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This article is centered on the cybersecurity research of dynamic state estimation for power systems with measurement delays. Relying on mixed measurements from phasor measurement units (PMUs) and remote terminal units (RTUs), a delayed measurement model is constructed. A modified state estimator based on the Kalman filter (KF) is designed, which can obtain the optimal estimated states under measurement delays. Moreover, the measurement data transmitted from the sensor to the estimator are vulnerable to cyberattacks. Especially, false data-injection (FDI) attacks are frequently encountered in the power system state estimation (PSSE) process. In the case of measurement delays, an FDI attack strategy is designed to interfere with the state estimator and evade detection by the chi-square detector. By utilizing the attacked estimated information and the uncorrupted measurement information, two measurement residual vectors are designed. According to these two residual vectors, a chi-square-based attack detection method is proposed, which has the ability to detect the attack without being affected by the delayed measurements. The proposed KF algorithm and attack detection method are implemented on an IEEE 14-bus system and they are confirmed to be effective and feasible.

Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, we give YOLOv4 the power to detect multiple objects in one cell. Center to our method is the carefully designed joint prediction scheme, which is executed through an assignment of bounding boxes and a joint loss. Equipped with the derived joint-object augmentation (DJA), refined regression loss (RL) and Score-NMS (SN), YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time. Furthermore, on the widely used general benchmark COCO, YOLO-CS still has a good performance, indicating its robustness to various scenes.

In molecular communications, the direct detection of signaling molecules may be challenging due to a lack of suitable sensors and interference in the environment. Motivated by research in molecular biology, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. We consider two implementations of the proposed detection mechanism, i.e., unrestricted probe movement and probes restricted to a volume around the receiver. The reaction-diffusion equations describing the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed form. Therefore, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables of the equations in each time step. Our results show that the concentrations of the product molecules and the signalling molecules share a similar characteristic over time, i.e., a single peak and a long tail. The peak and tail values of the product molecule concentration can be controlled by choosing probes with suitable parameters. By carefully choosing the molecular probe and optimizing the decision threshold, the BER can be improved significantly and outperform that of a direct detection system., 13 pages, 11 figures, 1 table. This journal version was submitted in April 2022 to the IEEE for possible publication. A part of this article was presented at the IEEE Wireless Communications and Networking Conference 2020 and stored in the previous version on arXiv

The challenging problem in the traffic system is lane detection. This Lane detection which attracts the computer vision community’s attention. For computer vision and machine learning techniques, the Lane detection which acts as the multi-feature detection problem. Many machine learning techniques are used for lane detection. Driver support system is one of the most important features in the modern vehicles to ensure the safety of the driver and decrease the vehicle accidents on road. Road Lane detection is the most challenging task and complex tasks now-a-days. Road localization and relative position between vehicle and roads which also includes with this. But in this journal, we propose a new method. Here, an on- board camera to be used which is looking outwards are presented here in this work. This proposed technique which can be used for all types of roads like painted, unpainted, curved, straight roads etc in different weather conditions. No need for camera calibration and coordination of the transform, may be any changing illumination situation, shadow effects, various road types. No representation for speed limits. This includes that the system acquires the front view using a camera mounted on the vehicles and detect the Lane by applying the code from the Python Programming process. This proposed system does not require any more information about roads. This system which demonstrates a robust performance for Lane detection.

RGB-D salient object detection aims to detect visually distinctive objects or regions from a pair of the RGB image and the depth image. State-of-the-art RGB-D saliency detectors are mainly based on convolutional neural networks but almost suffer from an intrinsic limitation relying on the labeled data, thus degrading detection accuracy in complex cases. In this work, we present a self-supervised self-ensembling network (S $^3$ Net) for semi-supervised RGB-D salient object detection by leveraging the unlabeled data and exploring a self-supervised learning mechanism. To be specific, we first build a self-guided convolutional neural network (SG-CNN) as a baseline model by developing a series of three-layer cross-model feature fusion (TCF) modules to leverage complementary information among depth and RGB modalities and formulating an auxiliary task that predicts a self-supervised image rotation angle. After that, to further explore the knowledge from unlabeled data, we assign SG-CNN to a student network and a teacher network, and encourage the saliency predictions and self-supervised rotation predictions from these two networks to be consistent on the unlabeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our network quantitatively and qualitatively outperforms the state-of-the-art methods.

Microstrip silicon detectors are one of the key trackers in particle physics experiment like LHC at CERN. There is a continuous need to improve upon various parameters of detectors like breakdown voltage, leakage current, full depletion voltage, post radiation hardness, charge collection efficiency and low thermal budget. We have introduced new doping less silicon microstrip detector for the very first time and investigated the various parameter of the detector. We have studied and compared the results of doped and dopingless charge plasma silicon microstrip detector. Our study shows improvement in Preradiation breakdown voltage of 2200 V with leakage current of 5.2 pA/um in dopingless charge plasma microstrip detector as compared to 1500 V of breakdown voltage with leakage current of 6.5pA/um as in the case of doped silicon microstrip detector.

Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects, provides rich and complex information about digital scenes. It also plays an important role in improving object detection and determining out-of-context objects. In this work, we present contextual models that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the performance of two of the state-of-the-art object detectors (i.e., Faster RCNN and YOLO), which are applied as a post-processing process for most of the existing detectors, especially for refining the confidences and associated categorical labels, without refining bounding boxes. We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO), where in some experiments PASCAL2012 is also used.We also show that iterating the process of applying our contextual models also enhances the detection performance further.

The fast and high response detection of neurotoxic H2S is of great importance for the environment. In this paper, directly electrospinning technology on the ceramic tube is developed to improve the response of H2S detector based on superlong SnO2 fibers. The submillimeter continuous fibers are deposited directly on ceramic tubes by in-situ electrospinning method and can keep morphology of fibers during calcination. By employing this technology, CuO-doped SnO2 fiber H2S detectors are fabricated, and 10% atom CuO-doped SnO2 H2S detector shows the highest response of 40 toward 1 ​ppm ​H2S at 150 ​°C while the response is only 3.6 for the H2S detector prepared in traditional route. In addition, the in-situ electrospinning H2S detectors show faster response and recovery compared to the H2S detectors fabricated by the conventional way. The high and fast response of H2S detectors based on in-situ electrospinning can be ascribed to the continuous fiber structure and CuO modification. The present in-situ electrospinning technology may provide a new strategy for the development of other gas-detectors and bio-detectors with fast and high response.

Small object detection is the main challenge for image detection of unmanned aerial vehicles (UAVs), especially with small pixel ratios and blurred boundaries. In this paper, a one-stage detector (SF-SSD) is proposed with a new spatial cognition algorithm. The deconvolution operation is introduced to a feature fusion module, which enhances the representation of shallow features. These more representative features prove effective for small-scale object detection. Empowered by a spatial cognition method, the deep model can re-detect objects with less-reliable confidence scores. This enables the detector to improve detection accuracy significantly. Both between-class similarity and within-class similarity are fully exploited to suppress useless background information. This motivates the proposed model to take full use of semantic features in the detection process of multi-class small objects. A simplified network structure can improve the speed of object detection. The experiments are conducted on a newly collected dataset (SY-UAV) and the benchmark datasets (CARPK and PUCPR+). To further demonstrate the effectiveness of the spatial cognition module, a multi-class object detection experiment is conducted on the Stanford Drone dataset (SDD). The results show that the proposed model achieves high frame rates and better detection accuracies than the state-of-the-art methods, which are 90.1% (CAPPK), 90.8% (PUCPR+), and 91.2% (SDD).

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

The purpose of this comprehensive research is to observe the impact of scintillator crystal type on entire detection process. For this aim, MCNPX (version 2.6.0) is used for designing of a physical plastic scintillation detector available in our laboratory. The modelled detector structure is validated using previous studies in the literature. Next, different types of plastic scintillation crystals were assessed in the same geometry. Several fundamental detector properties are determined for six different plastic scintillation crystals. Additionally, the deposited energy quantities were computed using the MCNPX code. Although six scintillation crystals have comparable compositions, the findings clearly indicate that the crystal composed of PVT 80% + PPO 20% has superior counting and detecting characteristics when compared to the other crystals investigated. Moreover, it is observed that the highest deposited energy amount, which is a result of the highest collision number in the crystal volume, corresponds to a PVT 80% + PPO 20% crystal. Despite the fact that plastic detector crystals have similar chemical structures, this study found that performing advanced Monte Carlo simulations on the detection discrepancies within the structures can aid in the development of the most effective spectroscopy procedures by ensuring maximum efficiency prior to and during use.

Hardware Malware Detectors (HMDs) have recently been proposed to make systems more malware-resistant. HMDs use hardware features to detect malware as a computational anomaly. Several aspects of the detector construction have been explored, leading to detectors with high accuracy. In this paper, we explore whether malware developers can modify malware to avoid HMDs detection. We show that existing HMDs can be effectively reverse-engineered and subsequently evaded. Next, we explore whether retraining using evasive malware would help and show that retraining is limited. To address these limitations, we propose a new type of Resilient HMDs (RHMDs) that stochastically switch between different detectors. These detectors can be shown to be provably more difficult to reverse engineer based on recent results in probably approximately correct (PAC) learnability theory. We show that indeed such detectors are resilient to both reverse engineering and evasion, and that the resilience increases with the number and diversity of the individual detectors. Furthermore, we show that an optimal switching strategy between the RHMDs base detectors not only reduces misclassification on evasive malware but also maintains high classification accuracy on non-evasive malware. Our results demonstrate that these HMDs offer effective defense against evasive malware at low additional complexity

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This article thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ~10% in terms of TDR (91.19% versus 80.74%).

Deep learning-based methods have achieved remarkable success in object detection, but this success requires the availability of a large number of training images. Collecting sufficient training images is difficult in detecting damages of airplane engines. Directly augmenting images by rotation, flipping, and random cropping cannot further improve the generalization ability of existing deep models. We propose an interactive augmentation method for airplane engine damage images using a prior-guided GAN to augment training images. Our method can generate many types of damages on arbitrary image regions according to the strokes of users. The proposed model consists of a prior network and a GAN. The Prior network generates a shape prior vector, which is used to encode the information of user strokes. The GAN takes the shape prior vector and random noise vectors to generate candidate damages. Final damages are pasted on the given positions of background images with an improved Poisson fusion. We compare the proposed method with traditional data augmentation methods by training airplane engine damage detectors with state-of-the-art object detectors, namely, Mask R-CNN, SSD, and YOLO v5. Experimental results show that training with images generated by our proposed data augmentation method achieves a better detection performance than that by traditional data augmentation methods.

Small-object detection has long been a challenge. High-megapixel cameras are used to solve this problem in industries. However, current detectors are inefficient for high-resolution images. In this work, we propose a new module called Pre-Locate Net, which is a plug-and-play structure that can be combined with most popular detectors. We inspire the use of classification ideas to obtain candidate regions in images, greatly reducing the amount of calculation, and thus achieving rapid detection in high-resolution images. Pre-Locate Net mainly includes two parts, candidate region classification and behavior classification. Candidate region classification is used to obtain a candidate region, and behavior classification is used to estimate the scale of an object. Different follow-up processing is adopted according to different scales to balance the variance of the network input. Different from the popular candidate region generation method, we abandon the idea of regression of a bounding box and adopt the concept of classification, so as to realize the prediction of a candidate region in the shallow network. We build a high-resolution dataset of aircraft and landing gears covering complex scenes to verify the effectiveness of our method. Compared to state-of-the-art detectors (e.g., Guided Anchoring, Libra-RCNN, and FASF), our method achieves the best mAP of 94.5 on 1920 × 1080 images at 16.7 FPS.

International audience; Fractional electron capture probability ratios of 125I were measured using metallic magnetic calorimeters (MMCs). Due to the 4π geometry of the source embedded in the absorber, detection efficiency of nearly 99% was observed for K, L, M, and N capture events. The fractional capture probabilities were calculated from peak area ratios.

Cloud computing is convenient to provide adequate resources for tenants. However, since multiple tenants share the underlying hardware resources, malicious tenants can use the shared processor to launch cache-based attacks. Such attacks can help malicious tenants steal private data of other tenants bypassing isolation mechanisms provided by the system, resulting in information leakage. Moreover, Spectre and Meltdown vulnerabilities can even extract memory contents arbitrarily with the help of cache attacks. Therefore, cache-based attacks pose a serious threat to the security of cloud platforms. To defeat such attacks, many detection methods have been proposed. However, most methods induce high false positives because they completely rely on the hardware performance counters (HPCs) and detect attacks with static criteria. To solve this problem, this paper proposes a self-feedback detector named CBA-Detector to detect cache-based attacks in real time. Specifically, CBA-Detector first uses machine learning technologies to create models for identifying suspicious programs with abnormal hardware behaviors, then analyzes suspicious programs from the instruction level to identify real attacks and provide feedback. Based on the feedback, the models can be updated to further improve their detection accuracy. As our experiments show, CBA-Detector can accurately identify cache-based attacks in real time and introduces a little overhead. Besides, the misjudgment rate decreases with the running time.

Multiplexing circuits in pixelated PET detectors are being studied with the goal of minimizing the degradation of detector performance. The purpose of this study was to develop a diode-based symmetric charge division (SCD) circuit with reducing the crosstalk caused by the leakage currents of adjacent channels connected in rows and columns. Four different SCD circuits (diode-based SCD with grounding path, diode-based SCD, resistor-based SCD with grounding path and resistor-based SCD) were constructed and their performance was evaluated and compared after being combined with a PET detector consisting of LYSO crystals optically coupled with SiPM. The diode-based SCD circuit with grounding path achieved a crosstalk ratio, energy resolution, coincidence timing resolution (CTR) and mean peak to valley ratio of 0.02, 14.3%, 0.5 ns and 27.9: 1, respectively; the diode-based SCD circuit achieved 0.03, 15.9%, 0.7 ns and 19.4: 1, respectively; the resistor-based SCD circuit with grounding path achieved 0.04, 14.5%, 0.9 ns and 19.4: 1, respectively; the resistorbased SCD circuit achieved 0.05, 17.8%, 1.8 ns and 17.7: 1, respectively. These results indicate that the proposed SCD has the potential to improve not only the crosstalk but also the energy linearity, energy resolution, and coincidence timing resolution characteristics in PET detectors.

Aim: A novel design for non-reversible as well as reversible parity generator and detector in Quantum-dot Cellular Automata (QCA) technology is presented in this research article. Parity generator and detector circuits are reliable error-checking components of a nano-communication system. Objective: The main focus of this research is to design an ultra-low-power fault-tolerant reversible gate implementation of the parity logic function in QCA. An efficient QCA design layout with minimal area, less latency and the least energy dissipation is desired. Methods: The proposed designs are developed using Quantum-dot Cellular Automata (QCA) technology. The circuits are optimized using majority gate reduction and clock zone reduction techniques. Also, the cell-cell interaction technique is employed to further optimize the QCA circuit. To increase the fault tolerance and ultra-low power operation, reversible QCA circuits are designed using cascaded Feynman gates. Results Conclusion: The efficiency of the parity generator and detector is calculated to be more than 25% compared to existing QCA layouts. It is demonstrated in this paper that the proposed circuits perform exceptionally well on every design parameter. The design parameters under consideration are cell count, cell area, complexity, crossover count, latency and energy dissipation. Conclusion: Using reversible logic, a fault-tolerant and defect-sensitive circuit are developed for parity generation and detection.

In this paper, we propose a novel deep learning architecture to classify unsafe driving maneuvers from dashcam and IMU data. Such architecture processes the output of an object detection algorithm in combination with raw video frames and GPS/IMU data. At the core of the architecture there is a novel Spatio-Temporal Attention Selector (STAS) module, which (1) extracts features describing the evolution of each object in the scene over time and (2) leverages multi-head dot product attention to select the relevant ones, i.e., the dangerous ones or the ones in danger, to perform classification. We also introduce a simple but effective methodology to increase the benefit of fine-tuning the backbone network. Our method is shown to achieve higher performance than other approaches in the literature applying attention over single frames.

The performance of traffic signal phasing and timing (SPaT) plans are directly related to temporal fluctuations in traffic volumes and their distribution across competing movements at the intersection. A well-timed signal plan generally allocates green times in proportion to the observed volumes. With the advance of data collection techniques and technology, it is possible to obtain and use high-resolution traffic data using detectors at intersections to measure the real-time performance of the SPaT plan to better achieve this goal. This study introduces novel green time metrics and methods to evaluate the efficiency of green time allocation along arterials and at individual intersections using automated, real-time, and high-resolution traffic data. An empirical application is presented for four intersections along a major radial arterial in Salt Lake City, Utah and a detailed analysis of the phases and movements for a single intersection located on that arterial. The proposed methods and results can help identify opportunities to improve the signal timing plan for the entire corridor or movements within secondary approaches by identifying locations and/or time periods where signal timing does not align well with observed traffic volumes. These metrics can be automated to provide traffic engineers with an alert as to when a particular intersection should be examined as a candidate for signal retiming, and they can also help identify what changes need to be made with the current SPaT plans.

Machine learning (ML) has been widely used for malware detection on different operating systems, including Android. To keep up with malware's evolution, the detection models usually need to be retrained periodically (e.g., every month) based on the data collected in the wild. However, this leads to poisoning attacks, specifically backdoor attacks, which subvert the learning process and create evasion tunnels for manipulated malware samples. To date, we have not found any prior research that explored this critical problem in Android malware detectors. In this paper, we are motivated to study the backdoor attack against Android malware detectors. The backdoor is created and injected into the model stealthily without access to the training data and activated when an app with the trigger is presented. We demonstrate the proposed attack on four typical malware detectors that have been widely discussed in academia. Our evaluation shows that the proposed backdoor attack achieves up to 99% evasion rate over 750 malware samples. Moreover, the above successful attack is realised by a small size of triggers (only four features) and a very low data poisoning rate (0.3%).

This article focused on the emission probability of the grey particles with black particles emitted in the interaction of 84Kr (as a projectile) having incident kinetic energy 1 GeV per nucleon with nuclear emulsion detector (as target detector). This study show that the emission probability of grey particles is depending on the number of emitted black particles.

In this manuscript we have focused on the shower particle characteristics produced at relativistics energy. We have also investigated the inequality of the shower particles emitted out of the interactivity of the projectiles with discrete target nuclei of the emulsion detector. In this investigation we have remarked that as we move to heavier target nuclei the release probability of shower particles is increases.

Defects on rail surfaces, which have become critical problems, need to be detected and removed as quickly as possible to ensure the fast, safe, and stable operation of trains. At present, although many solutions have been proposed to address these problems, the comprehensiveness, rapidity, and accuracy of defect detection remain unsatisfactory. This study aims to resolve these existing problems and accordingly proposes a multi-model rail surface defect detection system based on convolutional neural networks (MRSDI-CNN) from the standpoint of studying the squat on the rail surface. The convolutional neural networks utilized include the improved Single Shot MultiBox Detector (SSD) and You Only Look Once version 3(YOLOv3)--two types of one-stage networks. We expounded and analyzed the performance of the convolutional neural networks as well as their applicability to rail surface defect detection. We used a diverse range of rail defect sizes to improve the detection performance of the two deep learning networks, following which they could identify three types of squats in parallel with improved accuracy and without reduction of the detection speed. The experimental results confirm the effectiveness and superiority of the proposed method over those of previous studies.

This paper considers the stabilization problem of discrete-time semi-Markov jump singularly perturbed systems, in which the system operation mode is hidden but can be estimated by a detector with some emission probabilities. To model this circumstance, the hidden semi-Markov model with partially known emission probabilities is introduced, where the hidden state represents the real system operation mode while the emitted value represents the estimated value of the system operation mode and is available for the stabilizing controller. Based on the introduced hidden semi-Markov model, the stabilizing controller which only relies on the estimated value is designed to guarantee the $\delta$ -error mean square stability of the closed-loop system. A numerical example is used to verify the usefulness of the obtained results.

Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 - 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Supraharmonic (SH) disturbances are generated mainly by power electronics whose usage has increased significantly in the last decades. Consequently, supraharmonics have increased as well. The SH frequency range from 2~kHz to 150~kHz still lacks a normative measurement method. Compatibility levels and emission limits of SH are commonly presented as quasi-peak (QP) detector values for a bandwidth (BW) of 200~Hz measured by CISPR~16 receivers. In this context, this paper proposes an optimized method emulating the CISPR~16 receiver with QP detector for measuring grid disturbance levels. It makes innovative use of PLL (Phase-Locked Loop) units for estimating the highest distortion levels in the SH frequency range of the test signal, and in sequence, the QP detector with the appropriate specifications in the CISPR~16 standard is applied. Different cases of both synthetic and real signals were applied to the proposed method, which showed to be efficient in evaluating SH levels for current and voltage signals. An analysis of maximum permissible distortion levels is performed considering an individual SH distortion. In addition, the proposed method presents a computational cost reduction of approximately 91\% in relation to the CISPR~16 method onsidering a specific case of signal containing SH distortions used for power quality analysis.

In this paper we present how automatic maritime anomaly detection tools can be successfully applied in real-world situations such as the major event of the container vessel Ever Given, which grounded in the Suez Canal on March 23rd 2021. The anomaly detector is designed to process the available sequence of Automatic Identification System (AIS) reports, information from ground-based or satellite radar systems if available, and contextual information defining the expected nominal behavior of navigation. A statistical hypothesis testing procedure is sequentially run to decide whether or not a deviation from the nominal behavior happened within a specific time period, for instance two consecutive data points. We show, based on the recorded AIS data from the Ever Given, that the proposed detector could have been triggered and alerted to anomalous behavior fully 19 minutes before the grounding.

The amount of uranium in soil samples collected from a variety of residential, industrial, and agricultural sectors in the southern Basrah governorate in southern Iraq was determined using the neutron activation technique for solid-state nuclear track detectors CR-39. According to the findings, uranium concentrations in soil samples ranged from 0.65 ppm to 2.67 ppm. Soil samples were taken from a depth of 15 cm. The results were matched to publicly available data and determined to be within acceptable bounds.

Recently, 3D object detection based on deep learning has achieved impressive performance in complex indoor and outdoor scenes. Among the methods, the two-stage detection method performs the best; however, this method still needs improved accuracy and efficiency, especially for small size objects or autonomous driving scenes. In this paper, we propose an improved 3D object detection method based on a two-stage detector called the Improved Point-Voxel Region Convolutional Neural Network (IPV-RCNN). Our proposed method contains online training for data augmentation, upsampling convolution and k-means clustering for the bounding box to achieve 3D detection tasks from raw point clouds. The evaluation results on the KITTI 3D dataset show that the IPV-RCNN achieved a 96% mAP, which is 3% more accurate than the state-of-the-art detectors.

This paper investigates the cyber-security problem for autonomous vehicles under sensor attacks. In particular, a model-based framework is proposed which can detect sensor attacks and identify their sources in order to achieve the secure localization of self-driving vehicles. To ensure robustness of the vehicle against cyber-attacks, sensor redundancy is introduced, that is to deploy multiple sensors, each of which provides real-time pose observations of the vehicle. A bank of attack detectors is developed to capture anomalies in each sensor measurement, which is a combination of an extended Kalman filter (EKF) and a cumulative sum (CUSUM) discriminator. EKFs are employed to estimate the vehicle position and orientation recursively, while each CUSUM discriminator is designed to analyze the residual generated by its combined EKF to detect the possible deviation of the sensor measurement from the expected pose derived according to the mathematical model of the vehicle. To monitor the inconsistency amongst multiple sensor measurements, an auxiliary detector is introduced which fuses observations from multiple sensors. Based on the results of all the detectors, a rule-based isolation scheme is developed to identify the source anomalous sensor. The effectiveness of our proposed framework has been demonstrated on real vehicle data.

We have evaluated CTR performance of four different mixed-signal front-end electronic readout configurations with the goal to achieve 100 picoseconds (ps) coincidence time resolution (CTR). The proposed TOF-PET detector elements are based on two 3 × 3 × 10 mm(3) “fast LGSO” crystal segments, side-coupled to linear arrays of 3 × 3 mm(2) silicon photomultipliers (SiPMs), to form a total crystal length of 20 mm. We studied multiple configurations and components for the front-end readout: 1) high speed radio frequency (RF) amplifiers; 2) an ASIC-based discriminator; 3) combination of RF amplifier, balun transformer, and discriminator ASIC; and 4) combination of balun transformer, and discriminator ASIC. Using two 3 × 3 × 10 mm(3) fast LGSO crystals side coupled to a linear array of three SiPMs, coincidence data were experimentally acquired for each readout configuration in combination with a low jitter field programmable gate array (FPGA)-based time to digital converter (TDC). After evaluating timing performance of the three readout schemes, the best CTR value of 99.4 ± 1.9 ps FWHM was achieved for configuration (3), which is more than 20 ps better than the results achieved using configurations (1) and (2).

Subway security check systems (SSCS), which play an important role for smooth operation of subways of metropolises, exhibit a high complexity and randomness due to their multiple queues, mixed services, and heterogeneous passengers. Traditional approaches for evaluating configurations of SSCS either assume a single-queueing model or ignore various attributes of passengers for the sake of simplicity of models, leading to unsatisfactory results. In this paper, we propose a robust framework for evaluating the performance of configurations of SSCS. By considering passenger attributes (e.g., with or without bags) and special services (e.g., special check, additional search), we regard SSCS as two subsystems each of which is modeled as an iterative probabilistic formula, and introduce a hybrid queueing model to evaluate the waiting time of each passenger in SSCS. The parameters of the model are calibrated and validated using the field observation data collected in 24 subway stations of Beijing, which serve as the input of simulation studies. We conduct two simulation studies to evaluate three indices of SSCS: passengers' waiting time in SSCS, K-systematic time of SSCS, and density flow map, and draw three conclusions. (a) 1X2D, 2X3D, 2X4D configurations are suitable for small, medium and large passenger flows, respectively, where mXnD denotes the configuration of m X-ray machines and n detector doors; (b) acceptable efficiency of SSCS is achieved when the ratio between the numbers of X-ray machines and detector doors is in the range of 1:2-1:1, and the efficiency achieves highest at the ratio 1:2; (c) Increasing additional detector doors for no-bag channels can improve the efficiency of SSCS. However, switching existing doors to no-bag channels leads to opposite results. Our findings are beneficial to improving both the capacity and efficiency of SSCS, as well as allocating security check facilities reasonably.

Approximate multipliers are used in error-tolerant applications, sacrificing the accuracy of results to minimize power or delay. In this paper we investigate approximate multipliers using static segmentation. In these circuits a set of m contiguous bits (a segment of m bits) is extracted from each of the two n-bits operand, the two segments are in input to a small mx m internal multiplier whose output is suitably shifted to obtain the result. We investigate both signed and unsigned multipliers, and for the latter we propose a new segmentation approach. We also present simple and effective correction techniques that can significantly reduce the approximation error with reduced hardware costs. We perform a detailed comparison with previously proposed approximate multipliers, considering a hardware implementation in 28 nm technology. The comparison shows that static segmented multipliers with the proposed correction technique have the desirable characteristic of being on (or close to) the Pareto-optimal frontier for both power vs normalized mean error distance and power vs mean relative error distance trade-off plots. These multipliers, therefore, are promising candidates for applications where their error performance is acceptable. This is confirmed by the results obtained for image processing and image classification applications.

In the literature, giant magnetoresistive (GMR) point field detectors (PFDs) were integrated in terminals of Si IGBT modules for galvanically isolated current sensing in machine drives. This work presents a PFD-based current sensing system that can be fully integrated in the direct bonded copper (DBC) region of SiC power modules. The physics of phase shift errors caused by the Nickel (Ni) plating in the SiC power module is investigated. To address current sensing phase errors, a real-time phase shift reduction method is proposed. A bandwidth extension method is proposed to meet the sensing requirement of SiC devices that can switch at tens of kilohertz. The phase shift reduction and bandwidth extension methods are experimentally combined to sense the currents in the SiC DBC regions. The proposed current sensing has high bandwidth, minimal phase errors and low position sensitivity. The sensed currents can be used for high performance control and protection of SiC module-based converters.

The nuclear charge pickup cross sections of 40Ar on polyethylene(CH2), carbon, aluminum, copper and lead targets at the highest energy of 495 MeV/n were measured using CR-39 nuclear track detector. The cross sections for H were calculated from those measured on C and CH2 targets. The dependence of charge pickup cross section on target mass was studied, it was found that the nuclear charge pickup cross section exhibits a linear dependence on the target mass.

In this paper, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this paper. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector (OKD), the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the generalized likelihood ratio test detector for compound-Gaussian clutter with inverse Gaussian texture (GLRT-IG). Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure also named match filter (-MF) detector and its adaptive version (-AMF) are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the -MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in CG-IG clutter which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio (CFAR) property relative to the estimate of the speckle covariance matrix and Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data.

OBJECTIVE: Diffuse correlation spectroscopy (DCS) is an optical technique that allows for the non-invasive measurement of blood flow. Recent work has shown that utilizing longer wavelengths beyond the traditional NIR range provides a significant improvement to signal-to-noise ratio (SNR). However, current detectors both sensitive to longer wavelengths and suitable for clinical applications (InGaAs/InP SPADs) suffer from suboptimal afterpulsing and dark noise characteristics. To overcome these barriers, we introduce a cross correlation method to more accurately recover blood flow information using InGaAs/InP SPADs. METHODS: Two InGaAs/InP SPAD detectors were used for during in vitro and in vivo DCS measurements. Cross correlation of the photon streams from each detector was performed to calculate the correlation function. Detector operating parameters were varied to determine parameters which maximized measurement SNR. State-space modeling was performed to determine the detector characteristics at each operating point. RESULTS: Evaluation of detector characteristics was performed across the range of operating conditions. Modeling the effects of the detector noise on the correlation function provided a method to correct the distortion of the correlation curve, yielding accurate recovery of flow information as confirmed by a reference detector. CONCLUSION: Through a combination of cross-correlation of the signals from two detectors, model-based characterization of detector response, and optimization of detector operating parameters, the method allows for the accurate estimation of the true blood flow index. Significance: This work presents a method by which DCS can be performed at longer NIR wavelengths with existing detector technology, taking advantage of the increased SNR.

Most of the existing research focuses on electricity theft cyberattacks in the consumption domain. On the contrary, a high penetration level of distributed generators (DGs) may result in increased electricity theft cyberattacks in the distributed generation domain, which is the focus of this article. In these attacks, malicious customers can hack into the smart meters monitoring their DG units, which are usually photovoltaic, and manipulate their readings to report higher injected energy to the grid and claim more profit under feed-in tariff programs. This article proposes a data-driven approach based on machine learning to detect such thefts. We adopt an anomaly detection approach where a theft detection unit (TDU) based on a regression tree model is designed to detect suspicious data. Historical records of solar irradiance, temperature, and smart meter readings are utilized in the training stage of the detector. The probability density function of the error between the actual readings from DG meters and the predicted generation by the regression model is utilized as a metric to detect suspicious data. Several theft scenarios are used to assess the performance of the TDU. Furthermore, a comparison study with other detectors is presented to demonstrate the superiority of the proposed TDU.

Josephson junctions (JJs) with non-hysteretic I-V characteristic and high critical parameters are required for many applications; in particular, they are needed to create tunable THz range oscillators. We fabricated and studied shunted superconductor-insulator-superconductor (SIS) Nb-AlOx-Nb tunnel junctions with an area of about 1m2, estimated their parameters, and compared them with theoretical models. To assess the performance of shunted junctions in the terahertz range, their characteristics were studied under the influence of a high-frequency signal; the estimated value of the characteristic voltage for the shunted junction at a high frequency is about 1 mV. To improve the circuit parameters, a new type of the shunting based on application of NIS junctions was proposed, which reduces the influence of parasitic inductance, expands the frequency range of the generator, and decreases the size of the structure. The JJs with a new type of shunting have been fabricated and studied. The niobium integrated circuits intended for matching the superconducting oscillator and an SIS junction as a microwave detector were designed, fabricated, and tested. Such structures are required for studying the spectral characteristics and analyzing the operation of the oscillator. The developed circuits have demonstrated efficient operation at frequencies from 300 to 700 GHz; the experimental results are in good agreement with the simulated characteristics. The results obtained will be implemented for further development of the THz range oscillators.

Automatic License Plate Recognition is a crucial task for several applications related to Intelligent Transportation Systems, from access control to traffic monitoring. Most existing approaches are focused on a specific setup (e.g., toll control) or a single license plate (LP) region (e.g., European, US, Brazilian, Taiwanese, etc.), which limits their application. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. We present an Improved Warped Planar Object Detection Network (IWPOD-NET) that is able to detect the four corners of an LP in a variety of conditions, so that it can be warped to a fronto-parallel view and alleviate perspective-related distortions. Given the rectified LP, we test two different Optical Character Recognition (OCR) methods based on object detection. Our experimental results show that the proposed detector is competitive with state-of-the-art (SOTA) methods using a very limited training set. Regarding the full ALPR results, our method achieves top-scoring results for several datasets that include a variety of capture conditions and vehicle types (in particular, motorcycles).

Relay-assisted free space optical (FSO) systems offer several advantages such as cooperative diversity, mitigating turbulence-induced fading, and broad coverage area. Despite these manifold advantages, they can still be affected by unfavorable activities of jammer. Therefore, it is of high importance and need to investigate random jamming specially for security-constrained cooperative FSO communication protocol. This article develops a new system model of relay-assisted FSO system and studies a novel mathematical framework to analyze the performance of decode-and-forward protocol-based cooperative FSO system in presence of random relay jamming effects. In presence of jammer, the probability density functions (pdfs) of different additive noises, i.e., a mixture of negative exponential and Gaussian random variables (RVs) and mixture of negative exponential RVs are derived. A closed-form expression of the average bit error rate (ABER) is analytically evaluated by using the derived pdfs in the presence of random relay jamming effects. In presence of jammer, an approximate and simple expression of a threshold-based detector is obtained. By studying the ABER performance comparison, it is shown that the proposed approximate detector works satisfactorily compared to the maximum-likelihood detector in presence of random relay jammers. In addition to that, ABER performance of a general relay FSO system with an arbitrary number of relays is also obtained.

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in wireless sensor networks to leverage solutions for complex applications. However, some of the core SDN traits turn the networks prone to denial of service attacks (DoS). There are proposals in the literature to detect DoS in wireless SDN networks, however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported only for small networks, and the detection is disengaged from the identification of the type of the attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of the attack (from a list of known attacks). On the other hand, the distributed detector provides information that allows to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding $96\%$ in networks of 36 and 100 nodes and identification of the type of the attack with a probability exceeding $0.89$ when using the centralized approach. Additionally, for some types of attack it was possible to pinpoint the attackers with an identification probability over $0.93$ when using distributed detectors.

For achieving the goal of two-way communication and power flows, smart grids are integrated with much state-of-the-art hardware and software. However, these newly added components also introduce a lot of vulnerabilities into the power systems, which results in that malicious users can launch various cyber-physical attacks to steal electricity. Existing electricity theft detection techniques suffer from an implicit assumption that malicious users tamper with smart meter readings to values much less than their actual electricity consumptions. These are called Large-amount Electricity Theft (LET) attacks. Nevertheless, in the real world, some malicious users may be cautious enough to deliberately launch Small-amount Electricity Theft (SET) attacks where smart meter readings are manipulated to numbers slightly lower than actual values, mainly to escape detection. To address this limitation, we propose a detector able to deal with both LET and SET attacks effectively. This detector applies a cumulative sum (CUSUM) control chart and a Shewhart control chart together to analyze users’ reported readings and measurements of a central observer meter. It consists of an electricity theft detection phase which aims to detect the existence of LET/SET attacks timely and a malicious user identification phase which aims to identify malicious users exactly. Extensive experiments are conducted to evaluate the proposed detector, and the results show that it has good performance in terms of several metrics.

International audience; This paper presents a semi-analytical approximation of Symbol Error Rate (SER) for the well known LoRa Internet of Things (IoT) modulation scheme in the following two scenarios: 1) in multi-path frequency selective fading channel with Additive White Gaussian Noise (AW GN) and 2) in the presence of a second interfering LoRa user in flat-fading AW GN channel. Performances for both coherent and non-coherent cases are derived by considering the common Discrete Fourier transform (DF T) based detector on the received LoRa waveform. By considering these two scenarios, the detector exhibits parasitic peaks that severely degrade the performance of the LoRa receiver. We propose in that sense a theoretical expression for this result, from which a unified framework based on peak detection probabilities allows us to derive SER, which is validated by Monte Carlo simulations. Fast computation of the derived closedform SER allows to carry out deep performance analysis for these two scenarios.