IEEE Access

Journal: IEEE Access

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IEEE

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2169-3536

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Publications1 - 10 of 94

Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems
Moosmann, Julian; Müller, Hanna; Zimmerman, Nicky; et al. (2024)
IEEE Access
This paper deploys and explores variants of TinyissimoYOLO, a highly flexible and fully quantized ultra-lightweight object detection network designed for edge systems with a power envelope of a few milliwatts. With experimental measurements, we present a comprehensive characterization of the network’s detection performance, exploring the impact of various parameters, including input resolution, number of object classes, and hidden layer adjustments. We deploy variants of TinyissimoYOLO on state-of-the-art ultra-low-power extreme edge platforms, presenting a detailed comparison on latency, energy efficiency, and their ability to efficiently parallelize the workload. In particular, the paper presents a comparison between a RISC-V-based parallel processor (GAP9 from GreenWaves Technologies) with and without use of its on-chip hardware accelerator, an ARM Cortex-M7 core (STM32H7 from ST Microelectronics), two ARM Cortex-M4 cores (STM32L4 from ST Microelectronics and Apollo4b from Ambiq), and a multi-core platform aimed at edge AI applications with a CNN hardware accelerator (MAX78000 from Analog Devices). Experimental results show that the GAP9’s hardware accelerator achieves the lowest inference latency and energy at $\mathrm {2.12ms }$ and $\mathrm {150~\mu \text {J} }$ respectively, which is around 2x faster and 20% more energy efficient than the next best platform, the MAX78000. The hardware accelerator of GAP9 can even run an increased resolution version of TinyissimoYOLO with $112\times 112$ pixels and 10 detection classes within 3.2 ms, consuming $\mathrm {245~\mu \text {J} }$ . We also deployed and profiled a multi-core implementation on GAP9 at different core voltages and frequencies, achieving $\mathrm {11.3ms }$ with the lowest-latency and $\mathrm {490~\mu \text {J} }$ with the most energy-efficient configuration. With this paper, we demonstrate the flexibility of TinyissimoYOLO and prove its detection accuracy on a widely used detection dataset. Furthermore, we demonstrate its suitability for real-time ultra-low-power edge inference.
The potential of flexible reservations in a car sharing system with an auction scheme
Roca-Riu, Mireia; Menendez, Monica (2019)
IEEE Access
In the last 20 years car sharing has been a growing trend in personal mobility. Multiple aspects of these systems have been already discussed: different forms of car sharing, user’s preferences and behavior, or benefits estimation. Nevertheless, the management of these systems needs to be continuously improved to remain a competitive alternative. In this work, we propose a reservation scheme to manage rental reservations of a two-way station-based car sharing system. It allows the operator to better plan the necessary vehicles at each station, and encourages the drivers to make better use of the existing vehicles, by showing flexibility in the starting rental time. The reservation scheme is organized with an auction, where drivers bet for their preferred rental start time. Drivers participating in the auction are offered a reduced rental fare, which is then complemented with the reservation fee that results from the auction. The auction is solved under Vickrey-Clarke-Groves (VCG) mechanism for combinatorial auctions, which guarantees the desired properties for the operator and a fair assignment for the drivers. The proposed scheme is tested on instances inspired by the Mobility car sharing system in Zürich, Switzerland. The results show that operators could decrease their fleets with low to no impacts on the overall rental revenues, especially when drivers show flexibility in their rental start times. For certain levels of demand price elasticity, even positive impacts on the overall rental revenues can be expected. Moreover, reservation fees are proven to partially compensate for the decrease in rental revenues provided to the auction users.
Sealless Production Ultracentrifuge and Its Magnetically Self-Bearing Openable Motors for Purification in Viral Nanotechnology
Hubmann, Emanuel J.; Steinert, Daniel; Nussbaumer, Thomas; et al. (2025)
IEEE Access
Viral nanotechnology enables new possibilities for gene therapies and vaccines. However, the manufacturing of viral vectors lacks a satisfactory production capable method for purification of full capsids. Empty or partially empty capsids need to be removed to avoid immunotoxicity. The state of the art production ultracentrifuges (PUCFs) are limited, especially in rotational speed to approximately 40 krpm by rotary seals, by complexity and virus containment. This prevents PUCFs from widespread use in industry for viral vector production. This article proposes a novel PUCF type with rotational speed potential towards 100 krpm with a hermetically enclosed process chamber without any rotary seals, i.e. a sealless production ultracentrifuge (SL-PUCF) omitting contamination risk. The openable vertical axis self-bearing motors, the novel sealless flow path design and the needed ultra high-speed potential towards 100 krpm pose new technical problems, for which this article proposes and experimentally validates solutions. A SL-PUCF prototype with its vertical axis magnetically self-bearing openable motors (O-SBMs) and openable burst armor (O-BA) is realized, validated and operated as a system. The general working principle of the SL-PUCF system is experimentally validated by whey protein sedimentation. Furthermore, new future smart capability potential of the proposed new SL-PUCF technology suspended and driven by O-SBMs is unveiled as an outlook and examples shown in case studies.
GIS-Based Fuzzy-AHP Framework for Identifying Suitable Hubs for Offshore Wind and Clean Hydrogen Production
Paula, Karen F.; Pumalloclla Castilla, Hayro A.; Pourakbari-Kasmaei, Mahdi; et al. (2025)
IEEE Access
Integrating offshore wind power with clean hydrogen production in sustainable hubs offers a promising approach to enhancing the economic viability and sustainability of renewable energy projects. Such energy hubs are strategies to combine different infrastructures in specific regions, thus enabling synergies among them regions and make more efficient use of available resources. However, identifying suitable hubs requires a comprehensive evaluation of spatial, technical, financial, and environmental factors. This paper presents a Geographic Information System (GIS)-based Fuzzy Analytic Hierarchy Process (AHP) framework to assist stakeholders in identifying the most suitable sustainable hubs. The fuzzy-AHP technique, which integrates fuzzy logic with AHP, is particularly effective in managing the subjective and imprecise nature of decision-making criteria. Applied to the Brazilian coastline, the framework highlights the northeast as the most favorable region for sustainable hydrogen production hubs, with techno-economic-environmental feasibility exceeding 73% of all analyzed locations. The southeast also shows strong potential, with techno-economic-environmental feasibility above 64%, largely due to its proximity to industrial centers and spatial characteristics, which result in reduced Capital Expenditure (CAPEX) values. These findings show the effectiveness of the GIS-based Fuzzy-AHP framework in providing a nuanced ranking of potential hubs. Thus, offering energy companies and policymakers a robust tool for planning and investing in offshore wind and clean hydrogen production. Furthermore, this tool supports global efforts to reduce carbon emissions and promote sustainable energy transitions.
Sample-Efficient Spatio-Spectral Whitespace Detection Using Least Matching Pursuit
Gonultas, Emre; Soni, Sweta; Apsel, Alyssa B.; et al. (2021)
IEEE Access
Multi-antenna wireless communication improves spectral efficiency by reusing frequencies at different locations in space using beamforming and spatial multiplexing. In the past, research has extensively focused on dynamically reusing unused frequency bands to optimize spectrum usage, but methods that identify unused resources in space appear to be unexplored. In this paper, we propose a sample-efficient whitespace detection pipeline for multi-antenna radio-frequency (RF) transceivers that detects unused resources in both frequency and space. Our spatio-spectral whitespace detection pipeline relies on multi-antenna nonuniform wavelet sampling, which identifies unused frequencies in space at sub-Nyquist sampling rates. We demonstrate the efficacy of our approach via system simulations and show that reliable spatio-spectral whitespace detection is possible with 16× lower sampling rates than methods relying on Nyquist sampling.
Conceptualization and Experimental Verification of a 50 kW 600 V/4 kV/600 V DC Power Supply System for Ultra-Deep-Sea HyDrones
Menzi, David; Kakarla, Bhagyalakshmi; Capaul, Alexandre; et al. (2025)
IEEE Access
Remotely operated underwater vehicles (ROVs) are crucially important in deep-sea exploration. Today, ROVs are typically supplied from a surface vessel via a medium-voltage (MV) 50 Hz/60 Hz ac umbilical. The thus bulky and heavy step-down transformer aboard the ROV complicates achieving neutral buoyancy of the ROV. Further, for a given diameter, dc cables offer lower losses and/or higher power transfer capability than ac cables. Therefore, this paper conceptualizes an MVdc power supply system, which is based on using identical modular dc-transformer (DCX) converters in input-parallel output-series (IPOS) configuration aboard the vessel and in input-series output-parallel (ISOP) configuration aboard the ROV. The DCX concept ensures natural voltage sharing among the series-connected converter modules, full soft-switching operation of the 900 V and 1200 V SiC power transistors of the converter modules, and does not require any closed-loop control. A detailed analysis of the dynamic behavior of this open-loop DCX ROV power supply, including the MVdc umbilical, is conducted and safe operation in the presence of sudden load changes can be assured. Further, the performance limits of the converter system and the umbilical cable are systematically assessed by means of multi-objective Pareto optimization. A higher MVdc transmission voltage benefits the umbilical design and efficiency, but lowers the IPOS/ISOP DCX converter efficiency and/or power density. Hence, an MVdc level of 4 kV is identified as a system-level sweet spot for a 50 kW DCX work-class ROV power supply with a 4000 m umbilical, achieving an overall system efficiency (DCX-umbilical-DCX) of around 97%. Finally, two industrial prototypes of 50 kW, 600 V/4 kV DCX IPOS/ISOP converters are realized and successfully tested in a back-to-back configuration, indicating a rated-power efficiency of around 99% per DCX converter stage.
On the Effectiveness of BGP Hijackers That Evade Public Route Collectors
Milolidakis, Alexandros; Bühler, Tobias; Wang, Kunyu; et al. (2023)
IEEE Access
Routing hijack attacks have plagued the Internet for decades. After many failed mitigation attempts, recent Internet-wide BGP monitoring infrastructures relying on distributed route collection systems, called route collectors, give us hope that future monitor systems can quickly detect and ultimately mitigate hijacks. In this paper, we investigate the effectiveness of public route collectors with respect to future attackers deliberately engineering longer hijacks to avoid being recorded by route collectors. Our extensive simulations (and attacks we device) show that monitor-based systems may be unable to observe many carefully crafted hijacks diverting traffic from thousands of ASes. Hijackers could predict whether their attacks would propagate to some BGP feeders (i.e., monitors) of public route collectors. Then, manipulate BGP route propagation so that the attack never reaches those monitors. This observation remains true when considering plausible future Internet topologies, with more IXP links and up to 4 times more monitors peering with route collectors. We then evaluate the feasibility of performing hijacks not observed by route collectors in the real-world. We experiment with two classifiers to predict the monitors that are dangerous to report the attack to route collectors, one based on monitor proximities (i.e., shortest path lengths) and another based on Gao-Rexford routing policies. We show that a proximity-based classifier could be sufficient for the hijacker to identify all dangerous monitors for hijacks announced to peer-to-peer neighbors. For hijacks announced to transit networks, a Gao-Rexford classifier reduces wrong inferences by $\ge 91\%$ without introducing new misclassifications for existing dangerous monitors.
COVID-19 Control by Computer Vision Approaches: A Survey
Ulhaq, Anwaar; Born, Jannis; Khan, Asim; et al. (2020)
IEEE Access
The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.
Aerodynamic Performance and Impact Analysis of a MEMS-Based Non-Invasive Monitoring System for Wind Turbine Blades
Schärer, Nicolas; Mikhaylov, Denis; Sievi, Cédric; et al. (2025)
IEEE Access
Wind power generation plays a key role in transitioning away from fossil fuel-dependent energy sources, contributing significantly to the mitigation of climate change. Monitoring and evaluating the aerodynamics of large wind turbine rotors is crucial to enable more wind energy deployment. This is necessary to achieve the European climate goal of a reduction in net greenhouse gas emissions by at least 55% by 2030, compared to 1990 levels. This paper presents a comparison between two measurement systems for evaluating the aerodynamic performance of wind turbine rotor blades on a full-scale wind tunnel test. One system uses an array of ten commercial compact ultra-low power micro-electromechanical systems (MEMS) pressure sensors placed on the blade surface, while the other employs high-accuracy lab-based pressure scanners embedded in the airfoil. The tests are conducted at a Reynolds number of 3.5^10*6, which represents typical operating conditions for wind turbines. MEMS sensors are of particular interest, as they can enable real-time monitoring which would be impossible with the ground truth system. This work provides an accurate quantification of the impact of the MEMS system on the blade aerodynamics and its measurement accuracy. Our results indicate that MEMS sensors, with a total sensing power below 1.6 mW, can measure key aerodynamic parameters like Angle of Attack (AoA) and flow separation with a precision of 1°. Although there are minor differences in measurements due to sensor encapsulation, the MEMS system does not significantly compromise blade aerodynamics, with a maximum shift in the angle of attack for flow separation of only 1°. These findings indicate that surface and low-power MEMS sensor systems are a promising approach for efficient and sustainable wind turbine monitoring using self-sustaining Internet of Things devices and wireless sensor networks.
A Multiple Regression Approach for Traffic Flow Estimation
Pun, Lilian; Zhao, Pengxiang; Liu, Xintao (2019)
IEEE Access
Traffic flow information is of great importance for transport planning and related research. The conventional methods of automated data collection, such as annual average daily traffic (AADT) data, are often restricted by limited installation, while the state-of-the-art sensing technologies (e.g., GPS) only reflect some types of traffic flow (e.g., taxi and bus). Complete coverage of traffic flow is still lacking, thus demanding a rigorous estimation model. Most studies dedicated to estimating the traffic flow of the entire road network rely on single to only a few properties of the road network and the results may not be promising. This paper presents an idea of integrating five topological measures and road length to estimate traffic flow based on a multiple regression approach. An empirical study in Hong Kong has been conducted with three types of traffic datasets, namely floating car, public transport route, and AADT. Six measures, namely degree, betweenness, closeness, page rank, clustering coefficient, and road length, are used for traffic flow estimation. It is found that each measure correlates differently for the three types of traffic data. Multiple regression approach is then conducted, including multiple linear regression and random forest. The results show that a combination of various topological and geometrical measures has proved to have a better performance in estimating traffic flow than that of a single measure. This paper is especially helpful for transport planners to estimate traffic flow based on correlation available but limited flow data with road network characteristics.

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