Search

The current study revolves around development of paper from dhaincha fiber (Sesbania aculeata). Dhaincha is a basically cultivated for improvisation of soil quality. Due to deforestation worldwide, various sources of raw materials are used in paper industries. Indian pulp and paper industry is the world's major paper industry. In this study extraction of dhaincha fiber was done followed by retting, combing, chemical analysis and by making of pulp with dhaincha fiber. The dhaincha pulp was then used for making a paper from dhaincha fiber. The thickness of the dhaincha paper was then tested with paper thickness tester. By doing further processing dhaincha paper can be used commercially by paper industry.

The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to use safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.

The industry of disposables has undergone a huge revolution in this century owing to the busy lifestyles and technological advancements in its production and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be used to drink water as well as beverages, soups, liquids etc. This paper entirely focuses on increasing the quality factor of production and manufacturing process of these paper cups by adding additional circuitry to the existing machinery to reduce waste of raw materials as well as the per unit cost involved in wastage of electricity. This research work is based on using proximity sensors to sense the materials and molded paper cups infront of machinery and reduce the electric wastage through a relay Mechanism assembly. This is a PLC based live project example of effectively increasing the overall efficiency of the manufacturing plant

For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

Electromagnetic Fault Injection (EMFI) is a well known method of introducing faults for security analysis of digital devices. Such faults can be seen as analogous to the faults which are known to naturally occur in digital devices, a known problem with designing safety-critical systems.Numerous standards have been developed for safety-critical systems, including the development of standards for increasing the rate of naturally occurring faults using particle sources. In this work, we demonstrate that desktop EMFI tooling can be used to accomplish similar testing, but with more control, effectively speeding up the evaluation process.We demonstrate using EMFI tooling for safety evaluation to recreate a highly publicized safety issue present in an automotive ECU – one that could not easily be recreated with other techniques.

Oil-paper insulation is the key insulation structure of the transformers, whose aging condition is closely related to the operations of the equipment. The degree of polymerization (DP) is the direct parameter characterizing the aging condition of oil-paper insulation. Recently, the near infrared spectroscopy (NIRS) measurement powered by quantitative analysis is used to evaluate DP of the insulating papers mainly. While the applications of NIRS for DP evaluation is constrained because the present spectral quantitative analysis method is not accurate and stable enough, especially for onsite tests. In this paper, we propose a Gaussian process regression (GPR) method to predict DP of the oil- paper insulation in laboratory as well as in field. Firstly, the basic principles of GPR algorithm are illustrated. A GPR model for DP prediction is established based on the spectra of differently aged insulating paper samples which are prepared in laboratory. The GPR model show a high prediction accuracy both for training set and testing set. The established GPR model is finally applied on the DP prediction of insulating papers originating from a de-tanked transformer. The accurate predicted DP and the reliable aging assessment results indicate that the established model can be implemented on site.

This work is centered on the paper making process in an industrial paper mill. There exist plenty of sub-processes in a paper mill for transformation of raw material to finished product. A particular process that is taken up in this research is paper basis weight control process, which is very important for good quality paper production. But the controller design for this process is very challenging due to the involvement of severe dead time value, which in addition to delaying the control output, makes the control system unstable. So, two different approaches are used here, in MATLAB, for controller design, one is the dead time approximation using Pade approximation technique and the other is dead time compensation using Smith predictor technique. Hence, the performance of the designed control systems is compared.

The industry of disposables has undergone an astronomically immense revolution in this century owing to the diligent lifestyles and technological advancements in its engenderment and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be acclimated to imbibe water as well as beverages, soups, liquids etc. This paper entirely fixates on incrementing the quality factor of engenderment and manufacturing process of these paper cups by integrating adscititious circuitry to the subsisting machinery to truncate waste of raw materials as well as the per unit cost involved in wastage of electricity. There is a necessity to improvise the quality of overall production parameters using automation techniques. Replacing the whole machinery is a very high costing implementation hence economic feasibility of automation can be achieved by adding an additional circuitry and improving the overall perfoemance. This research work is predicated on utilizing proximity sensors to sense the materials and molded paper cups in front of machinery and minimize the electric wastage through a relay Mechanism assembly. This is a PLC predicated live project example of efficaciously incrementing the overall efficiency of the manufacturing plant

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.

In this paper, we describe the system of ad hoc search on statistical data based on categorization and metadata augmentation. The documents covered by this paper consist of metadata extracted from the governmental statistical data and the body of the corresponding statistical data. The metadata is characterized by the fact that its document length is short, and the main body of statistical data is almost always composed of numbers, except for titles, headers, and comments. We newly developed the categorical search that narrows the set of documents to be retrieved by category in order to properly capture the scope of the problem domain intended by the given query. In addition, to compensate for the short document length of metadata, we implemented a method of extracting the header information of the table from the main body of statistical data to augment documents to be searched. As a ranking model, we adopted BM25, which can be adjusted with few parameters to take into account term frequency and document length.

The recommendation system can recommend information to users efficaciously, which helps many users to obtain information in different fields. The paper recommendation is a research topic to provide authors with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring the author's interest and structure information of the academic network. In the paper recommendation system, authors and papers and the interaction of their information have a crucial impact on the efficiency and accuracy of the recommendations. However, most recommendation systems are usually designed based only on users. Therefore, we propose a method based on the author's periodic interest and academic graph network structure to obtain as much effective information as possible to recommend papers. Extensive offline experiments on large-scale real data show that our method outperforms the representative baselines.

Paper dating analysis is an important research direction of document inspection, which is widely used in the inspection and identification of cultural relics and ancient books. This article focuses on the analysis of paper dating in ancient books, and proposes a non-destructive inspection and analysis method based on paper fiber texture images. Aiming at the global stacking morphology of paper fibers and the local features of specific types of fiber morphology, we propose a neural network-based hybrid texture feature extraction and representation method: On the one hand, we use convolutional networks to obtain global features of fiber texture; at the same time, we designed a method of extracting and representing local fiber morphological features based on the attention mechanism. By mixing the above two types of features, we realize the extraction and representation of paper fiber features. Furthermore, we use the GRU(Gate Recurrent Unit) model to establish a paper dating time series model and design a new loss function. In order to verify the method, this paper selects 36 domestic books published from 1950 to 2000, and uses the document checker VSC 6000 to collect paper texture images as a dataset, and verifies the effectiveness of the proposed method on this dataset. Experiments prove that the method proposed in this paper has achieved ideal results in paper dating analysis.

In the process of cigarette, the dynamic splicing of running bobbin paper and spare bobbin paper plays a very important role in the continuous operation of Cigarette Making and Filter Assembling Group. However, at present, the dynamic splicing system of some domestic Cigarette Making and Filter Assembling Group mostly adopts magnetic particle clutch. After using the magnetic particle clutch for a period of time, it will not rotate or rotate too slowly due to wear and tear. It is easy to cause the rotation speed of spare bobbin paper and running bobbin paper to be inconsistent when splicing, resulting in relative speed, resulting in failure of splicing. Therefore, a dynamic splicing system based on Lenze 9400 servo controller is proposed. The system can guarantee the smooth splicing of the running and spare bobbin paper, reduce The Times of maintenance and improve the production efficiency.

In order to realize smart factory which is appeared in the concepts of Society 5.0 and Industry 4.0, wireless Internet of Things (IoT) becomes a key technology. Inside of the factory, there are heavy DATA traffics exist between moving/movable machine tools/robots/automated guided vehicle (AGV) with wireless access point (AP) and high speed moving/rotating objects/materials with wireless sensor node (SN). Currently, wireless systems used for wireless IoT are limited such as wireless-LAN/Bluetooth/RFID. Since these system’s frequency bands are limited and narrow, the interference between these systems becomes severe. In this paper, we propose a simplified beam forming wireless IoT communication using novel Wi-Fi backscatter system to notify the existence of SN who wants to send its DATA to AP. For Wi-Fi AP with beamforming function, knowing the numbers of existing SNs in each beam area will be a help for efficient beam control. This paper describes the Wi-Fi backscatter system in which the SNs backscatter the Wi-Fi signal transmitted from the AP then the AP detects the backscatter signals from SNs and know the existing SN’s number in its beam area. Since this backscatter system uses Wi-Fi modulated signal, the separation of SNs’ backscatter signals from AP’s transmitting Wi-Fi signal becomes complicated. This paper also describes the transceiver architecture of AP which reduces the complexity of signal processing and improves the dynamic range of reception.

Anstract- While wireless technologies are common solutions to the last mile problem for IoT systems, challenges exist with devices that are in remote locations, sparse deployments, have power and cost constraints, and low data applications as the cost of wireless and the supporting infrastructure presents barriers to entry. This work presents a last mile solution relying on smartphones to bridge the gap between the data generation device and the internet. A unique, low-power solution is presented based on QR codes and e-paper displays that allows a user's smartphone to carry the power and processing burden for data transmission. Utilizing the persistent nature of e-paper displays with low power requirements, QR codes can efficiently communicate data with minimal power impacts to a device's power budget and create large communication windows for user data pickup, leveraging the power of the smartphone without the need for extensive user training and costly infrastructure.

The forestry industry all over the world is seeing the need for modernization of its machines toward autonomy. In this paper, we focus on a wheel loader that should operate autonomously in the yard of a pulp-and-paper mill, scooping wood chips from a pile of wood and dropping them into a hopper, which is linked to a conveyor that carries them inside the mill. The modelling of the wheel loader is elaborated first, while taking into account that it is composed of two systems: (i) the vehicle and (ii) the arm carrying the bucket. Notice that the former pertains to the category of articulated vehicles that steer using a different mechanism than the conventional Ackermann steering used in car-like vehicles. As for the latter, it is a 2DOF serial manipulator. The navigation is considered then. Finally, simulation results of the kinematics model are shown in Matlab/Simulink first, then dynamics and 3D animation are added using ROS2/Gazebo. Notice that this work is a first step toward the development of the digital twin of the wheel loader. Later, it will be used as the virtual platform for the validation of the autonomous wheel loader.

In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.

Nowadays, the usage of smartphones keeps increasing day by day and it is also can be used effectively to increase the computational power and the security along with search and rescue. To maintain the attendance records, several organizations are depending on paper-based attendance. Manually recorded attendance for the employees has produced problems that related to information accuracy and workforce performance efficiency. In this paper, reviewed and discussed various methods, applications and systems that can be used by the employer to monitor their staff attendance using fingerprint, Iris, RFID and GPS. The outcome of this survey is getting knowledge on various techniques that used to record and monitor staff’s attendance.

The manufacturing business is one of the businesses in Indonesia that continues to show its development from year to year. Like a manufacturing business in general, one of the important efforts made in the printing business is the supply of raw paper materials to produce finished goods. The purpose of this research is making a forecasting of the raw paper material for printing company on 7 different types of 269 historical data with weekly intervals from January 2015 to February 2020 before the Covid19 pandemic season. Forecasting is done using the Long Short Term Memory method with Python language. The model architecture for training and testing is carried out using vanilla LSTM with single input, hidden and output layer with the configuration of 64 neurons in the hidden layer, 150 epoch, 12 batch size and Adam Optimizer (lr = 0.0001) which was repeated 10 times for best result. The test results show the best window size length in the model for each paper raw material differently from 4 to 16. All models was successfully forecasting the test data with an average MAPE of the overall forecast of 21.48%.

Even current OCR technology has made significant progress with the help of deep learning, rapid identification algorithms for specific information in documents still need elaborate design. In this paper, a lightweight operating framework based on deep neural network is established to deal with the extraction of date and time information from paper worksheets used in transformer station. In the framework, YOLOv4-Tiny is employed to detect the locations of the dates and the time in document images which are mixed with printing and handwritten fonts. Since the handwritten dates in pictures are difficult to segment, a novel lightweight CNN named MobileDateNet is proposed that can recognize the date serially without segmentation. The experimental results show that the identification accuracy of the whole framework reaches 92.1% at the average running speed of 36.5 FPS on an NVIDIA 3090 GPU, ensuring the recognition accuracy and the operating efficiency.

In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.

The formulation of good academic paper titles in English is challenging for intermediate English authors (particularly students). This is because such authors are not aware of the type of titles that are generally in use. We aim to realize a support system for formulating more effective English titles for intermediate English and beginner authors. This study develops an extractive title generation system that formulates titles from keywords extracted from an abstract. Moreover, we realize a title evaluation model that can evaluate the appropriateness of paper titles. We train the model with titles of top-conference papers by using BERT. This paper describes the training data, implementation, and experimental results. The results show that our evaluation model can identify top-conference titles more effectively than intermediate English and beginner students., 6 pages, 3 figures, 4 tables, 2 algorithms

China’s packaging industry has formed a packaging big data system from three aspects: implementing information engineering, building a collaborative symbiosis network platform of packaging information resources, and building an industrial chain knowledge map centered on product chain. Packaging big data visualization design uses big data information acquisition, hierarchical division and management, problem and audience, visual transformation and other thinking modes, and adopts text information visualization, multi-dimensional information visualization, hierarchical relationship visualization and other technologies to realize the visual presentation of packaging industry data information. This paper analyzes the network data, extracts the keywords related to the key elements, and then analyzes the construction method of product paper packaging container automation system based on the keywords extraction algorithm. On the one hand, it emphasizes the importance of big data in online shopping commodity packaging design; on the other hand, it explores new ideas and methods of online shopping commodity packaging design through the application of big data in online shopping commodity packaging design.

As a material, cellulose/paper has attracted significant attention for its fibrous and bendable properties. Paper is a renewable resource with the most common forms coming from trees. This brief article describes efforts to create electronics "on" and "in" paper. Examples of electronics on paper include touch sensors and cold plasma-based sanitizers. The skin-like touch sensors use capacitance and patterned resistive networks for passive, scalable sensing with a reduced number of interconnects. When touched or wetted with water, the interdigitated electrodes in the resistive networks detect significant changes in electrical impedance. The plasma generators with layered and patterned sheets of paper use a simple and flexible format for dielectric barrier discharge to create atmospheric plasma without an applied vacuum. Examples of electronics in paper include sheets with tunable electrical conductivity and piezoresistive force sensors, along with functionalized paper-based electrodes capable of detecting small proteins and clinically relevant biomarkers. These papertronic devices have the potential to supply multi-functionality to new forms of smart bandages, skin-like sensing, food packaging, and robotic sensing and actuation.

Academic papers is an important means to test students’ mastery of basic knowledge and professional ability. The quality of academic papers, on the one hand, is relected by the innovation and prefessionalism of content. On the other hand, the normalization of documents is an important index. In order to solve the problem of low efficiency that the paper standardization inspection is basically completed by manual work. This paper proposes an automatic detection framework based on document rule tree (DRT) and can solve the problem well. The practical application results show that good results have been achieved in terms of accuracy and query efficiency.

Industrial control systems have strict requirements for time-sensitive applications and clock synchronization services. Performance of such applications is adversely impacted by packet delays and jitters. The impact is especially critical in process industries due to harsh environmental conditions. In this paper, we analyze delays and jitters to assess the performance of time-sensitive applications. To this end, we captured and analyzed round trip delay data retrieved from a paper factory. Analysis shows that a sub-millisecond level average delays and the jitters derived from the observed data are sufficient to meet the minimum 10ms update frequency required for most critical control applications. Moreover, the filtered delay variations at the end devices are less than the recommended 150us, which guarantees an adequate time synchronization accuracy in the factory network. Besides, this analysis can provide significant insights into performance bottlenecks for factory applications.

Forgery of paper invoices and certificates negatively causes huge economic loss every year. In the molding process of paper, plant fibres inside will distribute randomly and form the unique microscopic surface. Accordingly, forensic researchers have presented many techniques to build its feature descriptor for paper anti-counterfeiting. Among those methods, normal vector field and reconstructed surface based on multi-view photos captured by mobile camera have indicated incredibly effectiveness and practicality. However, the inefficiency of existing models using mobile camera is the neck in real time detection scenarios. In this paper, we focus on the efficient microstructure orientation estimation of paper surface for authentication. To address this problem, we investigate the reflection characteristics of rough surface and quantify the light attenuation accurately. Then, we model the entire propagation path of light emitted by flash, and exploit single photo captured by mobile camera to reconstruct microscopic inclination based on beam energy loss. Reduction of required pictures significantly enhances forensics efficiency. Through comparison experiments, we demonstrate that our proposed method can be used for paper discrimination effectively with speedy feature extraction.

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.

This paper investigates the reconfigurable intelligent surface (RlS)-aided multiple-input-single-output (MISO) systems with imperfect channel state information (CSI), where RIS-related channels are modeled by Rician fading. Considering the overhead and complexity in practical systems, we employ the low-complexity maximum ratio combining (MRC) at the base station (BS), and configure the phase shifts of the RIS based on long-term statistical CSI. Specifically, we first estimate the overall channel matrix based on the linear minimum mean square error (LMMSE) estimator, and evaluate the performance of mean square error (MSE) and normalized MSE (NMSE). Then, with the estimated channel, we derive the closed-form expressions of the ergodic rate. The derived expressions show that with Rician RIS-related channels, the rate can maintain at a non-zero value when the transmit power is scaled down proportionally to 1 /M or 1 /N2, where M and N are the number of antennas and reflecting elements, respectively. However, if all the RIS-related channels are purely Rayleigh, the transmit power of each user can only be scaled down proportionally to $1/\sqrt M $ or 1/N. Finally, numerical results verify the promising benefits from the RIS to traditional MISO systems.

In this paper we propose to implements blockchain technology for Internet of Things (IoT) networks that use Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. To eliminate the reliance on a single leader and improve performance, we propose to allow multiple leaders to propose request batches independently and, possibly, concurrently, by using different overlay networks. Nodes participate in parallel consensus rounds without contention, and they only contend with others to reserve the next available spot for the atomic insertion of a new transaction batch or block into the replicated blockchain ledger. We develop an analytical model for the multiple leader PBFT ordering service by using a Discrete-time Markov chain. Our evaluations show that our model outperforms the original multiple entry point PBFT protocol in a wide range of parameter values, and that it scales well with the number of orderer nodes in the PBFT committee and block arrival rate.

There has been an increasing interest in mobile edge computing (MEC) in recent years. Different from the traditional centralized cloud computing, MEC servers are deployed at the edges of networks such as at base stations (BSs) and access points (APs), in order to support computation-intensive and latency-critical applications. In this paper, we consider a multi-user mobile edge computing (MEC) network in which the wireless data transmission/offloading is performed using finite blocklength (FBL) codes to satisfy the latency constraints. The reliability in the communication phase is characterized in the FBL regime, while the event of queue length violation in the computation phase is investigated via exploiting the extreme value theory. We first formulate the overall optimization problem in the scenario of multiple user equipments (UEs) aiming to minimize the maximal end-to-end error probability among all UEs under both the FBL and energy consumption constraints. We further propose a two-level learning-based approach to jointly determine time allocations in the FBL regime, the UEs' offloading decisions and MEC computational resource allocations to solve the overall optimization problem. Simulation results demonstrate that the proposed two-level learning-based algorithm solves the problem efficiently.

Long-term evolution in unlicensed spectrum (LTE-U) can make use of centralized scheduling, interference coordination and other technologies to achieve better spectrum efficiency (SE). As the evolution of cloud computing, mobile edge computing (MEC) sinks the computing and storage capacity from the centralized data center to the edge of the network, which is the key technology to achieve low delay and high speed. However, the traditional centralized scheme in small cell networks (SCNs) structure will bring huge signal overheads. In order to adapt to the complex and changeable network system, this paper proposes a distributed resource and power allocation scheme under MEC environment, which enables small base stations (SBSs) to work autonomously. The SBSs need only little information exchange through the information cloud (IC) and finally obtain the global optimal SE. Simulation results confirm the correctness and effectiveness of the proposed scheme, and demonstrate the superiority of the distributed scheme over the centralized scheme.

Fault Tree Analysis is widely used in the reliability evaluation of critical systems, such as railway and automotive systems, power grids, and nuclear power plants. While there are efficient algorithms for calculating the probability of failure in static fault trees, Mean Time to First Failure (MTFF) evaluation remains challenging due to state space explosion. Recently, structural and symmetry reduction methods were proposed to counteract this phenomenon. However, systems with a large number of different and highly interconnected components preclude the use of reduction techniques. Their MTFF analysis requires the solution of a system of linear equations whose size is exponential in the number of components in the system. In this paper, we propose a solution leveraging Tensor Trains as a compressed vector and matrix representation. We build upon Binary Decision Diagram-based techniques to avoid explicit state space enumeration and use linear equation solvers developed specifically for Tensor Trains to efficiently solve the arising linear systems. As a result, our novel approach complements the existing reduction-based techniques and makes some previously intractable models possible to analyze. We evaluate our approach on an industrial case study adapted from a railway system, and other openly available benchmark models.

The pervasive use of software systems and current threat environment demand that software systems not only survive cyberattacks, but also bounce back better, stronger, and faster. However, what constitutes a modern software system? Where should the security and resilience mechanisms be-in the application software or in the cloud environment where it runs? In this concept paper, we set up a context to pose these questions and present a roadmap to answer them. We describe challenges to achieving resilience and beyond, and outline potential research directions to stimulate discussion in the workshop.

The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.

The experiment of quality assurance of polymeric outdoor insulators service life is necessary to be checked the class of wettability by using the digital image processing tools. The image processing can improve its pictorial information for human interpretation, render it more suitable for autonomous machine perception. This paper provides a survey of the recent technology and theoretical concept to explain the development of the Hydrophobicity Class (HC) of porcelain insulator. The hydrophobic properties of the housing material were found degraded to a different extent between field-aged insulators due to differences in material structure and pollution conditions. This paper contributes to the latest development of reviews relating to image processing for the implementation of porcelain insulator hydrophobic systems testing., and their related studies. We categorized the computer vision mainstream into 2 group such as Machine Learning Methods Applied to Digital Image Processing and Deep Learning Methods Applied to Digital Image Processing. Then compare pros/cons in each type of technique. We also provide brief explanation on the up-to-date information about the techniques and their performance.

Paper influence analysis is essential technology of the literature management system. In recent years, research on influence analysis has received crucial attention from the industry and academia. In this field, there are many new challenges in how to evaluate valuable papers in the paper citation network. Therefore, in this paper, we have proposed an influence model in paper citation networks with integration features of PageRank and Hyperlink-Induced Topic Search (HITS). First, three ranking features are extracted with the combination of PageRank value, hub, and authority. Second, more features are added to our ranking classification method. Third, the ranking-based classification with $P$ ageRank and HITS is proposed to analyze the paper propagation. An influence score is computed to evaluate the importance of a paper. The experiments show that our ranking-based influence model is effective for paper propagation.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Theoretical analyses as well as case studies have established that behavioral models based on a recurrent neural network (RNN) are suitable for transient modeling of nonlinear circuits. After training, an RNN model can be implemented in Verilog-A and evaluated by a SPICE-type circuit simulator. This paper describes hurdles that have prevented wide-scale adoption of the RNN as an IP-obscuring behavioral model for circuits and presents recent advances. A new stability constraint is formulated and demonstrated to guide model training and improve performance. Augmented RNNs that can accurately capture aging effects and represent process variations are presented.

Noise optimization for FET low noise amplifiers (LNAs) are demonstrated through insights gained from analytical formulations derived using noise transformation matrix. The invited paper reviews the status of analytical noise optimization of FET LNAs including single-/dual-band FET LNAs with the lossless/lossy input match networks and broadband FET LNAs with the lossless input networks.

The space-based integrated information network is composed of space-based backbone network, space-based access network and ground-based node network. It is interconnected with the ground Internet and mobile communication network to build a “ global coverage, on-demand access, on-demand service, safe and reliable ” space-based integrated information network system. As an important part of the system, the space-ground laser communication equipment is faced with many problems, such as the integration of multiple modulation systems and the location of different places. In this paper, the related problems are analyzed and verified by experiments. The results show that the system works at 1550nm wavelength, adopts coherent communication system and IM/DD communication system, coherent communication system supports BPSK, QPSK and DPSK modulation format, IM/DD communication system adopts OOK modulation format, the communication rate reaches 5Gbps under the condition of 1E-9 bit error rate.

The traditional space telemetry, tracking, command and communication system works in the radio frequency band. With the improvement on measurement accuracy and communication capacity in space missions, the traditional means have a certain bottleneck in dealing with the development of future space missions. The inherent lightweight, high parallel, high capacity and fine processing capabilities of Optics and microwave photonics show the potential to solve the above problems. This paper summarizes the innovative work of the Southwest China Institute of Electronic Technology in the application of laser measurement and communication integration, microwave photonic RF front-end technology in space telemetry, tracking, command and communication system. The experimental platform is established for verification, the accuracy of ranging and velocity measurement reaches millimeter level at 10Gbps single channel transmission rate with 1E-9 bit error rate under a simulation satellite-ground link. The microwave photonic RF front-end can be compatible with S to Ka band, with a sampling rate of 20Gsps and a signal bandwidth of 5GHz. It is expected to be applied to next generation of space telemetry, tracking, command and communication system or navigation and relay system.

The rapid spread of COVID-19 prompted many to take precautions to control the transmission of the virus. The most crucial of these is face coverings, such as face masks, as the virus is an airborne pathogen capable of transmission via respiratory droplets. While there are developments in smart mask technologies, they only provide passive protection through filtration and internal decontamination against the virus. This paper describes a new approach to smart mask technology that actively monitors for airborne pathogens using a PM sensor and reduces the wearer’s exposure to them by an active mitigation strategy. This strategy involves using a mist spray to increase the pathogen’s particle size, which reduces their ability to linger in the air. This system can be controlled wirelessly via a mobile application, which also displays the monitoring data. Deployment areas of this smart mask include classrooms, hospitals, and workspaces. Our experimental results demonstrate that increasing droplet sizes reduces the settling time for pathogens. Our future work includes a more precise detection of pathogens and improvements in deploying mitigation using fine-grained analysis about the aerosol and artificial intelligence.

As opposed to multiple-antenna solutions, in-band full-duplex (IBFD) systems can utilize a shared antenna for signal transmission and reception to reduce the device form factor. These single-antenna designs, however, must additionally consider the self-interference (SI) that is generated by antenna impedance-mismatch reflections. This paper investigates the effect of different antenna loads and transmission line lengths for commercially-available magnetic circulator and omnidirectional antenna components operating between 2 and 6 GHz. The results indicate that both the load impedance and the distance between the antenna and interface device can drastically impact the composite SI frequency-selectivity, which influences the overall IBFD system bandwidth.

This paper discusses recent advancements in smart connected biophysical signal sensing technology focusing on the development of wearable biosensors and the use of Electro-Quasistatic Human Body Communication and Edge-Analytics in the human state measurement systems for lower power consumption and physical layer security.

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

This paper proposes a novel three-dimensional (3D) cluster-based sixth-generation (6G) model for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. In the proposed model, a novel method based on visibility region (VR) is developed, where the smooth and consistent birth and death of clusters in both the time domain and space domain are successfully captured. By using the developed method, the space-time non-stationarity and time-space consistency of channels can be modeled for the first time. Based on the proposed model, important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the smooth birth and death of clusters, time-space consistency, and space-time non-stationarity are modeled sufficiently.