Minimum Throughput Research Articles

Evaluating the Performance of the Carrot Slicer Machine

The study was carried out to evaluate the performance of the carrot slicer machine. An experiment was conducted with a multi-factor factorial design under a randomized complete block design. Using the Statistix 8 software, the experiments' collected data were statistically examined. The analysis of variance indicated that the effects of machine speed and feed rate on throughput capacity, efficiency, and percentage loss of the machine were significant at the 5% probability level. The results of the least significance difference pairwise comparison tests revealed that the treatment combination means did not differ significantly from one another at the 5% level. The key physical properties of the carrot including moisture content, angle of repose, bulk density, porosity, coefficient of friction, geometric mean diameter, arithmetic mean diameter, equivalent mean diameter, sphericity, surface area and aspect ratio were obtained as 84.3%, 39.4°, 469.5 kg m-3, 59.8%, 0.78, 47.8 mm, 62.18 mm, 83.7 mm, 0.55, 57.67 cm2, and 0.27, correspondingly. The results showed that the maximum throughput capacity of 621.4 kg h-1 was recorded at 550 rpm machine speed while the minimum throughput capacity of 511.6 kg h-1 was recorded at 350 rpm machine speed. It has been found that the maximum machine efficiency was 96.03% at 550 rpm machine speed whereas the minimum machine efficiency was 92.5% at 350 rpm machine speed. The investigation results revealed that the minimum percentage loss was 4.2% at 550 rpm machine speed whereas the maximum percentage loss was 7.8% at 350 rpm machine speed. The test results suggested that the carrot slicer machine was found to be very effective for processing the vegetable root crop of carrots for end users.

Self-aware collaborative edge inference with embedded devices for IIoT

Edge inference and other compute-intensive industrial Internet of Things (IIoT) applications suffer from a bad quality of experience due to the limited and heterogeneous computing and communication resources of embedded devices. To tackle these issues, we propose a model partitioning-based self-aware collaborative edge inference framework. Specifically, the device can adaptively adjust the local model inference scheme by sensing the available computing and communication resources of surrounding devices. When the inference latency requirement cannot be met by local computation, the model should be partitioned for collaborative computation on other devices to improve the inference efficiency. Furthermore, for two typical IIoT scenarios, i.e., bursting and stacking tasks, the latency-aware and throughput-aware collaborative inference algorithms are designed, respectively. Via jointly optimizing the partition layer and collaborative device selection, the optimal inference efficiency, characterized by minimum inference latency and maximum inference throughput, can be obtained. Finally, the performance of our proposal is validated through extensive simulations and tests conducted on 10 Raspberry Pi 4Bs using popular models. Specifically, in the case of two collaborative devices, our platform reaches up to 92.59% latency reduction for bursting tasks and 16.19× throughput growth for stacking tasks. In addition, the divergence between simulations and tests ranges from 1.64% to 9.56% for bursting tasks and from 3.24% to 11.24% for stacking tasks, which indicates that the theoretical performance analyses are solid. For the general case where the data privacy is not considered and the number of collaborative devices is optimally determined, up to 14.76× throughput speed up and 84.04% latency reduction can be obtained.

Exploring the interplay of costs and flow time in server allocation for flow lines with parallel non-identical machines

ABSTRACT The Server Allocation Problem is a significant challenge in manufacturing systems involving deciding the number and version of machines for each stage. This optimization problem is of utmost importance when there is a trade-off between minimizing investment costs and flow time while achieving target performance, such as a minimum throughput. Manufacturing systems often employ hybrid flow lines, with non-identical machines at each stage. This paper analyzes the trade-off between minimizing costs and flow time when allocating servers in hybrid flow lines by proposing a bi-criteria approach that uses an efficient pattern-based problem representation and a Variable Neighborhood Search solution algorithm. The objectives are to minimize total cost and flow time while ensuring a target throughput. A metric for comparing obtained solutions is proposed, providing insights for practical implementation. A comprehensive numerical analysis is conducted, to validate the proposed approach and demonstrate its applicability in real-world manufacturing settings.

A Performance Analysis of Security Protocols for Distributed Measurement Systems Based on Internet of Things with Constrained Hardware and Open Source Infrastructures.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

Self-Organization of UAV Networks for Maximizing Minimum Throughput of Ground Users

Self-Organization of UAV Networks for Maximizing Minimum Throughput of Ground Users

Minimal Throughput Maximization of UAV-Enabled Wireless Powered Communication Network in Cuboid Building Perimeter Scenario

Minimal Throughput Maximization of UAV-Enabled Wireless Powered Communication Network in Cuboid Building Perimeter Scenario

A GPU-Based, Real-Time Dealiasing Framework for High-Frame-Rate Vector Doppler Imaging.

Vector Doppler is well regarded as a potential way of deriving flow vectors to intuitively visualize complex flow profiles, especially when it is implemented at high frame rates. However, this technique's performance is known to suffer from aliasing artifacts. There is a dire need to devise real-time dealiasing solutions for vector Doppler. In this paper, we present a new methodological framework for achieving aliasing-resistant flow vector estimation at real-time throughput from precalculated Doppler frequencies. Our framework comprises a series of compute kernels that have synergized: 1) an extended least squares vector Doppler (ELS-VD) algorithm, 2) single-instruction, multiple-thread (SIMT) processing principles, and 3) implementation on a graphical processing unit (GPU). Results show that this new framework, when executed on an RTX-2080 GPU, can effectively generate aliasing-free flow vector maps using high-frame-rate imaging datasets acquired from multiple transmit-receive angle pairs in a carotid phantom imaging scenario. Over the entire cardiac cycle, the frame processing time for aliasing-resistant vector estimation was measured to be less than 16 ms, which corresponds to a minimum processing throughput of 62.5 fps. In a human femoral bifurcation imaging trial with fast flow (150 cm/s), our framework was found to be effective in resolving two-cycle aliasing artifacts at a minimum throughput of 53 fps. The framework's processing throughput was generally in the real-time range for practical combinations of ELS-VD algorithmic parameters. Overall, this work represents the first demonstration of real-time, GPU-based aliasing-resistant vector flow imaging using vector Doppler estimation principles.

Energy-Efficient Resource Allocation in Aerial Base Stations

Drones, or unmanned aerial vehicles, can be used as air base stations (UAV-BSs) for telecommunications. They prove useful in situations where the network is overloaded or unavailable due to natural disasters or maintenance work. UAV-BSs grant access to user/IoTs sensors on the ground, but their electromagnetic signals may suffer losses because of their dynamic capacity to provide access at different altitudes. These losses lead to transmission impairments, such as attenuation, fading, and distortion. To overcome these issues and improve signal quality, the UAV-BS position must be optimized. However, finding the optimal placement is a challenge, and a wide range of strategies employing different approaches have been adopted. In this study, we proposed a 3D positioning strategy for UAV-BSs that serves the maximum number of users with the smallest number of UAV-BSs. Results showed that the proposed heuristic could find the best position and altitude for the UAV-BSs, provide network access for mobile user/IoTs (Internet of things) sensors, maximize the number of devices connected to the UAV-BSs, and guarantee a minimum throughput for users. The proposed heuristic not only performs well in terms of coverage and performance, but is also more energy-efficient than other algorithms found in the literature.

Efficient Computation of the Max-Plus Semantics of Synchronous Dataflow Graphs

Streaming systems are naturally modeled with synchronous dataflow graphs (SDFGs). The max-plus semantics of an SDFG is a compact matrix representation of its timing behavior. The max-plus semantics enables us to analyze and control timing properties of the systems, such as the obtainable minimum guaranteed throughput and maximum latency. Deriving the max-plus semantics, and consequently, performance analysis may be computationally expensive since the state-of-the-art method simulates one iteration of an SDFG. This holds, in particular, for systems whose components operate at different levels of granularity, as this results in many executions of some components in one iteration. This paper aims at efficiently calculating the max-plus semantics of SDFGs. The paper proposes an optimization framework exploring decompositions of a given SDFG and finding a composition sequence whose computational effort for compositionally obtaining the max-plus semantics is minimal. Not only does our proposed technique accelerate the performance analysis of multiscale streaming systems, but it also allows us to compute the max-plus semantics of some systems for which the state-of-the-art method does not succeed because of memory limitations.

ANALYSIS OF ROUTING PROTOCOLS EFFECTIVENESS IN COMPUTER NETWORKS

The main task of networks is to transport information from the sending computer to the receiving computer. In most cases, you need to make several transfers to do this. The problem of choosing a path is solved by routing algorithms. The routing algorithm must have well-defined properties: reliability, correctness, stability, simplicity and optimality. The latter property is not as transparent as it may seem at first glance, it all depends on which or which parameters are optimized. This task is sometimes not at all easy even for relatively simple local networks. Optimization parameters may include minimum delivery delay, maximum throughput, minimum price, maximum reliability, or minimum error probability. In this paper, reactive, proactive and hybrid routing protocols are considered. When making their choice, we relied on the method of hierarchy analysis. Based on the results obtained during the simulation of a network with video conference traffic, a matrix was constructed for each of the criteria studied. The protocols were compared in terms of the possibility of information loss. The results of the research can be useful in the course of building modern computer networks.

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints.

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound.

Gain Without Pain: Recycling Reflected Energy From Wireless-Powered RIS-Aided Communications

In this paper, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching-and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are respectively formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we respectively develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding sub-optimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

URLLC-oriented secure communication for UAV relay-assisted network

In this paper, we investigate the unmanned aerial vehicle (UAV) relay-assisted secure short packet communication. The UAV acts as a decode-and-forward relay to transmit control signals from the source to the actuators in the presence of a ground eavesdropper (EV) whose imperfect channel state information is available at the UAV. Specially, non-orthogonal multiple access is adopted in our work to achieve more connections and improve the fairness of communication and the short packets are employed for data transmission to reduce the latency. Explicitly, we maximize the minimum average secrecy throughput among all actuators by jointly optimizing the UAV trajectory, transmit power and blocklength allocation, which generates a challenging optimization problem. Therefore, we propose an iterative algorithm based on block coordinate descent method and successive convex approximation technique to handle the non-convex problem. Numerical results show that the proposed scheme has better performance compared to the benchmark schemes.

Fair multigroup multicast precoding design based on traffic demands for multibeam satellite systems

Onboard resources are limited for multibeam satellite communication systems, while differences exist in traffic demands among users. Since conventional multigroup multicast precoding methods do not take traffic demands for users into consideration, it is difficult to flexibly adjust the offered throughput to users’ demands. Users with higher demand may obtain lower throughput while users with lower demand may be over-satisfied, which results in the requested-offered throughput mismatch and resource waste. This paper proposes a fair multigroup multicast precoding design based on traffic demands. To obtain the precoding design, the optimization problem aimed at maximizing the minimum throughput satisfaction ratio, which is defined as the ratio of the offered throughput to the traffic demand, is formulated, so as to provide fair service for all users while satisfying per-feed power constraints and traffic demand constraints. To address this problem, an auxiliary variable is first introduced to equivalently simplify it. Then, the semidefinite relaxation and the bisection search strategies are adopted to further handle the problem. Finally, the optimal precoding vectors are obtained by employing eigenvalue decomposition or Gaussian randomization. Simulation results indicate the effectiveness of the proposed precoding algorithm and demonstrate that it can better adapt to the scenarios with different traffic demands.

High sensitivity measurements of gas transport through films for food packaging and the O2 adsorption issue

New biodegradable polymers employed for food packaging require an accurate characterization of their O2 ultra-barrier permeation rates. Methods and procedures of gas transport measurements must guarantee high sensitivity and immunity to artefacts. Within this context, this paper reports two outcomes. The first one is to demonstrate the suitability of a specific high vacuum apparatus for challenging measurements of gas permeability on new polymers for food packaging. We show that our newly developed set-up is able to achieve a minimum detectable gas throughput 2×10-9mbarl/s , which is equivalent to permeability coefficient of 7×10−11cm2/s in case of 20 μm-thick polymer film with an area of 7×10−2cm2 under a 105Pa pressure differential. The second aim is to investigate the origin of its detection limits and possible measurement artefacts obtained with this instrument. In particular, we focus on the O2 adsorption effect on the inner surfaces of the set-up.

Применение усовершенствованного подхода к расчету характеристик нефтегазового сепаратора совмещением графоаналитического способа с методикой Катца на Янгурчинском месторождении

The work proposed the use of a new approach to the calculation of an oil and gas separator, combiningthe Katz method and the graphical analytical method, at the Yangurchinsky field, which will optimize the selection of a separator with a minimum throughput for the produced production of any field, reducing the costs associated with the loss of oil phase, and, as a result, increasing the profitability of the oil and gas production enterprise.

ОЦЕНКА ПРОИЗВОДСТВЕННЫХ МОЩНОСТЕЙ КАК ОСНОВА ОБЪЕКТИВИЗАЦИИ УПРАВЛЕНИЯ ПРОИЗВОДСТВЕННОЙ ДЕЯТЕЛЬНОСТЬЮ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ

Such indicators as normative production and equipment load factor are determined with-in the framework of the article in order to objectify the management of production activities. The as-sessment of these indicators is carried out through the production capacity indicator, which is calculat-ed using the “leading link” method, i.e., equipment is determined as part of the production line with a minimum throughput based on the passport data of the equipment. In this case, the calculation is carried out according to the principle of “upward calculation”: from the beginning of the production line to the end. According to the authors, this approach is not applicable to the conditions of multi-product indus-tries with a mobile assortment structure. Since most industrial enterprises produce a wide range of products, the authors propose an approach to calculating production capacity based on understanding it as a function of production factors, the main one of which is the assortment structure of finished prod-ucts. The authors also propose to use a single unit of measurement as part of the calculation of the de-clared indicators of objectification of management activities, namely a conventional assortment unit. The proposed methodological approach will make it possible to adequately assess the work of the pro-duction units of industrial enterprises, since changes in the assortment structure of products are explicit-ly taken into account, which do not depend on the efficiency of their work and, as a rule, are deter-mined by the work of the sales department responsible for the formation of production orders.

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

Intelligent Reflecting Surface-Aided Wireless Powered Hybrid Backscatter-Active Communication Networks

Wireless powered hybrid backscatter-active communications are recognized as the promising technique to provide the sustainable communication service for wireless devices. In this paper, we consider an intelligent reflecting surface-aided wireless powered hybrid backscatter-active communication network, where multiple sensors devices powered by a power beacon, transmit their information to an access point through backscatter and active communications. In particular, an intelligent reflecting surface is properly deployed to improve the communication quality between sensor devices and access point by utilizing the composite channel gain and phase beamforming gain. Furthermore, we formulate a system energy efficiency maximization problem under the minimum throughput requirements of sensor devices, to jointly optimize the phase shifts of intelligent reflecting surface, time allocation for backscatter communications and active transmissions, and transmit power of sensor devices and power beacon. To solve the formulated non-convex problem, we first unveil the optimal phase shifts of intelligent reflecting surface, and then utilize the variable substitutions and Dinkelbach-based method to develop an iterative method to solve the energy efficiency maximization problem. Simulation results reveal that the proposed method outperforms the existing several benchmark methods in terms of system energy efficiency.