Decoding Mechanism Research Articles

Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm

The current assembly line balancing studies ignore the preventive maintenance (PM) of machines in some workstations, implying that the already-known PM information has been completely missed. Moreover, PM may bring about a production stoppage for a considerable time. Hence, this paper considers PM scenarios into the assembly line balancing problem to improve the production efficiency and smoothness simultaneously. For this multi-objective problem, a heuristic rule relying on the tacit knowledge is dug up via gene expression programming to obtain an acceptable solution quickly. Then, an enhanced grey wolf optimizer algorithm with two improvements is proposed to achieve Pareto front solutions. Specifically, a variable step-size decoding mechanism accelerates the speed of the algorithm; the specially-designed neighbor operators prevent the algorithm from trapping in local optima. Experiment results demonstrate that the discovered heuristic rule outperforms other existing rules; the joint of improvements endows the proposed meta-heuristic with significant superiority over three variants and other six well-known algorithms. Besides, a real-world case study is conducted to validate the discovered rule and the proposed meta-heuristic.

On the Logical Computational Complexity Analysis of Turbo Decoding Algorithms for the LTE Standards

Evaluating the computational complexity of decoders is a very important aspect in the area of Error Control Coding. However, most evaluations have been performed based on hardware implementations. In this paper, different decoding algorithms for binary Turbo codes which are used in LTE standards are investigated. Based on the different mathematical operations in the diverse equations, the computational complexity is derived in terms of the number of binary logical operations. This work is important since it demonstrates the computational complexity breakdown at the binary logic level as it is not always evident to have access to hardware implementations for research purposes. Also, in contrast to comparing different Mathematical operations, comparing binary logic operations provides a standard pedestal in view to achieve a fair comparative analysis for computational complexity. The usage of the decoding method with fewer number of binary logical operations significantly reduces the computational complexity which in turn leads to a more energy efficient/power saving implementation. Results demonstrate the variation in computational complexities when using different algorithms for Turbo decoding as well as with the incorporation of Sign Difference Ratio (SDR) and Regression-based extrinsic information scaling and stopping mechanisms. When considering the conventional decoding mechanisms and streams of 16 bits in length, Method 3 uses 0.0065% more operations in total as compared to Method 1. Furthermore, Method 2 uses only 0.0035% of the total logical complexity required with Method 1. These computational complexity analysis at the binary logical level can be further used with other error correcting codes adopted in different communication standards.

An intelligent surveillance video analytics framework using NACT-Hadoop/MapReduce on cloud services

Video analytics has gradually increased in recent years. The intelligent CCTV cameras in public places, you-tube videos, etc. generate an enormous amount of video data. Generally, video analytics required more time as it contains several processes like encoding, decoding, etc. There are several existing approaches are evolved in improving the efficiency of video analytics but performance delay and loss of data still existing challenges. With our analysis, we strongly state VM migration will be an effective solution to overcome this delay and performance issues. In this paper, we propose NACT based map reducing mechanism (NACT-Map) for processing the real-time streaming videos. The NACT (Novel Awaiting Computation Time) enables the prediction of VM allocation and automatic migration. The scheduling and allocating of the optimal resource are done by task monitor who utilizes the Task manager (TM) system. The NACT based VM migration and MapReduce technique with Hadoop simplifies the process and minimizes the execution time. The splitting of video into chunks of frames speedup the process. Further efficiency is improved by the Map Reduce technique which uses video and its related content for clusters. The performance of our proposed system is executed in the cloudsim with a large dataset contains two real-time videos. Further, the result is compared with the existing methodologies such as distributed video decoding mechanism with extended FFmpeg and VideoRecordReader (VDMFF) (Yoon et al. in Distributed video decoding on Hadoop. IEICE Trans Inf Syst E101-D(1):2933–2941, 2018) and distributed Video Analytics Framework for Intelligent Video Surveillance (SIAT) (Uddin et al. in SIAT: a distributed video analytics framework for intelligent video surveillance. Symmetry 11:911, 2019). The obtained result shows our proposed NACT_Map consumes minimum Task processing time $$({\text{p}}_{{{\text{tix}}}} )$$ and about 90% of efficiency in overall system performance is increased.

Dynamic Image Quantization Using Leaky Integrate-and-Fire Neurons.

This paper introduces a novel coding/decoding mechanism that mimics one of the most important properties of the human visual system: its ability to enhance the visual perception quality in time. In other words, the brain takes advantage of time to process and clarify the details of the visual scene. This characteristic is yet to be considered by the state-of-the-art quantization mechanisms that process the visual information regardless the duration of time it appears in the visual scene. We propose a compression architecture built of neuroscience models; it first uses the leaky integrate-and-fire (LIF) model to transform the visual stimulus into a spike train and then it combines two different kinds of spike interpretation mechanisms (SIM), the time-SIM and the rate-SIM for the encoding of the spike train. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by counting the spikes. For that reason, the proposed mechanisms is called Dual-SIM quantizer (Dual-SIMQ). We show that (i) the time-dependency of Dual-SIMQ automatically controls the reconstruction accuracy of the visual stimulus, (ii) the numerical comparison of Dual-SIMQ to the state-of-the-art shows that the performance of the proposed algorithm is similar to the uniform quantization schema while it approximates the optimal behavior of the non-uniform quantization schema and (iii) from the perceptual point of view the reconstruction quality using the Dual-SIMQ is higher than the state-of-the-art.

Лингвокультурема как маркер идентичности (на примере перевода на английский язык романа Г. Яхиной «Зулейха открывает глаза»)

The article reveals the decoding mechanisms of linguoculturemes occurring in the translation of the novel “Zuleikha opens her eyes” by G. Yakhina into the English language. In the original text of the novel linguoculturemes express ethnical and socio-cultural identity of the main character Zuleikha. Working on the translation of the novel Lisa Hayden, the translator, uses different types of adaptive transcoding for interlanguage and intercultural communication. The translation is characterized by double transcoding that is based on three languages: Tatar, Russian and English. Tatar words and expressions with explicit national cultural elements form a cultural background in the novel and often have no equivalents or definitions in the English language. The comparative analysis of the original text and its translations highlight a number of different groups of linguoculturemes, such as terms for members of ethno-cultural community and types of address, names of mythical and religious characters, names of objects, elements of interior design of a peasant’s home, pieces of furniture, and clothes. Linguoculturemes also help to recreate the historical atmosphere in Russia in the 1920-1930s, as well as the relationships in a traditional patriarchal family, conventional values of a local ethno-cultural community and socio-political realia depicted in the novel. A complex hierarchy of contextual image levels of the novel in the process of translation of the novel. The outer level of the story (the plot) is being transformed and many story lines are translated into English without any significant semantic change. Universal human problems represented via archetypes are well received by the English-language readers regardless of their language and socio-cultural background. The inner levels of the story expressing specific social relationships and interactions, ethnocultural, religious, and ethnopsychological stands with the help of linguoculturemes appear to be “encoded” for readers with different language backgrounds, but open in their complete semantic value to the bearers of the given social, religious and ethnical cultures. The authors’ message is that the English translation of the text does not lack in national cultural identity or ethnocultural values, it is just that these values become secondary and, as a result, harm the intimacy of the unique world perception of the main character.

Outlier-Resistant Observer-Based Control for a Class of Networked Systems Under Encoding–Decoding Mechanism

This article is concerned with a new outlier-resistant observer-based control problem for a class of networked systems (NSs) under the encoding–decoding communication mechanism (EDCM). In order to lighten the communication burden and enhance the data security, the EDCM is introduced in the observer-to-controller channel. In case of the measurement outliers, a specific saturation function is adopted in the observer structure to restrain the abnormal innovations so as to mitigate the side effects brought from the outliers. The aim of this article is to design an observer-based controller, such that in the presence of measurement outliers, the closed-loop NS achieves the input-to-state stability (ISS) under the EDCM. By means of the uniform quantization technique, a criterion is first established in terms of the sizes of the encoding alphabet and the encoding period so as to ensure the detectability of the NS, and the requirement is also given on the capacity of the communication channel at each time instant. Then, with the help of ISS theory, the desired controller is obtained with its gain matrix parameterized by the solution to a certain inequality that can be solved via standard software packages. Finally, the effectiveness of the derived theoretical results is verified through three numerical simulation examples.

Recursive Filtering With Measurement Fading: A Multiple Description Coding Scheme

In this article, the recursive filtering problem is studied for a class of discrete-time nonlinear stochastic systems subject to fading measurements. In order to facilitate the data transmission in a resource-constrained communication network, the multiple description coding scheme is adopted to encode the fading measurements into two descriptions with the identical importance. Two independent Bernoulli distributed random variables are introduced to govern the occurrences of the packet dropouts in two channels from the encoders to the decoders. The channel fading phenomenon is characterized by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> th-order Rice fading model whose coefficients are mutually independent random variables obeying certain probability distributions. The purpose of the problem addressed is to design a recursive filter, such that in the simultaneous presence of the stochastic noises, the channel fading and the data coding–decoding mechanism, an upper bound of the filtering error variance is obtained and then minimized at each time step. In virtue of the Riccati difference equation technique and the stochastic analysis approach, the explicit form of the desired filter parameters is derived by solving a sequence of coupled algebraic Riccati-like difference equations. Finally, a simulation experiment is provided to show the applicability of the developed filtering scheme.

NMDARs Translate Sequential Temporal Information into Spatial Maps.

SummarySpatial representations of the sensory world are important for brain function. Timing is an essential component of sensory information. Many brain circuits transform the temporal sequence of input activity into spatial maps; however, the mechanisms underlying this transformation are unclear. Different N-methyl-D-aspartate receptor (NMDAR) response magnitudes result in synaptic potentiation or depression. We asked whether NMDAR response magnitude also affects the transformation of temporal information into directional spatial maps. We quantified retinotectal axon branch dynamics in Xenopus optic tectum in response to temporal sequences of visual stimulation. Reducing NMDAR responses by 50% inverts the spatial distribution of branch dynamics along the rostrocaudal axis in response to temporal patterns of input, suggesting that the magnitude of NMDAR signaling encodes the temporal sequence of inputs and translates the temporal code into a directional spatial map using structural plasticity-based branch dynamics. We discuss how this NMDAR-dependent decoding mechanism retrieves spatial information from sequential afferent activity.

Brain Dynamics Reflects Phonological And Semantic Top-Down Influences During Orthographic Processing Of Novel Word Forms

The acquisition of new vocabulary is usually mediated by previous experience with language. In visual domain, orthographically unfamiliar word-forms may already have corresponding phonological or even conceptual representations in linguistic system, which facilitates orthographic learning. The neural correlates of this advantage were investigated by recording EEG during reading novel and familiar words across three different experiments (n=22 each), manipulating the availability of previous knowledge for the novel written words. In Experiment 1, participants received no previous training before reading these stimuli; Experiments 2 and 3, however, provided a previous training (six exposures) in the phonology of novel words (auditory exposure) and in both phonology and meaning (auditory exposure + picture), respectively. During reading, a different pattern of ERP responses was found for the novel written words depending on their previous training, resembling cross-level top-down interactive effects during the process of vocabulary acquisition. Thus, whereas phonological experience produced a different modulation at early lexical (~180 ms) and post lexical (~520 ms) stages of visual recognition of novel written words, additional semantic training influenced their lexical processing at a later stage (~320 ms). Importantly, a clear lexicality effect was found at the early peak in experiment 1 (no previous training); however, neural responses for trained and familiar words were found indistinguishable at this latency regardless of the phonological or semantic nature of training, reflecting similar orthographic processing and word-form access. These results suggest the key role of phonology in orthographically transparent reading systems, in which bidirectional decoding mechanisms contribute to the rapid formation of novel word-form representations even in the absence of visual exposure.

Molecular Dynamics Simulations Suggest a Non-Doublet Decoding Model of -1 Frameshifting by tRNASer3.

In-frame decoding in the ribosome occurs through canonical or wobble Watson–Crick pairing of three mRNA codon bases (a triplet) with a triplet of anticodon bases in tRNA. Departures from the triplet–triplet interaction can result in frameshifting, meaning downstream mRNA codons are then read in a different register. There are many mechanisms to induce frameshifting, and most are insufficiently understood. One previously proposed mechanism is doublet decoding, in which only codon bases 1 and 2 are read by anticodon bases 34 and 35, which would lead to −1 frameshifting. In E. coli, tRNASer3GCU can induce −1 frameshifting at alanine (GCA) codons. The logic of the doublet decoding model is that the Ala codon’s GC could pair with the tRNASer3′s GC, leaving the third anticodon residue U36 making no interactions with mRNA. Under that model, a U36C mutation would still induce −1 frameshifting, but experiments refute this. We perform all-atom simulations of wild-type tRNASer3, as well as a U36C mutant. Our simulations revealed a hydrogen bond between U36 of the anticodon and G1 of the codon. The U36C mutant cannot make this interaction, as it lacks the hydrogen-bond-donating H3. The simulation thus suggests a novel, non-doublet decoding mechanism for −1 frameshifting by tRNASer3 at Ala codons.

On Information Extraction and Decoding Mechanisms Improved by Noisy Amplification in Signaling Pathways

The cells need to process information about extracellular stimuli. They encode, transmit and decode the information to elicit an appropriate response. Studies aimed at understanding how such information is decoded in the signaling pathways to generate a specific cellular response have become essential. Eukaryotic cells decode information through two different mechanisms: the feed-forward loop and the promoter affinity. Here, we investigate how these two mechanisms improve information transmission. A detailed comparison is made between the stochastic model of the MAPK/ERK pathway and a stochastic minimal decoding model. The maximal amount of transmittable information was computed. The results suggest that the decoding mechanism of the MAPK/ERK pathway improve the channel capacity because it behaves as a noisy amplifier. We show a positive dependence between the noisy amplification and the amount of information extracted. Additionally, we show that the extrinsic noise can be tuned to improve information transmission. This investigation has revealed that the feed-forward loop and the promoter affinity motifs extract information thanks to processes of amplification and noise addition. Moreover, the channel capacity is enhanced when both decoding mechanisms are coupled. Altogether, these findings suggest novel characteristics in how decoding mechanisms improve information transmission.

Error Correction Codes Derived from Orthogonal Latin Square Codes

The developments in IC technology and rapid increase of transistor densities and scaling factor, the use of ECC’s acquired prominence. Multiple bit errors in memories due to technology scaling demands advanced error correction codes. SEC-DEC, DEC, burst error detection, Golay code, Reed Solmon codes etc. have much decoding complexity and latency. The above drawbacks can be reduced with OLS codes. OLS codes with majority logic decoding technique, modular construction and simple decoding mechanisms it enables low delay improvements. MBU’S can be addressed using OLS-MLD codes. This paper presents a detail study of developments in multibit ECC’s using OLS-MLD mechanism

The Inferential Semantics of Comics

As a multimodally complex medium, graphic narratives have long been approached from a semiotic perspective, though the latter’s focus on general decoding mechanisms for reading meaning out of signs has increasingly come under criticism. Only recently has the applicability of inference to the analysis of visual narratives begun to attract attention, mostly with regard to bridging hypotheses between panels on the level of narrative structure. This article, in contrast, addresses the general question of meaning construction at the level of narrative panels and even smaller units in comics, focusing on their general semantics. It argues that the intersemiotic interplay of the various resources and the evolving meaning construction is one of the most challenging aspects of comic analysis today and that recipients need to construct complex representations of what is shown (and told) in the interplay of images, text, and other visual resources to be able to follow the comic’s narrative. How this interpretation can be described with the help of a multimodal discourse analytic approach is the central question this article addresses. It shows how recent developments in applied semiotics together with basic concepts of abduction and defeasible reasoning help capture the fine-grained mechanics of meaning construction in comics as an approach to their basic semantics.

FlipTracer: Practical Parallel Decoding for Backscatter Communication

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable and, hence, may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight; although the collided signal is time-varying and irregular, transitions between signals’ combined states follow highly stable probabilities, which offers important clues for identifying the collided signals and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then, FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mb/s, which is $6\times $ higher than the state of the art.

Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism

This paper investigates an iterative learning control for single-input, single-output, and linear time-invariant discrete system. The special design of the learning gain matrix is introduced, where a finite uniform quantizer is incorporated with an encoding and decoding mechanism to realize the zero-error convergence of a tracking problem. Furthermore, the boundary-level calculation is considerably improved using lifting technique and infinity-norm of vectors under this mechanism. Some illustrations of the simulations verify the theoretical results.

Mathematical model identifies effective P53 accumulation with target gene binding affinity in DNA damage response for cell fate decision

ABSTRACT Functional p53 signaling is essential for appropriate responses to diverse stimuli. P53 dynamics employs the information from the stimulus leading to selective gene expression and cell fate decision. However, the decoding mechanism of p53 dynamics under DNA damage challenge remains poorly understood. Here we mathematically modeled the recently dual-phase p53 dynamics under doxorubicin treatment. We found that p53 could perform sequential pulses followed by a high-amplitude terminal pulse at relatively low doxorubicin treatment, whereas p53 became steadily accumulated when damage level was high. The effective p53 integral above a threshold but not the absolute accumulation of p53 precisely discriminated survival and death. Silencing negative regulators in p53 network might promote the occurrence of terminal pulse. Furthermore, lower binding affinity and degradation rate of p53 target genes could favorably discriminate high and low dose doxorubicin treatment. Grouping by temporal profiles suggested that the p53 dynamics rather than the doxorubicin doses could better discriminate cellular outcomes and confer less variation for effective p53 integral reemphasizing the importance of p53 level regulation. Our model has established a theoretical framework that p53 dynamics can work cooperatively with its binding affinity to target genes leading to cell fate choice, providing new clues of optimized clinical design by manipulating p53 dynamics.

Novel decoding methods of constant weight coding for molecular communications

Molecular communications via diffusion (MCvD) is a popular research area thanks to its potential applications, mostly in nano communications. Despite many recent publications on MCvD, the studies on error correction/channel coding for molecular communications are limited. Low weight channel codes have been proposed in the literature due to their energy efficiency and ISI mitigation. We add the constant weight condition on top of being low weight for MCvD channel codes, since the information of all codewords having the same weight can be utilized at the receiver. Moreover, encoding simplicity of Constant Low Weight Codes (CLWC) compared to the conventional coding schemes makes them appealing for MCvD, considering nanodevice limitations. This paper focuses on the decoding strategies for CLWC at the receiver side. Threshold and sorting-based decoders for molecular communications are reviewed and two different novel decoding mechanisms for CLWC are proposed. Iterative sorting decoder is a decision-feedback heuristic method that iteratively calculates the ISI from a better estimation at each step. The second proposed method is the super trellis decoder, which is a maximum likely sequence estimator. Outer states in the trellis denote the number of total weights in a sequence, whereas inner states represent the memory of the ISI channel. Both proposed methods increase the communication quality significantly by lowering transmission error probabilities. Simulation comparisons with existing methods show performance superiority of the proposed algorithms. Depending on the capability of the receiver and the availability of the channel state information, both methods are viable to be used with CLWC.

Comparing decoding mechanisms for parsing argumentative structures

Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic structures, most approaches choose to learn a local model and then perform global decoding over the local probability distributions, often imposing constraints that are specific to the task at hand. Specifically for argumentation parsing, two decoding approaches have been recently proposed: Minimum Spanning Trees (MST) and Integer Linear Programming (ILP), following similar trends in discourse parsing. In contrast to discourse parsing though, where trees are not always used as underlying annotation schemes, argumentation structures so far have always been represented with trees. Using the 'argumentative microtext corpus' [in: Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon 2015 / Vol. 2, College Publications, London, 2016, pp. 801-815] as underlying data and replicating three different decoding mechanisms, in this paper we propose a novel ILP decoder and an extension to our earlier MST work, and then thoroughly compare the approaches. The result is that our new decoder outperforms related work in important respects, and that in general, ILP and MST yield very similar performance.

Zero‐error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism

In this study, the zero-error convergence of the iterative learning control for a tracking problem is realised by incorporating a uniform quantiser with an encoding and decoding mechanism. Under this scheme, the system output is first transformed and encoded. Then, the encoded information is transmitted back for updating the input. The results are extended to a finite quantisation level situation under the same framework and a simulation using a permanent magnet linear motor is performed to demonstrate the effectiveness of the proposed scheme.

Fast random motion biases judgments of visible and occluded motion speed

Human sensitivity to speed differences is very high, and relatively high when one has to compare the speed of an object that disappears behind an occluder with a standard. Nevertheless, different speed illusions (by contrast, adaptation, dynamic visual noise) affect proper speed judgment for both visible and occluded moving objects. In the present study, we asked whether an illusion due to non-directional motion noise (random dynamic visual noise, rDVN) intervenes at the level of speed encoding, thus affecting speed discrimination, or at the level of speed decoding by non-sensory decision-making mechanisms, indexed by speed overestimation of visible and invisible motion. In Experiment 1, participants performing a temporal two-Alternative Forced Choice task, judged the speed of a target moving in front of the rDVN or a static visual noise (SVN). In Experiment 2 and 3, the target disappeared behind the rDVN/SVN, and participants reported whether the target reappeared early or late (Experiment 2), or the time to contact (TTC) with the end of the occluded trajectory (Experiment 3). In Experiment 1 and 2, we found that rDVN affected the point of subjective equality (pse) of the individual’s psychometric function in a way indicating speed overestimation, while not affecting speed discrimination threshold (just noticeable differences, jnd). In Experiment 3 the rDVN reduced the TTC.Though not entirely consistent, our results suggest that a similar speed decoding mechanism, which read-out motion information to form a perceptual decision, operates regarding of whether motion is visible or invisible.