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Origami architecture (OA) is a fascinating papercraft that involves only a piece of paper with cuts and folds. Interesting geometric structures ‘pop up’ when the paper is opened. However, manually designing such a physically valid 2D paper pop-up plan is challenging since fold lines must jointly satisfy hard spatial constraints. Existing works on automatic OA-style paper pop-up design all focused on how to generate a pop-up structure that approximates a given target 3D model. This paper presents the first OA-style paper pop-up design framework that takes 2D images instead of 3D models as input. Our work is inspired by the fact that artists often use 2D profiles to guide the design process, thus benefited from the high availability of 2D image resources. Due to the lack of 3D geometry information, we perform novel theoretic analysis to ensure the foldability and stability of the resultant design. Based on a novel graph representation of the paper pop-up plan, we further propose a practical optimization algorithm via mixed-integer programming that jointly optimizes the topology and geometry of the 2D plan. We also allow the user to interactively explore the design space by specifying constraints on fold lines. Finally, we evaluate our framework on various images with interesting 2D shapes. Experiments and comparisons exhibit both the efficacy and efficiency of our framework.

This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity.

A digital fluorescence detector (DFD), a handheld fluorescence detection device, can convert the fluorescence signal of samples into the corresponding fluorescer concentration. Herein, by adopting a DFD as the readout, a novel intelligent platform was developed based on a ratiometric paper-based device (RPD) for multiple aminoglycoside detection. There are five layers and four parallel channels contained in the designed RPD, functioning as reagent storage, fluidic path control and signal processing, respectively. The rationale of this design lies in the fact that aptamer/graphitic carbon nitride nanosheet (Apt/g-C3N4 NS) modified layers can catalyze o-phenylenediamine to fluorescent 2,3-diaminophenazine (DAP) in the presence of H2O2. When Apt was removed from nanosheets via the Apt-target reaction, the peroxidase-like activity would be decreased, thus decreasing the production of DAP. All the changes of the fluorescence DAP signal can be read out using a portable DFD. Based on the DFD signal change related to the concentration of the target, a quantitative reaction platform was established. Furthermore, the sample flow and Apt-target reaction time can be reasonably regulated using the H2O2-cleavable hydrophobic compound modified layer placed between the target recognition region and detection region. Then, the practicality of this platform was verified through realizing sensitive analysis of streptomycin, tobramycin, and kanamycin simultaneously. Overall, with merits including portability and ease of operation, the platform shows great potential in on-site simultaneous detection of multiple targets, especially in resource-limited settings.

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.

Developing a facile, cost-saving, and environment-friendly method for fabricating a multifunctional humidity sensor is of great significance to expand its practical applications. However, most humidity sensors involve a complex fabrication process, resulting in their high cost and narrow application fields. Herein, a multifunctional paper-based humidity sensor with many advantages is proposed. This humidity sensor is fabricated using conventional printing paper and flexible conductive adhesive tape by a facile pasting method, in which the paper is used as both the humidity-sensing material and the substrate of the sensor. Owing to the moderate hydrophilicity of the paper and the rational structure design of the paper-based humidity sensor, the sensor exhibits an excellent humidity-sensing response of more than 103 as well as good linearity ( R2 = 0.9549) within the humidity range from 41.1 to 91.5% relative humidity. Furthermore, the paper-based humidity sensor has good flexibility and compatibility, endowing it with multifunctional applications for breath rate, baby diaper wetting, noncontact switch, skin humidity, and spatial localization monitoring. Although the resistance of the paper-based humidity sensor is relatively large, the humidity-sensing response signals of the sensor can be conveniently processed by the designed signal processing system. The readily available starting materials and facile fabrication technique provide useful strategies for the development of multifunctional humidity sensors.

A 3D modeling system with all-inclusive functionality is too demanding for a casual 3D modeler to learn. There has been a shift towards more approachable systems, with easy-to-learn, intuitive interfaces. However, most modeling systems still employ mouse and keyboard interfaces, despite the ubiquity of tablet devices and the benefits of multi-touch interfaces. We introduce an alternative 3D modeling and fabrication paradigm using developable surfaces, inspired by traditional papercrafting, and we implement it as a complete system designed for a multi-touch tablet, allowing a user to fabricate 3D scenes. We demonstrate the modeling and fabrication process of assembling complex 3D scenes from a collection of simpler models, in turn shaped through operations applied to virtual paper. Our fabrication method facilitates the assembly of the scene with real paper by automatically converting scenes into a series of cutouts with appropriately added fiducial markers and supporting structures. Our system assists users in creating occluded supporting structures to help maintain the spatial and rigid properties of a scene without compromising its aesthetic qualities. We demonstrate several 3D scenes modeled and fabricated in our system, and evaluate the faithfulness of our fabrications relative to their virtual counterparts and 3D-printed fabrications.

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter–electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.

Paper based sensor is the new technology to fabricate a simple, portable, and low cost device that exhibits the comparable properties and functions with those fabricated using complex fabrication process. Paper based sensor is usually applied in environmental monitoring, health diagnostics, and food quality control. This research is focusing on the paper based sensor that will contribute to the development of Ultraviolet (UV) sensor. The fabrication of the sensor was done by using different grade of pencil, namely 6B and 2B on different type of paper. The different grade of pencil corresponds to different percentage of graphite and clay. As for the type of paper, A4 printing paper and Whatman filter paper were used. UV sensing operation was analyzed from the measurement of current-voltage (I-V) characteristics under the exposure of UV light. Zinc oxide (ZnO) was coated on the sensor to facilitate the detection in the presence of UV. The sample fabricated using 6B pencil grade on A4 printing paper and with ZnO coating showed a better UV sensing performance compared to other samples. This is due to the high conduction of 6B pencil grade and smooth surface of A4 printing paper. The ZnO coating increased the sensor sensitivity and response towards the UV light. These findings provide valuable information which can be used in fabricating a low-cost and simple UV paper sensor.

When students believe they can get smarter, they understand that effort makes them stronger. Therefore, they put in extra time and effort, and that leads to higher achievement. Besides that, the arrangement of question papers and answer sheets process for a high number of students takes a long time. In this paper, the sheet exam has been proposed to change into an electric exam (E-exam). This system depended on the client-server framework to convert the traditional exam environment into an electronic exam, providing multiple and different questions at the same time. In additions, kept the grades of students and providing them automatically. In this paper, Visual Studio and Microsoft SQL server have been proposed to develop the electronic exam. The questionnaire is made by distributed it among 30 students, and after the data analysis, the results have been collected have been represented a response rate of 100%. It is recommended that, to take electronic exams and e-learning for students periodically. To improve their performance through continuous training on computerized exams and to increase the student’s efficiency in this type of learning.