Parallel Loops Research Articles

Mapping individual cortico-basal ganglia-thalamo-cortical circuits integrating structural and functional connectome: implications for upper limb motor impairment poststroke.

This study investigated alterations in functional connectivity (FC) within cortico-basal ganglia-thalamo-cortical (CBTC) circuits and identified critical connections influencing poststroke motor recovery, offering insights into optimizing brain modulation strategies to address the limitations of traditional single-target stimulation. We delineated individual-specific parallel loops of CBTC through probabilistic tracking and voxel connectivity profiles-based segmentation and calculated FC values in poststroke patients and healthy controls, comparing with conventional atlas-based FC calculation. Support vector machine (SVM) analysis distinguished poststroke patients from controls. Connectome-based predictive modeling (CPM) used FC values within CBTC circuits to predict upper limb motor function. Poststroke patients exhibited decreased ipsilesional connectivity within the individual-specific CBTC circuits. SVM analysis achieved 82.8% accuracy, 76.6% sensitivity, and 89.1% specificity using individual-specific parallel loops. Additionally, CPM featuring positive connections/all connections significantly predicted Fugl-Meyer assessment of upper extremity scores. There were no significant differences in the group comparisons of conventional atlas-based FC values, and the FC values resulted in SVM accuracy of 75.0%, sensitivity of 67.2%, and specificity of 82.8%, with no significant CPM capability. Individual-specific parallel loops show superior predictive power for assessing upper limb motor function in poststroke patients. Precise mapping of the disease-related circuits is essential for understanding poststroke brain reorganization.

FPGA‐Based Implementation of Real‐Time Cardiologist‐Level Arrhythmia Detection and Classification in Electrocardiograms Using Novel Deep Learning

ABSTRACTCardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine‐learning (ML) algorithms have extensive training cycles and require manual feature selection; to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT‐Cap. We conduct necessary preprocessing operations, including noise removal and signal‐to‐image conversion using short‐time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT‐BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi‐class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state‐of‐the‐art methodologies confirms the superiority of our framework. Additionally, we configure a field‐programmable gate array (FPGA) to implement the proposed model for real‐time arrhythmia classification, aiming to enhance user‐friendliness and speed. Despite numerous suggestions for high‐performance FPGA accelerators in the literature, our FPGA‐based accelerator utilizes optimization of loop parallelization, FP data, and multiply accumulation (MAC) unit. Our accelerator architecture achieves a 57% reduction in processing time and utilizes fewer resources compared to a floating‐point (FlP) design.

Optimizing coolant loop design across the stator core: A research study

Liquid cooling is widely used on high power motors. Optimal design of the coolant loop could help rise the power density. The coolant channels are placed across the stator core in this research. The heat dissipation performances of the coolant channel with different designs are compared and analyzed through simulation. Experiments were carried out to verify the simulation results and the feasibility of the cooling method. The results show that the coolant loop should be placed tightly close to the slots. The heat dissipation performance of the optimized coolant loop is efficient compared to the jacket cooling design. The coolant channels across the stator core could remain sealed under a 0.5 Mpa pressure at least according to the air proof test. The coolant pressure within the channels could be reduced efficiently through increasing the parallel loop number or moving the channels away from the slots properly. The winding temperature of the measured values and the simulated results of the motor with jacket cooling is within 2 °C.

An ultra-sensitive photonic crystal fiber magnetic field sensor based on Vernier effect using two parallel Sagnac loops

Magnetic field detection is of significant importance in various fields, including military, industrial, and power transmission systems. In this paper, we propose a novel ultra-sensitive photonic crystal fiber (PCF) magnetic field sensor based on the Vernier effect, employing two parallel Sagnac loops. Since magnetic field detection relies on the magneto-optical effect of magnetic fluids, all air holes in the PCF are assumed to filled with magnetic fluids. By inserting two slightly different lengths of PCFs into two parallel Sagnac loops, the Vernier effect can be excited to improve the sensitivity of magnetic field detection. The sensing characteristics of the PCF magnetic field sensor are theoretically studied using the finite element method (FEM). Moreover, the influences of the wavelength and magnetic field intensity on the sensing performance are also analyzed. The results show that the sensitivity and resolution of the PCF magnetic field sensor can reach 11.9 nm Oe−1 and 8.4 × 10−3 Oe, respectively, within the magnetic field intensity range of 80–150 Oe. To our best knowledge, the proposed magnetic field sensor exhibits the highest sensitivity among existing magnetic field sensors based on optical fiber interferometers. The proposed magnetic field sensor possesses ultra-high sensitivity and resolution, which exhibits good application prospects in the field of magnetic field detection.

Multi-channel decentralized decoupling FxLMS algorithm and active vibration control experiment

When vibrations generated by marine machinery propagate through a ship’s hull into the ocean, they produce low-frequency radiated noise with distinct “acoustic fingerprint” characteristics. This noise, characterized by stable and concentrated energy, long transmission distances, and difficulty in elimination, becomes the primary target for enemy sonar detection. Active vibration isolation serves as a critical method for reducing low-frequency vibrations in ships and enhancing their acoustic stealth performance. However, control challenges persist, including multi-frequency excitation, frequency fluctuation, multi-channel coupling, and slow convergence speed. To address these issues, this paper introduced an innovative multi-channel decentralized decoupling filtered-x least mean square (DMFxLMS) algorithm. Firstly, a recursive least squares identification algorithm with a forgetting factor was proposed, taking into account the characteristics of single-input, multi-output and multi-input, and multi-output control systems, effectively enhancing the algorithm’s convergence speed and control accuracy. Secondly, based on the decentralized decoupling control concept, the multi-channel control system was simplified into parallel single-channel control loops. The control weight coefficient updates were only related to adjacent error signals, significantly reducing the algorithm’s computational complexity. Thirdly, an anti-impact link was designed to improve the algorithm’s robustness, considering the interference caused by other mechanical equipment during the control process. The influence of abnormal error signals in the control weight coefficient correction term was suppressed, and a percentage function was introduced to limit the output signal. Finally, the feasibility and effectiveness of the DMFxLMS algorithm were verified through simulations and experiments. The results demonstrated that the DMFxLMS algorithm achieved significant control effects for both constant frequency line spectrum excitation and frequency fluctuating line spectrum excitation, fulfilling the objective of reducing base vibration. The DMFxLMS algorithm exhibited fast convergence and excellent robustness, making it suitable for practical engineering applications.

Precise cortical contributions to sensorimotor feedback control during reactive balance.

The role of the cortex in shaping automatic whole-body motor behaviors such as walking and balance is poorly understood. Gait and balance are typically mediated through subcortical circuits, with the cortex becoming engaged as needed on an individual basis by task difficulty and complexity. However, we lack a mechanistic understanding of how increased cortical contribution to whole-body movements shapes motor output. Here we use reactive balance recovery as a paradigm to identify relationships between hierarchical control mechanisms and their engagement across balance tasks of increasing difficulty in young adults. We hypothesize that parallel sensorimotor feedback loops engaging subcortical and cortical circuits contribute to balance-correcting muscle activity, and that the involvement of cortical circuits increases with balance challenge. We decomposed balance-correcting muscle activity based on hypothesized subcortically- and cortically-mediated feedback components driven by similar sensory information, but with different loop delays. The initial balance-correcting muscle activity was engaged at all levels of balance difficulty. Its onset latency was consistent with subcortical sensorimotor loops observed in the lower limb. An even later, presumed, cortically-mediated burst of muscle activity became additionally engaged as balance task difficulty increased, at latencies consistent with longer transcortical sensorimotor loops. We further demonstrate that evoked cortical activity in central midline areas measured using electroencephalography (EEG) can be explained by a similar sensory transformation as muscle activity but at a delay consistent with its role in a transcortical loop driving later cortical contributions to balance-correcting muscle activity. These results demonstrate that a neuromechanical model of muscle activity can be used to infer cortical contributions to muscle activity without recording brain activity. Our model may provide a useful framework for evaluating changes in cortical contributions to balance that are associated with falls in older adults and in neurological disorders such as Parkinson's disease.

A new flat electronics cooling device composed of internal parallel loop heat pipes

A new flat electronics cooling device composed of internal parallel loop heat pipes

Parallel executive pallio-motor loops in the pigeon brain.

A core component of the avian pallial cognitive network is the multimodal nidopallium caudolaterale (NCL) that is considered to be analogous to the mammalian prefrontal cortex (PFC). The NCL plays a key role in a multitude of executive tasks such as working memory, decision-making during navigation, and extinction learning in complex learning environments. Like the PFC, the NCL is positioned at the transition from ascending sensory to descending motor systems. For the latter, it sends descending premotor projections to the intermediate arcopallium (AI) and the medial striatum (MSt). To gain detailed insight into the organization of these projections, we conducted several retrograde and anterograde tracing experiments. First, we tested whether NCL neurons projecting to AI (NCLarco neurons) and MSt (NCLMSt neurons) are constituted by a single neuronal population with bifurcating neurons, or whether they form two distinct populations. Here, we found two distinct projection patterns to both target areas that were associated with different morphologies. Second, we revealed a weak topographic projection toward the medial and lateral striatum and a strong topographic projection toward AI with clearly distinguishable sensory termination fields. Third, we investigated the relationship between the descending NCL pathways to the arcopallium with those from the hyperpallium apicale, which harbors a second major descending pathway of the avian pallium. We embed our findings within a system of parallel pallio-motor loops that carry information from separate sensory modalities to different subpallial systems. Our results also provide insights into the evolution of the avian motor system from which, possibly, the song system has emerged.

Stability analysis and vibration suppression in an overhung rotor system using active magnetic damper

The present work demonstrates the effectiveness of an active magnetic damper (AMD) in improving the vibration characteristics of an overhung rotor system (ORS) considering the effects of synchronous mass-unbalance, rotor bow, and viscous internal damping. A finite element-based rotordynamic analysis has been conducted to evaluate the system response using state-space formulation. MATLAB codes are developed for the numerical implementation of the rotordynamic model pertaining to the flexible ORS integrated with an AMD consisting of three parallel feedback loops. The internal damping is found to reduce the vibration amplitudes at the bearing and disk locations. However, it leads to system instability in the absence of AMD beyond the first critical speed (670 RPM) which is indicated by the change in the real part of eigenvalues. Under these circumstances, the use of AMD causes a remarkable improvement and the system stabilizes for almost the entire speed range considered here, 320–10,000 RPM. In addition, the AMD causes a maximum reduction of 95.8%, 96.5%, and 83.3% in the vibration amplitudes at the locations of Bearing 1, Bearing 2, and the overhung disk, respectively. Using an appropriate set of AMD parameters, the system characteristics can be altered effectively as per the requirement.

A new thread-level speculative automatic parallelization model and library based on duplicate code execution

Loop-efficient automatic parallelization has become increasingly relevant due to the growing number of cores in current processors and the programming effort needed to parallelize codes in these systems efficiently. However, automatic tools fail to extract all the available parallelism in irregular loops with indirections, race conditions or potential data dependency violations, among many other possible causes. One of the successful ways to automatically parallelize these loops is the use of speculative parallelization techniques. This paper presents a new model and the corresponding C++ library that supports the speculative automatic parallelization of loops in shared memory systems, seeking competitive performance and scalability while keeping user effort to a minimum. The primary speculative strategy consists of redundantly executing chunks of loop iterations in a duplicate fashion. Namely, each chunk is executed speculatively in parallel to obtain results as soon as possible and sequentially in a different thread to validate the speculative results. The implementation uses C++11 threads and it makes intensive use of templates and advanced multithreading techniques. An evaluation based on various benchmarks confirms that our proposal provides a competitive level of performance and scalability.

Ordered scheduling in control-flow distributed transactional memory

Consider the control-flow model of transaction execution in a distributed system modeled as a communication graph where shared objects positioned at nodes of the graph are immobile but the transactions accessing the objects send requests to the nodes where objects are located to read/write those objects. The control-flow model offers benefits to applications in which the movement of shared objects is costly due to their sizes and security purposes. In this paper, we study the ordered scheduling problem of committing dependent transactions according to their predefined priorities in this model. The considered problem naturally arises in areas, such as loop parallelization and state-machine-based computing, where producing executions equivalent to a priority order is needed to satisfy certain properties. Specifically, we study ordered scheduling considering two performance metrics fundamental to any distributed system: (i) execution time - total time to commit all the transactions and (ii) communication cost - the total distance traversed in accessing required shared objects. We design scheduling algorithms that are individually or simultaneously efficient for both the metrics and rigorously evaluate them through several benchmarks on random and grid graphs, validating their efficiency. To our best knowledge, this is the first study of ordered scheduling in the control-flow model of distributed transaction execution.

Parallel GEMM-based convolution for deep learning on multicore RISC-V processors

We address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication (gemm) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm.

Development of embedded fuzzy control using reconfigurable FPGA technology

The purpose of this work is to investigate the construction of fuzzy embedded control systems combining fast execution and parallel processing capabilities provided by Field Programmable Gate Arrays (FPGAs) and Reconfigurable Inputs/Outputs (RIO) chips. A fixed-point fuzzy controller is developed and implemented on a fast mechatronic system with high-speed control and high channel count on an FPGA target. This paper provides a brief introduction to deploying Fuzzy Logic Control (FLC) methods using RIO-FPGA technology. It suggests a technique for implementing the three stages that constitute the FLC and the PD-like FLC and PID-like FLC structures into practice. Controllers with 1 and 2 degrees of freedom are developed and tested experimentally. Parallel loops, key challenging advantage of LabVIEW programming, are utilized for decoding feedback signals, generating pulse trains for actuator's drivers and for calculation of control gains. An NI-SbRIO board that combines deployable devices with a real-time processor, a re-configurable FPGA, and analogue and digital input–output ports is used. The experimental work demonstrates the significant enhancement of implementing reconfigurable embedded fuzzy control upon such mechatronic systems.

COWS for High Performance: Cost Aware Work Stealing for Irregular Parallel Loop

Parallel libraries such as OpenMP distribute the iterations of parallel-for-loops among the threads, using a programmer-specified scheduling policy. While the existing scheduling policies perform reasonably well in the context of balanced workloads, in computations that involve highly imbalanced workloads it is extremely non-trivial to obtain an efficient distribution of work (even using non-static scheduling methods like dynamic and guided). In this paper, we present a scheme called COst aware Work Stealing (COWS) to efficiently extend the idea of work-stealing to OpenMP. In contrast to the traditional work-stealing schedulers, COWS takes into consideration that (i) not all iterations of a parallel-for-loops may take the same amount of time. (ii) identifying a suitable victim for stealing is important for load-balancing, and (iii) queues lead to significant overheads in traditional work-stealing and should be avoided. We present two variations of COWS: WSRI (a naive work-stealing scheme based on the number of remaining iterations) and WSRW (work-stealing scheme based on the amount of remaining workload). Since in irregular loops like those found in graph analytics it is not possible to statically compute the cost of the iterations of the parallel-for-loops, we use a combined compile-time + runtime approach, where the remaining workload of a loop is computed efficiently at runtime by utilizing the code generated by our compile-time component. We have performed an evaluation over seven different benchmark programs, using five different input datasets, on two different hardware across a varying number of threads; leading to a total number of 275 configurations. We show that in 225 out of 275 configurations, compared to the best OpenMP scheduling scheme for that configuration, our approach achieves clear performance gains.

Cerebellum's Contribution to Attention, Executive Functions and Timing: Psychophysiological Evidence from Event-Related Potentials.

Since 1998, when Schmahmann first proposed the concept of the "cognitive affective syndrome" that linked cerebellar damage to cognitive and emotional impairments, a substantial body of literature has emerged. Anatomical, neurophysiological, and functional neuroimaging data suggest that the cerebellum contributes to cognitive functions through specific cerebral-cerebellar connections organized in a series of parallel loops. The aim of this paper is to review the current findings on the involvement of the cerebellum in selective cognitive functions, using a psychophysiological perspective with event-related potentials (ERPs), alone or in combination with non-invasive brain stimulation techniques. ERPs represent a very informative method of monitoring cognitive functioning online and have the potential to serve as valuable biomarkers of brain dysfunction that is undetected by other traditional clinical tools. This review will focus on the data on attention, executive functions, and time processing obtained in healthy subjects and patients with varying clinical conditions, thus confirming the role of ERPs in understanding the role of the cerebellum in cognition and exploring the potential diagnostic and therapeutic implications of ERP-based assessments in patients.

A novel approach of multi-loop control based-ADRC for improving lower knee position exoskeleton system

AbstractThis study revealed the system of a lower limb exoskeleton created for knee rehabilitation. The exoskeleton has been extensively used in rehabilitation robotic device research, but its practical applicability is limited due to its high nonlinearity and uncertain behavior. As a result, the control technique is critical in increasing the efficacy of rehabilitation devices. For the rehabilitation and help of a patient with a lower-limb condition, a sliding mode control (SMC) with proportional derivative (PD) control approach are used as parallel loops. Active disturbances rejection control (ADRC) is used by these controllers to cancel any external influences. To overcome the degradation of disturbance rejection and robustness caused by a failure to fully adjust for the entire disturbance, a (SMC) loop was introduced to the control regulation. By assessing performance indices related to the estimated inaccuracy, the results demonstrate the effectiveness of the suggested controller. Simulink is used for simulation and analysis.

Medication only improves limb movements while deep brain stimulation improves eye and limb movements during visually-guided reaching in Parkinson's disease.

Antiparkinson medication and subthalamic nucleus deep brain stimulation (STN-DBS), two common treatments of Parkinson's disease (PD), effectively improve skeletomotor movements. However, evidence suggests that these treatments may have differential effects on eye and limb movements, although both movement types are controlled through the parallel basal ganglia loops. Using a task that requires both eye and upper limb movements, we aimed to determine the effects of medication and STN-DBS on eye and upper limb movement performance. Participants performed a visually-guided reaching task. We collected eye and upper limb movement data from participants with PD who were tested both OFF and ON medication (n = 34) or both OFF and ON bilateral STN-DBS while OFF medication (n = 11). We also collected data from older adult healthy controls (n = 14). We found that medication increased saccade latency, while having no effect on reach reaction time (RT). Medication significantly decreased saccade peak velocity, while increasing reach peak velocity. We also found that bilateral STN-DBS significantly decreased saccade latency while having no effect on reach RT, and increased saccade and reach peak velocity. Finally, we found that there was a positive relationship between saccade latency and reach RT, which was unaffected by either treatment. These findings show that medication worsens saccade performance and benefits reaching performance, while STN-DBS benefits both saccade and reaching performance. We explore what the differential beneficial and detrimental effects on eye and limb movements suggest about the potential physiological changes occurring due to treatment.

A Low-Level Virtual Machine Just-In-Time Prototype for Running an Energy-Saving Hardware-Aware Mapping Algorithm on C/C++ Applications That Use Pthreads

Low-Level Virtual Machine (LLVM) compiler infrastructure is a useful tool for building just-in-time (JIT) compilers, besides its reliable front end represented by a clang compiler and its elaborated middle end containing different optimizations that improve the runtime performance. This paper specifically addresses the part of building a JIT compiler using an LLVM with the scope of obtaining the hardware architecture details of the underlying machine such as the number of cores and the number of logical cores per processing unit and providing them to the NUMA-BTLP static thread classification algorithm and to the NUMA-BTDM static thread mapping algorithm. Afterwards, the hardware-aware algorithms are run using the JIT compiler within an optimization pass. The JIT compiler in this paper is designed to run on a parallel C/C++ application (which creates threads using Pthreads), before the first time the application is executed on a machine. To achieve this, the JIT compiler takes the native code of the application, obtains the corresponding LLVM IR (Intermediate Representation) for the native code and executes the hardware-aware thread classification and the thread mapping algorithms on the IR. The NUMA-Balanced Task and Loop Parallelism (NUMA-BTLP) and NUMA-Balanced Thread and Data Mapping (NUMA-BTDM) are expected to optimize the energy consumption by up to 15% on the NUMA systems.

Robust multi‐loop control of a floating wind turbine

AbstractA principal challenge facing the control of floating offshore wind turbines (FOWTs) is the problem of instability, or “negative damping,” when using blade pitch feedback to control generator speed. This closed‐loop instability can be attributed to non‐minimum phase zeros in the transfer function from blade pitch to generator speed. Standard approaches to improving stability and performance include robust tuning of control gains and introducing multiple feedback loops to respond to platform motion. Combining these approaches is nontrivial because multiple control loops complicate the impact of coupling in the system dynamics. The single‐loop approach to analyzing stability robustness neglects inter‐loop coupling, while a simplistic multi‐loop approach is highly sensitive to dimensional scaling and overestimates the robustness of the single‐loop controller. This work proposes a sensitivity representation that separates some of the natural FOWT dynamic coupling into a parallel feedback loop in the sensitivity function loop to address both of these concerns. The modified robustness measure is used with a simplified linear FOWT model to optimize scheduled multi‐loop control parameters in an automated tuning procedure. This controller is implemented for the 10‐MW Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) and compared against conventional single‐ and multi‐loop controllers tuned using frequency‐domain analysis and high‐fidelity OpenFAST simulations. The multi‐loop robust controller shows the highest overall performance in generator speed regulation and tower load reduction, though consideration of power quality, actuator usage, and other structural loading leads to additional trade‐offs.

Voltage Detection-Based Selective Harmonic Current Compensation Strategies for Photovoltaic Inverters

Voltage and current detection-based harmonic current compensation (VDB-HCC and CDB-HCC, respectively) strategies allow the use of photovoltaic (PV) inverters to enhance the grid power quality. CDB-HCC strategies require converter hardware retrofit by inserting an extra current sensor to measure load or downstream grid currents. On the other hand, VDB-HCC strategies are straightforward solutions employing only embedded measurements used for protection, control, and synchronization purposes. This paper provides a comprehensive comparison of three VDB-HCC strategies: VDB-HCC based on a single current loop (SCL); VDB-HCC based on dual parallel current control loops (DCL); and VDB-HCC based on parallel voltage and current control loops (VCL). The comparison includes the requirement of harmonic detection algorithms, PV array maximum power point tracking (MPPT) performance, stability over grid impedance variations and inverter power rating, steady-state harmonic compensation, enabling transient response, and computational burden. Experimental results using a 1.5-kW commercial PV inverter are conducted. Based on distributed HCC applications by PV systems, VCL has shown the best performance in terms of voltage distortion reduction under weak grids.