Conventional Pipeline Research Articles

Structural Optimization and Simulation of a Magnetic Leak Detection Device for Streak-like Defects in Oil Pipes

Striped defects are a common type of defects in oil pipelines, divided into horizontal and longitudinal stripes, and it is difficult to accurately quantify the results of conventional pipeline leakage magnetic detection to correspond to the type of defects and their extent. In this paper, a new type of oil pipe non-destructive testing equipment is proposed and designed based on the principle of magnetic leakage detection using three-dimensional finite element simulation, and the magnetic field distribution inside the equipment is simulated using ANSYS finite element analysis software to provide original data reference for the development of the new equipment.

A local resistance coefficient model of aircraft hydraulics bent pipe using laser powder bed fusion additive manufacturing

Laser powder bed fusion (L-PBF) technology has remarkable advantages in achieving lightweight and miniaturized products, and a short manufacturing cycle. However, for the inner suface of L-PBF bent pipe, the distribution of surface roughness is not uniform. The surface roughness at the top of the bent pipe is much greater than that of other positions, which results that the local resistance coefficient of the bent pipe manufactured by L-PBF cannot be estimated accurately through the existing theory dedicated for conventional pipelines. Aiming at resolving this shortcoming, a new model is proposed to predict the local resistance coefficient of aircraft hydraulic bent pipes. In this regard, numerous 90°-bend Ti6Al4V (TC4) pipelines with different diameter-bend ratios (D/R) are fabricated using the L-PBF technique, and the geometry and surface roughness of the machined samples are measured experimentally and calculated numerically. Based on the obtained results from the finite element simulation and measurements from the experiment for local resistance coefficients of various bent pipes, a model is established to predict local resistance coefficients. The obtained results indicate that the surface roughness, Reynolds number, and diameter-bend ratios affect the local resistance coefficient of the bent pipe. Transition between flow regimes is observed in L-PBF bent pipes. The Reynolds number range in the transition region increases with the decrease of surface roughness, and the critical value of the transition region is modeled. Compared with the experimental data, the predicted values have an average error of less than 10%, indicating that the prediction accuracy is greatly improved.

Safeguarding Connected Health: Leveraging Trustworthy AI Techniques to Harden Intrusion Detection Systems Against Data Poisoning Threats in IoMT Environments

Internet of Medical Things (IoMT) environments introduce vast security exposures including vulnerabilities to data poisoning threats that undermine integrity of automated patient health analytics like diagnosis models. This research explores applying trustworthy artificial intelligence (AI) methodologies including explainability, bias mitigation, and adversarial sample detection to substantially enhance resilience of medical intrusion detection systems. We architect an integrated anomaly detector featuring purpose-built modules for model interpretability, bias quantification, and advanced malicious input recognition alongside conventional classifier pipelines. Additional infrastructure provides full-lifecycle accountability via independent auditing. Our experimental intrusion detection system design embodying multiple trustworthy AI principles is rigorously evaluated against staged electronic record poisoning attacks emulating realistic threats to healthcare IoMT ecosystems spanning wearables, edge devices, and hospital information systems. Results demonstrate significantly strengthened threat response capabilities versus baseline detectors lacking safeguards. Explainability mechanisms build justified trust in model behaviors by surfacing rationale for each prediction to human operators. Continuous bias tracking enables preemptively identifying and mitigating unfair performance gaps before they widen into operational exposures over time. SafeML classifiers reliably detect even camouflaged data manipulation attempts with 97% accuracy. Together the integrated modules restore classification performance to baseline levels even when overwhelmed with 30% contaminated data across all samples. Findings strongly motivate prioritizing adoption of ethical ML practices to fulfill duty of care around patient safety and data integrity as algorithmic capabilities advance.

Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings

Dense prediction tasks are common for 3D point clouds, but the uncertainties inherent in massive points and their embeddings have long been ignored. In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds. Inspired by metric learning, the key idea of CUE is to explore cross-point embeddings upon a conventional 3D dense prediction pipeline. Specifically, CUE involves building a probabilistic embedding model and then enforcing metric alignments of massive points in the embedding space. We also propose CUE+, which enhances CUE by explicitly modeling cross-point dependencies in the covariance matrix. We demonstrate that both CUE and CUE+ are generic and effective for uncertainty estimation in 3D point clouds with two different tasks: (1) in 3D geometric feature learning we for the first time obtain well-calibrated uncertainty, and (2) in semantic segmentation we reduce uncertainty's Expected Calibration Error of the state-of-the-arts by 16.5%. All uncertainties are estimated without compromising predictive performance.

Dynamic Monitoring System of Oil Pipeline Leakage for Oil and Gas Safety

The conventional oil pipeline leakage dynamic monitoring system has the problem of imperfect risk assessment link, which leads to the long alarm response time of the system. An oil and gas safety oriented oil pipeline leakage dynamic monitoring system is designed. Hardware part: select MLLD sensor as voltage acquisition device, and connect RS-485 serial standard bus and other circuit devices. Software part: identify the leakage hazard source of oil pipeline, capture the corresponding characteristic points of leakage negative pressure wave, determine the location of leakage point, build the risk assessment model based on oil and gas safety theory, design the safety management function of dynamic monitoring system software, and clarify the potential safety hazards of oil and gas pipeline. Experimental results: the average alarm response time of the oil pipeline leakage dynamic monitoring system based on data mining in this paper and the other two systems are 95.746s, 122.026s and 122.426s respectively, which proves that the oil pipeline leakage dynamic monitoring system integrating oil and gas safety theory has a higher value.

Plasma catalytic non-oxidative methane conversion to hydrogen and value-added hydrocarbons on zeolite 13X

Non-thermal plasma has unfolded highly efficient, safe to operate novel routes for methane conversion to hydrogen. In this work, methane conversion is performed under atmospheric dielectric barrier discharge (DBD) plasma with and without 13X zeolite-based catalysts i.e., 13X, Ga/13X, Pd/13X, and Pd-Ga/13X. Experimental results indicate that the plasma catalytic process delivered almost twofold higher product yield than the plasma only route. The binary Pd-Ga catalyst possesses highest catalytic performance with about 40 % CH4 conversion at an input flowrate of 5 cm3min−1 and 2 W due to the formation of the Pd-Ga alloy, which acts as catalytic active centre for activating C–H bonds. Product yield can be tailored by the catalyst design where the bimetallic Pd-Ga/13X preferably favours the hydrocarbon formation while H2 is the dominant product obtained over the Pd/13X. The cleavage of C–H bonds of methane molecule over the plasma only route is mainly governed by energetic electrons in the gaseous phase the catalyst activity, and plasma-catalyst synergism play a significant role in the plasma catalysis process. The findings from this work provide significant insights into the methane activation for subsequent optimization of the methane conversion processes operated on floating production, storage, and offloading vessels (FPSOVs). Exploiting untapped offshore natural gas reserves, where conventional pipeline systems are less economical.

End-to-end weakly-supervised single-stage multiple 3D hand mesh reconstruction from a single RGB image

In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from a single 2D image. Previous studies either focus on single hand reconstruction or solve this problem in a multi-stage way. Moreover, the conventional two-stage pipeline firstly detects hand areas, and then estimates 3D hand pose from each cropped patch. To reduce the computational redundancy in preprocessing and feature extraction, for the first time, we propose a concise but efficient single-stage pipeline for multi-hand reconstruction. Specifically, we design a multi-head auto-encoder structure, where each head network shares the same feature map and outputs the hand center, pose and texture, respectively. Besides, we adopt a weakly-supervised scheme to alleviate the burden of expensive 3D real-world data annotations. To this end, we propose a series of losses optimized by a stage-wise training scheme, where a multi-hand dataset with 2D annotations is generated based on the publicly available single hand datasets. In order to further improve the accuracy of the weakly supervised model, we adopt several feature consistency constraints in both single and multiple hand settings. Specifically, the keypoints of each hand estimated from local features should be consistent with the re-projected points predicted from global features. Extensive experiments on public benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD demonstrate that our method outperforms the state-of-the-art model-based methods in both weakly-supervised and fully-supervised manners. The code and models are available at https://github.com/zijinxuxu/SMHR.

Energy-based analog neural network framework.

Over the past decade a body of work has emerged and shown the disruptive potential of neuromorphic systems across a broad range of studies, often combining novel machine learning models and nanotechnologies. Still, the scope of investigations often remains limited to simple problems since the process of building, training, and evaluating mixed-signal neural models is slow and laborious. In this paper, we introduce an open-source framework, called EBANA, that provides a unified, modularized, and extensible infrastructure, similar to conventional machine learning pipelines, for building and validating analog neural networks (ANNs). It uses Python as interface language with a syntax similar to Keras, while hiding the complexity of the underlying analog simulations. It already includes the most common building blocks and maintains sufficient modularity and extensibility to easily incorporate new concepts, electrical, and technological models. These features make EBANA suitable for researchers and practitioners to experiment with different design topologies and explore the various tradeoffs that exist in the design space. We illustrate the framework capabilities by elaborating on the increasingly popular Energy-Based Models (EBMs), used in conjunction with the local Equilibrium Propagation (EP) training algorithm. Our experiments cover 3 datasets having up to 60,000 entries and explore network topologies generating circuits in excess of 1,000 electrical nodes that can be extensively benchmarked with ease and in reasonable time thanks to the native EBANA parallelization capability.

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs.

We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.

Combined Use of Nonlinear Measures for Analyzing Pathological Voices

Automatic voice pathology detection enables an objective assessment of pathologies that influence the voice production strategy. By utilizing the conventional pipeline model as well as the modern deep learning-centric end-to-end methodology, numerous pathological voice analyzing techniques have been developed. The conventional methodology is still a valid choice owing to the lack of enormous amounts of training data in the study region of pathological voice. In the meantime, obtaining higher precision, higher accuracy, and stability is still a complicated task. Therefore, by amalgamating the nonlinear measure, the pathological voices are analyzed to abate such risks. The viability of six nonlinear discriminating measures derived from the phase space realm, involving healthy and pathological voice signals, is studied in this work. The analyzed parameters are Singularity spectrum coefficients ([Formula: see text], [Formula: see text] and [Formula: see text]). Correlation entropy at optimum embedding dimension ([Formula: see text]) and correlation dimension at optimum embedding dimension ([Formula: see text]). Analyzing the pathological voices with better accuracy rates is the major objective of the proposed methodology. Here, the Support Vector Machine (SVM) was utilized as the classifier. Experimentations were performed on VOiceICarfEDerico (VOICED) databases subsuming 208 healthy, as well as pathological voices, amongst these 50 samples, were utilized. Here, the model obtained 97% of accuracy with 99% as of the classifier with Gaussian kernel function. Therefore, to differentiate normal as well as pathological subjects, the six proposed characteristics are highly beneficial; in addition, they will be supportive in pathology diagnosis.

Stress Analysis of Pipe-In-Pipe Systems under Free Span for Deep Water Pipeline Applications

This study examined the phenomena of free span for a pipe -in- pipe (PIP) system for pipeline application. Two different span length of 8 and 30 meters are modelled and simulated using nonlinear stress analysis. The effect of pressure, temperature and gravity on the PIP system are determined and compared with conventional single pipeline. From the results obtained, it is clear that the finite element analysis (FEA) results correlated very well with those calculated using analytical methods. Percentage differences were generally less than 10%, with some discrepancies which were due to assumption of thin-walled theory which assumes a radial stress equals to zero, whereas the FEA calculates a non- zero radial stress. The key finding in this study demonstrated the strong potentials of PIP system in terms of structural reliability for deepwater pipeline application. Specifically, the 30m single pipe in free span (with pressure and temperature) deflected 205.1mm, more than double the corresponding PIP. This knowledge can be beneficial to selection and design considerations for pipeline system responses to both the gravity, thermal and pressure loading as well as the potential failure modes that may results in a typical scenario. Various theoretical calculations of stresses are used to validate the finding in this study of the single pipe and PIP models for flat seabed and free span.

Distractor-aware Event-based Tracking.

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.

Enhancing Conventional Geometry-Based Visual Odometry Pipeline Through Integration of Deep Descriptors

Geometry-based Visual Odometry (VO) techniques are renowned in the fields of computer vision and robotics. They use methods from multi-view geometry to estimate camera motion from visual data obtained from one or more cameras. Tracking the camera motion precisely between different views is dependent on the correct estimation of correspondences between salient points of the views. In practice, geometry-based methods are found to be quite effective but do not perform well in challenging cases due to tracking failures caused by abrupt motion, occlusions, textureless and low-light scenes, etc. On the contrary, end-to-end learning from visual data using deep neural networks is an emerging area of research and deals with challenging cases successfully. Despite being computationally expensive, these methods do not outperform their counterparts in conditions favorable to geometry-based methods. Considering these facts in this work, our goal is to integrate deep descriptors to improve the correspondence between image points for tracking in a traditional geometry-based VO pipeline. We propose a simple stereo VO pipeline inspired by popular techniques found in the literature. Two conventional and four deep descriptors have been used in our experiments conducted on various image sequences of the challenging KITTI benchmark dataset. We have determined empirically that deep descriptors can effectively minimize drift in the VO estimates and produce better camera trajectories. The experimental results on the KITTI dataset demonstrate that our VO method performs at par with the state-of-the-art works reported in the literature.

Simultaneously Training and Compressing Vision-and-Language Pre-Training Model

Model compression is an essential step for large-scale pre-training models toward practical application and deployment on the edge device. However, when conventional compression methods following ‘pre-training then compressing’ two-phase pipeline are applied to Vision-and-Language Pre-training (VLP) models, it will lead to a high calculation and memory overhead. In this work, we break the two-phase pipeline and propose an efficient and effective one-phase VLP model compression mechanism, named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">REDUCER</b> , which stands for ‘simultaneously training and comp <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RE</b> ssing’ VLP model via progressive mo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DU</b> le repla <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ing and n <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> twork <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> ewiring. Specifically, REDUCER consists of three insightful designs. Firstly, we design a one-phase compression framework to train and compress the VLP model simultaneously to avoid the extra calculation and memory cost caused by an isolated model compression phase in the conventional two-phase pipeline. Secondly, we propose an adaptive progressive module replacing mechanism to compress the model depth free from explicit knowledge distillation losses, relieving the multi-task optimization problems. Thirdly, we integrate pruning techniques into VLP model compression to simultaneously compress the model in width and depth. Overall, we obtain a lightweight VLP model with only one pre-training phase, and it is the first one-phase compression method for VLP models. Extensive experiments have been conducted on representative VLP models, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ClipBERT and VICTOR, and the experimental results show a superior trade-off between performance and efficiency.

AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI.

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.

Prediction model and risk analysis of hydrate deposition and blockage in reduced-diameter pipelines

In deep-water gas production and transportation, reduced-diameter structures are used extensively for connecting pipeline. The flow distribution is more complex and the processes of hydrate formation, migration, and deposition are yet to be characterized compared with the conventional pipelines. A new hydrate formation model in a reduced-diameter pipe is developed that considers the throttling effect and uneven thickness distributions of the liquid film on the wall. By solving the deposition velocities of hydrate particles inside and outside of low-speed eddy zones and considering the shedding process of the hydrate layer, the thickness of the deposition layer and blockage time can be accurately predicted. The results suggest that the radial growth rate attains the maximum value at the pipe shrinkage, which is thusly determined to be a high-risk area for hydrate blockage. The temperature decreased rapidly along the pipelines and attained its lowest value in the reduced-diameter region. Parameters such as the mass flow rate of the droplet and film, rate of hydrate formation, and hydrate distribution were highly correlated with the thickness distribution of the hydrate layer. The study establishes a theoretical basis for the prediction and prevention of hydrate risk during gas transportation in pipelines involving complex structures.

Arquitecturas Pipeline Asincrónicas Register Less NULL Convention Logic (RL-NCL) Usando Puertas Básicas

Asynchronous circuits is an alternative to design digital systems that is becoming the interest of many researchers in the digital design area mainly due to it’s low-power consumption and robustness. One of the most compelling design paradigms of asynchronous circuits is the NULL Convention Logic (NCL). The pipeline is a very common technique used in digital circuits to achieve high throughput. Although one can implement a pipeline using NCL gates, recent works have shown that register-less pipelines are possible using modified NCL gates. In this paper we propose two new Register-Less NCL (RL-NCL) pipeline architectures and two new methods to design NCL gates, which can be implemented even in Field Programmable Gate Arrays (FPGAs) or using the standard cells method. The new design of the proposed architecture was able to achieve an average area reduction of 27,32%, an average latency reduction of 14,1% and an average throughput increase of 5,54% comparing with the conventional NCL pipeline architecture.

Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory performance, automatic sleep staging solutions are not widely adopted in a clinical context yet. We argue that most deep learning solutions for sleep scoring are limited in their real-world applicability as they are hard to train, deploy, and reproduce. Moreover, these solutions lack interpretability and transparency, which are often key to increase adoption rates. In this work, we revisit the problem of sleep stage classification using classical machine learning. Results show that competitive performance can be achieved with a conventional machine learning pipeline consisting of preprocessing, feature extraction, and a simple machine learning model. In particular, we analyze the performance of a linear model and a non-linear (gradient boosting) model. Our approach surpasses state-of-the-art (that uses the same data) on two public datasets: Sleep-EDF SC-20 (MF1 0.810) and Sleep-EDF ST (MF1 0.795), while achieving competitive results on Sleep-EDF SC-78 (MF1 0.775) and MASS SS3 (MF1 0.817). We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models. This observation opens the door to clinical adoption, as a representative feature vector allows to leverage both the interpretability and successful track record of traditional machine learning models.

Evaluation of somatic and/or germline mosaicism in congenital malformation of the eye.

Microphthalmia, Anophthalmia and Coloboma (MAC) form a spectrum of congenital eye malformations responsible for severe visual impairment. Despite the exploration of hundreds of genes by High-Throughput Sequencing (HTS), most of the patients remain without genetic diagnosis. One explanation could be the not yet demonstrated involvement of somatic mosaicism (undetected by conventional analysis pipelines) in those patients. Furthermore, the proportion of parental germline mosaicism in presumed de novo variations is still unknown in ocular malformations. Thus, using dedicated bioinformatics pipeline designed to detect mosaic variants, we reanalysed the sequencing data obtained from a 119 ocular development genes panel performed on blood samples of 78 probands with sporadic MAC without genetic diagnosis. Using the same HTS strategy, we sequenced 80 asymptomatic parents of 41 probands carrying a disease-causing variant in an ocular development gene considered de novo after Sanger sequencing of both parents. Reanalysis of the previously sequencing data did not find any mosaic variant in probands without genetic diagnosis. However, HTS of parents revealed undetected SOX2 and PAX6 mosaic variants in two parents. Finally, this work, performed on two large cohorts of patients with MAC spectrum, provides for the first time an overview of the interest of looking for mosaicism in ocular development disorders. Somatic mosaicism does not appear to be frequent in MAC spectrum and might explain only few diagnoses. Thus, other approaches such as whole genome sequencing should be considered in those patients. Parental mosaicism is however not that rare (around 5%) and challenging for genetic counselling.

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information.

Efficient processing of large-scale time-series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand-engineered feature extraction often involve huge computational costs with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with the increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multidimensional time-series data with spatial information. The cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time-series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for the classification of multiclass time-series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series data sets: a multichannel scalp electroencephalogram (EEG) data set for seizure detection, and a machine fault detection data set obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.