Optimal Throughput Research Articles

Covert Communication Over Adversarially Jammed Channels

Suppose that a transmitter Alice potentially wishes to communicate with a receiver Bob over an adversarially jammed binary channel. An active adversary James eavesdrops on their communication over a binary symmetric channel (BSC( q)), and may maliciously flip (up to) a certain fraction p of their transmitted bits based on his observations. We consider a setting where the communication must be simultaneously covert as well as reliable, i.e., James should be unable to accurately distinguish whether or not Alice is communicating, while Bob should be able to correctly recover Alice's message with high probability regardless of the adversarial jamming strategy. We show that, unlike the setting with passive adversaries, covert communication against active adversaries requires Alice and Bob to have a shared key (of length at least Ω(logn)) even when Bob has a better channel than James. We present lower and upper bounds on the information-theoretically optimal throughput as a function of the channel parameters, the desired level of covertness, and the amount of shared key available. These bounds match for a wide range of parameters of interest. We also develop a computationally efficient coding scheme (based on concatenated codes) when the amount of shared key available is Ω(√n logn), and further show that this scheme can be implemented with much less amount of shared key when the adversary is assumed to be computationally bounded.

A Novel Adaptive OFO-OFDM Modulation for Visible Light Communication

Radio frequency techniques have been deployed in the past time for wireless communication and following its spectral crunch and limited throughput, the visible light spectrum with enormously large bandwidth and potentials for high throughput is being investigated in this work. This study is aimed at investigating and modeling a signal conditioning scheme in the visible light spectrum in view of enhancing the throughput of the network. To effectively enhance communication throughput, a possible solution is to deploy multicarrier techniques. In this work, an adaptive Optimized Flipped Optical (OFO) OFDM is proposed for improved throughput using Lagrange Multiplier and Broyden Fletcher Goldfarb Shanno Algorithm (BFGSA). The Lagrange Multiplier technique was used to formulate the model for the optimization of the throughput constrained by the bit error rate (BER) and the total subcarrier transmit power whereas the BFGSA was used for the estimation of the approximation to the Hessian matrix for the computation of the optimal throughput value. Results showed improved spectral efficiency in favor of the proposed algorithm when compared with the conventional schemes. Further validations revealed the performance superiority of the proposed algorithm when compared with Castel, Wyglinski and Bedeer algorithms under comparable operating conditions for a given average signal to noise ratio (SNR).

Improving The Performances of WSN Using Data Scheduler and Hierarchical Tree

Users of data-intensive implementation needs intelligent services and schedulers that will provide models and strategies to optimize their data transfer jobs. Normally sensor nodes are connected to consecutive sensor nodes depending on frequent transmission. To enhance end-to-end data flow parallelism for throughput optimization in high speed WSNs. The major objective is to maximize the WSNs throughput, minimizing the model overhead, avoiding disputation among users and using minimum number of end-system resources. Data packets are broadcasted from sender node to target node. Though, all nodes operate concurrently in various communications, the analysis shows that more packet latencies are occurred and priority-based transmission tasks are performed. Then the proposed Bearing parallelism-based Data Scheduler (BPDS) is used for data scheduling to enhance the end-to-end throughput input parameter. Sensor nodes are fast working node, it verifies each and every node before allocating packet transmission for that node. Busy resources are monitored to inform the nodes that are in processing, based on the schedule it allocates various paths to particular node and monitors the node capacity. Sampling algorithm supports for fixing threshold value, based on the values, they are further allocated to communicate between channels. It assigns the routing path with minimum resources and reduces end to end delay, to improve throughput, and network lifetime.

Characterization and kinetic modeling for pyrolytic conversion of cotton stalks

AbstractHerein, cotton stalk biomass was initially characterized to understand its physicochemical properties as a raw material for biochar production. Furthermore, thermal analysis was conducted using thermogravimetric analysis (TGA), and the results were further utilized to evaluate the cotton stalk's kinetic behavior under thermal decomposition in an inert environment. Advanced kinetics and technology solutions (AKTS) software was for the first time employed to compute the kinetic parameters of cotton stalk pyrolysis, as well as provide kinetic predictions under isothermal conditions. Three methods were used to compute the activation energy (Ea) value, namely ASTM‐E698, Flynn‐Wall‐Ozawa (FWO), and Friedman's differential iso‐conversional model. The results obtained using the ASTM‐E698 method indicate an activation energy of 127.23 kJ·mol−1. Furthermore, the FWO method presented an Ea value ranging 35‐250 kJ·mol−1. The differential iso‐conversional method is the most robust approach as it adequately represents the complex nature of lignocellulosic biomass decomposition, showing an Ea range between 4 and 250 kJ·mol−1. Based on the differential iso‐conversional method, kinetic predictions under isothermal conditions were provided. The predictions offer valuable insight for industrial‐scale biochar project developers in relation to production throughput optimization. Furthermore, the kinetic parameters obtained can be utilized in process modeling.

Achieving high reliability and throughput in software defined networks

Flow routing is one of the most important issues in a software defined network (SDN), and faces various challenges. For example, each link may not be reliable all the time (or with a failure probability), and the flow-table size on each switch is limited. Existing solutions about reliable flow routing may result in a longer failure recovery delay, a large number of flow entries or massive control overhead. To this end, we propose to achieve throughput optimization with the constraint that the forwarding reliability probability of each switch pair should exceed a threshold (e.g., 99.9%). We formally define the reliable flow routing (RLFR) problem with flow-table size constraint. We present a rounding-based algorithm and analyze its approximation performance. We further extend our algorithm to preserve the throughput of each switch pair even with link failures. We implement our proposed algorithms on the SDN platform. The experimental results and large-scale network simulation results show that our algorithms can improve the network throughput by about 48.0% and reduce the maximum number of required flow entries by about 53.1%, compared with the existing solutions under the reliability requirement.

Throughput Optimization of Parallel Sensing and Energy Harvesting Cognitive Radio Network

In cognitive radio, throughput of secondary user (SU) will depend on spectrum sensing performance and available power of secondary user to transmits data. As the secondary user dissipates energy for spectrum sensing operation and to maintain cooperation among multiple SUs can results in reduction of transmission power. To compensate this energy, an energy harvesting technique has introduced in cognitive radio by which SU can harvest energy from primary (PU) signal and this harvested energy will be utilized to transmit its data and increases the lifetime. In a traditional Energy Harvesting Cognitive Radio Network (EHCRN), SU can perform sensing and harvesting in separate slots which decrease the transmission time of secondary user results in reduction in throughput. To enhance the throughput of secondary user, a parallel operation of spectrum sensing and energy harvesting has been discussed. This parallel operation results in reduction of energy consumption and increases harvested energy that makes more energy to be available for transmission, which results in an increase of SU throughput. Simulation results using MATLAB shows that the proposed Parallel Sensing and Energy Harvesting CRN have improved the throughput compared to Traditional Energy Harvesting CRN and are analyzed with different parameters.

Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala

An accurate container throughput forecast is vital for any port. Since overall improvements in port performance and competitiveness can be derailed by port bottlenecks, ports need to find leverage to identify and prioritize measures to improve weak key performance indicators (KPIs) to attain growth opportunities. Prior studies had modeled container throughput from socio-economic and growth projection factors. This study aims to provide a practical method for forecasting the optimal container throughput a port can physically handle/attract given a certain level of terminal operation efficiency through random forest (RF) and multilayer perceptron (MLP) models. The study variables are derived from the port operations dimension and include ship turnaround time, vessel draft, container dwell time, berth productivity, container storage capacity, and custom declaration time. Evaluations are made based on the R-squared, NRMSE, MAE and MAPE. Model comparison is deduced with seven competing models in container throughput forecasting. The findings indicate that the RF model is a potential candidate for forecasting the engineering optimal throughput of Douala port. Model interpretation is provided through feature importance and partial dependence plots. The findings from this study will help reduce uncertainty and provide leverage for port management to spot bottlenecks and engage in better port planning and development projects which will strengthen their international competitive advantage.

PLANNING FOR OPTIMUM SUPPLY CHAIN THROUGHPUT AS A CRITICAL FACTOR IN MAKING A SUCCESSFUL CHAIN IN THE MARKET

The article discusses the need to ensure the optimal throughput of the supply chain as the most important factor in maintaining the effective and efficient operation of the chain. The notion is given. The importance of throughput planning is noted. It is presented as a search for a compromise between the speed of response to demand and the internal efficiency of the chain links. In addition, some of the barriers that hinder the simplicity of the throughput planning process in the chain are considered. They are the “whip effect” and the conflict of interests of the participants in the chain. The article also proposes approaches that allow to achieve the most efficient planning of throughput in the chain, taking into account all the existing requirements and restrictions. The author proposes supply chain management as a single system and its synchronization with every moment of actual market demand. The next propose is the introduction of strategic alliances.

The optical throughput of near-infrared static wind imaging interferometer

The basic principle of the near-infrared static wind imaging interferometer (NIR-SWINDII) is expounded. The instrument can simultaneously detect the atmospheric wind speed, temperature, and ozone concentration in the middle and lower thermosphere (25–110 km). According to the optical transmission principle, the exact expression of the ideal and actual optical throughput of the NIR-SWINDII is obtained. To analyze and discuss the influence of the field of view, wavelength, and refractive index systematically, we select a high refractive index and a low refractive index glass matching schemes according to the “full compensation” principle of the wide-field Michelson interferometer, the simulation of NIR-SWINDII’s optical throughput is carried out. For the high refractive index matching scheme, the results show that when the field of view is within 3 degrees, the transmittance of the NIR-SWINDII can reach 23.38%, and the optical throughput of the entire system is about 0.1024 cm2⋅sr. Comparing with the parameters of other instruments, it is found that our instrument’s optical throughput performance is comparable to other instruments. This research provides the theoretical basis and practical guidance for the throughput optimization of the NIR-SWINDII.

Efficient heterogeneous matrix profile on a CPU + High Performance FPGA with integrated HBM

In this work, we study the problem of efficiently executing a state-of-the-art time series algorithm class – SCAMP – on a heterogeneous platform comprised of CPU + High Performance FPGA with integrated HBM (High Bandwidth Memory). The geometry of the algorithm (a triangular matrix walk) and the FPGA capabilities pose two challenges. First, several replicated IPs can be instantiated in the FPGA fabric, so load balance is an issue not only at system-level (CPU+FPGA), but also at device-level (FPGA IPs). And second, the data that each one of these IPs accesses must be carefully placed among the HBM banks in order to efficiently exploit the memory bandwidth offered by the banks while optimizing power consumption.To tackle the first challenge we propose a novel hierarchical scheduler named Fastfit, to efficiently balance the workload in the heterogeneous system while ensuring near-optimal throughput. Our scheduler consists of a two level scheduling engine: (1) the system-level scheduler, which leverages an analytical model of the FPGA pipeline IPs, to find the near-optimal FPGA chunk size that guarantees optimal FPGA throughput; and (2) a geometry-aware device-level scheduler, which is responsible for the effective partitioning of the FPGA chunk into sub-chunks assigned to each FPGA IP. To deal with the second challenge we propose a methodology based on a model of the HBM bandwidth usage that allows us to set the minimum number of active banks that ensure the maximum aggregated memory bandwidth for a given number of IPs. Through exhaustive evaluation we validate the accuracy of our models, the efficiency of our intra-device partition strategies and the performance and energy efficiency of our Fastfit heterogeneous scheduler, finding that it outperforms state-of-the-art previous schedulers by achieving up to 99.4% of ideal performance.

Throughput and delay optimality of power-of-d choices in inhomogeneous load balancing systems

It is well-known that the power-of-d choices routing algorithm maximizes throughput and is heavy-traffic optimal in load balancing systems with homogeneous servers. However, if the servers are heterogeneous, throughput optimality does not hold in general. We find necessary and sufficient conditions for throughput optimality of power-of-d choices when the servers are heterogeneous, and we prove that almost the same conditions are sufficient to show heavy-traffic optimality. Additionally, we generalize the sufficient condition for throughput optimality to a larger class of routing policies.

Efficient method to identify hidden node collision and improving Quality-of-Service (QoS) in wireless sensor networks

The proposed approach is designed to boost the IEEE 802.15.4 / ZigBee-based WSN streaming efficiency by introducing multipurpose routing. We investigate how those networks can be designed in order to achieve optimum throughput using computational models to explain intra-track and inter-track interference in multipath routing networks. One mode of action in particular – Spatial-TDMA (S-TDMA), Zig Bee dependent WSNs, will greatly reduce the amount of interference, both in one- and multi-track environments. It is also seen that two-way networks can be stronger than their one-way counterparts by means of deliberately selected implementation parameters. Finally, a greater degree of spatial isolation between the routes used indicates improved average efficiency in multi-path scenarios. The simulation is one of our results.

Throughput optimization of multi-hop and multi-path cooperation in WPSNs with hardware noises

This paper proposes a novel multi-path and multi-hop wireless powered sensor network in case of hardware impairment, constituting an energy node, one source node, single sink node, and a series of distributed relay sensor nodes, where the energy node transmits wireless energy to all terminals in the first stage, and the relay sensor nodes relay the information of the source node to the sink node in the second stage. There exists M available paths between the source node and sink node, one of which is chosen for serving source-sink communication. To enhance the minimum achievable data rate, we propose a multi-hop communication protocol based on time-division-multiple-access and an optimal throughput path algorithm. We formulate the time allocation optimization problem about energy and information transmission of the proposed multi-hop cooperation, and confirm through abundant simulation experiments that the proposed scheme can availably improve user unfairness and spectral efficiency, and thus enhance its throughput performance.

Latency and Throughput Optimization In Modern Networks

The paper briefly describes about the subject which is Latency and Throughput Optimization in Modern Networks. Modern applications are highly sensitive to communication delays and throughput. This paper surveys major attempts on reducing latency and increasing the throughput. These methods are surveyed on different networks and surrounding’s such as wired networks, wireless networks, application layer transport control, Remote Direct Memory Access, and machine learning based transport control. On one hand every user likes to send and receive their data as quickly as possible. On the other hand the network infrastructure that connects users has limited capacities and these are usually shared among users. Thus Latency and Throughput Optimization is important in Modern networking.

Optimal performance comparison of the simulated moving bed process variants based on the modulation of the length of zones and the feed concentration

The VariCol and ModiCon processes are two variants of the simulated moving bed (SMB) process, characterized by the modulation of the length of zones of the chromatographic column train and the feed concentration. These features give more flexibility than the conventional operation, leading to essential improvements in the separation and purification of mixtures. The optimal performance comparison of these two variants, the hybrid formed by their combination, and the conventional SMB process are scarce in the literature. This comparison helps discover new characteristics of each single and combined operation mode and creates guidelines to select the appropriate operation mode for possible real applications. In this work, the performance comparison of the ModiCon, VariCol, ModiCon+VariCol, and SMB processes is carried out in terms of maximal throughput for specific product purity values. Particular emphasis is placed on both the ModiCon and the hybrid ModiCon+VariCol processes characteristics. A strategy for combining and optimizing the ModiCon and the VariCol processes was determined. As a case study, the enantioseparation of guaifenesin was considered. In the ModiCon process, more than two modulation subintervals did not improve the performance in the separation. The optimal pattern, based on two subintervals, has zero feed concentration in the first subinterval and the maximal concentration in the second one. The best result for the hybrid operation (ModiCon+VariCol) was reached when the feed port moves simultaneously as the SMB process switching period. The optimal throughput of the ModiCon and the ModiCon+VariCol processes was almost doubled than that of the SMB process. These performances were based on larger zones I and II and not in zones II and III as occur with the SMB and VariCol process. The throughput in the hybrid operation increases more significantly than the ModiCon process when 5 columns were considered instead of 6. The hybrid operation could be more attractive for a system with a few numbers of columns.

Deep Reinforcement Learning-Based Dynamic Spectrum Access for D2D Communication Underlay Cellular Networks

This letter investigates a deep reinforcement learning (DRL)-based spectrum access scheme for device-to-device (D2D) communication underlay cellular networks. Specifically, cellular users (CUEs) and D2D pairs attempt to access the time slots (TSs) of a shared spectrum, and TSs are dynamically scheduled to CUEs in different frames. Based on the DRL theory, D2D pairs can be seen as a centralized agent which aims to learn an optimal spectrum access strategy to maximize the sum throughput without any prior information. In particular, with different locations of CUEs, the spectrum access manners for D2D communication are changed to ensure the communication quality of CUEs at the cell edge. Then, a double deep Q-network (DDQN) based D2D spectrum access (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> SA) algorithm is proposed, which makes D2D pairs learn to decide whether to access the spectrum in different TSs. Moreover, to ensure the fairness of resource allocation among D2D pairs, we improve the proposed algorithm and incorporate fairness into the objective function. Simulation results show that our proposed algorithm can achieve an optimal sum throughput close to the theoretical upper bound, where the performance is significantly improved compared to the scheme based on base station cooperation.

High throughput optimization of hard and tough TiN/Ni nanocomposite coatings by reactive magnetron sputter deposition

A combinatorial thin-film synthesis approach together with a high throughput analysis methodology was successfully applied to synthetize TiN/Ni coatings with optimum mechanical properties. The synthesis approach consists of the deposition of TiN/Ni coatings with the Ni content ranging from around 0 to 20 at.% by reactive magnetron sputtering with a well-defined composition gradient, so that almost all possible compositions are produced in one single coating under identical conditions, removing uncertainties from processing conditions. The analysis methodology continuously screened the microstructure, residual stresses, hardness and fracture toughness of TiN/Ni coatings. The results show that a nanocomposite microstructure of equiaxed nanograins of crystalline δ-TiN embedded in a Ni rich amorphous phase is formed in the composition window between 8 and 12 Ni at.%. This microstructure offers a superior combination of hardness and toughness and a simultaneous reduction of residual stresses with respect to TiN coatings grown in the same conditions. Higher Ni contents are however detrimental because they compromise the integrity of the coatings.

RETRACTED ARTICLE: Cooperative Virtual Connectivity Control in Uplink Small Cell Network: Towards Optimal Resource Allocation

Nowadays, the application of soft computational techniques in power systems has been increasing with good acceptance under the machine learning umbrella. In this paper, first we presented a deep reinforcement learning approach to find the optimal configuration for HetNet systems. We used a huge quantity of radial configurations from a test system for training purposes. We also studied the joint carrier/power allocation problem in a multi-layer hierarchical networks to achieve the optimal power efficiency in addition to ensure the quality-of-experience for all subscribers. The proposed approach utilizes an adaptive Load Balancing model that aims to obtain “almost optimal” fairness among all servers from the key performance indicator viewpoints. Contrary to current model-based energy efficiency methods, we proposed a Joint Resource Allocation, Energy Efficiency and Flow Control algorithm to solve conventional non-convex and hierarchical optimization problems. Also, referred to SLA-based continuous resource allocation, we generalized the proposed algorithm to Joint Flow/Power Control, and Throughput Optimization algorithm to achieve the optimal energy efficiency beside of guaranteeing user throughput constraints. The simulation results exhibit that the proposed flow-controlled power optimization approach using the capability of network topology adjustment, is able to considerably enhance the energy efficiency compared to the conventional schemes.

Model-Predictive Control for Discrete-Time Queueing Networks With Varying Topology

In this article, we equip the conventional discrete-time queueing network with a Markovian input process which, in addition to the usual short-term stochastics, governs the mid- to long-term behavior of the links between the network nodes. This is reminiscent of the so-called Jump–Markov Systems in control theory, allowing the network topology to change over time and, thus, facilitating to model a plethora of useful applications in wireless communication, traffic, or logistics. We argue that the common back-pressure control policy is inadequate to control such network dynamics and propose a novel control policy inspired by the paradigms of Model-Predictive Control . Specifically, by defining a suitable but arbitrary prediction horizon, our policy takes into account future network states and possible control actions. This stands in clear contrast to most other policies which are myopic, i.e., only consider the immediate next state. We show numerically that such an approach can significantly improve the control performance and introduce several variants of the policy, thereby trading off performance versus computational complexity. In addition, we prove so-called throughput optimality of our policy, which guarantees stability for all network flows that can be maintained by the network topology. Interestingly, in contrast to general stability proofs in model-predictive control, our proof does not require the assumption of a terminal set, i.e., the prediction horizon is not required to be large enough as to reach a predetermined set of states with special properties. Finally, we provide several illustrating examples, one of which being a network of assembly-queues. This network, in particular, constitutes an interesting system class for which our policy exerts superiority over general backpressure policies, with the latter ones even losing their throughput optimality.

Advanced FPGA Implementation of AES Algorithm

Along with the enhance in computation as well as information safe-keeping in cloud servers, the requirement for a devoted computer hardware accelerator with regard to encryption is arising to be able to decrease the processor work. Highly efficient Advanced Encryption Standard (AES) 128-bit implementation, that could be utilized as an accelerator. In this research paper resource optimization and higher throughput were obtained. Memory segmentation is actually carried out to be able to assign several ports for simultaneous information accessibility. When algorithm is in proceed and examined time delay and initiation time period of various procedures, with regard to every crucial route delay, a fresh multistage solitary initiation time period sub-pipelined structure is suggested for making the initiation time period to a single for smallest route latency. Consequently, almost all operations in AES could be started within a single clock cycle and also can easily accept input in each and every clock cycle. The suggested approach while examined on latest Field Programmable Gate Array (FPGA) XC7VX690T unit that offers a throughput of 104.06 Gbps at a highest frequency of 813MHz and also 1.23-ns route delay. The useful resource utilization is reduced whenever compared along with other alternatives. The suggested method offers 30.74Mbps efficiency on device, which usually was 27.13% much more compared to the best efficiency documented in an earlier research study