System Throughput Research Articles

High-speed independent polarization modulation based on dual channel 2DEG anisotropic terahertz metasurfaces

In communication and imaging systems, anisotropic metasurfaces bring new degrees of freedom for the independent control of electromagnetic waves with different polarizations. This paper proposes a dual-polarization modulation metasurface (DPMM) operating in the terahertz (THz) frequency range, which can effectively and independently modulate two orthogonally linearly polarized electromagnetic waves. The metasurface unit adopts an anisotropic structure, and by integrating multiple GaN-based high electron mobility transistors (GaN-HEMTs) into the anisotropic structure, independent control of the two-dimension electron gas (2DEG) carrier concentration in the dual-polarization channel is achieved, enabling selective modulation of x-polarization and y-polarization waves. Simulation and static experimental results demonstrate the excellent modulation isolation performance of the DPMM, with a maximum isolation exceeding 45 dB. Dynamic experiments further highlight the high-speed modulation effect, with modulation speeds exceeding 1 GHz for a single channel and an overall modulation speed of 2 GHz for dual polarization, highlighting its potential for enhancing channel capacity and information throughput in THz communication and imaging systems.

Recent development in reconfigurable dielectric resonator antenna and microwave filter: design and application

SummaryDeveloping wireless communication systems depends on reconfigurable microwave filters (MF) and dielectric resonator antenna (DRA) because the functionality of the various filters and antennas available for a wireless communication system is limited, and reconfigurable DRA and MF are utilized to overcome these limitations. The throughput of a multiantenna system can be achieved with reconfigurable DRA and filter. Various switching strategies and classifications of reconfigurable DRA and MF are presented. The electrical reconfiguration technique is most suitable for developing a reconfigurable DRA and filter among all the switching techniques. In this approach, reconfigurability may be accomplished by utilizing a PIN diode, a varactor diode, and a radio‐frequency microelectromechanical switch (RF‐MEMS). These switches are more reliable, highly efficient, and easily integrated with the microwave circuit. Reconfigurable DRA with machine learning is demonstrated. The application of reconfigurable DRA and filters in cognitive radio (CR) is also demonstrated. Also, the review article focused on the performance parameters, design, and challenges of reconfigurable antenna and MF.

Modeling of Automobile Assembly Line Performance Using ARENA Simulation Software

Due to the intense competition in today's business environment, companies must continuously analyze and enhance their existing manufacturing systems. Discrete event simulation, which is especially helpful for simulating queuing systems, involves describing the system as it evolves. The building and study of a simulation model of an existing production line are the main topics of this paper. The findings demonstrate that the current system's throughput is low due to bottlenecks, extended processing times at workstations, and inefficient resource utilization, all contribute to productivity losses in automotive assembly lines. This paper aims to evaluate the efficiency of the production line performance of automobile assembly lines using Arena modeling and simulation. Data provided by the company's management is utilized to calculate the processing time and standard time for each step in the production line. Additional information is gathered through direct observation of the assembly line. A car assembly manufacturing line was selected as a case study, and Arena 16.0 software was employed for basic modeling and analysis to achieve these objectives.

Performance Limit: Fuzzy Logic Based Anti-Collision Algorithm for Industrial Internet of Things Applications

Passive RFID has the advantages of rapid identification of multi-target objects and low implementation cost. It is the most critical technology in the Industrial Internet of Things information-gathering layer and is extensively applied in various industries, such as smart production, asset management, and monitoring. The signal collision caused by the communication between the reader/writer and tags sharing the same wireless channel has caused a series of problems, such as the reduction of the identification efficiency of the reader/writer and the increment of the missed reading rate, thus restricting the further development of RFID. At present, many hybrid anti-collision algorithms integrate the advantages of Aloha and TS algorithms to optimize RFID system performance, but these solutions also suffer from performance bottlenecks. In order to break through such performance bottleneck, based on the ISE-BS algorithm, we combined the sub-frame observation mechanism and the Q value adjustment strategy and proposed two hybrid anti-collision algorithms. The experimental results show that the two algorithms proposed in this paper have obvious advantages in system throughput, time efficiency and other metrics, surpassing existing UHF RFID anti-collision algorithms.

Bottleneck identification and transfer prediction for automated production lines based on FNN

Abstract In a re-entrant production system, the throughput of the whole system depends on the capacity of the bottleneck machine. In this study, a new definition of bottleneck is proposed for a precision forging blade shop. The reinforcement learning algorithm is used to optimize the production scheduling to determine the most suitable scheduling scheme, which lays the foundation for bottleneck identification. Subsequently, the bottleneck identification index system was established according to the optimization objective, and the bottleneck identification problem was transformed into a multi-attribute decision-making problem. Finally, a fuzzy neural network is used for training, and the basic scheduling examples of each flow shop are utilized for bottleneck identification and prediction to verify their effectiveness.

Thermal Safe Power Constrained Dynamic Mapping for Heterogeneous Multicore System

Heterogeneous multicore system could provide high flexibility for applications through integrating different types of cores. As the number of cores increases, more and more transistors are integrated which could lead to the rise of chip temperature, thus have a negative impact on the system performance. At present, the typical method to tackle this problem is dynamic voltage and frequency scaling (DVFS) which reduces the voltage and frequency to achieve cooling. However, DVFS will make the heterogeneous multicore system unable to take full advantage of its performance. This paper proposes a dynamic mapping method called TspDM which maximizes the system performance target under the premise of satisfying the temperature safety power. TspDM exploits the runtime performance counters of running threads to dynamically adjust thread mapping. Specifically, a lightweight Artificial Neural Network (ANN) model is proposed to obtain the thread-to-core type mapping when a remapping process is activated. Subsequently, TspDM determines the specific core locations of threads according to the number of threads mapped to a certain core type and the thermal safety power computation. The experimental results show that the proposed method is 21% better than that of PCMig while ensuring the thermal safety of heterogeneous platform. In addition, TspDM outperforms recent performance constrained and power constrained literature in terms of system throughput.

Development of a Smart Shopping Cart for Enhanced Retail Experience

Abstract: This research introduces a novel approach to revolutionize the shopping experience through the development of a Smart Shopping Cart. The system integrates technologies such as RFID, weight sensors, and real-time inventory management to provide instantaneous updates and an engaging user interface. The project’s primary objectives include enhancing inventory management efficiency and establishing a seamless RFID connection. Future enhancements envisage increased automation, enhanced security measures, personalization options, and connectivity improvements. The system employs RFID, weight sensors, a Tkinter GUI, and MySQL for database management. Key performance indicators include response time, database efficiency, RFID scanning speed, and overall system throughput. By amalgamating these technologies, this project aspires to transform the retail sector, augmenting customer satisfaction and operational efficiency

GRF: A Global Range Filter for LSM-Trees with Shape Encoding

Log-structured merge-trees (LSM-trees) are widely used in key-value stores because of its excellent write performance. To reduce LSM-tree's read amplification due to overlapping sorted runs, each file (i.e., SSTable) in an LSM-tree is typically associated with a point or range filter to reduce unnecessary I/Os to the runs that do not contain the target key (range). However, as modern SSDs get faster, probing multiple in-memory filters per query often makes the system CPU bottlenecked, thus compromising the system's throughput. In this paper, we developed the Global Range Filter (GRF) for RocksDB that reduces the number of filter probes per query to one. We follow the pioneering Chucky's approach by storing the sorted run IDs within the filter. However, we identify two practical challenges in building a global range filter: correctness in multi-version concurrency control and efficiency in frequent updates. We solve both challenges by the novel Shape Encoding algorithm. With further optimizations, GRF achieves a dominating performance over the state-of-the-art filters under different workloads when integrated into RocksDB.

Subchannel assignment for social-assisted UAV cellular networks using dynamic hypergraph coloring

Device-to-Device (D2D) communication when used in conjugation with Unmanned Aerial Vehicle (UAV) Femtocell and unlicensed spectrum can effectively tackle the ever-increasing mobile data demands. However, D2D communication raises security and privacy concerns among users due to the absence of a centralized entity such as a base station. Therefore, exploring social connections among users becomes imperative to enable secure and trustworthy D2D communication. This paper seeks to improve the performance of a social-assisted UAV cellular network augmented by D2D communication by proposing a novel subchannel assignment technique employing hypergraph coloring. Considering the real-world scenario, we incorporate the user/UAV mobility by using dynamic hypergraph coloring for subchannel assignment instead of the static one. Our proposed technique shifts a set of cellular users from the licensed to the unlicensed band based on their social connection with other co-channel D2D users. Additionally, we assign subchannels to different users to optimize the throughput while minimizing overall interference. Our proposed technique demonstrates significant improvements in system throughput, energy efficiency, and interference efficiency compared to conventional techniques. For our proposed technique, we observed improvements of 69%, 42%, and 15% in the per user system throughput when compared with the conventional techniques (graph, hypergraph, and dynamic-hypergraph, respectively). Moreover, our technique achieves higher per user energy efficiency by 120%, 70%, and 25%, and higher per user interference efficiency by 92%, 43%, and 22%, respectively, compared to graph, hypergraph, and dynamic-hypergraph techniques.

IoT-based MANET performance improvement against jamming attackers in different mobile applications

Internet of Things (IoT) is a cutting-edge technology and effective solution that enables real-time communication between physical objects. In order to manage the mobility of nodes in the IoT, mobile ad hoc networks (MANETs) are crucial. It enables IoT-based systems to construct and connect networks autonomously. IoT based MANET is more vulnerable to Jamming attacks (Jammers) due to the decentralized nature of the ad hoc network. Therefore, secure communication is a crucial requirement for nodes. In this paper, jammers interfere with the IoT MANET communication resulting in order to degrade network performance in terms of delay, throughput and PDR (Packet Delivery Rate) which are the important QoS (Quality of Service) parameters to measure the network performance. The IoT ad hoc system's networking is based on MANET routing protocols so that (GRP, AODV and OLSR) routing protocols had been examined in this paper to improve the QoS (Packet Delivery rate, throughput and delay) of the IoT-MANET system using Riverbed Modeler (17.5) simulation program for different mobile applications. The outcomes showed that the selection of a suitable routing protocol would improve the degradation in delay, throughput and PDR of the ad hoc network.

Performance Analysis of Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems

Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints. Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .

A Generic High-Performance Architecture for VPN Gateways

Virtual private network (VPN) gateways are widely applied to provide secure end-to-end remote access and to relay reliable interconnected communication in cloud computing. As network convergence nodes, the performance of VPN gateways is limited by traditional methods of packet receiving and sending, the kernel protocol stack and the virtual network interface card. This paper proposes a generic high-performance architecture (GHPA) for VPN gateways in consideration of its generality and performance. In terms of generality, we redesign a generic VPN core framework by modeling a generic VPN communication model, formulating generic VPN core technologies and presenting corresponding core algorithms. In terms of performance, we propose a three-layer GHPA for VPN gateways by designing a VPN packet processing layer based on a data plane development kit (DPDK), implementing a user space basic protocol stack and applying our proposed generic VPN core framework. On the basis of the research work above, we implement a high-performance VPN (HP-VPN) and a traditional VPN (T-VPN) that complies with GHPA and traditional methods, respectively. Experimental results prove that the performance of HP-VPN based on GHPA is superior to T-VPN and other common VPNs in RTT, system throughput, packet forwarding rate and jitter. In addition, GHPA is extensible and applicable for other VPN gateways to improve their performance.

Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.

Transaction fee mechanisms with farsighted miners

AbstractWe investigate the recent fee mechanism EIP1559 of the Ethereum network. Whereas previous studies have focused on myopic miners, we here focus on strategic miners in the sense of miners being able to reason about the future blocks. We derive expressions for optimal miner behavior (in terms of setting block sizes) in the case of two‐block foresight and varying degrees of hashing power. Results indicate that a sufficiently large mining pool will have enough hashing power to gain by strategic foresight. We further use a simulation study to examine the impact of both two‐block and three‐block foresight. In particular, the simulation study indicates that for realistic levels of hashing power, mining pools do not gain from being able to reason more than two blocks ahead. Moreover, even though the presence of strategic miners increases the variation in block sizes and potentially empty blocks, overall system throughput tends to increase slightly compared with myopic mining. We further analyze the effect of varying the base fee updating rule.

Performance Analysis of CSMA/NP under Finite Population Environments.

In this study, we analyze the CSMA Non-Persistent protocol with a finite number of nodes, providing more accurate results for applications like wireless sensor networks. The finite model addresses scenarios where the node count is moderate, capturing realistic system dynamics. Our analysis reveals a dependency on the node count, impacting system throughput. As the node count increases, throughput behavior aligns with Kleinrock's infinite model. We derive a complex closed-form throughput expression for a finite quantity of nodes in the system, solved numerically, and offer an approximate expression for specific conditions. These insights advance understanding of low-contention network performance, especially in scenarios where the infinite model becomes inadequate.

Resource allocation-aware efficient interference management technique for ultra-dense Femto environment

Addressing the challenge of severe inter-layer and intra-layer interference in densely deployed Femtocells, we introduce an efficient resource allocation strategy centered on clustering. Initially, we employ the Fractional Frequency Reuse (FFR) technique to mitigate interference by segmenting cells into distinct zones. Subsequently, leveraging graph theory and convex optimization, we cluster the Femto Base Stations (FBS) to enhance efficiency. An algorithm prioritizing user fairness governs sub-channel allocation among FBSs. Additionally, a distributed power control algorithm dynamically adjusts FBS power levels, further enhancing system throughput. Comparative analysis reveals that our approach surpasses traditional non-clustered methods and three other comparison methods, exhibiting superior system throughput and Signal-to-Interference Ratio (SINR). Notably, our method significantly enhances user fairness, promoting greater satisfaction among Femto User Equipment (FUE) users.

Efficient information reconciliation for high-dimensional quantum key distribution

The information reconciliation phase in quantum key distribution has significant impact on the range and throughput of any QKD system. We explore this stage for high-dimensional QKD implementations and introduce two novel methods for reconciliation. The methods are based on nonbinary LDPC codes and the Cascade algorithm, respectively, and achieve efficiencies close the Slepian–Wolf bound on q-ary symmetric channels.

Testing of Permeability of RFID Access Control System for the Needs of Security Management

Access control systems are part of the overall protection of objects. It is often the first system with which it is necessary to start the contact system; therefore, it is necessary to ensure its proper functioning. In the event of a malfunction, it can cause downtime in production, and it is triggered by a bad replacement of workers. Access control systems have their own specificities that need to be considered when designing security. Poor selection of access control devices can cause inefficient system functionality, resulting in downtime and loss. Based on experimental testing and related work in access control systems, this paper discusses the possibilities of testing the throughput of access control systems. The manuscript presents the design of a unique test device that can be used in the assessment of the reliability and throughput of access control systems. With the help of tests, we were able to determine the probability of downtime due to inappropriately set time intervals for changing employees on a work shift.

Hermes, a low-latency transactional storage for binary data streams from remote devices

In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by third parties, such as human operators or specific business logic, even while the media files are still being collected. However, using transactions to ensure data persistence often limits system throughput and latency. This paper presents a solution that enables both high ingestion rates with transactional data persistence and near real-time, low-latency access to the stream during collection. This immediate access enables the prompt application of specialized data engineering algorithms during data acquisition. The proposed solution is particularly suitable for binary data sources such as audio and video recordings in surveillance systems, and it can be extended to various big data scenarios via well-defined general interfaces. The scalability of the approach is based on the microservice architecture. Preliminary results obtained with Apache Kafka and MongoDB replica sets show that the proposed solution provides up to 3 times higher throughput and 2.2 times lower latency compared to standard multi-document transactions.

Investigating the angular distortion impact on vehicular optical camera communication (OCC) systems

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.