Panoptic 3D reconstruction from a monocular video is a fundamental perceptual task in robotic scene understanding. However, existing efforts suffer from inefficiency in terms of inference speed and accuracy, limiting their practical applicability. We present EPRecon, an efficient real-time panoptic 3D reconstruction framework. Current volumetric-based reconstruction methods usually utilize multi-view depth map fusion to obtain scene depth priors, which is time-consuming and poses challenges to real-time scene reconstruction. To address this issue, we propose a lightweight module to directly estimate scene depth priors in a 3D volume for reconstruction quality improvement by generating occupancy probabilities of all voxels. In addition, compared with existing panoptic segmentation methods, EPRecon extracts panoptic features from both voxel features and corresponding image features, obtaining more detailed and comprehensive instance-level semantic information and achieving more accurate segmentation results. Experimental results on the ScanNet V2dataset demonstrate the superiority of EPRecon over current state-of-the-art methods in terms of both panoptic 3D reconstruction quality and real-time inference. Code is available at https://github.com/zhen6618/EPRecon.

The Five Hundred Meter Spherical Radio Telescope (FAST) is the largest spherical radio telescope in the world, and there are more than 2,000 prism targets distributed on its reflector that require regular maintenance. These prism targets are screwed into the corresponding threaded holes by target bolts. At present, manual maintenance is mainly used, which is inefficient and unsafe. To address this issue, we develop an autonomous prism target maintenance robotic system, called APTMRS. This system integrates a mobile robot with an assembly robot and a collaborative robot, enabling it to move and perform the prism target replacement task on the reflector. Its technical development is threefold. First, a robotic end-effector that can be used for screwing the non-standard target bolt from the side is designed. Second, a novel pose measurement framework, which incorporates a feature point extraction and matching method, is utilized to measure the pose of the prism target. Third, the manipulation policies for the challenging steps in the workflow are presented, including picking and placing, initial thread mating, and final tightening. Independent experiments indicate that the performances of these methods are satisfactory. Moreover, tests in both simulated and real FAST scenarios confirm the effectiveness of APTMRS in achieving autonomous prism target maintenance.Note to Practitioners—Due to its excellent versatility, threaded connections have been widely adopted in a multitude of scenarios. Threads are not only present on standard bolts and nuts but also on a myriad of non-standard components. The robotic system proposed in this paper offers a solution for assembling and disassembling target bolts for FAST. Specifically, the scenario applied in this paper covers several extremely challenging problems in autonomous threaded assembly. Firstly, the object of assembly is a non-standard bolt, with obstacles above it, rendering traditional tools inapplicable. Secondly, the surface of the object is weakly textured and highly reflective. These pose significant challenges for the vision system to accurately acquire its pose. Lastly, the assembly environment is harsh. The threaded holes reside on an incline that is hard to access, with a maximum slope of$56^\circ$. To address these challenges, this paper presents a robotic system supplemented by three techniques including a novel robotic end-effector, a new pose measurement framework, and manipulation policies. The experimental results indicate that this robotic system is capable of autonomously disassembling and assembling non-standard target bolts. We explore the potential for generalizing these designs and methods to other scenarios, with the hope of contributing new insights to the development of threaded assembly systems.

Compared with traditional underwater vehicles, robotic fish have been receiving increasing attention in recent years due to their excellent maneuverability. However, the characteristics of fishlike undulatory motions and complex underwater working environment have posed significant challenges to robotic fish speed measurement, limiting their autonomy. To overcome these challenges, an artificial lateral line sensor (ALLS) was developed, drawing inspiration from the tactile system of fish. It captured the real-time speed of robotic fish through assessing the deformation of the stressed component under laminar flow impact. To mitigate turbulence disturbances near the ALLS, three flow control components, fairing, flow conditioner, and flow collector, were proposed to attenuate turbulence noise under the viscous effect. Furthermore, a physics-informed calibration method was presented to establish the nonlinear model of ALLS. Specifically, a physical model embedding algorithm based on data resampling was used to mitigate the risk of overfitting by the multilayer perceptron, considering the influence of turbulence disturbance and fishlike undulatory noise. Compared with the classical calibration method based on physical model fitting, the calibration method proposed in this paper reduced the error by 36.0%. Our ALLS’s final mean absolute error was 0.016 m/s with a linearity (R2) of 0.956. The experimental results indicated that the significant changes in the motion state of robotic fish reduced the accuracy of ALLS. The fusion with other sensors is expected to enhance the robustness of ALLS in the future. Note to Practitioners—The motivation of this paper is to design an artificial lateral line sensor based on surface flow field detection and turbulence noise suppression, providing a small-sized and high-precision solution to the speed measurement problem of bionic robotic fish. Most existing ALLS research focused on developing new types of sensors based on different measurement principles, without suppressing the noise caused by fishlike motions, and most experiments were conducted in environments with excessive controls rather than free-swimming robotic fish. To this end, we developed an ALLS based on deformation measurement and proposed three flow control components to make the measured flow more stable. Furthermore, a physics-informed overfitting suppression method was used for the calibration task of the ALLS. A series of simulations and experiments demonstrated that the proposed turbulence noise suppression and calibration method were practical and effective. Hopefully, our methods can provide theoretical and technical guidance to marine engineers for underwater vehicle speed measurement and flow sensing. The recommended flow control component is applicable for conditioning surface fluids in pneumatic control systems. Furthermore, the proposed biomimetic tactile sensor is poised to inspire tactile-based human-machine interaction methods.

Underwater collision-free navigation and dense reconstruction are essential for marine refined exploration. However, existing passive vision-based methods are difficult to apply in low-light and weak-feature underwater environments. In this article, a more adaptable three-dimensional (3-D) dense mapping robotic system based on self-designed scanning binocular structured light (BSL), named ROV-Scanner, is developed to address this challenge. First, the measurement principle based on the refraction model ensures its high accuracy. Second, an underwater 3-D dense mapping algorithm fusing the Doppler velocity log (DVL), inertial measurement unit (IMU), and pressure sensor multifrequency information is proposed to realize dense mapping during robot motion. Then, an air–water two-stage extrinsic calibration algorithm is proposed. In particular, the extrinsic parameters between DVL and camera are innovatively calibrated using BSL, enhancing robustness. Furthermore, for the first time, a framework of BSL-based collision-free navigation is presented to guarantee the safe movement of the system in unknown environments. Experimental results show that our system can simultaneously achieve autonomous collision-free navigation and dense mapping in dark underwater environments, which has great potential for application in marine refined exploration.

Structured light systems are widely used in underwater dense reconstruction due to their excellent accuracy. However, the current related methods mainly focus on fixed positions. The reconstruction performance in motion is insufficient. Therefore, we propose an underwater movable binocular structured light (MBSL) based high-precision dense reconstruction framework, named WaterMBSL, to realize the robot reconstruction while moving. Specifically, an onboard binocular structured light system based on mirror-galvanometer is developed first. Then, a simplified underwater point cloud acquisition algorithm is presented to quickly obtain 3-D information of the scene. Besides, a new underwater motion compensation algorithm combining inertial measurement unit and uniform velocity model is proposed. Moreover, the generalized-ICP point cloud registration algorithm is introduced to achieve accurate motion estimation. Finally, an underwater movable reconstruction platform is developed by integrating the self-designed structured light system with the underwater robot BlueROV for validating the performance of our proposed Water-MBSL. Experimental results show that satisfactory motion reconstruction performance can be obtained.

In July 2022, the National Institute of Standards and Technology (NIST) announced its selection of four algorithms for post-quantum cryptography standardization in advance. Among these algorithms, Kyber was chosen as the only key encapsulation mechanism (KEM). In the Kyber KEM, the modular reduction function is utilized in numerous areas. We have discovered that by modeling controllable modular reduction functions, unknown modular reduction functions can be targeted. And attacks can then be constructed. Henceforth, profiling can be mounted on the target device. In this paper, we present a machine-learning-based key recovery attack on Kyber, without needing a reference device. We have effectively attacked the modular reduction function. Furthermore, this vulnerability that enables the reuse of the same function could be utilized in other attacks.

Number theoretic transform (NTT) is useful for the acceleration of polynomial multiplication, which is the main performance bottleneck in the next-generation cryptographic schemes. Different NTT-based cryptographic algorithms have different security settings. The diverse application scenarios introduce different cost-performance tradeoffs and hardware constraints. Motivated by the emerging demand for more versatile NTT hardware accelerators, we propose a new design methodology that can generate area-efficient and high-performance NTT accelerators for any length and modulus of NTT polynomials and single processing element (PE) or PE array with a varying number of layers. The proposed NTT accelerator architecture pivots on a conflict-free memory access pattern for adaptation to different combinations of security and PE array configuration parameters. The proposed memory access pattern is formally proved to be conflict-free for any parametric configurations. The criterion for read-after-write conflict without pipeline stall is also established. Our proposed design methodology can produce NTT accelerators with single PE or multilayer PE array for different polynomial size and modulus, with hardware area and computational efficiency comparable to accelerators customized for a fixed set of parameters. Our proposed methodology produces parameterized accelerator with higher scalability than the existing parameterized accelerator design. On average, the accelerators generated by our proposed method are 71.4% more area-time efficient. Up to 30.7% area-time reduction over the most area-time efficient state-of-the-art scalable NTT accelerator can be achieved for the same security parameters.

The rapid development of IoT (Internet of Things) brings great convenience to people through the utilization of IoT applications, but also brings huge security challenges. Existing IoT security breaches show that many IoT devices have authentication flaws. Although many IoT authentication schemes were proposed, they are not applicable to recent smart IoT applications covering IoT device, back-end sever, and user-end mobile applications. To build the first line of defense for trending IoT systems, this paper proposes a new authentication scheme. The proposed scheme first models the entire life cycle of the IoT device for real-world scenarios of smart IoT systems, which contains factory manufacturing, daily usage, and system resetting. For each stage in the life cycle, the proposed scheme employs efficient symmetric key mechanisms to achieve the authentication between IoT device, back-end server, and mobile application. The proposed scheme supports both server-free local area network communication and sever-involved remote public area communication. Formal security verification shows that the proposed scheme resists existing attacks. The open-source experimental evaluations also show that the proposed scheme is efficient and promising for practical usage.

To realize high-quality robotic welding, an efficient and robust complex weld seam feature point extraction method based on a deep neural network (Shuffle-YOLO) is proposed for seam tracking and posture adjustment. The Shuffle-YOLO model can accurately extract the feature points of butt joints, lap joints, and irregular joints, and the model can also work well despite strong arc radiation and spatters. Based on the nearest neighbor algorithm and cubic B-spline curve-fitting algorithm, the position and posture models of the complex spatially curved weld seams are established. The robot welding posture adjustment and high-precision seam tracking of complex spatially curved weld seams are realized. Experiments show that the method proposed in this article can extract weld seam feature points quickly and robustly, which enables welding robots to accurately track the weld seams and adjust the welding torch postures simultaneously.

Kyber is one of the four final Key Encapsulation Mechanism (KEM) competitors of the National Institute of Standards and Technology PostQuantum Cryptography standardization competition. This paper reveals the vulnerability of Kyber under a voltage template side channel attack: the modular polynomial subtraction operation in Kyber.CCAKEM.Dec. In this paper, by splicing data under different selected ciphertexts, a small number of traces are required to recover the secret key. Experiments show that the recovering accuracy of secret key achieves 100% when using 330 traces, and it still achieves 98% when only using 44 traces.

To prevent private training data leakage in Federated Learning systems, we propose a novel secure aggregation scheme based on seed homomorphic pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic property of SHPRG to simplify the masking and demasking scheme, which for each of the clients and for the server, entails a overhead linear w.r.t model size and constant w.r.t number of clients. We prove that even against worst-case colluding adversaries, SASH preserves training data privacy, while being resilient to dropouts without extra overhead. We experimentally demonstrate SASH significantly improves the efficiency to 20× over baseline, especially in the more realistic case where the numbers of clients and model size become large, and a certain percentage of clients drop out from the system.

In recent years, deep learning methods make a big difference in side-channel attack (SCA) community especially in the profiled scenario. Multiclass classification method is the common way to complete such classification task. In this article, we propose a novel SCA method utilizing multilabel classification from bit-to-byte view. Accordingly, each leakage trace has eight labels when considering a byte. The experimental results on several datasets show that our multilabel classification method is efficient and even performs better in some situations compared with the original multiclass classification model while model complexity is much reduced. Besides, our multilabel model can be seen as ensemble of monobit models and we verify the ensemble effect experimentally.

Seam feature point acquisition is the premise of the intelligent welding process such as initial point guiding and seam tracking. However, conventional seam feature point acquisition methods based on geometric feature have shortcomings of poor flexibility and robustness. In this article, a seam feature point acquisition method based on efficient convolution operator (ECO) and particle filter (PF) is proposed, which could be applied to different weld types and could achieve fast and accurate seam feature point acquisition even under the interference of welding arc light and spatter noises. First, a structured light vision sensor is developed to acquire welding image. Second, the ECO algorithm is adopted to track the seam region and acquire seam feature point during gas metal arc welding process. Third, the state and measurement equations of the weld seam position are established, and PF is applied to improve seam feature point acquisition accuracy. Finally, a welding experiment system is built and a series of seam feature point acquisition experiments of butt joint, lap joint, and fillet joint are carried out to validate the performance of the proposed method. The experiment results demonstrate that the processing speed of the proposed method could reach up 35 Hz, and the seam feature point acquisition errors are smaller than 0.15 mm, which could meet the real-time and accuracy requirement for subsequent initial point guiding and seam tracking.

The hybrid-structured flexible manipulator has a complex structure and strong coupling between state variables. Meanwhile, the natural frequency of the hybrid-structured flexible manipulator varies with the motion of the telescopic joint, so it is difficult to suppress the vibration quickly. In this article, the tip state signal of the hybrid-structured flexible manipulator is decomposed into elastic vibration signal and tip vibration equilibrium position signal, and a combined control method is proposed to improve tip positioning accuracy and trajectory tracking accuracy. In the proposed combined control method, an improved nominal model-based sliding mode controller (NMBSMC) is used as the main controller to output the driving torque, and an actor-critic-based reinforcement learning controller (ACBRLC) is used as an auxiliary controller to output small compensation torque. The improved NMBSMC can be divided into a nominal model-based sliding mode robust controller and a practical model-based integral sliding mode controller. Two sliding mode controllers with different structures make full use of the mathematical model and the measured data of the actual system to improve the vibration equilibrium position tracking accuracy. The ACBRLC uses the tip elastic vibration signal and the prioritized experience replay method to obtain the small reverse compensation torque, which is superimposed with the output of the NMBSMC to suppress tip vibration and improve the positioning accuracy of the hybrid-structured flexible manipulator. Finally, several groups of experiments are designed to verify the effectiveness and robustness of the proposed combined control method.

Recently, laser vision sensors are widely applied in initial point alignment and seam tracking to improve the level of intelligent welding because of good characteristics. However, since the deformation of laser stripe is unobvious at the narrow weld with 0.2 mm width, these methods are not applicable for the narrow weld. Moreover, there are rare researches that could achieve initial point alignment and seam tracking of narrow weld simultaneously. Therefore, an initial point alignment and seam tracking system for narrow weld is proposed in this paper. At first, a laser vision sensor with extra light emitting diode light is used to obtain laser and weld seam image. Besides, the seam feature point is extracted and three-dimensional coordinates can be obtained with vision model. In addition, three controllers including decision controller, initial point alignment controller, and seam-tracking controller are proposed to achieve initial point alignment and seam tracking control in X- and Z-axis directions. Moreover, feature verification, Kalman filter, and output pulse verification are designed to improve the accuracy and stability of this system. Finally, many initial point alignment and seam-tracking experiments of narrow weld are conducted. Experimental results demonstrate that proposed system can well achieve initial point alignment and seam tracking of planar and curved surface narrow weld.

Probing attack is a severe threat for the security of hardware cryptographic modules (HCMs). In this paper, we make the first step to evaluate the vulnerability of HCMs against probing attack, wherein we investigate the probing complexity and the key candidate reduction capability for probing attack on every signal in the circuit. We also present approximate solutions for the calculation of the proposed metrics to reduce computational complexity. Experimental results demonstrate that the proposed evaluation metric is both effective and efficient.

Koblitz curves are a class of computationally efficient elliptic curves where scalar multiplications can be accelerated using τNAF representations of scalars. However, conversion from an integer scalar to a short τNAF is a costly operation. In this paper, we improve the recently proposed scalar conversion scheme based on division by τ2. We apply two levels of optimizations in the scalar conversion architecture. First, we reduce the number of long integer subtractions during the scalar conversion. This optimization reduces the computation cost and also simplifies the critical paths present in the conversion architecture. Then we implement pipelines in the architecture. The pipeline splitting increases the operating frequency without increasing the number of cycles. We have provided detailed experimental results to support our claims made in this paper.

An unprotected scan-chain makes a perfect back-door in a secure chip. Therefore, many secure chips disable the access to the scan-chains once the testing is completed. In this paper, we consider an attack that is carried out on a blank chip. The scan-chain is still accessible, yet there is no key to be attacked. We show that the attacker can use the scan chain to reveal information that can be used to improve other physical attacks, such side-channel analysis, fault analysis and probing analysis.

The parameter selection of Residue Number Systems (RNS) has a great impact on its computational efficiency. This paper shows that a base extension, the most costly operation in RNS Montgomery multiplication, can be more efficient when the intervals between the RNS moduli are small. We propose a systematic RNS parameter selection procedure and two methods to select RNS moduli that lead to a reduced complexity. Our experimental results confirm the advantages of the selected moduli.

Smart medical devices will only be smart if they also include technology to provide security and privacy. In practice this means the inclusion of cryptographic algorithms of sufficient cryptographic strength. For battery operated devices or for passively powered devices, these cryptographic algorithms need highly efficient, low power, low energy realizations. Moreover, unique to cryptographic implementations is that they also need protection against physical tampering either active or passive. This means that countermeasures need to be included during the design process.

This paper describes a new method to speed up {\hbox{\rlap{I}\kern 2.0pt{\hbox{F}}}}_p-arithmetic in hardware for pairing-friendly curves, such as the well-known Barreto-Naehrig (BN) curves. We explore the characteristics of the modulus defined by these curves and choose curve parameters such that {\hbox{\rlap{I}\kern 2.0pt{\hbox{F}}}}_p multiplication becomes more efficient. The proposed algorithm uses Montgomery reduction in a polynomial ring combined with a coefficient reduction phase using a pseudo-Mersenne number. As an application, we show that the performance of pairings on BN curves in hardware can be significantly improved, resulting in a factor 2.5 speedup compared with state-of-the-art hardware implementations.

The first contribution of our paper is that we propose a platform, a design strategy, and evaluation criteria for a fair and consistent hardware evaluation of the second-round SHA-3 candidates. Using a SASEBO-GII field-programmable gate array (FPGA) board as a common platform, combined with well defined hardware and software interfaces, we compare all 256-bit version candidates with respect to area, throughput, latency, power, and energy consumption. Our approach defines a standard testing harness for SHA-3 candidates, including the interface specification for the SHA-3 module on our testing platform. The second contribution is that we provide both FPGA and 90-nm CMOS application-specific integrated circuit (ASIC) synthesis results and thereby are able to compare the results. Our third contribution is that we release the source code of all the candidates and by using a common, fixed, publicly available platform, our claimed results become reproducible and open for a public verification.

When using Elliptic Curve Cryptography (ECC) in constrained embedded devices such as RFID tags, López-Dahab's method along with the Montgomery powering ladder is considered as the most suitable method. It uses x-coordinate only for point representation, and meanwhile offers intrinsic protection against simple power analysis. This paper proposes a low-cost fault detection mechanism for Elliptic Curve Scalar Multiplication (ECSM) using the López-Dahab algorithm. Introducing minimal changes to the last round of the algorithm, we make it capable of detecting faults with a very high probability. In addition, by reusing the existing resources, we significantly reduce both performance losses and area overhead compared to other methods in this scenario. This method is suitable especially for constrained devices.

Elliptic Curve Cryptography (ECC) is considered as the best candidate for Public-Key Cryptosystems (PKC) for ubiquitous security. Recently, Elliptic Curve Cryptography (ECC) based on Binary Edwards Curves (BEC) has been proposed and it shows several interesting properties, e.g., completeness and security against certain exceptional-points attacks. In this paper, we propose a hardware implementation of the BEC for extremely constrained devices. The w-coordinates and Montgomery powering ladder are used. Next, we also give techniques to reduce the register file size, which is the largest component of the embedded core. Thirdly, we apply gated clocking to reduce the overall power consumption. The implementation has a size of 13,427 Gate Equivalent (GE), and 149.5 ms are required for one point multiplication. To the best of our knowledge, this is the first hardware implementation of binary Edwards curves.

This paper reports a new speed record for FPGAs in cracking Elliptic Curve Cryptosystems. We conduct a detailed analysis of different F2(m)multiplication approaches in this application. A novel architecture using optimized normal basis multipliers is proposed to solve the Certicom challenge ECC2K-130. We compare the FPGA performance against CPUs, GPUs, and the Sony PlayStation 3. Our implementations show low-cost FPGAs outperform even multicore desktop processors and graphics cards by a factor of 2.

Testing of cryptographic chips or components has one extra dimension: physical security. The chip designers should improve the design if it leaks too much information through side-channels, such as timing, power consumption, electric-magnetic radiation, and so on. This requires an evaluation of the security level of the chip under different side-channel attacks before it is manufactured. This paper presents an FPGA-based testing strategy for cryptographic chips. Using a block-based architecture, a testing bus and a shadow FPGA, we are able to check information leakage of each block. We describe this strategy with an Elliptic Curve Cryptosystem (ECC) for RFID tags.

NTRU is a public-key cryptosystem based on the shortest vector problem in a lattice which is an alternative to RSA and ECC. This work presents a compact and low power NTRU design that is suitable for pervasive security applications such as RFIDs and sensor nodes. We have designed two architectures, one is only capable of encryption and the other one performs both encryption and decryption. The strategy for the designs includes clock gating of registers, operand isolation and precomputation. This work is also the first one to present a complete NTRU design with encryption/decryption circuitry. Our encryption-only NTRU design has a gate-count of 2:8 kgates and dynamic power consumption of 1:72μW. Moreover, encryption-decryption NTRU design consumes about 6μW dynamic power and consists of 10:5 kgates.

Algebraic torus-based cryptosystems are an alternative for Public-Key Cryptography (PKC). It maintains the security of a larger group while the actual computations are performed in a subgroup. Compared with RSA for the same security level, it allows faster exponentiation and much shorter bandwidth for the transmitted data. In this work we implement a torus-based cryptosystem, the so-called CEILIDH, on a multicore platform with an FPGA. This platform consists of a Xilinx MicroBlaze core and a multicore coprocessor. The platform supports CEILIDH, RSA and ECC over prime fields. The results show that one 170-bit torus T6exponentiation requires 20 ms, which is 5 times faster than 1024-bit RSA implementation on the same platform.