Optimal Throughput Research Articles

The technological landscape and applications of single-cell multi-omics.

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Mapping adsorbent properties to optimal process performance of two-bed temperature swing adsorption by superstructure optimization

Temperature swing adsorption (TSA) is one of the promising separation techniques for large-scale CO2 capture, and many adsorbents are being developed today. However, it is difficult to identify the optimal operating configurations of multi-bed TSA process to fully utilize the potential of a given adsorbent. This is because optimal operating conditions and cycle configurations are highly dependent on the adsorbent properties. In this work, we propose a comprehensive approach that maps adsorption isotherms to the optimal multi-bed TSA operation identified from various alternatives. A case study for separation of CO2 and N2 is presented where isotherm properties of the adsorbent are parameterized to analyze the influence on the optimal TSA operating cycle configurations and process performance. Our analysis shows that the optimal throughput increases by 68% when the CO2 selectivity is doubled, and 96% when the CO2 selectivity and capacity are both doubled. Furthermore, the frequency of the reflux operations and the cycle time in the optimal operating cycle are found to be dependent on the CO2 capacity.

Hybrid-Mux Signal Structure and Resource Allocation for In-Band Distribution Link and ITND Transmission in SFN Environment

This paper investigates the multiplexing and resource allocation to improve the spectrum efficiency and reduce demultiplexing complexity and latency in the wireless in-band distribution link (IDL) and inter-tower networks and datacasting (ITND) for terrestrial broadcasting systems. A novel Hybrid-Mux technology is proposed, which combines orthogonal multiplexing (TDM/FDM), non-orthogonal multiplexing (NOM, or LDM), and hierarchical modulation (HM) with the non-uniform constellation (NUC). Hybrid-Mux technology can achieve high spectrum efficiency, low complexity, and low latency to provide versatile services of mobile/fixed broadcast services, inter-tower communication networks and datacasting, as well as the in-band distribution link to support SFN operation. Two Hybrid-Mux signal structures with 2-layer and 3-layer power-based non-orthogonal multiplexing of different broadcast and network data services are introduced. The throughput optimization of each Hybrid-Mux structure is formulated and resolved under the constraint of ITND and IDL data capacity, SNR requirement, and optimized NUC HM. Simulation results show that, by properly implementing resource allocation and constellation design, the proposed 3-layer Hybrid-Mux structures can achieve higher capacity than the simple 2-layer Hybrid-Mux structure, while the demodulation and demultiplexing complexity is not much increased. A 2-tier ITND service is proposed. It consists of a robust control and datacasting (ITCD) tier and a high data rate intertower networking (ITCN) link. This design can increase the total aggregated system data rate substantively. MIMO structures supporting ITND and IDL transmission are also proposed to further improve the spectrum efficiency.

Throughput delays: causes, predictors, and outcomes – observational cohort in a Swiss emergency department

Optimal throughput times in emergency departments can be adjudicated by emergency physicians. Emergency physicians can also define causes of delays during work-up, such as waiting for imaging, clinical chemistry, consultations, or exit blocks. For adequate streaming, the identification of predictors of delays is important, as the attribution of resources depends on acuity, resources, and expected throughput times. This observational study aimed to identify the causes, predictors, and outcomes of emergency physician-adjudicated throughput delays. Two prospective emergency department cohorts from January to February 2017 and from March to May 2019 around the clock in a tertiary care centre in Switzerland were investigated. All consenting patients were included. Delay was defined as the subjective adjudication of the responsible emergency physician regarding delay during emergency department work-up. Emergency physicians were interviewed for the occurrence and cause of delays. Baseline demographics, predictor values, and outcomes were recorded. The primary outcome - delay - was presented using descriptive statistics. Univariable and multivariable logistic regression analyses were performed to assess the associations between possible predictors and delays and hospitalization, intensive care, and death with delay. In 3656 (37.3%) of 9818 patients, delays were adjudicated. The patients with delays were older (59 years, interquartile range [IQR]: 39-76 years vs 49 years, IQR: 33-68 years) and more likely had impaired mobility, nonspecific complaints (weakness or fatigue), and frailty than the patients without delays. The main causes of delays were resident work-up (20.4%), consultations (20.2%), and imaging (19.4%). The predictors of delays were an Emergency Severity Index of 2 or 3 at triage (odds ratio [OR]: 3.00; confidence interval [CI]: 2.21-4.16; OR: 3.25; CI: 2.40-4.48), nonspecific complaints (OR: 1.70; CI: 1.41-2.04), and consultation and imaging (OR: 2.89; CI: 2.62-3.19). The patients with delays had an increased risk for admission (OR: 1.56; CI: 1.41-1.73) but not for mortality than those without delays. At triage, simple predictors such as age, immobility, nonspecific complaints, and frailty may help to identify patients at risk of delay, with the main reasons being resident work-up, imaging, and consultations. This hypothesis-generating observation will allow the design of studies aimed at the identification and elimination of possible throughput obstacles.

Algorithm-hardware Co-optimization for Energy-efficient Drone Detection on Resource-constrained FPGA

Convolutional neural network (CNN)-based object detection has achieved very high accuracy; e.g., single-shot multi-box detectors (SSDs) can efficiently detect and localize various objects in an input image. However, they require a high amount of computation and memory storage, which makes it difficult to perform efficient inference on resource-constrained hardware devices such as drones or unmanned aerial vehicles (UAVs). Drone/UAV detection is an important task for applications including surveillance, defense, and multi-drone self-localization and formation control. In this article, we designed and co-optimized an algorithm and hardware for energy-efficient drone detection on resource-constrained FPGA devices. We trained an SSD object detection algorithm with a custom drone dataset. For inference, we employed low-precision quantization and adapted the width of the SSD CNN model. To improve throughput, we use dual-data rate operations for DSPs to effectively double the throughput with limited DSP counts. For different SSD algorithm models, we analyze accuracy or mean average precision (mAP) and evaluate the corresponding FPGA hardware utilization, DRAM communication, and throughput optimization. We evaluated the FPGA hardware for a custom drone dataset, Pascal VOC, and COCO2017. Our proposed design achieves a high mAP of 88.42% on the multi-drone dataset, with a high energy efficiency of 79 GOPS/W and throughput of 158 GOPS using the Xilinx Zynq ZU3EG FPGA device on the Open Vision Computer version 3 (OVC3) platform. Our design achieves 1.1 to 8.7× higher energy efficiency than prior works that used the same Pascal VOC dataset, using the same FPGA device, but at a low-power consumption of 2.54 W. For the COCO dataset, our MobileNet-V1 implementation achieved an mAP of 16.8, and 4.9 FPS/W for energy-efficiency, which is ∼ 1.9× higher than prior FPGA works or other commercial hardware platforms.

Throughput optimization for backscatter-and-NOMA-enabled wireless powered cognitive radio network

Throughput optimization for backscatter-and-NOMA-enabled wireless powered cognitive radio network

Energy Efficiency and Throughput Optimization in 5G Heterogeneous Networks

Device-to-device communication offers a promising technology for the 5G network that aims to enhance the data rate, reduce latency and cost, improve energy efficiency, and provide other desired features. The 5G heterogeneous network (5GHN) with a decoupled association strategy of downlink (DL) and uplink (UL) is a promising solution for the challenges faced in the 4G heterogeneous network (4GHN). The research presented in this paper evaluates the performance of the 4GHN as well as a DL-and-UL-coupled (DU-CP) access scheme in comparison with the 5GHN with a DL-and-UL-decoupled (DU-DCP) access scheme in terms of the energy efficiency and network throughput in four-tier heterogeneous networks. The energy and throughput are optimized for both scenarios, i.e., DU-CP and DU-DCP, and the results are compared. Detailed performance analyses of the DU-CP and DU-DCP access schemes were conducted with the help of comparisons of the results achieved by implementing a genetic algorithm (GA) and particle swarm optimization (PSO). Both of these algorithms are suited for the non-linear problem under investigation in which the search space is large. The simulation results have shown that the DU-DCP access scheme gives a better performance than the DU-CP scheme in a four-tier heterogeneous network in terms of network throughput and energy efficiency. The PSO achieves an energy efficiency of 12 Mbits/joule for the DU-CP and 42 Mbits/joule for the DU-DCP, whereas the GA yields an energy efficiency of 28 Mbits/joule for the DU-CP and 55 Mbits/joule for the DU-DCP. The performance of the proposed method is compared with those of three other schemes. The results show that the DU-DCP scheme using the GA outperforms the compared methods.

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput.

Throughput optimization for IRS-assisted multi-user NOMA URLLC systems

Throughput optimization for IRS-assisted multi-user NOMA URLLC systems

An MCDM optimization based dynamic workflow scheduling used to handle priority tasks for fault tolerance in IIOT

Cloud computing's prominence can be mainly attributed to its convenient on-demand service delivery via the internet. The difficulty in managing these requests and allocating them optimally is exacerbated by the growing number of users making them and the variety of criteria used to apply cloud resources. Some of these difficulties include scheduling work while considering the user's Quality of Service (QoS) preferences, ensuring adequate load balancing, and adapting to the ever-changing conditions of the cloud environment. As server capabilities evolve, static load balancers, which distribute requests using fixed ratios of available resources, fail to provide optimum throughput. It has been shown that no other scheduling algorithms offer an adaptive method that considers both load balancing and maximizing the QoS criteria. The authors of this study present a novel adaptive strategy for dealing with this issue by fusing the best-worst multi-criteria decision-making method (BWM) with the compromise ranking method (VIKOR). Task priorities are determined by using the VIKOR method as a decision-maker. Numerical experiments are used to verify the validity of the suggested method, and the methodology is compared to other scheduling methods in terms of a variety of performance criteria. The simulation findings show that the presented method outperforms its rivals regarding throughput, makespan, waiting time, virtual machine (VM) utilization, and VM usage cost across the board of simulated experimental settings.

Continuous wave multi-pass imaging flow cytometry

Biological cells have weak optical properties that limit the sensitivity of measurements and imaging when the total illumination is limited by dynamics or throughput. Multi-pass microscopy enhances the sensitivity of classical light fields to these weak properties. We present a wide-field multi-pass microscope for label-free imaging flow cytometry. By passing the optical field through cells in a high-speed flow up to 4 times, we realize a ×4 enhancement in contrast and signal-to-noise ratio. We show that this technique approaches close to the quantum limit of measurement sensitivity, extending the range of optimal imaging to samples in the weakly absorbing regime. This allows for near optimal imaging sensitivity and throughput in practical scenarios involving dynamic biological samples and surpasses the sensitivity achieved with currently available quantum light sources.

Intelligent Access to Unlicensed Spectrum: A Mean Field Based Deep Reinforcement Learning Approach

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

Spectrum sharing mechanisms in the unlicensed band: Performance limit and comparison

AbstractDeploying networks in unlicensed spectrum has been drawing significant attention, which serves to alleviate the increasing demands in licensed spectrum. However, the network coexistence in unlicensed channel may lead to throughput degradation and unfairness. An appropriate spectrum‐sharing mechanism is therefore of great significance. In this paper, we study the performance limit of two representative mechanisms used in the coexistence with WiFi, including Duty Cycle (DC) and Listen‐Before‐Talk (LBT). In particular, both the throughputs of the coexisting network and WiFi under two mechanisms are derived as explicit expressions of system parameters, based on which the maximum total throughput of the coexisting network and WiFi is characterized under throughput fairness and 3GPP fairness, respectively. A systematic comparison between the optimal throughput performance of DC and LBT is conducted. It is found that if the coexisting network with LBT occupies the channel for a large period each time it successfully accesses the channel, then the maximum total throughput in LBT would be close to that in DC under both throughput fairness and 3GPP fairness. The optimal settings for DC and LBT mechanisms to achieve maximum total throughput are obtained, respectively, which sheds important light on the design of fair and efficient spectrum‐sharing protocols.

A Privacy-Preserving Scheme for Medical Diagnosis Records Based on Encrypted Image Steganography

Healthcare diagnosis records are essential as they establish perpetual reports on the health of patients. These records are in nature meant to be kept confidential. It is for this reason that medical data should be secured before transmission. Steganography and cryptography are old concepts used to secure communications. Steganography is hiding data into a carrier, for instance, images, videos, texts, and files. It seeks to hide the existence of communications. Cryptography is communication in deciphering secret writings or ciphers. In our privacy-preserving scheme for healthcare records, a hybrid approach is used to ensure the maximum multi-level security of medical records before transmission. The proposed system utilizes AES-128 which is a symmetric key encryption algorithm to scramble the data. As compared to asymmetric key encryption, symmetric key encryption is faster as only one key is used for both encryption and decryption. To resolve the problem of key distribution that plagues symmetric cryptosystems, the proposed system makes use of the Diffie-Hellman key exchange algorithm for key agreement. In this way, the encryption essentials are securely exchanged before the actual transmission of the date. To ensure efficient and optimal steganography throughput, the Least significant bit (LSB) which involves the hiding of information in the most repetitive bits of each pixel of an image was implemented for steganography. The technique was chosen because the level of distortion made to the image is low hence the image quality change is minimal. As such, the resultant robust system, is highly impeccable, utilises efficient compression, and has improved capacity which are all desirable features for securing medical health records.

How Should We Regulate Cryptocurrencies via Consensus?: A Strategic Framework for Optimal Legal Transaction Throughput

Permissionless blockchain consensus protocols have been leveraged for defining decentralized economies for the (commercial or private) trade of virtual and physical assets, using cryptocurrencies. In most instances, the assets being traded are regulated , which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formalize proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in illegal activities in the associated crypto-economies. In this contribution, unlike existing non-consensus solutions, which are prone to be more compute-time and audit-time intensive, we present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain consensus. As per our regulatory framework, we derive, through a stochastic game analysis, block proposal strategies under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that when a majority of the consensus protocol participants are licensed by the regulator to propose legal transactions, there exists a fair consensus execution policy to maximize the legal transaction throughput in the blockchain network.

Improve Throughput and spectrum efficiency using Cell – Free MIMO-NOMA network with user–centric clustering

In this article, the user resource allocation and user scheduling in a cell-free user-centric clustering network and coherent transmissions is investigated. However, the setup of the user-centric clustering is not sufficient for multi-user transmissions, and user scheduling becomes difficult. To overcome the aforementioned problem and to provide user fairness, an optimum resource allocation algorithm with perfect user scheduling, beam forming, and radio frequency transmitter (RFT) clustering is proposed. Thereby, the cell edge user achieves the optimum throughput and spectrum efficiency. The simulation result shows that the proposed method outperforms the conventional method. In addition, the pilot-reusing factor is used to obtain loss due to imperfect channel state information and pilot train overhead.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

Dynamic Super Round-Based Distributed Task Scheduling for UAV Networks

Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE– a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate.

Throughput Optimization for Noma Energy Harvesting Cognitive Radio With Multi-UAV-Assisted Relaying Under Security Constraints

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints.