Abstract Non-Fungible Token (NFT) creators use digital signatures to ensure the ownership, authenticity, integrity, and nonrepudiation of their digital works. However, if the private key is compromised, an attacker can generate unauthorized NFTs by using the creator’s private key to issue valid signatures. These valid but unauthorized signatures will be accepted in the NFT market and cannot be revoked. Even if the NFT creators update their private-public key pairs, they cannot deny the NFTs generated by the attacker. To mitigate these risks, we propose revocable signature by introducing commitment mechanism and an Auxiliary Embedded Key ( AEK ) into the signature, while the regular verification process does not involve this AEK . If a valid but unauthorized signature is detected and needs to be revoked, AEK will be disclosed to perform the revocation operation. To illustrate the application of revocable signatures in NFT, we design and implement a revocable Elliptic Curve Digital Signature Algorithm (ECDSA) scheme with provable security. Experimental evaluations on the FIPS-recommended elliptic curves show that the performance of revocable ECDSA is comparable to the basic ECDSA, with additional 0.0303 s (P-256 curve) and 0.15 USD gas fee in Remix VM for revoking a signature.

Effective management of private keys is crucial to ensure the security and ownership of users’ data and digital assets in the Web3 environment. However, existing solutions often fail to adequately address private key management from the user’s perspective. Private key leakage and loss incidents occur frequently, resulting in significant losses of digital assets. Moreover, the conventional approach of revoking both the private and public keys after a leakage or loss accident is inconvenient in Web3, where the public key serves as the user’s wallet address or digital identity. To tackle the issue of user-side private key management in Web3, this paper presents KeyShield which is a leakage-and-loss-resilient private key protection scheme. KeyShield divides the user’s private key into three shares, securely stored across a primary device and a secondary device owned by the user, and a third storage module owned by the user or a semi-trusted service provider. For daily use of the private key, the user only needs to connect the primary and secondary devices. In the event of a leakage or loss, such as device theft or attack, an update process will be triggered to update the three shares, immediately invalidating the leaked or lost share while causing no changes to the public key. As a demonstration of KeyShield, we developed KeyShieldECC accessible on both Android and iOS platforms for managing Elliptic Curve Cryptography (ECC) private keys. The testing results show that for a 256-bit ECC private key, the daily use only needs 0.05 seconds and update needs 0.25 to 0.3 seconds on an ordinary smart phone.

Data trading has become a fundamental category of commerce in the current digital era, bringing along substantial opportunities for economic activities. The emergence of blockchain facilitates decentralized data trading, empowering users to maintain complete control over their own data and enabling direct peer-to-peer transactions. However, while users possess complete control over their data product and asset in a decentralized trading environment, they must take responsibility for their behaviors and face the consequences of dishonest behaviors either from themselves or counterparties. Therefore, behavior fairness problems arise, such as the seller refusing to deliver the correct data product after receiving the money and the buyer failing to pay the outstanding balance after receiving the data product. This article addresses these issues by proposing a decentralized fair data trading ecosystem leveraging the capability of cryptography and smart contracts. A novel verifiable proxy re-encryption (VPRE) scheme is designed, which introduces the verifiability of re-encryption keys into the original PRE schemes. The scheme is implemented using smart contracts, which ensures that trading can succeed if and only if the data seller provides a valid re-encryption key and the buyer pays the correct amount of money. Furthermore, experiments are conducted to evaluate the proposed ecosystem in terms of trading fairness, security, and cost. The results show that the proposed scheme can effectively terminate transactions involving fairness violations with affordable costs ranging from 39 276 Gwei to 296 983 Gwei. Without our solution, these unfair transactions will proceed and consume 77 301 Gwei to 754 447 Gwei gas fees.

Embodied perception is essential for intelligent vehicles and robots in interactive environmental understanding. However, these advancements primarily focus on vision, with limited attention given to using 3D modeling sensors, restricting a comprehensive understanding of objects in response to prompts containing qualitative and quantitative queries. Recently, as a promising automotive sensor with affordable cost, 4D millimeter-wave radars provide denser point clouds than conventional radars and perceive both semantic and physical characteristics of objects, thereby enhancing the reliability of perception systems. To foster the development of natural language-driven context understanding in radar scenes for 3D visual grounding, we construct the first dataset, Talk2Radar, which bridges these two modalities for 3D Referring Expression Comprehension (REC). Talk2Radar contains 8,682 referring prompt samples with 20, 558 referred objects. Moreover, we propose a novel model, T-RadarNet, for 3D REC on point clouds, achieving State-Of-The-Art (SOTA) performance on the Talk2Radar dataset compared to counterparts. Deformable-FPN and Gated Graph Fusion are meticulously designed for efficient point cloud feature modeling and cross-modal fusion between radar and text features, respectively. Comprehensive experiments provide deep insights into radar-based 3D REC. We release our project at https://github.com/GuanRunwei/Talk2Radar.

4D millimeter-wave (MMW) radar, which provides both height information and dense point cloud data over 3D MMW radar, has become increasingly popular in 3D object detection. In recent years, radar-vision fusion models have demonstrated performance close to that of LiDAR-based models, offering advantages in terms of lower hardware costs and better resilience in extreme conditions. However, many radar-vision fusion models treat radar as a sparse LiDAR, underutilizing radar-specific information. Additionally, these multi-modal networks are often sensitive to the failure of a single modality, particularly vision. To address these challenges, we propose the Radar Depth Lift-Splat-Shoot (RDL) module, which integrates radar-specific data into the depth prediction process, enhancing the quality of visual Bird's-Eye View (BEV) features. We further introduce a Unified Feature Fusion (UFF) approach that extracts BEV features across different modalities using shared module. To assess the robustness of multimodal models, we develop a novel Failure Test (FT) ablation experiment, which simulates vision modality failure by injecting Gaussian noise. We conduct extensive experiments on the View-of-Delft (VoD) and TJ4D datasets. The results demonstrated that our proposed Unified BEVFusion (UniBEVFusion) network significantly outperforms state-of-the-art models on the TJ4D dataset, with improvements of 3.96% in 3D and 4.17% in BEV object detection accuracy.

Recently, visual grounding and multi-sensors setting have been incorporated into perception system for terrestrial autonomous driving systems and Unmanned Surface Vessels (USVs), yet the high complexity of modern learning-based visual grounding model using multi-sensors prevents such model to be deployed on USVs in the real-life. To this end, we design a low-power multi-task model named NanoMVG for waterway embodied perception, guiding both camera and 4D millimeter-wave radar to locate specific object(s) through natural language. NanoMVG can perform both box-level and mask-level visual grounding tasks simultaneously. Compared to other visual grounding models, NanoMVG achieves highly competitive performance on the WaterVG dataset, particularly in harsh environments. Moreover, the real-world experiments with deployment of NanoMVG on embedded edge device of USV demonstrates its fast inference speed for real-time perception and capability of boasting ultra-low power consumption for long endurance.

Battery-powered IoT devices incur significant maintenance cost and complexity in remote and large-scale environmental monitoring deployments due to regular battery replacement. Energy harvesting in combination with supercapacitor charge storage offers a promising solution, but practical deployments are few and far between. This paper contributes to application research in this space by presenting an experience report on deploying a battery-free sensing system for wireless monitoring of forest microclimates. Our approach combines adaptive power management hardware and intelligent scheduling algorithms, which are compared against a baseline rechargeable battery-based solar alternative. In both cases, the wireless devices are used to drive a sensing system that reports atmospheric pressure, temperature, humidity, air quality and light levels over the LoRaWAN network. This data is used by environmental scientists to study and better understand the forest ecosystem. We evaluate our battery-free approach against the battery-powered baseline in a two-month forest monitoring deployment, demonstrating reliable operation using only harvested energy, while highlighting important lessons learned for researchers and practitioners deploying battery-free sensing systems.

Over the past decade, the size of neural network models has gradually increased in both breadth and depth, leading to a growing interest in the application of neural network pruning. Unstructured pruning provides fine-grained sparsity and achieves better inference acceleration under specific hardware support. Unstructured Pruning at Initialization (PaI) optimizes the iterative pruning pipeline, but sparse weights increase the risk of underfitting during training. More importantly, almost all PaI algorithms focus only on obtaining the best pruning mask without considering whether the retained weights are suitable for training. Introducing Lipschitz constants during model initialization can reduce the risk of model underfitting and overfitting. As a result, we firstly analyze the impact of Lipschitz initialization on model training and propose the Repaired Initialization (ReI) algorithm for common modules with BatchNorm. Then, we utilize the same idea to repair the weight of unstructured pruned model, and name it Repaired Pruning at Initialization Resilience (RePaIR) algorithm. Extensive experiments and demonstrate that our proposed ReI and RePaIR can improve the training robustness of unpruned and pruned models, respectively, and achieve up to 1.7% accuracy gain with the same sparse pruning mask on TinyImageNet. Furthermore, we provide an improved SynFlow algorithm called Repair SynFlow (ReSynFlow), which employs Lipschitz scaling to overcome the problem of score computation in deeper models. ReSynFlow can effectively improve the maximum compression rate and is suitable for deeper models, with an accuracy improvement of up to 1.3% compared to the SynFlow algorithm on TinyImageNet.

Waterway perception is critical for the special operations and autonomous navigation of Unmanned Surface Vessels (USVs), but current perception schemes are sensor-based, neglecting the interaction between humans and USVs for embodied perception in various operations. Therefore, inspired by visual grounding, we present WaterVG, the inaugural visual grounding dataset tailored for USV-based waterway perception guided by human prompts. WaterVG contains a wealth of prompts describing multiple targets, with instance-level annotations, including bounding boxes and masks. Specifically, WaterVG comprises 11,568 samples and 34,987 referred targets, integrating both visual and radar characteristics. The text-guided two-sensor pattern provides a fine granularity of text prompts aligned with the visual and radar features of the referent targets, containing both qualitative and numeric descriptions. To enhance the endurance and maintain the normal operations of USVs in open waterways, we propose Potamoi, a low-power visual grounding model. Potamoi is a multi-task model employing a sophisticated Phased Heterogeneous Modality Fusion (PHMF) mechanism, which includes Adaptive Radar Weighting (ARW) and Multi-Head Slim Cross Attention (MHSCA). The ARW module utilizes a gating mechanism to adaptively extract essential radar features for fusion with visual inputs, ensuring prompt alignment. MHSCA, characterized by its low parameter count and computational efficiency (FLOPs), effectively integrates contextual information from both sensors with linguistic features, delivering outstanding performance in visual grounding tasks. Comprehensive experiments and evaluations on WaterVG demonstrate that Potamoi achieves state-of-the-art results compared to existing methods. The project is available athttps://github.com/GuanRunwei/WaterVG.

With the rapid advancements of sensor technology and deep learning, autonomous driving systems are providing safe and efficient access to intelligent vehicles as well as intelligent transportation. Among these equipped sensors, the radar sensor plays a crucial role in providing robust perception information in diverse environmental conditions. This review focuses on exploring different radar data representations utilized in autonomous driving systems. Firstly, we introduce the capabilities and limitations of the radar sensor by examining the working principles of radar perception and signal processing of radar measurements. Then, we delve into the generation process of five radar representations, including the ADC signal, radar tensor, point cloud, grid map, and micro-Doppler signature. For each radar representation, we examine the related datasets, methods, advantages and limitations. Furthermore, we discuss the challenges faced in these data representations and propose potential research directions. Above all, this comprehensive review offers an in-depth insight into how these representations enhance autonomous system capabilities, providing guidance for radar perception researchers. To facilitate retrieval and comparison of different data representations, datasets and methods, we provide an interactive website at https://radar-camera-fusion.github.io/radar.

Panoptic Driving Perception (PDP) is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). A PDP model typically integrates multiple tasks, necessitating the simultaneous and robust execution of various perception tasks to facilitate downstream path planning. The fusion of visual and radar sensors is currently acknowledged as a robust and cost-effective approach. However, most existing research has primarily focused on fusing visual and radar features dedicated to object detection or utilizing a shared feature space for multiple tasks, neglecting the individual representation differences between various tasks. To address this gap, we propose a pair of Asymmetric Fair Fusion (AFF) modules with favorable explainability designed to efficiently interact with independent features from both visual and radar modalities, tailored to the specific requirements of object detection and semantic segmentation tasks. The AFF modules treat image and radar maps as irregular point sets and transform these features into a crossed-shared feature space for multitasking, ensuring equitable treatment of vision and radar point cloud features. Leveraging AFF modules, we propose a novel and efficient PDP model, ASY-VRNet, which processes image and radar features based on irregular super-pixel point sets. Additionally, we propose an effective multi-task learning method specifically designed for PDP models. Compared to other lightweight models, ASY-VRNet achieves state-of-the-art performance in object detection, semantic segmentation, and drivable-area segmentation on the WaterScenes benchmark. Our project is publicly available at https://github.com/GuanRunwei/ASY-VRNet.

Abstract Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7% precision and 89.4% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC [1]. From loading the command into VehicleFinder to identifying whether the target vehicle is consistent with the command, the time cost is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle , and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM .

Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivor rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct benchmark experiments on the uni-modality of radar and camera, as well as the fused modalities. Experimental results demonstrate that 4D radar-camera fusion can considerably improve the accuracy and robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public onhttps://waterscenes.github.io.

A significant feature of smart door lock systems is the Temporary Key Function (TKF) for visitors. However, existing TKFs in smart door lock systems in use are either vulnerable to attacks or inconvenient for use. This paper thereby proposes elliptic curve cryptography (ECC)-based innovative authentication module for smart door lock systems which enables highly secure and convenient TKF. Based on the authentication module, two protocols are designed for the host and visitor respectively to unlock the door lock system. Security features for the two protocols are verified via Gong Needham Yahalom (GNY) logic. Besides, the proposed prototypes are realized, and the simulation experiments are carried out to study the performance of the protocols. The results show that our scheme is secure and efficient.

The short lifespan of conventional smartphone batteries leads to toxic waste and increases replacement costs. In addition, contemporary devices require reliable electrical infrastructure which remains unavailable in major parts of the developing world. To address these problems, we propose a BAttery-free Mobile Interactive device (BaMbI), which aims to offer a scaled down set of smartphone features. BaMbI is based around an ARM Cortex M33 module with integrated LTE-M. In place of a battery, BaMbI uses a solar panel and an array of super-capacitors that offer sustainable operation and that can be fully charged in under one minute when mains power becomes available.

In this work, oxide resistive random access memory (OxRRAM) devices with stacked solution-processed (SP) metal oxide (MO) layers were fabricated to investigate artificial synaptic behavior such as long-term potentiation (LTP) and long-term depression (LTD). The stacked RRAM devices exhibited stable and repeated bipolar IV curves with operation voltage lower than the ~0.5 V and a switching ratio larger than 2*104. Also, with the stimuli from external consecutive pulses, the stacked devices demonstrated learning-forgetting-relearning behavior similar to neuron-induced behavior in the human brain. Finally, based on stable long-term memory performance, the pattern recognition system with an artificial neuron network (ANN) algorithm was simulated with the recognition accuracy higher than 95%.

A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the PV generator. In this paper, deep Q-network based reinforcement learning strategy is proposed to optimize MPPT process for the photovoltaic system. The proposed system uses a novel control method which introduces agent to interface with the environment and finally gets the strategy of maximum reward accordingly. Simulations and experiments show the feasibility and effectiveness of the proposed system. Compared with the traditional perturb and observe (P&O) and incremental conductance (InC) methods, this method prominently saves tracking steps.

Bluetooth wireless technology is widely deployed in consumer electronics such as mobile phones, headsets, medical wearables and so on. Security is a significant concern of users, since such devices collect and store personal data. Security schemes are provided in the Bluetooth Core Specification version 5.0, but they cannot resist the emerging side-channel attacks which can leak long-term secrets through physical manners. In this paper, the security of Bluetooth under side-channel attacks is studied. Specifically, we improved the security of Secure Simple Pairing (SSP) protocol in Bluetooth 5.0 by developing two leakage-resilient methods to resist side-channel attacks. The leakage-resilient SSP protocols are designed using exponent splitting and multiplicative mask leakage-resilient methods respectively to prevent the long-term secrets from being leaked to side-channel attackers. Prototypes of the new and original protocols are realized, and we simulate a set of experiences to evaluate and compare their performance. We find the increased computation cost of the new protocols is slight and acceptable.

This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low energy of P2. To identify such split, HVD method is used to decompose the S2 into a number of components while preserving the phase information. Further, A2s and P2s are localized using smoothed pseudo Wigner-Ville distribution followed by reassignment method. Finally, the split is calculated by taking the differences between the means of time indices of A2s and P2s. Experiments on total 33 clips of S2 signals are performed for evaluation of the method. The mean ± standard deviation of the split is 34.7 ± 4.6 ms. The method measures the split efficiently, even when A2-P2 overlap is ≤ 20 ms and the normalized peak temporal ratio of P2 to A2 is low (≥ 0.22). This proposed method thus, demonstrates its robustness by defining split detectability (SDT), the split detection aptness through detecting P2s, by measuring up to 96 percent. Such findings reveal the effectiveness of the method as competent against the other baselines, especially for A2-P2 overlaps and low energy P2.

Feature ordering is a significant data preprocessing method in incremental attribute learning (IAL), where features are gradually trained according to a given order. Previous research showed feature ordering is crucial to the IAL performance. It is relevant to each feature's discrimination ability, which can be calculated by single discriminability (SD). However, when feature dimensions increase, feature discrimination ability should also be calculated incrementally, because discrimination ability in lower dimensional spaces is different from that in higher spaces. Thus based on SD, accumulative discriminability (AD), a new statistical metric for incremental feature discrimination ability estimation, is designed. Moreover, a criterion that summarizes all the produced values of AD is employed to obtain the optimum feature ordering for classification problems based on neural networks by means of IAL. In addition, in order to reduce the time consumption, an effective feature ordering approach is developed. Compared with the feature ordering obtained by other approaches, the method outlined in this paper obtained good final classification results, which indicates that, firstly, feature discrimination ability should be incrementally estimated in IAL; and secondly, feature ordering derived by AD and its corresponding approaches are applicable with IAL.

This paper presents an integration of a wireless sensor network design project in an introductory course about circuits and systems. In the project, students will design a wireless sensor network that constitutes of sensors, for a creative surveillance application. Through a versatile project vehicle, project-oriented learning modules, a comprehensive assessment strategy and public learning communities, students can learn contemporary concepts of circuits and systems from the system perspective, as well as develop ability to design a basic electronic system.

Maximum Power Point Tracking (MPPT) methods can be classified into direct and indirect approaches. They are used to improve the efficiency of power conversion in Photovoltaic (PV) systems. However, a review of present literature implies that the indirect methods never produce accurate results. Meanwhile, the conventional direct Perturb and Observe (P&O) method has two problems: oscillations at steady state and slow dynamic response under changing environment conditions. Estimation and Revision (ER) method is proposed in this paper to overcome these limitations by the alternative use of MPP estimation and MPP revision process. The efficiency of the ER method is verified in an MPPT system implemented with a specific DC-DC converter and an adopted PV module.

Maximum Power Point Tracking (MPPT) is a technique applied to improve the efficiency of power conversion in Photovoltaic (PV) systems. Under partially shadowed conditions, the Power-Voltage (P-V) characteristic exhibits multiple peaks and the existing MPPT methods such as the Perturb and Observe (P&O) are incapable of searching for the Global Maximum Power Point (GMPP). This paper proposes a low-cost on-line MPPT scheme to overcome this drawback. By using hybrid numerical searching process, the operating point approaches Local Maximum Power Points (LMPPs) gradually and the GMPP is caught by comparing all the LMPPs. Simulation results prove the effectiveness and correctness of the proposed method.

In this paper, we define a formal communication semantics for SystemC/sup FL/ that deals with concurrency and interaction. The communication semantics of SystemC/sup FL/ is formally defined in a standard structured operational semantics (SOS) style. The correctness of the proposed communication semantics of SystemC/sup FL/ is also validated. A case study (RPC protocol with refinement) is given to illustrate the use of the newly developed communication semantics of SystemC/sup FL/.

HyPA is an algebraic theory based on the classical process algebra Algebra of Communicating Processes (ACP) for the specification and analysis of hybrid systems. We have the idea that HyPA is also well suited for addressing various aspects of digital embedded systems including hardware, software and concurrency, as well as mixed-signal designs. To show that HyPA is useful for the specification and analysis of hybrid systems and that our idea is correct, we illustrate the use of HyPA with some case studies: a point-to-point communication , a thermostat, a positive-edge-triggered D flip flop, and a small part of a mixed-signal fuzzy controller.