Transmission Time Research Articles

Delay estimation for cortical-muscular interaction with wavelet coherence time lag

BackgroundCortico-muscular coherence (CMC) between the cerebral cortex and muscle activity is an effective tool for studying neural communication in the motor control system. To accurately evaluate the coherence between electroencephalogram (EEG) and electromyogram (EMG) signals, it is necessary to accurately calculate the time delay between physiological signals to ensure signal synchronization. New methodWe proposed a new delay estimation method, named wavelet coherence time lag (WCTL) and the significant increase areas (SIA) index as a measure of the specific region enhancement effect of the magnitude squared coherence (MSC) image. ResultsThe grip strength level had a small effect on the information transmission time from the cortex to the muscles, while the transmission time from the cortex to different muscle channels was different for the same task. A positive correlation was found between the grip strength level and the SIA index on the β band of C3-B and the α and β bands of C3-FDS.Comparison with Existing Method: The WCTL method was found to accurately calculate the delay time even when the number of repeated segments was low in a simple motor control model, and the results were more accurate than the rate of voxels change (RVC) and CMC with time lag (CMCTL) methods. ConclusionsThe WCTL is an effective method for detecting the transmission time of information between the cortex and muscles, laying the foundation for future rehabilitation treatment for stroke patients.

Emulation-based stabilization for networked control systems with stochastic channels

This paper studies the stabilization problem of networked control systems (NCSs) with random packet dropouts caused by stochastic channels. To describe the effects of stochastic channels on the information transmission, the transmission times are assumed to be deterministic, whereas the packet transmission is assumed to be random. We first propose a stochastic scheduling protocol to model random packet dropouts, and address the properties of the proposed stochastic scheduling protocol. The proposed scheduling protocol provides a unified modelling framework for a general class of random packet dropouts due to different stochastic channels. Next, the proposed scheduling protocol is embedded into the closed-loop system, which leads to a stochastic hybrid model for NCSs with random packet dropouts. Based on this stochastic hybrid model, we follow the emulation approach to establish sufficient conditions to guarantee uniform global asymptotical stability in probability. In particular, an upper bound on the maximally allowable transmission interval is derived explicitly for all stochastic protocols satisfying Lyapunov conditions that guarantee uniform global asymptotic stability in probability. Finally, two numerical examples are presented to demonstrate the derived results.

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

A real-time field bus architecture for multi-smart-motor servo system

The multi-motor servo system (MMSS) is an electro-mechanical system widely used in various fields, including electric vehicles, robotics, and industrial machinery. Depending on the application, the number of motors in the system can range from several dozens to tens of thousands, which imposes additional communication demands. Thus, ensuring synchronization and control precision of the system requires addressing the challenge of guaranteeing the performance and reliability of communication among motors in the MMSS. In this paper, we design a smart servo motor (SSM) to upgrade the system to the multi-smart-motor servo system (MSMSS) based on a distributed real-time field bus architecture, namely, Multi-Motor Bus (MMB) architecture. The proposed MMB architecture is lightweight and stable, providing real-time support for Control Area Network connections to a central user computer and inter-integrated circuit connections to SSM units. This MMB architecture facilitates the synchronization of command transmission across SSMs and ensures the consistency of motors in the MSMSS. Additionally, a serial experiments to examine 3 key system performance and reliability characteristics are conducted, including command transmission time, transmission jitters, and rotation consistency. The analysis of these characteristics demonstrates the system’s potential and feasibility to be applicable in industry.

An Efficient FPGA-based Depthwise Separable Convolutional Neural Network Accelerator with Hardware Pruning

Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks. However, the computation and resource intensive characteristics of CNN bring obstacles to its application on embedded systems. This article proposes an efficient inference accelerator on Field Programmable Gate Array (FPGA) for CNNs with depthwise separable convolutions. To improve the accelerator efficiency, we make four contributions: (1) an efficient convolution engine with multiple strategies for exploiting parallelism and a configurable adder tree are designed to support three types of convolution operations; (2) a dedicated architecture combined with input buffers is designed for the bottleneck network structure to reduce data transmission time; (3) a hardware padding scheme to eliminate invalid padding operations is proposed; and (4) a hardware-assisted pruning method is developed to support online tradeoff between model accuracy and power consumption. Experimental results show that for MobileNetV2 the accelerator achieves 10× and 6× energy efficiency improvement over the CPU and GPU implementation, and 302.3 frames per second and 181.8 GOPS performance that is the best among several existing single-engine accelerators on FPGAs. The proposed hardware-assisted pruning method can effectively reduce 59.7% power consumption at the accuracy loss within 5%.

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

An enhanced multilevel secure data dissemination approximate solution for future networks.

Sensitive data, such as financial, personal, or classified governmental information, must be protected throughout its cycle. This paper studies the problem of safeguarding transmitted data based on data categorization techniques. This research aims to use a novel routine as a new meta-heuristic to enhance a novel data categorization based-traffic classification technique where private data is classified into multiple confidential levels. As a result, two packets belonging to the same confidentiality level cannot be transmitted through two routers simultaneously, ensuring a high data protection level. Such a problem is determined by a non-deterministic polynomial-time hardness (NP-hard) problem; therefore, a scheduling algorithm is applied to minimize the total transmission time over the two considered routers. To measure the proposed scheme's performance, two types of distribution, uniform and binomial distributions used to generate packets transmission time datasets. The experimental result shows that the most efficient algorithm is the Best-Random Algorithm ([Formula: see text]), recording 0.028 s with an average gap of less than 0.001 in 95.1% of cases compared to all proposed algorithms. In addition, [Formula: see text] is compared to the best-proposed algorithm in the literature which is the Modified decreasing Estimated-Transmission Time algorithm (MDETA). The results show that [Formula: see text] is the best one in 100% of cases where MDETA reaches the best results in only 48%.

Adaptive simulation of digital signal transmission

High-order modulated signals have lower anti-interference ability than low-order modulated signals with the same modulation method and conditions. In situations with low signal-to-noise ratios, the quality of high-order signals can degrade significantly. An algorithm capable of intelligently switching the modulation order based on the current signal-to-noise ratio can effectively address this issue. This paper presents an adaptive signal transmission algorithm that intelligently selects different orders of Phase shift keying (PSK) modulation depending on varying signal noise ratio (SNR) conditions, while ensuring the user-specified upper limit of bit error rate (BER). This approach guarantees signal transmission quality. The algorithm is implemented in Python and involves simulating the relationship curve between SNR and BER for different PSK orders. This simulation is combined with theoretical transmission rate analysis, resulting in an adaptive algorithm that intelligently switches modulation methods under complex conditions to meet transmission requirements. The proposed algorithm dynamically adapts to diverse user requirements for signal-to-noise ratios in various environments. It achieves this by adjusting the modulation order, calculating theoretical transmission times based on the given signal frequency, and ultimately verifying the actual bit error rate through transmission and testing. Upon testing, this design successfully achieved its intended goals.

LoRaSync: Energy efficient synchronization for scalable LoRaWAN

AbstractLow‐power wide‐area networks connect a large number of battery‐powered wireless devices over long distances. Among them, long range wide area networks (LoRaWAN) implement a pure ALOHA medium access scheme to save device energy by minimizing the radio usage. However, frame collisions restrain the network scalability when the traffic load increases. In this context, synchronization can be used to exploit the available bandwidth more efficiently by controlling the timing of frame transmissions and reducing the collision probability. Such strategy allows to increase the network throughput at the cost of an extra energy demand due to the inherent overhead. In that, the entailed network scenario is still supposed to address low power applications. Therefore this article timely presents LoRaSync, an energy‐efficient synchronization scheme designed for LoRa networks of any size. An accurate clock drift model was established based on measurements made on real cheap devices, and leveraged to support the design of LoRaSync. Our mechanism has been used to evaluate the same ALOHA‐based random access but on a time‐slotted basis, thus increasing the maximum achievable throughput compared to the legacy access. Throughput and energy efficiency models are established to evaluate the performances of a LoRaSync‐operated network. These models are validated with a simulation environment mimicking large‐scale deployments, and then used to determine the most energy efficient slot size for any traffic load. As a final proof of concept, LoRaSync has been implemented and tested on a LoRa testbed to demonstrate the feasibility of our solution on real hardware.

A Link Status-Based Multipath Scheduling Scheme on Network Nodes

Traditional internet protocol (IP) networks, adhering to a “best-effort” service model, typically utilize shortest-path routing for data transmission. Nevertheless, this methodology encounters limitations, especially considering the increasing demands for both high reliability and high bandwidth. These demands reveal shortcomings in this routing strategy, notably its inefficient bandwidth utilization and fault recovery capabilities. The method of multipath transmission has been extensively researched as a solution to these challenges. With the emergence of innovative Internet architectures, notably information-centric networking (ICN), network nodes have gained enhanced capabilities, opening new avenues for multipath transmission design. This paper introduces a multipath scheduling approach for network nodes, capitalizing on the advanced features of these modern nodes. It reimagines the conventional next-hop node as a group of potential next-hop nodes based on both global and local routing strategies and assigns traffic shares to each node within this group for balanced traffic distribution. Network nodes are configured to periodically review and adjust traffic shares according to the link statuses. If scheduling cannot be completed within the set, feedback is sent to upstream nodes. Simulations demonstrate that this approach effectively leverages network path variety, improves bandwidth usage and throughput, and minimizes average data transmission time.

A framework for efficient cross-chain token transfers in blockchain networks

The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.

School Attendance Among Pediatric Oncology Patients During the COVID-19 Pandemic in Ontario, Canada.

Supporting schooling for current and past pediatric oncology patients is vital to their quality of life and psychosocial recovery. However, no study has examined the perspectives toward in-person schooling among pediatric oncology families during the COVID-19 pandemic. In this online survey study, we determined the rate of and attitudes toward in-person school attendance among current and past pediatric oncology patients living in Ontario, Canada during the 2020-2021 school year. Of our 31-family cohort, 23 children (74%) did attend and 8 (26%) did not attend any in-person school during this time. Fewer children within 2 years of treatment completion attended in-person school (5/8; 62%) than those more than 2 years from treatment completion (13/15; 87%). Notably, 22 of 29 parents (76%) felt that speaking to their care team had the greatest impact compared to other potential information sources when deciding about school participation, yet 13 (45%) were unaware of their physician's specific recommendation regarding whether their child should attend. This study highlights the range in parental comfort regarding permitting in-person schooling during the COVID-19 pandemic. Pediatric oncologists should continue to address parental concerns around in-person school during times of high transmission of COVID-19 and potentially other communicable diseases in the future.

Information Transmission in a Drone Swarm: A Temporal Network Analysis

We consider an ensemble of drones moving in a two-dimensional domain, each one of them carrying a communication device, and we investigate the problem of information transfer in the swarm when the transmission capabilities are short range. The problem is discussed under the framework of temporal networks, and special attention is paid to the analysis of the transmission time of messages transported within the swarm. Traditional theoretical methods of graph theory are extended to tackle the problem of time-varying networks and a numerical analysis of the detection time statistics is performed in order to evaluate the efficiency of the communication network as a function of the parameters characterizing the swarm dynamics.

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

Implementasi Kompresi File MP3 dengan Menerapkan Algoritma Levenstein

MP3 is an audio format that is often used because the data stored in MP3 format resembles the actual data when recorded.Storage space requirements will also rise if you keep a large number of MP3 files. Because of this, MP3 files must be compressed in order to prevent the storage space from filling up too quickly. This is because the compressed data will be lower in size. The Lossless compression method can be used to compress audio. The author uses the Levenstein Algorithm to implement audio compression in MP3 format. Levenstein's algorithm compresses data without any loss. When a file is compressed using lossless compression, the resulting file has the exact same size as the original file upon decompression. The author will be able to assess the effectiveness of MP3 file compression by using the Levenstein Algorithm to compress MP3 files. This will allow for faster transmission times and less data storage space consumption because the compressed MP3 file will shrink from its initial large size. After compression, the MP3 file's size—which was originally 128 bits—was reduced to 104 bits, yielding an 81.25% compression ratio. The purpose of this computation is to determine whether the compressed files are similar enough to be either decompressed or restored to their original size.

Evaluation of indicators of railway-induced annoying vibration based on building measurements developed by TOD: a discussion

The main negative impact of the residential buildings located on the cover structure of a Transit Oriented Development (TOD) metro depot is that residents would suffer from the train-induced annoying vibrations. Arguments accordingly arose on the possibility of evaluating indicators controversy for this application that acceleration data was more captured in the past while velocity would be more related to structure born noise. Therefore, this paper provided a relative comparison based on a measurement campaign that both velocity and acceleration sensors were installed together at the same time. The collected vibration accelerations and velocities are analyzed and discussed using the methods of transmission loss, coherence, and time and frequency domain analysis. The results indicated that even there was no difference for identifying the resonance frequency of structure, but the transit mobility and human annoyance evaluation were obviously different by using velocity or acceleration. Attempts to establish a relationship between the two indicators using the calculus method failed because they showed the largest error at the peak frequency at the same vibration level, which would have affected the evaluation results and the judgment on the choice of mitigation measures.

Complementary feeding in infants born preterm: Aspects needing improvement.

The aim of our study was to collect data on complementary feeding (CF) in preterm infants (PIs). We enrolled PI ≤ 34 weeks of gestational age discharged from the neonatal intensive care unit (NICU) of the University Hospital of Padova. At 12 months of corrected age (CA), CF was investigated with questionnaires to the parents and a 24-h dietary recall. In a subgroup of newborns, we also evaluated bone status at a CA of 12 months using quantitative ultrasound. We studied 167 ex PI at 1 year of CA. CF was introduced in 67.1% of them between 5 and 8 months of chronological age, with fruit as the first food (81%, n = 136). Sweet drinks were consumed by 17.4% of our sample, and salt was added in 33.5% of cases. PIs, at 1 year CA, introduced extra energy compared to the theoretical requirement (121 ± 31 kcal/kg/day) and higher protein intake than recommended (39 ± 11 g/day), while the intake of both total lipids and carbohydrates was slightly lower. Vitamins and minerals were adequate, except vitamin D. Regarding bone status, we found a correlation between vitamin D intakes from the diet and bone parameters (metacarpus-bone transmission time: r = 0.36, p = 0.01) at 1 year of CA. Our population of PIs started CF in agreement with current suggestions though with a notable heterogeneity and with some mistakes. Vitamin D intake was correlated with bone status at 1 year of CA.

Dimensioning payload size for fast retransmission of MQTT packets in the wake of network disconnections

The Internet of Things (IoT) is spreading rapidly around the world, and Message Queue Telemetry Transport (MQTT) is one of the main protocols used to explore device-to-device (D2D) communication. The industry typically requires communication systems that can transmit data continuously while optimizing both bandwidth and transmission time. Due to the vast amount of data that can be lost, companies often find that even short periods of network downtime lead to significant costs. In this paper, we propose a retransmission mechanism to allow sensor nodes to relay missing data via MQTT to a local server when it reconnects after an unexpected disconnection. To assess its performance, several tests in a digital healthcare use case scenario have been designed. Since the procedure involves transferring a considerable amount of data, our main goal is to determine the maximum payload of each message to restore the missing information, while minimizing the retransmission time without information loss.

Post-Cancellation-Based LLR Refining for MIMO Multiple ARQ Systems

In multiple-input multiple-output (MIMO) multiple automatic repeat request (ARQ) systems, multiple streams with independent hybrid ARQ (HARQ) processes can be simultaneously sent. Thus, the interference from other streams can affect future retransmissions of a packet as well as the current transmission, and proper management of interference at the receiver is required. Therefore, in this paper, a post-cancellation-based log-likelihood ratio (LLR) refining scheme is proposed for MIMO multiple ARQ (MMARQ) systems. In the proposed scheme, after the end of the entire conventional reception procedure for packet decoding, LLR refining is performed for the non-terminated packets that will be sent during the next transmission time interval. For LLR refining, the packet cancellation is performed to cancel only the successfully decoded packets. Thus, the LLRs of the non-terminated packets are refined without any error propagation, including the inter-transmission error propagation. Consequently, the proposed scheme can compensate for the interference problem in MMARQ systems and improve system performance. In order to utilize the error detection results of decoded codewords, the proposed scheme should be performed after the end of the entire reception procedure for packet decoding. Therefore, as the post-processing scheme, the proposed scheme can be employed to any existing LLR-level combining-based MMARQ receiver without changing the original procedure. Simulation results verify that the proposed scheme can significantly improve the error performance and throughput of MMARQ systems, especially for hard-decision interference cancellation-based receivers and high-order modulation. In addition, compared with the conventional reception procedure, the proposed scheme requires a smaller computational complexity in most of the simulated SNR region. Therefore, the proposed LLR refining scheme can be considered as an effective and practical post-processing scheme for an MMARQ receiver.

Transmission time and spin polarization for electron in magnetically confined semiconducotr nanostructure modulated by spin-orbit coupling

Because digital information in semiconductor spintronics is encoded, stored, processed, and transferred by electron spins instead of its charge, the operation of a spintronic device requires that electrons in semiconductors be spin polarized. But spin states of electrons in traditional semiconductor materials are usually degenerate, therefore, conventional semiconductors cannot be directly used to design spintronic devices. Thus, how to spin polarized electrons in ordinary semiconductors (also called spin injection) including its effective manipulation, has become an important direction of research. In physics, either Zeeman effect between electron spins and external magnetic fields or spin-orbit coupling of electron spins and its spatial momentums can be employed to achieve electron-spin polarization. According to these physical mechanisms, some effective schemes have been developed successfully, such as spin filtering, temporally separating electron-spins, and spatial separations of electron spins. Utilizing the combination of theoretical analysis and numerical calculation, transmission time is investigated by considering both Zeeman effect as well as Rashba and Dresselhaus spin-orbit couplings for electron in magnetically confined semiconductor nanostructure, which is constructed on the GaAs/Al<sub><i>x</i></sub>Ga<sub>1–<i>x</i></sub>As heterostructure. Schrödinger equation of an electron is numerically solved by matrix diagonalization and improved transfer-matrix method. Adopting H.G. Winful’s theory, dwell time of electron is calculated and spin polarization ratio is given. Due to Zeeman effect and spin-orbit coupling, dwell time of electron is obviously associated with the spins, which is used to separate electron-spins in time dimension and to realize spin polarization of electrons in semiconductors. Because the semiconductor GaAs has a small effective g-factor, which is about 4 times larger than that induced by Zeeman effect, electron-spin polarization originates mainly from spin-orbit coupling including Rashba and Dresselhaus types. Dwell time of electron and its spin polarization can be efficaciously modified by interfacial confining electric-field or strain engineering, because the effective potential of electron is related to spin-orbit coupling’s strength. These interesting findings not only have some references for spin injection into semiconductors, but also provide a controllable temporal electron-spin splitter for semiconductor spintronics device applications.