ReTMiC: Reliability-Aware Thermal Management in Multicore Mixed-Criticality Embedded Systems
As the number of cores in multicore platforms increases, temperature constraints may prevent powering all cores simultaneously at maximum voltage and frequency level. Thermal hot spots and unbalanced temperatures between the processing cores may degrade the reliability. This paper introduces a reliability-aware thermal management scheduling (ReTMiC) method for mixed-criticality embedded systems. In this regard, ReTMiC meets Thermal Design Power as the chip-level power constraint at design time. In order to balance the temperature of the processing cores, our proposed method determines balancing points on each frame of the scheduling, and at run time, our proposed lightweight online re-mapping technique is activated at each determined balancing point for balancing the temperature of the processing cores. The online mechanism exploits the proposed temperature-aware factor to reduce the system’s temperature based on the current temperature of processing cores and the behavior of their corresponding running tasks. Our experimental results show that the ReTMiC method achieves up to 12.8°C reduction in the chip temperature and 3.5°C reduction in spatial thermal variation in comparison to the state-of-the-art techniques while keeping the system reliability at a required level.
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Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D
The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user’s choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2D and 3D. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.
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A Hierarchical Graph-Based Approach for Recognition and Description Generation of Bimanual Actions in Videos
Nuanced understanding and the generation of detailed descriptive content for (bimanual) manipulation actions in videos is important for disciplines such as robotics, human-computer interaction, and video content analysis. This study describes a novel method, integrating graph-based modeling with layered hierarchical attention mechanisms, resulting in higher precision and better comprehensiveness of video descriptions. To achieve this, we encode, first, the spatio-temporal interdependencies between objects and actions with scene graphs and we combine this, in a second step, with a novel 3-level architecture creating a hierarchical attention mechanism using Graph Attention Networks (GATs). The 3-level GAT architecture allows recognizing local, but also global contextual elements. This way several descriptions with different semantic complexity can be generated in parallel for the same video clip, enhancing the discriminative accuracy of action recognition and action description. The performance of our approach is empirically tested using several 2D and 3D datasets. By comparing our method to the state of the art we consistently obtain better performance concerning accuracy, precision, and contextual relevance when evaluating action recognition as well as description generation. In a large set of ablation experiments we also assess the role of the different components of our model. With our multi-level approach the system obtains different semantic description depths, often observed in descriptions made by different people, too. Furthermore, better insight into bimanual hand-object interactions as achieved by our model may portend advancements in the field of robotics, enabling the emulation of intricate human actions with heightened precision.
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TRIO-TCAM: An Area and Energy-Efficient Triple-State-in-Cell Ternary Content-Addressable Memory Architecture
Ternary content-addressable memory (TCAM) is critical for applications requiring high-speed data retrieval and pattern matching, such as networking, cybersecurity, artificial intelligence, and real-time data analytics. While recent advancements in TCAM architectures have addressed challenges related to power consumption and scalability, significant potential for further optimization remains, particularly in enhancing energy efficiency and cell density. In this paper, we introduce TRIO-TCAM, a novel area- and energy-efficient TCAM architecture that utilizes a 10-transistor SRAM cell. Building on and surpassing the performance of state-of-the-art (SOTA) designs, TRIO-TCAM, designed using 28nm FD-SOI technology, achieves a 7.6% reduction in cell area. Moreover, it reduces searching delay by up to 37.4%, searching energy per bit by as much as 67.6%, and cell leakage by more than 41.7% compared to SOTA designs. These advancements position TRIO-TCAM as a promising solution for high-density, low-power TCAM applications in future high-performance systems.
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Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information in the form of sub-pixel offsets in the scene sampling, and can be significantly more effective than its single-image counterpart. Its main difficulty lies in accurately registering and fusing the multi-image information. Currently studied settings, such as burst photography, typically involve assumptions of small geometric disparity between the LR images and rely on optical flow for image registration. We propose EpiMISR, a novel MISR method that can increase the resolution of sets of images acquired with arbitrary, and potentially wildly different, camera positions and orientations, generalizing the currently studied MISR settings. Our proposed model moves away from optical flow and explicitly uses the epipolar geometry of the acquisition process, together with transformer-based processing of radiance feature fields to substantially improve over state-of-the-art MISR methods in presence of large disparities in the LR images.
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Compact GaN HEMT Power Amplifier MMIC Delivering Over 40 W for Ku-Band Applications
This paper presents the design and implementation of a high-power amplifier (HPA) using a 250-nm gallium nitride (GaN) high electron mobility transistor (HEMT) process on a silicon carbide substrate. The HPA is engineered to optimize both output power and power density relative to chip size. The 1st and 2nd drive stages utilize individual source via transistors (ISV TRs) for high gain and efficiency, while the output stage employs outside source via transistors (OSV TRs) to achieve high power density. The output matching network is initially designed for a unit TR with a high impedance transformation ratio of 114 and then expanded to a 16-way binary power combining circuit. RC stabilizers with shunt inductors are tailored in the input and interstage matching networks to address the very low input impedance of the drive stage TRs. These stabilizers effectively increase the input impedance of the TRs. The bias circuit is designed with a DC bus-bar structure, enhancing flexibility for large-scale power combining. The fabricated HPA demonstrated a maximum small-signal gain of 26.3 dB at 16.2 GHz and a 3-dB bandwidth ranging from 15.1 to 17.7 GHz. It also achieved an output power of 46.1 dBm (40.7 W) under pulsed operation from 16.0 to 16.75 GHz with a drain voltage of 28 V. When the drain voltage was increased to 32 V, it reached a maximum output power of 63 W at 16.5 GHz, demonstrating an excellent power density of 2.03 W/mm2 per chip area.
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Biodegradable and Renewable Antennas for Green IoT Sensors: A Review
The development and integration of the Internet of Things (IoT) sensor technology across various domains have significantly transformed our work and daily lives, enriching society. However, the increase in the number of IoT devices leads to electronic waste (e-waste), which is a growing global concern. The continued development of sustainable IoT sensors utilizing biodegradable and renewable materials will not only help with reducing e-waste but also ensure wider adaptation of sensing applications thereby benefitting the global community. This review article examines the use of biodegradable and renewable materials in developing antennas for various sensing applications, emphasizing their sustainability, biodegradability, and recyclability. The main contributions of our work are six-fold. First, we review common bio-based materials used in microwave components, detailing the selection process for biodegradable and renewable materials, as well as their comparative advantages and limitations. Second, we examine biodegradable and renewable materials in antenna technologies for sensing applications, providing a comparative analysis based on microwave component type, material properties, dielectric constant, measurement method, relative permittivity, and relevant applications. Third, we analyze design requirements for antennas utilizing these materials, comparing antenna design type/technique, substrate and conductive materials, operating frequency band, size, and gain/directivity. Fourth, we evaluate antenna fabrication techniques, discussing their advantages and challenges. Fifth, we comprehensively review applications of biodegradable and renewable antennas in green IoT sensors, with focus areas including agriculture, environmental monitoring, healthcare, wearable electronics, logistics, and food processing. Finally, we address key research challenges, future prospects, and the potential of these technologies moving forward.
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AD-VILS: Implementation and Reliability Validation of Vehicle-in-the-Loop Simulation Platform for Evaluating Autonomous Driving Systems
Vehicle-in-the-loop simulation (VILS) is a vehicle-testing technique that integrates high-fidelity simulation environments with real-world vehicles. Among existing simulation approaches for evaluating autonomous driving systems (ADS), VILS is particularly noteworthy because it faithfully reflects the dynamic characteristics of real-world vehicles and ensures repeatable and reproducible testing in diverse virtual scenarios. While researchers strive to implement a VILS platform that closely approximates real-world vehicle-testing environments, the performance of vehicles in VILS testing may differ from that observed in real-world testing, depending on the platform’s reliability. Therefore, methods must be established to validate the reliability of VILS platforms. Herein, we present the essential components of a VILS platform for evaluating ADS (AD-VILS) and propose a metho dology to validate the reliability of the implemented AD-VILS platform. This methodology includes scenario definition, techniques for VILS testing and real-world vehicle testing, and procedures for evaluating consistency and correlation based on statistical and mathematical comparisons between the datasets from virtual and real-world tests. Moreover, we empirically derive reliability evaluation criteria through iterative testing. This methodology aims to enhance the precision and reliability of ADS evaluations conducted on AD-VILS platforms.
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Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine
To solve large-scale real-world problems, attempts have been made to realize high-speed simulations of quantum Ising machines using field-programmable gate arrays (FPGAs) and to virtually realize networks with a large number of fully coupled spins, which are difficult to achieve in physical systems. We developed an FPGA-implemented cyber coherent Ising machine (cyber CIM) that is much more versatile than previous implementations using FPGAs. Our architecture is versatile because it can be applied to the open-loop CIM, which was proposed when CIM research began, to the closed-loop CIM, which has been used recently, and to the Jacobi successive over-relaxation method. By modifying the sequence control code for the calculation control module, other algorithms such as Simulated Bifurcation (SB) can also be implemented. Earlier studies on large-scale FPGA implementations of SB and CIM used binary or ternary discrete values for connections, whereas cyber CIM used single-precision floating-point format (FP32) values. In addition, cyber CIM uses Zeeman terms that are represented in FP32, which are not present in other large-scale FPGA systems. Our implementation with continuous interaction achieves
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Flexible Switching Control of Aircraft Skin Inspection Robot via Adaptive Dynamic Programming
This article considers the flexible switching control problem of a two-frame aircraft skin inspection robot (TFASIR) with full-state time-varying constraints, input saturation, uncertainty, and unknown disturbance. Initially, this control problem is also treated as a tracking control problem of the dual-coupled adsorption system (DCAS). A novel nonlinear time-varying state-dependent function (NTVSDF) is first designed to tackle the full-state constraint problem. Subsequently, a feedforward tracking control method is designed, that uses the command-filtered backstepping technique, to transform the tracking control problem into an equivalent differential game problem (DGP) of closed-loop systems. Then, a zero-sum game strategy is presented, that uses the idea of adaptive dynamic programming (ADP) algorithm, to determine the DGP. The whole control method ensures that the closed-loop signals are uniformly ultimately bounded (UUB). Furthermore, another problem is that the partial system states are not accessible. To overcome this problem, a high-gain observer is utilized to reconstruct the state vector, and an output feedback controller is developed. The feasibility of the proposed control scheme is demonstrated in simulation.
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