Parallel Transmission Research Articles

Low-Traffic Aware Hybrid MAC (LTH-MAC) Protocol for Wireless Sensor Networks

This paper proposes LTH-MAC (Low-Traffic Aware Hybrid MAC), a novel MAC protocol designed to improve energy efficiency and message delivery reliability in Wireless Sensor Networks (WSNs). LTH-MAC achieves this through innovative techniques like flexible timeslots, channel selection, collision-avoiding, parallel transmissions, and efficient backoff schemes. These optimizations lead to reduced idle listening, minimized collisions, and simplified synchronization. Simulations using OPNET environment demonstrate that LTH-MAC significantly reduces energy consumption, especially under light traffic loads. Additionally, LTH-MAC provides lower end-to-end latency and higher message delivery reliability compared to ECoMAC. These advancements position LTH-MAC as a compelling solution for WSN applications demanding efficient and reliable communication.

Bioinspired In-Sensor Multimodal Fusion for Enhanced Spatial and Spatiotemporal Association.

Multimodal perception can capture more precise and comprehensive information compared with unimodal approaches. However, current sensory systems typically merge multimodal signals at computing terminals following parallel processing and transmission, which results in the potential loss of spatial association information and requires time stamps to maintain temporal coherence for time-series data. Here we demonstrate bioinspired in-sensor multimodal fusion, which effectively enhances comprehensive perception and reduces the level of data transfer between sensory terminal and computation units. By adopting floating gate phototransistors with reconfigurable photoresponse plasticity, we realize the agile spatial and spatiotemporal fusion under nonvolatile and volatile photoresponse modes. To realize an optimal spatial estimation, we integrate spatial information from visual-tactile signals. For dynamic events, we capture and fuse in real time spatiotemporal information from visual-audio signals, realizing a dance-music synchronization recognition task without a time-stamping process. This in-sensor multimodal fusion approach provides the potential to simplify the multimodal integration system, extending the in-sensor computing paradigm.

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

Calibration-free parallel transmission of the cervical, thoracic, and lumbar spinal cord at 7T.

To address the limitations of spinal cord imaging at ultra-high field (UHF) due to time-consuming parallel transmit (pTx) adjustments. This study introduces calibration-free offline computed universal shim modes that can be applied seamlessly for different pTx RF coils and spinal cord target regions, substantially enhancing spinal cord imaging efficiency at UHF. A library of channel-wise relative maps for the cervical spinal cord (six datasets) and thoracic and lumbar spinal cord (nine datasets) was constructed to optimize transmit homogeneity and efficiency for these regions. A tailored B0 shim was optimized for the cervical spine to enhance spatial magnetic field homogeneity further. The performance of the universal shims was validated using absolute saturation based mapping and high-resolution 2D and 3D multi-echo gradient-recalled echo (GRE) data to assess the image quality. The proposed universal shims demonstrated a 50% improvement in efficiency compared to the default (zero phase) shim mode. homogeneity was also improved by 20%. The optimized universal shims achieved performance comparable to subject-specific pTx adjustments, while eliminating the need for lengthy pTx calibration times, saving about 10 min per experiment. The development of universal shims represents a significant advance by eliminating time-consuming subject-specific pTx adjustments. This approach is expected to make UHF spinal cord imaging more accessible and user-friendly, particularly for non-pTx experts.

Advancing Light Scattering Control Through the Exploration of Temperature‐Sensitive Optical Modifiers

AbstractLight scattering holds immense significance in various applications, including LED lighting, display enhancement, and solar cell optimization, emphasizing the critical role of understanding scattering mechanisms, especially light–matter interactions, achieving effective optical diffusion. Consequently, the influence of stimuli on material properties, including refractive indices and thermal expansion behavior, has emerged as a pivotal consideration, leading to the development of stimuli‐responsive light scattering modifier. Here, a temperature‐sensitive light scattering modifier composed of poly(methyl methacrylate‐co‐styrene) and poly(ethylene‐co‐vinyl acetate) is presented as scattering particles and matrix, respectively, using a melt‐blending process. Optical properties are evaluated across controlled temperatures 0–70 °C. These findings demonstrate an increasing trend in diffuse transmission and haze as temperatures increased across all light scattering modifiers. Significantly, the optimized light scattering modifier, integrating a dual particle system, showcased uniform reduction gaps in parallel transmission and adjustable haze characteristics across the visible spectrum under temperature control. This optical modifier achieved a total transmission of 92% and adjustable haze levels ranging from 35% to 90% under temperature control. Furthermore, this fabrication of three‐layer films incorporating the light scattering modifier exhibited identical characteristics to the developed light scattering modifier, emphasizing their potential for seamless integration across diverse technological domains.

Identification of symmetrical faults and overloads on parallel transmission lines based on compensated voltage cosine volume

Identification of symmetrical faults and overloads on parallel transmission lines based on compensated voltage cosine volume

PaCHNOC: Packet and Circuit Hybrid Switching NoC for Real-Time Parallel Stream Signal Processing.

Real-time heterogeneous parallel embedded digital signal processor (DSP) systems process multiple data streams in parallel in a stringent time interval. This type of system on chip (SoC) requires the network on chip (NoC) to establish multiple symbiotic parallel data transmission paths with ultra-low transmission latency in real time. Our early NoC research PCCNOC meets this need. The PCCNOC uses packet routing to establish and lock a transmission circuit, so that PCCNOC is perfectly suitable for ultra-low latency and high-bandwidth transmission of long data packets. However, a parallel multi-data stream DSP system also needs to transmit roughly the same number of short data packets for job configuration and job execution status reports. While transferring short data packets, the link establishment routing delay of short data packets becomes relatively obvious. Our further research, thus, introduced PaCHNOC, a hybrid NoC in which long data packets are transmitted through a circuit established and locked by routing, and short data packets are attached to the routing packet and the transmission is completed during the routing process, thus avoiding the PCCNOC setup delay. Simulation shows that PaCHNOC performs well in supporting real-time heterogeneous parallel embedded DSP systems and achieves overall latency reduction 65% compared with related works. Finally, we used PaCHNOC in the baseband subsystem of a real 5G base station, which proved that our research is the best NoC for baseband subsystem of 5G base stations, which reduce 31% comprehensive latency in comparison to related works.

Self-supervised dynamic learning for long-term high-fidelity image transmission through unstabilized diffusive media

Multimode fiber (MMF) which supports parallel transmission of spatially distributed information is a promising platform for remote imaging and capacity-enhanced optical communication. However, the variability of the scattering MMF channel poses a challenge for achieving long-term accurate transmission over long distances, of which static optical propagation modeling with calibrated transmission matrix or data-driven learning will inevitably degenerate. In this paper, we present a self-supervised dynamic learning approach that achieves long-term, high-fidelity transmission of arbitrary optical fields through unstabilized MMFs. Multiple networks carrying both long- and short-term memory of the propagation model variations are adaptively updated and ensembled to achieve robust image recovery. We demonstrate >99.9% accuracy in the transmission of 1024 spatial degree-of-freedom over 1 km length MMFs lasting over 1000 seconds. The long-term high-fidelity capability enables compressive encoded transfer of high-resolution video with orders of throughput enhancement, offering insights for artificial intelligence promoted diffusive spatial transmission in practical applications.

Design and control of a compliant robotic actuator with parallel spring-damping transmission

AbstractPhysically compliant actuator brings significant benefits to robots in terms of environmental adaptability, human–robot interaction, and energy efficiency as the introduction of the inherent compliance. However, this inherent compliance also limits the force and position control performance of the actuator system due to the induced oscillations and decreased mechanical bandwidth. To solve this problem, we first investigate the dynamic effects of implementing variable physical damping into a compliant actuator. Following this, we propose a structural scheme that integrates a variable damping element in parallel to a conventional series elastic actuator. A damping regulation algorithm is then developed for the parallel spring-damping actuator (PSDA) to tune the dynamic performance of the system while remaining sufficient compliance. Experimental results show that the PSDA offers better stability and dynamic capability in the force and position control by generating appropriate damping levels.

A Mathematical Analysis of Clustering-Free Local SAR Compression Algorithms for MRI Safety in Parallel Transmission.

Parallel transmission (pTX) is a versatile solution to enable UHF MRI of the human body, where radiofrequency (RF) field inhomogeneity appears very challenging. Today, state of the art monitoring of the local SAR in pTX consists in evaluating the RF power deposition on specific SAR matrices called Virtual Observation Points (VOPs). It essentially relies on accurate electromagnetic simulations able to return the local SAR distribution inside the body in response to any applied pTX RF waveform. In order to reduce the number of SAR matrices to a value compatible with real time SAR monitoring ( << 103) , a VOP set is obtained by partitioning the SAR model into clusters, and associating a so- called dominant SAR matrix to every cluster. More recently, a clustering-free compression method was proposed, allowing for a significant reduction in the number of SAR matrices. The concept and derivation however assumed static RF shims and their extension to dynamic pTX is not straightforward, thereby casting doubt on the strict validity of the compression approach for these more complicated RF waveforms. In this work, we provide the mathematical framework to tackle this problem and find a rigorous justification of this criterion in the light of convex optimization theory. Our analysis led us to a variant of the clustering-free compression approach exploiting convex optimization. This new compression algorithm offers computational gains for large SAR models and for high-channel count pTX RF coils.

Data-Acquisition System of a Multidetector Device with Tagged Neutrons with Parallel Transmission of Events

Data-Acquisition System of a Multidetector Device with Tagged Neutrons with Parallel Transmission of Events

Manufacturing and contact characteristics analysis of a novel 2K-H internal meshing planetary reducer with involute-cycloid combined tooth profiles

Rotate Vector (RV) reducer involves multiple crankshafts to drive the cycloidal-pin transmission in parallel, leading to an over-positioning structure and high manufacturing and assembly requirements for the reducer, restricting the development of industrial robots. In view of the above problems, a novel configuration of Abnormal Cycloidal Gear (ACG) reducer with a 2K-H internal meshing planetary transmission is proposed. The design innovatively replaces the multiple crankshafts with a pin mechanism, thereby addressing the over-positioning and simplifying the manufacturing and assembly processes. To improve the performance of the reducer, the “epicycloid-involute-hypocycloid” combined tooth profiles are designed and adopted as the ACG reducer’s drive teeth. The configuration principle of the ACG reducer is conducted. Next, a manufacturing method utilizing a rack cutter to generate the profile of drive teeth is presented. Finally, the meshing performance and mechanical behaviors of the designed internal meshing pair for the ACG reducer are evaluated. Results show that ACG reducer has the advantages of a simple structure and large transmission ratio. The combined tooth profiles can be machined at one time using the designed cutter, simplifying the tooth machining process. The internal meshing pair is linear contact and exhibits an obviously multi-teeth meshing effect, which benefits the carrying capacity and transmission performance of the ACG reducer.

Large-Scale Data Processing and Machine Learning Analysis Model Based on Distributed Algorithm

The model of large-scale data processing and ML(machine learning) analysis based on DA(distributed algorithm) is a powerful computing method, which aims at processing huge data sets and performing efficient ML analysis. In this paper, a cluster topology driver module based on gradient switching and aggregate communication is designed, and its core goal is to adapt the distributed system to various underlying network topologies. By designing decentralized gradient exchange algorithm and aggregate communication framework, the parallel transmission ability of multi-interface network can be fully exerted, thus improving the model synchronization efficiency of ML task. The experimental results show that the cluster topology driver module can provide better performance than the existing methods in terms of training convergence, cluster scalability and communication overhead. Large-scale data processing and ML analysis model based on DA is widely used in processing massive data and realizing complex analysis tasks.

Comparative Study on Power Transmission Modeling in Large Scale AGC Power System

This paper presents a comparative analysis for various type of power transmission modeling in multi-area AGC power system. AC power transmission, parallel AC-AC transmission lines and parallel AC-DC transmission modeling in two-area LFC power system is presented and the results are compared. Based on simulation results the positive effects of HVDC links under random load disturbance are demonstrated.

On line monitoring of transmission line

Continuous online monitoring of transmission line is required for proper functioning of power system. This paper presents two simulink model one for single transmission line, singly fed and the other for parallel transmission line doubly fed and presents the simulation result which shows the continuous variation in transmission line impedance before, during and after the fault. This paper shows the effect of different factors like prefault system condition, ground fault resistance, series compensation effect individually and then taking together on impedance plot under line to ground fault and three phase to ground fault. In case of parallel transmission line the paper have taken into account of mutual coupling effect also.

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

A novel anomaly detection model for secure multipath QUIC communications by jointly using empirical mode decomposition and long short-term memory networks

In the era of rapid development of modern internet technology, network transmission techniques are continuously iterating and updating. The Quick UDP Internet Connections (QUIC) protocol has emerged as a timely response to these advancements. Owing to the strong compatibility and high transmission speed of QUIC, its extended version, Multipath QUIC (MPQUIC), has gained popularity. MPQUIC can integrate various transmission scenarios, achieving parallel transmission with higher bandwidth. However, due to some security flaws in the protocol, MPQUIC is susceptible to attacks from anomalous network traffic. To address this issue, we propose an MPQUIC traffic anomaly detection model based on Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) networks, which can decompose and denoise data and learn the long-term dependencies of the data. Simulation experiments are conducted by obtaining MPQUIC traffic data under normal and anomalous conditions for prediction, analysis, and evaluation. The results demonstrate that the proposed model exhibits satisfactory prediction performance when trained on both normal and anomalous traffic data, enabling anomaly detection. Moreover, the evaluation metrics indicate that the EMD-LSTM-based model achieves higher accuracy compared to various traditional single models.

Bifurcation and Chaotic Characterization of Incommensurate Fractional-Order Model for AC/DC Parallel Transmission System

Bifurcation and Chaotic Characterization of Incommensurate Fractional-Order Model for AC/DC Parallel Transmission System

An 8-Channel Transceiver Coil for Carotid Artery Imaging at 7T Using an Optimized Shield Design.

To design and fabricate a transmit/receive (T/R) radiofrequency (RF) coil array for MRI of the carotid arteries at 7T with optimal shielding to improve transmit performance in parallel transmit (pTx) mode. The carotid coil included 8 total RF elements, with left and right subarrays, each consisting of 4 overlapping loops with RF shields. Electromagnetic (EM) simulations were performed to optimize and improve the transmit performance of the array by determining the optimal distance between the RF shield and each subarray. EM simulations were further used to calculate local specific absorption rate (SAR) matrices. Based on the SAR matrices, virtual observation points (VOPs) were applied to ensure safety during parallel transmission. The efficacy of the coil design was evaluated by measuring coil performance metrics when imaging a phantom and by acquiring in-vivo images. The optimal distance between the RF shield and each subarray was determined to be 45 mm. This resulted in a maximum B1+ efficiency of 1.23 μT/ √W in the carotid arteries and a peak, 10-g-average SAR per Watt of 0.86 kg-1 when transmitting in the nominal CP+ mode. Optimizing the RF shield resulted in up to 37% improvement in B1+ efficiency and 14% improvement in SAR efficiency compared to an unshielded design. Optimizing the distance between the RF shield and coil array provided significant improvement in the transmit characteristics of the bilateral carotid coil. The bilateral coil topology provides a compelling platform for imaging the carotid arteries with high field MRI.

Research on Gear Synthesis of Automatic Transmission

Abstract The lever method facilitates a straightforward analysis of the motion dynamics of an automatic transmission under various gear settings, offering a simple and intuitive approach to examining planetary gear transmissions. Specifically, the equivalent lever diagram for a parallel-double planetary gear transmission comprises four component points. From a control perspective, a transmission system with two planetary rows can accommodate up to two reduction gears, one direct gear, one overdrive gear, and one reverse gear. In contrast, the equivalent lever diagram for a parallel three-planetary gear transmission includes five component points, with such systems typically supporting up to three reduction gears, one direct gear, two overdrive gears, and two reverse gears. This methodological approach underscores the lever diagram’s utility in delineating the functional capabilities of complex transmission systems.