This paper describes the Verifiable Automatic  Language Analysis and Recognition for Inputs (VALARIN) system to process, evaluate, and flag unsafe PDFs. The system extracts error features from a collection of PDF parsers, and organizes the different types of error messages by how they impact file safety. An ontology was designed to describe the relationships between parsers, error messages, safety, and PDF properties to  support PDF-based malware classification efforts. Our domain specific PDF ontology shows that PDF parsers exhibit mutual  biases when recovering from specification ambiguities. Consensus on extracted error features among parsers had a direct relationship to the safety of the PDF. The PDF OWL ontology serves as a shareable method for information security and forensics efforts to highlight discrepancies and aid understanding by standardizing and describing the hierarchical relationship of diverse parsers, PDF structure, and validity.

Specific Emitter Identification (SEI) detects, characterizes, and identifies emitters by exploiting distinct, inherent, and unintentional features present in their transmitted signals. Since its introduction a significant amount of work has been conducted; however, most assume the emitters are passive and that their identifying signal features are immutable and difficult to mimic. Suggesting the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures; however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples and Software-Defined Radios (SDRs) are capable of manipulating them. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures; “off-the-self” DL; two SDRs of different sizes, weights, power, and cost (SWaP-C); handcrafted and DL-based SEI processes, and a “coffee shop” deployment. Our results show “off-the-shelf” DL algorithms and SDR enables SEI mimicry; however, adversary success is hindered by: (i) the use of decoy emitter signals, (ii) integration of a denoising autoencoder, and (iii) SDR SWaP-C constraints.

In this paper we present and evaluate the performance of a routing and link scheduling algorithm for millimeter wave backhaul networks. The proposed algorithm models the access point behavior as being selfish by considering access points always aiming to maximize their individual utility, rather than the global optimization objective. Our system utilizes popular concepts from the economics and fairness literature. Specifically, in order to forward packets between the access points that comprise the backhaul network, the Shapley value method is applied, which is shown to induce solutions with reduced latency. The performance of the proposed algorithm is evaluated in terms of total delay and price of anarchy, which represents the inefficiency of a scheduling policy when users are allowed to adapt their rates in a selfish manner and reach an equilibrium. A relaxed version of the problem is also presented, providing a lower bound on the value of the optimal solution. According to simulation results, the system that employs the proposed algorithm outperforms in terms of delay and price of anarchy a system that considers a First-In-First-Out packet forwarding policy, and a system that employs local search global optimization, under which users aim at optimizing the overall network delay.

Hyperspectral images are known to contain an extensive range of bands that offer a wealth of information that tri-spectral images cannot match. With an increased number of bands, hyperspectral images provide an enhanced level of reflectance and absorption values, making it possible to obtain more precise details at each point in the image. The HSI field continues to evolve and has already proven useful in various applications such as age prediction, handwritten OCR, word segmentation, ecological and hydrological science, forensics, and much more. This study involves showing the total number of bands of the hyperspectral image along with the starting and ending wavelength, displaying the 1st, 30th, 60th and last band of the HSI image, plotting the spectral responses of foreground pixels and applying k-means clustering to determine the number of inks present in the hyperspectral image document. Additionally, color-labeling techniques were utilized to identify the various colors of ink in the document.

As a promising implementation model of payment channel network (PCN), payment channel hub (PCH) could achieve high throughput by providing stable off-chain transactions through powerful hubs. However, existing PCH schemes assume hubs preplaced in advance, not considering payment requests’ distribution and may affect network scalability, especially network load balancing. In addition, current source routing protocols with PCH allow each sender to make routing decision on his/her own request, which may have a bad effect on performance scalability (e.g., deadlock) for not considering other senders’ requests. This paper proposes a novel multi-PCHs solution with high scalability. First, we are the first to study the PCH placement problem and propose optimal/approximation solutions with load balancing for small-scale and large-scale scenarios, by trading off communication costs among participants and turning the original NP-hard problem into a mixed-integer linear programming (MILP) problem solving by supermodular techniques. Then, on global network states and local directly connected clients’ requests, a routing protocol is designed for each PCH with a dynamic adjustment strategy on request processing rates, enabling high-performance deadlock-free routing. Extensive experiments show that our work can effectively balance the network load, and improve the performance on throughput by 29.3% on average compared with state-of-the-arts.

Clock domain crossing (CDC) is a critical aspect of digital design where data is transferred between different clock domains in a synchronous system. It entails making sure that the data is appropriately synchronized and transported between the source and destination clock domains without any loss or errors. Modern digital systems that employ numerous clock domains to accommodate various modules or components operating at different frequencies place a high value on CDC. Potential timing problems such as setup and hold time violations, clock skew, and metastability pose difficulties for clock domain crossing. To overcome these difficulties and guarantee dependable data transport, appropriate synchronization techniques and design considerations are used, including the usage of synchronization components like flip-flops and synchronizers. This paper presents the advantage gained by using VC Spyglass tool for easier CDC analysis. CDC is crucial to maintain system functionality, performance, and reliability, and it requires careful design, verification, and analysis to mitigate potential issues and ensure the correct operation of complex digital systems.

In survivable elastic optical networks, multi-path protection combined with traffic squeezing has recently gained attention. Link-disjoint multipath routing (LD-MPR) and bandwidth squeezing protection (BSP) when applied to the routing, modulation level and spectrum assignment problem are efficient strategies to address the excessive bandwidth demanded by protected services, spectral fragmentation and link-load balancing. LD-MPR enables the division of service transmission bit rate into independent flows whereas BSP tolerates traffic reduction under link failure. An open issue in the literature is how to efficiently divide demanded traffic among the link-disjoint routes under dynamic-traffic and BSP, while complying the SLA. We propose in this paper a multi-objective-  optimization genetic algorithm that defines how service transmission bit rate should be partitioned among the candidate link-disjoint routes. A customized partitioning definition is made for each source-destination node pair in the network, aiming the simultaneous minimization of network blocking probability and average squeezing transmission bit rate experienced by the services during single-link failure. We also propose, for the first time, a fixed-alternate routing using groups of disjoint paths (FARgdp). Complex dynamic-traffic network scenarios that simultaneously consider BSP, LD-MPR and FARgdp are addressed.

HAM10000 consists of 10015 dermatoscopic images with a size of 450x600. There are seven types of skin lesions: actinic keratosis/intraepithelial carcinoma (AKIEC), basal cell carcinoma (BCC), benign keratosis (BKL), dermatofibroma (DF), melanoma (MEL), melanocytic nevi (NV), and vascular lesions (VASC). Dataset： https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000 https://challenge.isic-archive.com/data/#2018 https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T https://aistudio.baidu.com/aistudio/datasetdetail/218024 (ours)

Objective: Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical therapy monitoring and provide insight on how data collection choices may impact classifier performance. Methods: We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units on the wrist, pelvis, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. Results: Exercise groups (n=10) could be classified with 96% accuracy using a set of 10 inertial measurement units (IMUs) and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. Conclusions: Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

In this paper, we present a Jacobian vector correction (JVC) method for calibrating tactile force in EIT-based tactile sensors. The JVC method addresses the non-uniform sensitivity distribution of EIT reconstruction by constructing a scaling vector for sensitivity correction based on the Jacobian vector. A thresholding segmentation strategy is employed to enhance the correction results within a region of interest (ROI). The proposed method achieves approximately constant sensitivity across all locations and eliminates the need for extensive data collection during force calibration. We provide a straightforward sensitivity analysis and visualizations to improve comprehension of EIT measurement properties and the calibration methodology. The effectiveness of the JVC method is evaluated through phantom experiments using various target objects with different conductivities or sizes, and on a non-array EIT-based tactile sensor composed of a porous elastic polymer and ionic liquid. The results demonstrate the ability of the JVC method to accurately capture strength information without being constrained by location dependency. Furthermore, the practicality of our proposed method is validated by integrating two calibrated flexible tactile sensors (each with a 100 cm2 area) into a robot arm platform.

We explore the use of distributed PV power measurements for real-time short-term forecasting of the maximum potential power output of a utility-scale PV power plant, to support future incorporation of PV plants in automatic generation control (AGC) systems. PV plants operating under AGC may run in a curtailed state but must be capable of accurately forecasting their maximum potential non-curtailed power output, or potential high limit (PHL), 10-20 seconds in advance – despite variable environmental conditions. One forecast approach estimates PHL from a subset of inverters, designated reference inverters, which are never curtailed. We propose an alternative approach using in-situ I V tracers to measure maximum power point of reference modules distributed throughout a PV plant. This would allow for greater sampling of variations in irradiance, module temperature, soiling, and albedo, permitting better PHL prediction accuracy. To study potential benefits of this method, we analyzed a one-year data set from an approximately 100 MW PV power plant in the US Southeast, using non-curtailed string combiner outputs as a proxy for distributed module-level power measurements. We compare the reference inverter and distributed power measurement approaches. Results indicate that for highly flexible operation, substantial improvement in prediction accuracy can be achieved using distributed power measurements compared to using a low fraction of reference inverters. We review the study methodology, conclusions, and shortcomings and discuss prospective future work.

Terahertz (THz) massive MIMO with wideband hybrid precoding has been considered one of the crucial techniques to compensate the high path loss in 6G high-data-rate Internet of Things (IoT). However, the beam split in wideband hybrid precoding makes the beam of different sub-carriers aim at different directions, which results in only partial channel state information (CSI) from the users to the base station. The efficiency of the CSI-based THz wideband beamforming scheme which is more efficient than the hardware-based scheme in the narrow band would degrade severely. To address the degradation, in this paper, we firstly propose a sensing-aided THz wideband hybrid precoding which restores the full CSI. Through sensing and deducing the angle-frequency information, we construct a channel selecting matrix and inverse the full CSI from our complete channel dictionary. Moreover, in order to satisfy the multi-user access requirements in IoT, we also propose dynamic RF chains and dynamic power allocation schemes to further enhance the performance in multi-user scenario based on a new precoding perspective in which each RF chains serves only one user. This benefits from the highly sparse THz channel characteristic. The spectral efficiency and energy efficiency are employed to validate that the proposed is efficient. The numerical results demonstrate that our proposed sensing-aided wideband hybrid precoding scheme achieves similar performance to the optimal precoding and much better performance to the true time delay scheme and the full CSI based scheme.

This preprint is based on an accepted paper for the Annals of Scientific Society for Assembly, Handling, and Industrial Robotics 2023, and may undergo further revisions before the final publication. This preprint presents a groundbreaking research study that focuses on the application of incremental learning processes to enhance high-throughput product inspection and minimize false labeling in industrial environments. The paper tackles a critical challenge faced by industries today, which is the accurate identification and labeling of products during the inspection process. Traditional methods often fall short in maintaining high accuracy rates due to evolving production lines and ever-changing product variations. However, this preprint introduces an innovative approach that leverages incremental learning techniques to adapt and improve the inspection process continuously. By employing incremental learning, the proposed system can autonomously learn from new data and make adjustments to its inspection algorithms in real-time. This adaptive capability allows the system to keep up with changing product characteristics, ensuring accurate and reliable labeling throughout high-throughput operations. The result is a significant reduction in false labeling, leading to enhanced quality control and improved efficiency in industrial environments. We invite researchers, industry professionals, and enthusiasts in the field of assembly, handling, and industrial robotics to explore this preprint. The findings presented here have the potential to revolutionize the way product inspection is conducted in industrial settings, addressing a critical aspect of quality control. We encourage readers to engage in discussions, share feedback, and contribute to the advancement of this exciting research area. Abstract: In the circular economy, remanufacturing success relies heavily on the accurate identification and classification of used products. Processes, which rely on worker experience, lack objective validation, leading to the potential for mislabeling and inaccurate damage assessment. This, in turn, results in additional manual evaluations and unproductive costs, which run counter to the principles of sustainability. To address these issues, machine learning and artificial intelligence have been applied with promising results. However, producing reliable large amounts of labeled data remains a challenge, as workers are susceptible to human error. This paper addresses process design in production. It proposes a new design to ensure that only valid labels enter the prediction models, reducing the potential for false labels in the dataset. Through this, the aim is to improve the accuracy and reliability of remanufacturing, ultimately reducing costs and mitigating the carbon footprint in the manufacturing, repair, and maintenance industries.

One of the challenges of dynamic wireless power transfer (DWPT) systems for electric vehicle (EV) charging applications is to reduce pick-up power fluctuation. Building a multiphase transmitter (Tx) is proven to be effective in stabilizing the pick-up power in literature, while the major drawback is a significant increment in the material cost on the Tx side. This paper proposed a new magnetic coupler mechanism that can achieve a low-ripple pick-up power without increasing the material cost on the Tx side. The operating principle of the proposed DWPT system is explained in detail. Besides, an optimization method is introduced to find the optimal deployment of Tx and receiver (Rx) coils, and the optimal ripple of the Rx induced voltage is below 17.1%. Finally, a prototype is built according to the optimization results and the measured curve of the direct current (DC) voltage across the load resistor has the same pattern as the calculated results with a 7.8% higher ripple due to the fabricating error of the coils.

In this paper, we propose an anomalous sound detection (ASD) method that feeds audio signals through a feature extractor trained with a discriminative model and then through an anomaly detector trained with a generative model. The proposed method uses outlier exposure and Mixup to focus the feature extractor on differences between normal and other sounds in the embedding space, improving ASD results. Evaluation of the proposed method on the DCASE2020 Task 2 dataset showed a 4.7 % improvement in AUC and a 6.5 % improvement in detection performance stability compared to the conventional methods. Moreover, previous ASD studies have rarely investigated the possibility of improving detection per?formance through anomalous data during model training or model performance when this anomalous data is contaminated with normal data, even though the proportion of anomalous data used for training is an important factor. The proposed method effectively utilizes small amounts of anomalous data during training, outperforming conventional methods. It also achieves higher performance even when this anomalous training data is contaminated with normal data. Experimental results show that the proposed method improves ASD performance by 10.4 % when anomalous data is utilized during training, and 8.9 % when this anomalous data is contaminated compared to conventional methods. The source code for Serial-OE is available at https://github.com/ibkuroyagi/Serial-OE.

The Internet of Things (IoT) paradigm has widespread applications across many fields in which private and sensitive user or environmental data are sensed and shared. Most present-day IoT applications depend on centralized cloud servers for authentication and access control. Validating the identity of a user and determining the legitimacy of his/her access requests require multiple rounds of data communications over the untrusted Internet, exposing sensitive data to potential attacks. Thus, protecting these data from security and privacy attacks and ensuring legitimate access is imperative. To address this challenge, we adopt an emerging technology called blockchain to propose a decentralized security framework called BloAC. It ensures secure access control in IoT networks without the intervention of the back-end cloud. We have used the Hyperledger Fabric, an open-source, permissioned blockchain platform, for implementing a prototype system using customized attribute-based access control (ABAC) policies. We have performed simulated and real test bed-based experiments to illustrate that BloAC outperforms the cloud server-based access control in latency and scalability. Finally, we conduct a security analysis to formally verify the ABAC policies used in BloAC and establish its robustness against attacks theoretically and using the AVISPA tool.

The paper briefly explains on CAN protocol, On-board Diagnostics and about the establishment of wireless data acquisition in the automotive domain. This paper can really help people to understand the overall network of in-vehicle system and about the STM32 mcu.

Dynamic modeling and its control of two nonlinear processes are presented in this work. The processes emulate a clean water storage plant and are configured by interconnecting two tanks. According to the way, the tanks are interconnected, i.e., in cascade or in series, the dynamic behavior of the process changes from a non-interacting to an interacting system, both nonlinear. Three controllers (two linear and one nonlinear) have been designed and applied to the process. In order to evaluate the performance of the system, as a function of the controller applied, three figures of merit have been selected and compared. From the comparison results of these figures of merit, the advantages and drawbacks of each designed controller are identified. Finally, through simulation results, the feasibility of the system operation is verified.