Short Input Research Articles

IoT-Based Power Theft Detector

Today energy theft or energy meter tampering is the worldwide problem that contributes heavily to revenue losses. Consumers have been finding new methods to manipulate their electric meters. In any way their intention is to stop working energy meters and at the same time they are enjoying free power. The most common method is by passing the meter by shorting input & output terminals and using power without paying for it. Power theft through energy meter tampering is one of the major causes of revenue losses. In this method of tampering to bypass the meter, one must remove the terminal block top cover where input and output terminals are connected. After removing the top cover, a person uses to short input and output terminal through a piece of wire. We are not shorting terminals, but using an IR sensor network arranged inside the top cover, automatically supply to the house will be automatically disconnected and the alarm will be energized. Once the supply is disconnected, automatically power theft information will be transmitted to the concerned mobile phone through the WiFi module. Whenever the top cover is removed, power will be disconnected through relay and power will not resume until the reset key is activated, in practical this reset key must be arranged inside the meter somewhere in a secret place such that it will not be accessible to the energy user. For demo purposes, it is arranged outside for easy accessible. In this project work an electronic energy meter is used which generates pulses according to the load applied to it, therefore energy consumption information also will be displayed through an LCD interfaced with main processing unit designed with an 89C52 microcontroller chip.

Cascaded redundant convolutional encoder-decoder network improved apnea detection performance using tracheal sounds in post anesthesia care unit patients

Objective. Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise.Approach. Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients' tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising.Results. Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively.Significance. The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness.

Implementing Learner Autonomy in English as a Foreign Language Classroom Through a Flipped Classroom Pedagogical Approach

This study is a small-scale action research on the problem of using the Flipped Classroom pedagogical approach to enhance learner autonomy in the target group of high school students in an EFL classroom. The main stages of the research were conducted in the spring semester of the academic year of 2023/24. The participants of the study are seven Ukrainian Grade 9 students and three teachers. The research aims at designing and implementing a method for engaging learners in autonomous practices through the Flipped Classroom model. The methodological basis of this study is the works of (Benson & Voller, 2014), the Flipped Learning Network (2014), (Moore, 2015), (Velegol, Zappe, & Mahoney, 2015), (O’Flaherty & Phillips, 2015), (Hung, 2015), (Huang & Hong, 2016), (Nichols, Burgh, & Kennedy, 2017), (Olakanmi, 2017), (Låg & Sæle, 2019), (Jung, Park, Kim, & Park, 2022), (Little, 2022) and other scientists. In this research, the mixed-method approach is used; the quantitative and qualitative data have been collected through guided observation, surveys (teacher interviews and student questionnaires), and mathematical statistics (descriptive and frequency methods). The essence of the designed method lies in the gradual engagement of students in reviewing short video lectures and reading relevant input before class; then, in class, discussing content and doing quizzes and exercises to reinforce understanding, and, finally, actively participating in creating content-related presentations or projects. The results of this study demonstrate that the Flipped Classroom approach has a considerable impact on learner self-awareness, beliefs, and attitudes implemented in autonomous classroom practices, in particular: effective use of free time for English studying; increased motivation to explore new content and understanding it; perception of a teacher as a facilitator, suggesting a move towards a more student-centered or collaborative learning environment; perception of knowledge as something to be gained rather than “transmitted”, taking on responsibility for evaluating learning outcomes through self-assessment or collaborative evaluation methods. Nevertheless, there are some limitations to using FC in the English language classroom: the willingness of teachers to apply learner-centered methods; the willingness of students to take responsibility for their learning; learners’ cognitive and metacognitive skills; the difficulty of the learning material meant for flipping (self-study), access to resources and technology. The results of this study can be used by researchers and practitioners working in the sectors of secondary and tertiary education.

Contribution of Sweetpotato Production to Economic Empowerment of Farming Households in Niger-Delta Area of Nigeria

Many farming households in Nigeria are deprived, and poor, hence, the need for economic empowerment intervention. Sweetpotato production, due to its short cycle and low input requirement could be projected on a larger scale for farming households’ economic empowerment. However, data regarding ability of sweetpotato farming for farming households’ economic empowerment is scarce. Therefore, farming households’ sweetpotato production and economic empowerment in Nigeria’s Niger-Delta Area was investigated. 364 sweetpotato farming households were chosen through a five-stage sample process. Descriptive statistics, Pearson Product Moment Correlation, T-test, and multiple regression were used to analyze the data that interview schedule was used to collect. Most respondents were female and aged 42.7±11.9 years. Farm size; farming experience; and income from sweetpotato, other crops, and non-crop livelihood activities were 2.6±3.4 hectares; 17.2±12.4 years; and ₦1,327,723.0±1,639,414.0 yearly, ₦2,385,815.0±2,615,273.0 yearly, and ₦1,828,004.0±2,336,270.0 yearly, respectively. The sweetpotato production and economic empowerment were low and high amongst farming households, respectively. Age, farm size and income from sweet potato were significantly related to economic empowerment. The economic empowerment status of high and low sweetpotato producers differed significantly. Between farming households with low and high income from sweetpotato production, their economic empowerment status significantly differed. Farming households’ economic empowerment was mostly predicted by farming experience; and income from sweetpotato, and non-crop livelihood activities. Sweetpotato production influenced farming households' ability to prosper economically. Agricultural policies oriented towards improving economic empowerment should be promoted to emphasize increased sweetpotato production.

Acapella-based music generation with sequential models utilizing discrete cosine transform

Making musical instruments that accompany vocals in a song depends on the mood quality and the music composer’s creativity. The model created by other researchers has restrictions that include being limited to musical instrument digital interface files and relying on recurrent neural networks (RNN) or Transformers for the recursive generation of musical notes. This research offers the world’s first model capable of automatically generating musical instruments accompanying human vocal sounds. The model we created is divided into three types of sound input: short input, combed input, and frequency sound based on the discrete cosine transform (DCT). By combining the sequential models such as Autoencoder and gated recurrent unit (GRU) models, we will evaluate the performance of the resulting model in terms of loss and creativity. The best model has a performance evaluation that resulted in an average loss of 0.02993620155. The hearing test results from the sound output produced in the frequency range 0-1,600 Hertz can be heard clearly, and the tones are quite harmonious. The model has the potential to be further developed in future research in the field of sound processing.

Study on the evolution stages of the flow field induced by an alternating current sliding discharge plasma actuator in different actuation modes

The present study investigates the discharge and flow characteristics of a sliding discharge (SD) driven by alternating current (AC) and negative direct current (DC) high voltage in continuous operation and burst-mode actuation in quiescent air. The burst frequency f is set at 20, 40, 50, and 100 Hz with a duty cycle τ fixed at 50%. Different actuation cases exhibit similar discharge morphologies and electrical properties. The results indicate that the flow induced by the horizontal body force generated by the SD undergoes the following stages: formation, intensification, accumulation, and stabilization. Based on the effects of the body force, the evolution of the induced flow field can be divided into three stages: the initial stage (starting-vortex stage), the transition stage, and the final stage. In continuous operation, the transition stage is marked by a complex flow structure, while the final stage is distinguished by a deflecting jet. When the burst frequency f ≤ 50 Hz, the duration of the transition stage increases with the burst frequency, and it becomes transient at f = 100 Hz due to the short voltage input time. Phase-averaged particle image velocimetry results indicate that the final stage of the burst-mode actuation can be categorized into three types mostly based on the interaction of the vortices from the AC and DC electrodes. Compared to the continuous operation, the application of the burst-mode actuation in this study has a shorter transition stage duration, resulting in a more rapid realization of flow control.

TileCalibWeb Robot tool for ATLAS Calorimeter conditions and calibrations data handling in Run 3

Every sub-detector in the ATLAS experiment at the LHC, including Liquid Argon and TileCal, writes conditions and calibration data into an ORACLE Database (DB). In order to provide an interface for reliable interactions both with the Conditions Online and Offline DBs a unique semiautomatic web based application, TileCalibWeb Robot, has been developed. TileCalibWeb is being used in LHC Run 3 by ATLAS Calorimeter data preparation, data quality and calibration experts as well as by shifters on duty. The architecture and functionality of the tool is discussed. Use cases for updating the conditions data of various types and formats are presented. The interface provides the user with the option to choose the strategy of the DB update. Options include a short text input typed directly into the browser as well as more complicated methods using data files prepared in advance. TileCalibWeb significantly simplifies the procedure of the preparation and writing of conditions and calibration data both into Online and Offline DBs in parallel, verifies user input and automatically publishes bookkeeping records.

Scalable Data Concentrator with Baseline Interconnection Network for Triggerless Data Acquisition Systems

Triggerless data acquisition systems (DAQs) require that the data stream is transmitted from multiple links and into the processing node. The short input data words must be concentrated and packed into the longer bit vectors the output interface (e.g., PCI Express) uses. In that process, the unneeded data must be eliminated, and a dense stream of useful DAQ data must be created. Additionally, the time order of the data should be preserved. This paper presents a new solution using the Baseline Network with Reversed Outputs (BNRO) for high-speed data routing. A thorough analysis of the network’s operation enabled increased scalability compared to the previously published concentrator based on an 8 × 8 network. The solution may be scaled by adding additional layers to the BNRO network while minimizing resource consumption. Simulations were performed for four and five layers (16 and 32 inputs). The FPGA implementation and tests in the actual hardware were successfully performed for 16 inputs. The pipeline registers may be added in each layer independently, shortening the critical path and increasing the maximum acceptable clock frequency.

Exploring the food-energy-water nexus in China's national industries: Insights from network structure and production disturbances

Understanding the food-energy-water nexus (FEWN) among industries' input–output activities is vital for the management of regional strategic resources. However, identifying key nexuses and quantifying their effects within an economy-wide dimension remain a challenge. This study integrated the frameworks of social network analysis (SNA) and computable general equilibrium (CGE) methods based on input–output data, presenting a new methodology to assess inter-industrial FEWN in China. Results indicated that: (1) Agriculture and catering industries were the principal food provision industries in the national economy. They also linked production activities among the food, energy, and water (FEW) sectors. (2) Fossil energy extraction industries with high betweenness centrality were the product circulation media in the industrial network. Their increased outputs could clearly boost domestic production. For instance, the yield of most national industries was expected to expand by 0.5 %–1.5 % due to a 10 % increase in coal supply. The electricity and petroleum processing industries with the highest response coefficients were the mainstays of China's economy. They exerted wide-ranging effects on other industries through short input–output paths. (3) Water-related industries could readily use products and services from other sectors and create demand-driven effects on FEW industries. In contrast, the output scale of national industries was not sensitive to changes in water input. These findings also highlighted the importance of conducting a FEWN evaluation within a balanced macroeconomic system. The proposed SNA–CGE analytical framework showcased good applicability and accuracy in the context of China and could serve as a reference method for FEWN research in other regions.

Classifying Face Features For Better Recognition And Detection

Recognition of facial features is in high demand currently because of its value in individual proof of identity, wherein a short input picture or a glance can assist in recognizing an individual using a database of images. This type of identity isn't commonly used in India, although it is necessary. Fingerprint identity systems ought to utilise facial recognition to render the system frictionless and more secure. As a result, these researchers attempted to assess the suitability of two separate face recognition computational methods, facet, which came, and facial features, in this work. The traits are retrieved and then prepared before being turned to a list and then compared with the area of interest to determine the individual in question [5]. The characteristics considered were the Euclidean gap between the pupils, the contours of the nostrils, plus the lip-to-lip length. This initiative will assist in guarding factories wherein trespassing is possible, identifying people in crowds, tracing those who have vanished, and keeping tabs upon some disruptive elements of the community. It's used for a wide range of applications, including legislative bodies and vaults at financial institutions. At the exact same period of time the identification and detection percentages with the three distinct criteria for lateral recognition of faces, opacity identification, and face dramatic emotion are contrasted using the contrast test, and the adequacy for both methods is improved [6]. These results demonstrate the fact that every instance can be assessed individually. Conventional detectors may "perceive" an object or message, turn it into an electrical signal, store it, and then utilise a conversion circuitry to turn the electricity into an amount or another observable screen format.

Review on Neural Question Generation for Education Purposes

Questioning plays a vital role in education, directing knowledge construction and assessing students’ understanding. However, creating high-level questions requires significant creativity and effort. Automatic question generation is expected to facilitate the generation of not only fluent and relevant but also educationally valuable questions. While rule-based methods are intuitive for short inputs, they struggle with longer and more complex inputs. Neural question generation (NQG) has shown better results in this regard. This review summarizes the advancements in NQG between 2016 and early 2022. The focus is on the development of NQG for educational purposes, including challenges and research opportunities. We found that although NQG can generate fluent and relevant factoid-type questions, few studies focus on education. Specifically, there is limited literature using context in the form of multi-paragraphs, which due to the input limitation of the current deep learning techniques, require key content identification. The desirable key content should be important to specific topics or learning objectives and be able to generate certain types of questions. A further research opportunity is controllable NQG systems, which can be customized by taking into account factors like difficulty level, desired answer type, and other individualized needs. Equally important, the results of our review also suggest that it is necessary to create datasets specific to the question generation tasks with annotations that support better learning for neural-based methods.

Uranium mining – challenges and lessons to learn for nuclear disposal in view of participation and safety

Abstract. Uranium mining and milling is where the nuclear fuel chain starts. Nevertheless, it is the most disregarded part of this chain when thinking about resilience and safety, both in academic debates and in the process of resource extraction. This workshop aims at addressing this gap in addition to improving our understanding of the lessons we can learn for nuclear waste disposal. Uranium mining and milling is challenging when considering safety, from the first day a mine is in operation to the final day of land rehabilitation. During the operation of the mine and mill, the health of the miners is at risk and so is the well-being of the population living in the surrounding area of the mine and the ore-processing plant. Moreover, environmental issues such as pollution pose a risk during the entire lifetime of a mine. The remnants of mining activities can be regarded as long-term liabilities, since they must be monitored for many generations. Uranium mining and milling facilities are often situated on the land of indigenous people. Their rights to information and their participation in the decision-making processes regarding the mining operations, in addition to being informed about basic health outcomes, were and frequently continue to not be given, leading to injustices by mining companies and governmental authorities. Participation, however, is crucial for positive societal outcomes, such as avoiding the deepening of structural inequalities and the development of violent conflict, and for building trust – and thus also for security. This is not only true for operations at the beginning of the nuclear fuel chain but also for actions taken at the end (i.e., during the quest for the safest possible disposal site for nuclear waste). Based on insights from the processes of uranium extraction, we can draw conclusions about the site selection process of a final repository, specifically regarding licensing, operation and land rehabilitation processes. Bearing this in mind, it is worth having a detailed look at the beginning of the nuclear fuel chain as we move forward to resolve the issues we will face during the final stage of nuclear waste disposal. Learning in this context means looking closely at the processes during the licensing, operation, closure and rehabilitation of uranium mines and mills and drawing conclusions about the site selection process, in particular with respect to inclusion of the individual stakeholders and safety concepts. As a result, we can develop recommendations for the ongoing revision of the search process for a final repository for radioactive waste. Therefore, safety and participation issues are the focus of the uranium mining and milling workshop. The workshop will gather academics and practitioners from different fields in a transdisciplinary setting. We plan about five short inputs (up to 10 min each) based on some visualizations (slides, posters or similar), followed by a World Café related to the input talks and a concluding plenary discussion. The entire workshop will last about 2.5 h. The workshop is topped off by an excursion to the Wismut facilities in Ronneburg (Thüringen/Thuringia, Germany) on the Saturday after the conference. The field trip will be guided by Wismut GmbH, the Church Environmental Group Ronneburg and the local Miners' Association. This workshop will enhance the transdisciplinary capabilities of the participants with respect to the logic and externalities of uranium extraction, in addition to providing opportunities for exchange and networking. We aim to compose a publication format for the Safety of Nuclear Waste Disposal platform, based on the contributions of all workshop participants.

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

In this paper, we compare air‐conducted (AC) speech with bone‐conducted (BC) speech for the purpose of utilizing them in packet loss concealment (PLC) for speech quality of voice over internet protocol (VoIP). Instead of the autocorrelation method of linear prediction (LP), which was utilized in the conventional PLC techniques, we employ the modified covariance (MC) method. The MC method provides accurate LP estimation from short input data samples and avoids the numerical problem the autocorrelation method suffers from. The lost frame is compensated from both forward and backward directions in which linear gain and weighting are applied. When BC speech is used as the input speech data for PLC in the case where the speech sender is in noisy environments, BC speech behaves more accurately than the corresponding AC speech, resulting in an excellent performance of the LP‐based PLC technique. This unveils a useful use of BC speech in speech information systems. Experiments show that in severe noise environments of AC speech‐to‐noise ratio being less than 10 dB, BC speech is superior to AC speech for PLC. It is also shown that the transmitted BC speech is more accurately reconstructed for PLC than transmitted AC speech is done. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A bio-inspired hierarchical spiking neural network with biological synaptic plasticity for event camera object recognition

With inherent sparse spike-based coding and asynchronous event-driven computation, spiking neural network (SNN) is naturally suitable for processing event stream data of event cameras. In order to improve the feature extraction and classification performance of bio-inspired hierarchical SNNs, in this paper an event camera object recognition system based on biological synaptic plasticity is proposed. In our system input event streams were firstly segmented adaptively using spiking neuron potential to improve computational efficiency of the system. Multi-layer feature learning and classification are implemented by our bio-inspired hierarchical SNN with synaptic plasticity. After Gabor filter-based event-driven convolution layer which extracted primary visual features of event streams, we used a feature learning layer with unsupervised spiking timing dependent plasticity (STDP) rule to help the network extract frequent salient features, and a feature learning layer with reward-modulated STDP rule to help the network learn diagnostic features. The classification accuracies of the network proposed in this paper on the four benchmark event stream datasets were better than the existing bio-inspired hierarchical SNNs. Moreover, our method showed good classification ability for short event stream input data, and was robust to input event stream noise. The results show that our method can improve the feature extraction and classification performance of this kind of SNNs for event camera object recognition.

Laser-based ultrasonic measurement of the influence of PBF-LB/M-typical thermal cycles on sheet metal components of hybrid parts

In order to cope with the upcoming challenges from industrial trends, such as mass customization, hybrid parts made out of sheet metal and additively manufactured components offer a possible approach. Hybrid parts can be used for instance in the field of medical and aerospace industries. The combination of the two technologies forming and additive manufacturing allows to use the advantages of each while at the same time avoiding the disadvantages. During the additive manufacturing process, however, the sheet metal substrates, which are a component of the later part, are subjected to heat input such as substrate heating and laser radiation. Each heat input has a different temperature and duration, which are simulated to detect changes in the material properties. Experiments are performed for 316L and Ti-6Al-4 V, which are both commonly used in additive manufacturing. The investigations aim at proving that the substrate heating has no influence on either material. To investigate the influence of substrate heating during additive manufacturing, tensile tests in heat-treated state as well as laser-based ultrasonic measurements are used to detect changes in mechanical or microstructural properties. However, the heat input of the laser is expected to lead to a phase transformation for the titanium alloy due to the high cooling rate. Therefore, the feasibility of using a laser based ultrasonic measurement system to detect microstructural changes during heat input similar to the laser radiation during laser-based powder bed fusion processes is tested. Based on the results, the substrate heating does not influence the sheet material, despite long holding times. However, the short laser beam-like heat inputs lead to phase transformations for the titanium alloy, which can be detected temperature and time dependent via laser-based ultrasonic measurements.

Constitution of a Camelina sativa L. Synthetic Population and Agronomic Comparison between Spring and Winter Cultivation in North Italy

In recent years, the interest in increasingly sustainable agriculture has also turned attention towards new cover crops suitable for use in marginal areas that could enter the food chain as new protein and oil sources or for biodiesel production. In this scenario, Camelina sativa is a perfect crop to study. Camelina is an annual herbaceous plant belonging to the Brassicaceae which is interesting in terms of its oil content, since the seeds contain about 40% oils, with a high level of polyunsaturated fatty acids (30–40% alpha linolenic acid, 15–25% linoleic acid, 15% oleic acid and about 15% eicosenoic acid). It is a hexaploid species (2n = 40, genome size ~782 Mb) characterized by rapid growth, a short life cycle (85–100 days for spring varieties, 190–210 for autumn varieties) and low input cultivation needs. However, its use in feed and food is limited by the presence of glucosinolates (GLS). GLS are sulfur molecules involved in plant defense. In recent years, they have been studied not only as antinutritionals but also for their anti-carcinogenic effects against chronic inflammatory and heart diseases and for their use as natural pesticides. Given the recent interest in camelina and its highly nutritious oil, eight pure lines and a synthetic population were compared in two different growing periods, spring and winter. In this work, the genetic materials were characterized for different phenotypic traits, yields and yield components, and bromatological and glucosinolate content. The results confirmed that in North Italy, camelina has higher yields if cultivated in the autumn–winter period (about 2 t/ha vs. 0.6 t/ha); furthermore, a negative correlation was found between spring and winter yields, indicating that varieties that produce more in winter cultivation produce less in spring cultivation. Moreover, to our knowledge, it is the first work in which a synthetic population of Camelina sativa has been tested and proved to be a valid solution for use in various environments both for its adaptability and for the low content of glucosinolates (about 17 mmol/kg).

Drosophila photoreceptor systems converge in arousal neurons and confer light responsive robustness.

Lateral ventral neurons (LNvs) in the fly circadian neural circuit mediate behaviors other than clock resetting, including light-activated acute arousal. Converging sensory inputs often confer functional redundancy. The LNvs have three distinct light input pathways: (1) cell autonomously expressed cryptochrome (CRY), (2) rhodopsin 7 (Rh7), and (3) synaptic inputs from the eyes and other external photoreceptors that express opsins and CRY. We explored the relative photoelectrical and behavioral input contributions of these three photoreceptor systems to determine their functional impact in flies. Patch-clamp electrophysiology measuring light evoked firing frequency (FF) was performed on large LNvs (l-LNvs) in response to UV (365 nm), violet (405 nm), blue (450 nm), or red (635 nm) LED light stimulation, testing controls versus mutants that lack photoreceptor inputs gl60j, cry-null, rh7-null, and double mutant gl60j-cry-null flies. For UV, violet, and blue short wavelength light inputs, all photoreceptor mutants show significantly attenuated action potential FF responses measured in the l-LNv. In contrast, red light FF responses are only significantly attenuated in double mutant gl60j-cry-null flies. We used a light-pulse arousal assay to compare behavioral responses to UV, violet, blue and red light of control and light input mutants, measuring the awakening arousal response of flies during subjective nighttime at two different intensities to capture potential threshold differences (10 and 400 μW/cm2). The light arousal behavioral results are similar to the electrophysiological results, showing significant attenuation of behavioral light responses for mutants compared to control. These results show that the different LNv convergent photoreceptor systems are integrated and together confer functional redundancy for light evoked behavioral arousal.

Pulsed Eddy Current Sensor for Cascade Electrical Conductivity and Thickness Estimation in Nonferrous Metal Plates

This paper presents a pulsed eddy current (PEC) sensor design for cascade electrical conductivity and thickness estimation in non-ferrous metal plates. With a single excitation coil design, the combined change in magnetic flux density caused by the excitation coil and the induced eddy current (EC) in the test plate is detected by a compact anisotropic magneto-resistive (AMR) sensor. The effects of the varying input pulse width are discussed with both numerical simulations in commercial finite element analysis (FEA) software and experimental results, and the time-domain characteristics of the responses are analyzed in relation to the properties of the test plates. It is revealed that the electrical conductivity can be gauged based on the peak value of the short pulse response, and with a known electrical conductivity value, plate thickness can be subsequently derived with the rise time of the long pulse response. A cascade estimation process is therefore proposed to calculate the parameters sequentially by adopting hybrid input signals with both short and long input pulses. Verified by the FEA results, this cascade estimation process was then experimentally validated with a prototype of the PEC sensor.

Cryptanalysis of Reduced-Round SipHash

AbstractSipHash is a family of ARX-based MAC algorithms optimized for short inputs. So far, a lot of implementations and applications for SipHash have been proposed, whereas the cryptanalysis of SipHash still lags behind. In this paper, we study the property of truncated differential in reduced-round SipHash. By exhaustively testing all kinds of 1-bit input differences, we find out the greatest differential biases from corresponding output bits through 3 or 4 SipRounds. Making use of these results, we construct distinguishers for SipHash-2-1 and SipHash-2-2 with practical complexities of $2^{12}$ and $2^{36}$, respectively. However, one limitation of the latter is that it begins with 1-bit input differences on the most significant message bit, which means it can only work when neglecting the padding rules of SipHash. Furthermore, we reveal the relations between the value of output bias and the difference after the first modular addition step, which is directly determined by corresponding key bits. Based on these relations, we propose a key recovery method for SipHash-2-1 that can obtain a significantly nonuniform distribution of the 128-bit secret key. It is summarized that about $97\%$ of random keys can be fully recovered under this method within a complexity of $2^{83}$.

Realizing 32-time Scan Duration Reduction of 18F-FDG PET Using Deep Learning Model with Image Augmentation

Purpose: 32-time scan duration reduction of 18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) images through the generation of standard scan duration images using a multi-slice cycle-consistent Generative Adversarial Network (cycle-GAN) was studied. Also, the effect of the image augmentation methods on the performance of the cycle-GAN model was evaluated. Materials and Methods: Four subsets of standard and 32-time short scan duration PET image pairs, each contacting image data of 10 patients were used to train and test (80 percent for training and 20 percent for testing) a multi-slice cycle-GAN separately. Another patient’s image data was used as the validation dataset for different training subsets. When training the cycle-GAN model for each subset, two approaches were followed: with and without image augmentation. Common image quality metrics of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Normalized Root Mean Squared Error (NRMSE) were used to assess the generation performance of the cycle-GAN model. Paired sample t-test statistical testing with a confidence interval of 0.95 was used to determine whether the differences between approaches were statistically significant or not. Results: For subsets 1-3, both training approaches improved the image quality of the short scan duration inputs (p < 0.001) while for subset 4 only the training approach with image augmentation was capable of improving the image quality. However, the training approach with image augmentation offered better results than the approach without image augmentation (p < 0.001). Conclusion: Employing the training approach with image augmentation, the cycle-GAN model was capable of improving the image quality of 1/32nd short scan duration images through the generation of synthetic standard scan duration images. In the case of the training approach without image augmentation, except for subset 4, the model trained on all subsets 1-3 was capable of improving the image quality. Image augmentation does indeed improve the performance of the cycle-GAN model, especially in the case of insufficient available training datasets.