionality of FPGAs can be attacked, particularly when the FPGA is remotely located or the configuration bitstreams are sent through insecure networks. The existing FPGA technologies provide some built-in security mechanisms; however, these are often inadequate. The existing solutions still impose a significant impact on the reconfiguration process and on the available resources. This article proposes a solution to improve the security of dynamic partial reconfiguration of FPGAs, without significantly affecting the reconfiguration performance. The proposed solution changes the encryption key of the remotely received bitstream by a randomly generated key, unique for each configuration, when storing them in the external unsecured memory. The native frame-wise error detection mechanism combined with an additional CBC-MAC authentication mechanism, allows for an improved countermeasure against replay attack and wrongful bitstream usage. The proposed solution introduces an overhead of 1% of the available resources on the target FPGA and provides the lowest impact on the reconfiguration process when compared to the state of the art, achieving a reconfiguration throughput of 2.5Gbps. Regarding the built-in security mechanism provided by the Xilinx FPGAs, the solution herein proposed provides better security and improves the reconfiguration performance by more than 3 times.