Data Throughput Research Articles

Fast polarization-sensitive second-harmonic generation microscopy based on off-axis interferometry.

We propose polarization-sensitive second-harmonic generation microscopy based on off-axis interferometry (OI-PSHG) by recording the complex field of a wide-field second-harmonic generation (SHG) image and performing polarization measurements. With the ability to record the SHG signals associated with different positions simultaneously, the proposed method exhibits a higher imaging frame rate than raster scanning-based SHG microscopy. The molecular orientation (in terms of their symmetric axis) of tendon collagen fibrils and myosin in muscle is resolved in three dimensions from a subset of polarization-resolved SHG holograms. With the present configuration, it takes approximately 0.01 s to acquire an image with 128 × 128 pixels, which is mainly limited by the excitation power density for wide-field illumination. For the same data throughput using pixel-by-pixel scanning, 0.16-s-long acquisition is required, with the pixel dwell time of 10 µs. Offering the ability to perform wide-field imaging and polarization measurements, the present work lays the foundation for fast SHG microscopy using complex deconvolution and harmonic tomography.

Performance of Sensor Data Process Offloading on 5G-Enabled UAVs.

Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network's capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV's sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.

Ultrafast quantum key distribution using fully parallelized quantum channels.

The field of quantum information processing offers secure communication protected by the laws of quantum mechanics and is on the verge of finding wider application for the information transfer of sensitive data. To improve cost-efficiency, extensive research is being carried out on the various components required for high data throughput using quantum key distribution (QKD). Aiming for an application-oriented solution, we report the realization of a multichannel QKD system for plug-and-play high-bandwidth secure communication at telecom wavelengths. We designed a rack-sized multichannel superconducting nanowire single photon detector (SNSPD) system, as well as a highly parallelized time-correlated single photon counting (TCSPC) unit. Our system is linked to an FPGA-controlled QKD evaluation setup for continuous operation, allowing us to achieve high secret key rates using a coherent-one-way protocol.

Distinctive analysis of power and frequency allocation for Device to Device (D2D) communication

Wireless communication makes an impeccable advancement from 1 G to 5 G and beyond. Simultaneously, modern technologies like Internet of Things (IoT) and Vehicular Communications also emerged. Improved connectivity between many different devices is provided by 5 G, which maximises data throughput and reduces interference. Non-Orthogonal Multiple Access (NOMA) schemes or Orthogonal Multiple Access techniques (OMA) suit 5 G. It is a NOMA-based device-to-device communication by correlating it with the Orthogonal Frequency Division Multiple Access (OFDMA). NOMA is also utilized to propose an optimal power allocation scheme and duly evaluated. The aim of such a scheme is to achieve a minimized outage and a maximized sum rate. A NOMA based Greedy Interference Algorithm is proposed for interference management by exquisite frequency allocation. The results generated from a comprehensive evaluation justify the algorithm utilization. Simulation results infer that the algorithms proposed improves system throughput and enhance energy efficiency.

Nonlinear polarization tensor measurement with a vectorial complex field in second-harmonic-generation microscopy

Polarization-sensitive second-harmonic-generation microscopy (pSHGM) allows one to determine the molecular orientation of harmonophores. Conventional point scanning based pSHGM is time consuming and subject to the assumption of the cylinder symmetry of the sample. Here, we propose a wide-field pSHGM measurement scheme that is able to measure the second-order nonlinear polarization tensor. The measurement scheme is based on first-order Born approximation, from which the relation between incident fundamental wave, second harmonic wave, and nonlinear polarization tensor has been established. It suggests that the polarization tensor can be solved by measuring the vectorial second harmonic complex fields corresponding to three independent polarization states of the incident fundamental wave. An experiment on measuring the supramolecular orientations, in terms of their symmetric axis, of myosin in rat muscle tissue has been carried out to demonstrate the proposed measurement scheme. Benefiting from the ability of recording the second harmonic signal from different positions parallelly, the proposed method possesses a higher imaging frame rate compared to point scanning based pSHGM. With the present configuration, it takes 0.01 s to acquire a 128\ifmmode\times\else\texttimes\fi{}128 pixels image, which is mainly limited by the excitation power density for wide-field illumination. For the same data throughput using pixel-by-pixel scanning, 0.16 s acquisition time is required for a pixel dwell time of 10 \textmu{}s. Having the ability of wide-field imaging and polarization measurement, the present work also lays a foundation for high-resolution SHG microscopy using computed tomography.

Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare

Smart Health Care offers efficient, sustainable, along with real-time human services, and the concept of enhanced Internet of Things (IoT) lies behind the emergence of this smart Health Care (HC). Nevertheless, the association of these IoT-centric sensors with other organizations generates security threats that an illegal user might utilize it owing to the data’s openness. In the HC sector, data security and privacy are the prime concern in which the alteration in data values as sensors could modify the diagnosis process, which may cause serious health problems. The immutability and transparency of the blockchain make it a viable option for the safe storage and management of healthcare data. However, the Cloud Storage Server (CSS), consensus latency, majority attacks, Byzantine problem, uncomfortable data Throughput (TP), et cetera, make the blockchain vulnerable to healthcare data transmission. To solve the current disadvantages, a secure IoT was introduced in healthcare using Brooks Iyengar Quantum Byzantine Agreement-centered Blockchain Networking (BIQBA-BCN). This study ensures the sincerity and equity of health data exchange. Based on Blum Blum Shub and Okamoto Uchiyana Cryptosystem (OUCS) mutual authentica1tion, the proposed work provides an OUCS-based mutual authentication system (BBS-OUC). Attackers are prevented from entering the BCN, ensuring the storage remains trustworthy. In addition, the Key Weight Block Function-Quasi-Cyclic Moderate Density Parity Check (KWBF-QCMDPC) algorithm safeguards the confidentiality and dependability of IoT user data. The cryptosystem technique protects sensitive data when the blockchain platform is distributed. The proposed methodology, known as BIQBA-BCN, results in a security level that is %94 effective. Finally, the BIQBA-BC consensus mechanism is used to distribute data to the corresponding Hospital Server (HS). By achieving a higher data TP, lower delay, higher Success Rate (SR), shorter consensus latency, and node communication time, the experimental results show that the proposed methodology is exceptionally safe against assaults and highly scalable.

Intelligent Trajectory Design for Mobile Energy Harvesting and Data Transmission

Energy harvesting technology enables wireless sensor networks (WSNs) to be self-sustainable, for maintaining long-term key performance indicators, such as the data throughput and sensing coverage. Due to the highly dynamic and complex environment, energy sources (ES) cannot provide stable energy supply, which needs the efficient learning algorithm to enable system adaptations. This article reports on the development of reinforcement learning (RL) methodology to long-term data collection in self-sustainable WSNs. Specifically, we consider the WSN as a 2-D rectangular region, where a mobile sensor (MS) can harvest energy from ambient environments while transmitting the collected data to a fixed sink. Due to the changing environment and the mobility of the MS, the harvested energy by the MS at each slot presents spatiotemporal dynamics within the network, which severely affects the performance of data throughput from the MS to the sink. The MS’s trajectory is investigated to maximize the long-term average MS-to-sink data throughput. Due to the unknown energy arrival information as well as the locations of ESs, we formulate the problem as a Markov decision process, which is then solved with model-free RL. In particular, the deep deterministic policy gradient (DDPG) is applied to tackle the continuous and deterministic movement space. Results show that the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received energy over slots. Finally, the MS can identify and move to the optimal location where the maximized long-term average MS-to-sink data throughput is achieved. Extensive numerical evaluations are conducted to investigate the impact of various system parameters on the network performance.

Design of a virtual simulation interaction system based on enhanced reality

<abstract><p>Traditional virtual simulated interaction systems experience data fragmentation during the process of converting from two-dimensional to three-dimensional information, resulting in reduced realism and an inability to meet the teaching requirements of computer courses. Therefore, the integration of augmented reality (AR) technology into the educational environment remains an urgent and unresolved issue. To address the aforementioned issues, this paper investigates the data throughput limitations present in virtual simulation interaction systems. In response to this problem, an application solution utilizing AR technology is proposed, specifically a design concept for a virtual simulation interactive system tailored to computer-related courses. This system achieves its objectives through the collaborative interaction of AR hardware and supplementary software algorithms. The AR hardware is subdivided into framework design and functional hardware design, while the software components encompass AR models, virtual interaction models, and fusion methods. Through testing and comparison of the data throughput of this system with two other virtual simulation interaction systems, it was found that the virtual simulation interactive system optimized using AR technology can effectively enhance data throughput and address the issue of reduced realism in virtual interaction scenes caused by data fragmentation. This design system provides a more realistic and efficient mode of interaction for teaching computer-related courses.</p></abstract>

A Low-Latency, Low-Power CMOS Sun Sensor for Attitude Calculation Using Photovoltaic Regime and On-Chip Centroid Computation

The demand for sun sensors has skyrocketed in the last years due to the huge expected deployment of satellites associated with the New Space concept. Sun sensors compute the position of the sun relative to the observer and play a crucial role in navigation systems. However, the sensor itself and the associated electronics must be able to operate in harsh environments. Thus, reducing hardware and post-processing resources improves the robustness of the system. Furthermore, reducing power consumption increases the lifetime of microsatellites with a limited power budget. This work describes the design, implementation, and characterization of a proof-of-concept prototype of a low-power, high-speed sun sensor architecture. The proposed sensor uses photodiodes working in the photo-voltaic regime and event-driven vision concepts to overcome the limitations of conventional digital sun sensors in terms of latency, data throughput, and power consumption. The temporal resolution of the prototype is in the microsecond range with an average power consumption lower than 100 μW. Experimental results are discussed and compared with the state-of-the-art.

The Prism Bridge: Maximizing Inter-Chip AXI Throughput in the High-Speed Serial Era

In this paper, we present the Prism Bridge, a soft IP core developed to bridge FPGA-MPSoC systems using high-speed serial links. Considering the current trend of ubiquitous serial transceivers with staggeringly increasing line rates, minimizing overhead and maximizing data throughput becomes paramount. Hence, our main design goal is to maximize bandwidth utilization for AXI data, which we realize through an advanced packetization mechanism. We give an overview of the Prism Bridge’s design and analyze its half-duplex bandwidth utilization. Additionally, we discuss the results of the experiments we conducted to assess its real-world performance, including measurements of throughput and latency of various combinations of line rates, link-layer cores, and bridge cores. Using a serial link with a 16.375 Gbit/s line rate, the Prism Bridge with an advanced packetizing mechanism achieved an AXI write throughput of 1368.81 MiB/s and an AXI read throughput of 1376.61 MiB/s, an increase of 46.19% and 45.85%, respectively, compared with the de-facto industry-standard core. The advanced packetization mechanism had negligible impact on latency but required 69.14%–73.91% more LUTs and 33.62%–36.19% more flip-flops. We conclude that for most designs that support inter-chip AXI transactions and will not be limited to short transaction lengths, the higher data throughput of the Prism Bridge with an advanced packetization mechanism is worth its cost in additional logic resource utilization.

An Energy-Efficient MAC Protocol for Three-Dimensional Underwater Acoustic Sensor Networks With Time Synchronization and Power Control

In recent years, Underwater Acoustic Sensor Networks (UASNs) have gained considerable attention for their unique role in detecting and monitoring the underwater environment. However, due to the long propagation time, high bit error rate, and limited bandwidth of underwater acoustic systems, the design of media access control (MAC) protocols is extremely complex, especially for the power consumption of UASNs. Therefore, this paper proposes an energy-efficient MAC protocol for three-dimensional UASNs with time synchronization and power control (TDTSPC-MAC). The proposed protocol is a hybrid access scheme for three-dimensional UASN using techniques such as time synchronization, power control, clustering, layering, and sleep mechanisms. Moreover, the TDTSPC-MAC protocol uses the hierarchical concept and distributed clustering algorithm to divide the three-dimensional space, and combines time synchronization and power control strategies to avoid collisions. Besides, energy consumption is reduced through monitoring and sleep mode. Simulation results demonstrate that the proposed TDTSPC-MAC protocol has reasonable data transmission delay time, throughput, energy consumption, and other performance.

THz Channel Sounding and Modeling Techniques: An Overview

As the world warms up to the idea of millimeter wave (mmWave) communication and fifth generation (5G) mobile networks, realization slowly dawns that the data rate, latency, throughput, and other performance metrics that are used to assess a new wireless communication technology will not be enough to support the demands of envisioned futuristic applications. Thus there is an eagerness to further climb up the frequency ladder to use the large swathes of available spectrum in the 0.1 - 10 THz band which is expected to act as the key technology enabler to fulfill the requirements of the sixth generation (6G) wireless communication and possibly even beyond. Channel measurement and modeling are crucial to the design and deployment of future wireless communication systems and researchers across the world are putting their best foot forward to accelerate the process. The present article presents comprehensive assimilation of research efforts in the context of THz channel sounding. A detailed overview of the current channel sounding techniques is first introduced followed by their relevance to THz band channel measurement. An in-house novel channel sounder developed for THz band measurement is also briefly introduced in this context. The paper next provides elaborate dissemination of various measurement campaigns in the band of interest followed by the modeling techniques that are available in the literature and are being adopted for the THz band. Post the description of different challenges and future research directions in the context of sounding, measurement, and modeling the article is concluded.

VAR2CSA Ectodomain Labeling in Plasmodium falciparum Infected Red Blood Cells and Analysis via Flow Cytometry.

Presentation of the variant antigen Plasmodium falciparum erythrocyte membrane protein 1 (EMP1) at the surface of infected red blood cells (RBCs) underpins the malaria parasite's pathogenicity. The transport of EMP1 to the RBC surface is facilitated by a parasite-derived trafficking system, in which over 500 parasite proteins are exported into the host cell cytoplasm. To understand how genetic ablation of selected exported proteins affects EMP1 transport, several EMP1 surface presentation assays have been developed, including: 1) trypsinization of surface-exposed EMP1 and analysis by SDS-PAGE and immunoblotting; and 2) infected RBC binding assays, to determine binding efficiency to immobilized ligand under physiological flow conditions. Here, we describe a third EMP1 surface presentation assay, where antibodies to the ectodomain of EMP1 and flow cytometry are used to quantify surface-exposed EMP1 in live cells. The advantages of this assay include higher throughput capacity and data better suited for robust quantitative analysis. This protocol can also be applied to other cellular contexts where an antibody can be developed for the ectodomain of the protein of interest.

Four levels of in-sensor computing in bionic olfaction: from discrete components to multi-modal integrations.

Sensing and computing are two important ways in which humans attempt to perceive and understand the analog world through digital devices. Analog-to-digital converters (ADCs) discretize analog signals while the data bus transmits digital data between the components of a computer. With the increase in sensor nodes and the application of deep neural networks, the energy and time consumption limit the increment of data throughput. In-sensor computing is a computing paradigm that integrates sensing, storage, and processing in one device without ADCs and data transfer. According to the integration degree, herein, we summarize four levels of in-sensor computing in the field of artificial olfactory. In the first level, we show that different functions are conducted by using discrete components. Next, the data conversion and transfer are exempt within the in-memory computing architecture with necessary data encoding. Subsequently, in-sensor computing is integrated into a single device. Finally, multi-modal in-sensor computing is proposed to improve the quality and reliability of the classification results. At the end of this minireview, we provide an outlook on the use of metal nanoparticle devices to achieve such in-sensor computing for bionic olfaction.

Multi-channel spectral-domain optical coherence tomography using single spectrometer

Multi-channel detection is an effective way to improve data throughput of spectral-domain optical coherence tomography (SDOCT). However, current multi-channel OCT requires multiple detectors, which increases the complexity and cost of the system. We propose a novel multi-channel detection design based on a single spectrometer. Each camera pixel receives interferometric spectral signals from all the channels but with a spectral shift between two channels. This design effectively broadens the spectral bandwidth of each pixel, which reduces relative intensity noise (RIN) by √M times with M being the number of channels. We theoretically analyzed the noise of the proposed design under two cases: shot-noise limited and electrical noise or RIN limited. We show both theoretically and experimentally that this design can effectively improve the sensitivity, especially for electrical noise or RIN-dominated systems.

Communication capacity maximization in drone swarms

Employment of unmanned aerial vehicles (UAVs) or drones as swarms of coordinating nodes offers multiple advantages for commercial as well as military applications. However, the complex communication requirements of these swarms, coupled with high data rates of advanced UAV payloads, require innovative techniques for optimizing data throughput. Channel capacity being the key resource, optimum communication architecture and network topology is critical to ensure quality of service while remaining within transmission power constraints. This paper proposes a capacity maximization approach for swarm communication architectures using mixed-integer nonlinear programming (MINLP). These techniques are designed to tackle optimization applications involving both discrete variables and nonlinear system dynamics. Mathematical model formulated considering system constraints and desired objective function establishes applicability of MINLP. Since MINLP problems are NP-hard in general, computational overheads and search space exponentially grow with number of nodes in the swarm. Therefore, outer approximation algorithm has been applied that achieves near-optimal solutions with reduced convergence time and complexity compared with exhaustive search. Applicability of algorithm regardless of selected communication architecture has been established through realistic simulations.

Optimization of Data Throughput for Improved Traffic Management in a Data Switched Network

Optimization of Data Throughput for Improved Traffic Management in a Data Switched Network

Blockchain for Dynamic Spectrum Access and Network Slicing: A Review

Dynamic spectrum access (DSA) and network slicing are some of the principal concepts to realize the emerging applications in Beyond 5th Generation (B5G) and 6th Generation (6G) networks. The frequency spectrum remains scarce and underutilized, while the performance requirements in terms of data throughput and latency of the network tenants have diversified. DSA allows for the efficient utilization of spectrum resources, while network slicing aims to serve network users with highly distinctive service needs. Lack of incentivization, sharing of spectrum resources among multiple operators, and lack of trust between operators are some of the challenges faced by centralized DSA approaches. Similarly, secure network slice orchestration, slice-isolation, secure access to network resources, privacy of user sensitive data, assuring the provision of Service Level Agreements (SLAs), are some of the challenges in existing network slicing techniques. Blockchain due to its innate capabilities can be a promising technology to solve the key issues pertaining to current DSA and network slicing approaches. Blockchain through smart contracts, provides traceability of network resources, auditability and accountability of network operators and service providers. Smart contracts facilitate the automation of resource sharing and network slice orchestration, while ensuring that SLAs are met, and network operators are compensated. This paper first provides a comprehensive overview of the DSA concept, the existing DSA techniques, and the challenges posed by these techniques. This is followed by a detailed description of network slicing, its key elements and architecture, various network slicing parameters, existing network slicing techniques and their challenges. Then an in- depth review on blockchain, its working principle, factors that affect blockchain implementation, and a comparison of various open-source blockchain platforms that support smart contracts is presented. This discussion is summarized by presenting the state-of-the-art in the blockchain-enabled DSA and network slicing, challenges and trade-offs of these techniques, and the gaps and future directions in this research area. Finally, we conclude by providing some future research directions.

Integration of FPGA RDMA into the ATLAS readout with FELIX in High Luminosity LHC

The FELIX system is used to interface the front-end electronics and the commodity hardware in the server farm of the ATLAS experiment. FELIX is using RDMA through RoCE to transmit data from its host servers to the Software Readout Driver using off-the-shelf networking equipment. In the current version of FELIX, RDMA communication is implemented using software on both ends of the links. Improvements of the data throughput as part of the High Luminosity LHC upgrade, by implementing RDMA support in the front-end FELIX FPGA, have been tested. A version of FELIX that uses the FPGA implementation of RDMA is being proposed and demonstrated.

An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant.

Phasic small interfering RNAs are plant secondary small interference RNAs that typically generated by the convergence of miRNAs and polyadenylated mRNAs. A growing number of studies have shown that miRNA-initiated phasiRNA plays crucial roles in regulating plant growth and stress responses. Experimental verification of miRNA-initiated phasiRNA loci may take considerable time, energy and labor. Therefore, computational methods capable of processing high throughput data have been proposed one by one. In this work, we proposed a predictor (DIGITAL) for identifying miRNA-initiated phasiRNAs in plant, which combined a multi-scale residual network with a bi-directional long-short term memory network. The negative dataset was constructed based on positive data, through replacing 60% of nucleotides randomly in each positive sample. Our predictor achieved the accuracy of 98.48% and 94.02% respectively on two independent test datasets with different sequence length. These independent testing results indicate the effectiveness of our model. Furthermore, DIGITAL is of robustness and generalization ability, and thus can be easily extended and applied for miRNA target recognition of other species. We provide the source code of DIGITAL, which is freely available at https://github.com/yuanyuanbu/DIGITAL.