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Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose Iota, a logic programming-based framework to perform system-level security analysis for IoT systems. Iota generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building Iota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that Iota's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply Iota to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices., Comment: This manuscript has been accepted by IoTDI 2022

Autonomous following is one of the key issues in mobile robotics with a wide range of applications in commercial, industrial and military areas. To achieve autonomous following, the robot needs not only to robustly detect and track the target, but also efficiently follow the target while avoiding obstacles. In this paper, we propose a method that combines a Line-of-sight sensor (a 2D laser range finder) and a Non-line-of-sight sensor (a low-cost Angle-of-Arrival (AOA) tag) to identify, track and follow the target person in dynamic environments. First, in order to get smooth and continuous moving trajectory of the target person, a Kalman Filter is used to fuse person tracking information from AOA data and laser data. Then, a real-time robot-centric rolling grid map is constructed using the laser data. On top of the rolling grid map, a target potential field is generated by using the fast marching method, and then a direction gradient field is created based on the target potential field. With the rolling grid map, the target potential field and the direction gradient field, an improved dynamic window algorithm, FMM-DWA, is proposed to control the robot to move towards the target. The proposed method considers not only the distance between the robot and the target, but also the difference between the heading of the robot and the reference direction provided by the direction gradient field, to avoid falling into the local optimum. To validate the performance of the proposed method, a series of person following experiments are carried out in complex dynamic environments. The experimental results show that the proposed following algorithm can effectively deal with the occlusion problem and robustly follow the target person while quickly avoiding the static and dynamic obstacles.

As an important component of non-speech audio classification technology, environmental sound classification (ESC) has attracted the attention of researchers in recent years. Benefiting from the rapid development of deep learning technology, researchers input manually extracted speech features into a convolutional neural network (CNN) to extract deeper abstract features to complete the final classification task. Make the accuracy of ESC reach a higher level. However, the improvement of accuracy often resorts to the constantly deepening network structure. This caused a lot of parameter redundancy. The huge amount of floating-point operations (FLOPs) also slows down the running speed of CNN and increases the burden on storage and computing resources. To this end, we propose a convolutional neural network pruning technology to compress the CNN model, reduce the amount of CNN parameters and FLOPs, thereby eliminating the redundancy of CNN. Specifically, we use ResNet-20 as the backbone network. First, pre-train the network to make it have good classification performance. Then randomly remove a small number of convolution channels and fine-tune them to restore accuracy. Iterate this process until the model reaches the target compression ratio. We conduct experiments on the UrbanSound8K dataset. Thanks to the strong plasticity of the CNN model, the pruned model does not have a significant decrease in accuracy, even when the compression rate is low, because the network redundancy is reduced, the accuracy will be slightly improved. Our model achieves an accuracy that competes with state-of-the-art method while being lighter in weight.

Understanding complex social interactions among agents is a key challenge for trajectory prediction. Most existing methods consider the interactions between pairwise traffic agents or in a local area, while the nature of interactions is unlimited, involving an uncertain number of agents and non-local areas simultaneously. Besides, they treat heterogeneous traffic agents the same, namely those among agents of different categories, while neglecting people's diverse reaction patterns toward traffic agents in ifferent categories. To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area. Meanwhile, a hierarchical graph attention module is proposed to obtain category-to-category interaction and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model are estimated for generating the future trajectories. Extensive experimental results on benchmark datasets demonstrate a significant performance improvement of our method over the state-of-the-art methods.

An ultra-wideband THz metamaterial absorber (MMA) loaded with lumped elements and graphene is proposed. The MMA has achieved an ultra-wideband absorption from 6.1THz to 18.5THz with average absorptance over 90%, and the relative bandwidth reaches 112.7%. The excellent absorption properties superior to the ordinary MMAs are owing to the effect comes from lumped resistors and graphene surface plasmon (GSP).

We propose an extremely high Q-factor terahertz metamaterial microfluidic sensor based on semiconductor materials. The unit cell consists of four layers: Au baseboard, dielectric and InN strips mixed layer, InN slab and a cover dielectric layer. Excellent sensing characteristics have been achieved with a high quality-factor (Q-factor) of 199.6 and an absorbance of 96.1% at 4.672 THz, the RI sensitivities reaches 1251GHz/RIU.

Classical ship target classification algorithms in synthetic aperture radar (SAR) images mainly rely on manual extraction of simple physical features to distinguish different ship types. A novel multiscale dual-attention (MDA) based convolutional neural network (CNN) is proposed in this paper to highlight the deep distinguishable features extracted from SAR images for classification. First, we apply the data enhancement method to expand the limited labeled sample set to alleviate the defect of unbalanced dataset. Second, the squeeze-and-excitation(SE) block is adopted to learn the initial weight of the feature maps to mitigate the negative effects of large similarity between different ship classes. Third, the weighted feature maps are sent into two parallel self-attention blocks, and the semantic dependence on spatial dimension and channel dimension is modeled respectively. Feature fusion is carried out to further improve feature discrimination ability. The results of ablation experiments and comparative experiments on the open SAR ship dataset show that this method can mine deep discriminant features effectively, and further improve the classification accuracy of ships in SAR images compared with baseline method and other latest methods.

In this paper, we consider a motion compensation scheme using Kalman filtering to airborne Synthetic Aperture Radars (SAR). The parameters to be estimated include sensor positions and sensor attitudes. We will show that Kalman filtering is superior to extract motion parameters compared with traditional methods. In addition, the particle filter may further take advantages to nonlinear Gaussian issues. Based on the parameters by a tracking filter, the Chirp Scaling algorithm integrated with motion compensation was implemented to generate target impulse response. It may provide good results to motion errors in both cross track and along track. Computer simulations demonstrate that Kalman filtering is computationally efficient and numerically accurate to airborne SAR motion compensation.

Event-based vision sensors are a paradigm shift in the way that visual information is obtained and processed. These devices are capable of low-latency transmission of data which represents the scene dynamics. Additionally, low-power benefits make the sensors popular in finite-power scenarios such as high-speed robotics or machine vision applications where latency in visual information is desired to be minimal. The core datatype of such vision sensors is the ‘event’ which is an asynchronous per-pixel signal indicating a change in light intensity at an instance in time corresponding to the spatial location of that sensor on the array. A popular approach to event-based processing is to map events onto a 2D plane over time which is comparable with traditional imaging techniques. However, this paper presents a disruptive approach to event data processing that uses a tree-based filter framework that directly processes raw event data to extract events corresponding to interest point features, which is then combined with a Harris interest point approach to isolate features. We hypothesise that since the tree structure contains the same spatial information as a 2D surface mapping, Harris may be applied directly to the content of the tree, bypassing the need for transformation to the 2D plane. Results illustrate that the proposed approach performs better than other state-of-the-art approaches with limited compromise on the run-time performance.

In this paper, we propose a vanishing point aided LiDAR-Visual-Inertial estimator to achieve real-time, low-drift and robust pose estimation. The proposed method is mainly composed of 3 sequential modules, namely IMU-aided vanishing point (VP) detection module, voxel-map based feature depth association module, and visual inertial fixed-lag smoother module. The IMU-aided VP detection module will detect feature points, line segments and vanishing points to establish robust correspondences in successive frames. In particular, we propose to use 1-line RANSAC method to provide stable VP hypotheses and polar grid to accelerate vanishing point hypothesis validation. After that, we propose a novel voxel-map based feature depth association method, to retrieve depth and assign depth to visual feature efficiently. Finally, the visual inertial fixed-lag smoother module is proposed to jointly minimize error terms. Experiments show that our method outperforms the state-of-the-art visual-inertial odometry and LiDAR-visual estimator in both indoor and outdoor environments.

The 3D reconstruction of weak feature indoor scenes is a challenging task that cannot be effectively solved by existing methods based on image features. In this paper, we propose an indoor scene reconstruction method based on Hector SLAM [1] and floorplan optimization to generate a standard and realistic 3D mesh model. First, we use Hector SLAM [1] and Lidar to generate a 2D grid map and extract the edge points as the initial floorplan by filtering the discrete noise. Second, we classify these edge points into different classes through the region growing algorithm and fit each class with a line. Then we optimize these lines according to the topological regularity of the man-made scene to obtain a standard floorplan. Finally, we combined the optimized floorplan and texture images to generate a realistic 3D Mesh model of the weak feature indoor scene. We evaluate our approach on four weak feature scenes and demonstrate the advantages over existing alternative methods.

This paper presents a novel Reducing-Over-Time (ROT) binary tree structure for event-based vision data and subtypes of the tree structure. A framework is presented using ROT, that takes advantage of the self-balancing and self-pruning nature of the tree structure to extract spatial-temporal information. The ROT framework is paired with an established motion classification technique and performance is evaluated against other state-of-the-art techniques using four datasets. Additionally, the ROT framework as a processing platform is compared with other event-based vision processing platforms in terms of memory usage and is found to be one of the most memory efficient platforms available.

A high-performance ultrawideband terahertz absorber is proposed. Constituted by silicon hemi-ellipsoid and monolayer graphene that is separated by a dielectric spacer from a bottom metal reflector. The absorber achieved an ultrawide absorption bandwidth from 2 THz to more than 10 THz with an average absorbance of 95.72%, and the relative bandwidth is 133%. The excellent absorption properties are owing to the presence of graphene and the shape morphing of the silicon hemi-ellipsoid, in which multiple discrete graphene plasmon resonances and continuous multimode Fabry–Perot resonances can be excited. By coupling the graphene plasmon resonances and Fabry–Perot resonances, the absorption spectrum is extended and smoothed to realize an ultrawideband absorber. The incident angular tolerance can reach 60°. The results will shed light on the better performance of terahertz trapping, imaging, communication, and detection.

To data processing of Synthetic Aperture Radars (SAR), Doppler centroid and Doppler frequency rate are crucial parameters. It is an efficient way to derive Doppler parameters from radar echo information. To achieve accurate performance, we try to apply an advanced signal processing algorithm, namely, Estimating Signal Parameter via Rotation Invariance Techniques (ESPRIT), into SAR parameter inference. Doppler frequency rate can be estimated by the phase difference principle. Doppler ambiguity can be solved by beat frequency of two range looks. To facilitate the algorithm, we also implemented an adaptive filter to lower the computational complexity. Numerical simulations demonstrate the ESPRIT algorithm a supreme tool when applied.

An ultra-sensitive THz metamaterial sensor with asymmetric duble-F-shaped metal resonators (ADFSMR) is proposed, and its application in terahertz is analyzed. The sensor achieved perfect narrow-band absorption spectrum at 5.95 THz, with a high Q factor of 49.6. It is ultra-sensitive to the refractive index of the surrounding medium and the thickness variation of the medium. Simulation results show that the sensitivity is 1800 GHz / RIU for analyte with thickness of 5um. Additionally, for a typical biomedical analyte of n = 1.3, the sensitivity was 134 GHz/μm. It has important application in THz high-sensitive biomedical sensor and detector.

An ultra-wideband terahertz absorber is presented with dielectric hemi-ellipsoid on a monolayer graphene layer. The absorber has achieved an ultra-wideband absorption bandwidth from 2 THz to more than 10 THz with an average absorbance of 95.72%, the relative bandwidth reaches 133%. The excellent absorption properties owing to multiple discrete graphene plasmonic resonances(GPRS) and the continuous multimode Fabry-Perot (FP) resonances, the mechanism for the its ultrawideband absorption are analyzed in details.

To achieve robust motion estimation in visually degraded environments, thermal odometry has been an attraction in the robotics community. However, most thermal odometry methods are purely based on classical feature extractors, which is difficult to establish robust correspondences in successive frames due to sudden photometric changes and large thermal noise. To solve this problem, we propose ThermalPoint, a lightweight feature detection network specifically tailored for producing keypoints on thermal images, providing notable anti-noise improvements compared with other state-of-the-art methods. After that, we combine ThermalPoint with a novel radiometric feature tracking method, which directly makes use of full radiometric data and establishes reliable correspondences between sequential frames. Finally, taking advantage of an optimization-based visual-inertial framework, a deep feature-based thermal-inertial odometry (TP-TIO) framework is proposed and evaluated thoroughly in various visually degraded environments. Experiments show that our method outperforms state-of-the-art visual and laser odometry methods in smoke-filled environments and achieves competitive accuracy in normal environments.

Event-based image processing is a relatively new domain in the field of computer vision. Much research has been carried out on adapting event-based data to comply with established techniques from frame-based computer vision. On the contrary, this paper presents a descriptor which is designed specifically for direct use with event-based data and therefore can be considered to be a pure event-based vision descriptor as it only uses events emitted from event-based vision devices without transforming the data to accommodate frame-based vision techniques. This novel descriptor is known as the Post-stimulus Time-dependent Event Descriptor (P-TED). P-TED is comprised of two features extracted from event data which describe motion and the underlying pattern of transmission respectively. Furthermore a framework is presented which leverages the P-TED descriptor to classify motions within event data. This framework is compared against another state-of-the-art event-based vision descriptor as well as an established frame-based approach.

Stable extinction ratio (ER) of the probe pulse is crucial to the measurement stability of Brillouin Optical Time-Domain Reflectometry (BOTDR). In this paper, a dynamic and continuous ER measurement scheme is proposed to evaluate the ER stability. Besides, a modified optical pulse modulation scheme based on gain switch is proposed to replace the conventional electric-optic modulation (EOM), aiming at enhancing the stability of BOTDR strain sensing system. The gain switch utilizes semiconductor optical amplifier (SOA) to generate probe pulse with the higher and more stable ER. Experiment results in 4 hours show the improvement of the ER stability with the proposed gain-switched modulation, which further improves the stability of BOTDR strain sensing system.

Internet of Things (IoT) has been emerging as one of the most significant technologies that may change the world. The development and deployment of IoT systems significantly improve the efficiency of work, advance the development of productive force, and affect the human society’s modes of production, working, and life deeply. The number of deployed IoT devices and applications has had explosive growth in the past years. While people enjoy the benefits brought by IoT, the addressing of its security and privacy issues does not keep pace with the development of IoT technologies. We find that encrypted wireless IoT traffic can still leak information about user privacy. We can infer what the user is doing at home by deploying a wireless sniffer outside the user’s house.In this paper, we propose a method to eavesdrop different kinds of user privacy via analyzing the wireless context. First, we extract the packet sequence features to fingerprint and detect the IoT events. Then we analyze the detected IoT events and infer the user’s activities and even the user’s moods. Furthermore, by analyzing the wireless context, which is discovered by mining the IoT event dependencies, we can infer the user’s living habits, routines, and even installed IoT applications. The leakage of such information not only exposes the user’s privacy but also extends the attack surface that an attacker can utilize to control the smart home. We implement our eavesdropping approach and conduct extensive experiments on the SmartThings platform. The evaluation demonstrates the feasibility and effectiveness of our eavesdropping approach.

Relation extraction and entity linking are two fundamental procedures to extend knowledge bases. Most existing methods typically treat them separately and ignore the semantic relevance between entities and relations. In this paper, we pioneer a general joint learning framework for relation extraction and entity linking, which allows these two tasks boost each other. Based on the framework, a demonstration model is proposed with neural networks. We conduct experiments on variants of a standard benchmark dataset (NYT-10) to verify the effectiveness of our approach. Experimental results show that our approach significantly outperforms traditional separate methods without reducing efficiency, especially on datasets with many ambiguous entity mentions. Furthermore, various mainstream methods for relation extraction and entity linking can be easily integrated into our loosely-coupled framework due to its flexible architecture.

It is reasonable to consider combing IRE and RFA. Thus the changes of liver properties after electoporation were studied. The electrical and thermal conductivity of rabbit livers treated by electric shocks with different conditions were measured in vivo through the four-electrode method and the thermistor method respectively. Frequency and electric field were considered, as two main factors of IRE. Experiments then showed that changes occurred in both electrical and thermal conductivity after electoporation treatment. Electric field strength contributed more on electrical conductivity than frequency did. Besides, electrical conductivity significantly increased in all protocols, while thermal conductivity didn't perform so until electric field strength was more than 500V/cm. The related formulas were also given after curve fitting.

Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.

Multi-view feature learning has garnered much attention recently since many real world data are comprised of different representations or views. How to explore the consensus structure and eliminate the inconsistency noise in different views remains a challenging problem in multi-view feature learning. In this paper, we propose a multi-way deep autoencoder for multi-view feature learning to explore the deep consensus structure and reconcile the efficiency of encoding process meanwhile. Through a multi-way encoding process, we embed the original data feature views to nonnegative representations of multiple levels which are structured hierarchically. Along the structure of embedded representations, we recover the diversity and important information layer by layer in the decoding process. The experiments on two image datasets show the superior performance of our method.