bluuebunny/crossref_metadata_2025 huggingface dataset:
Degradable Poly(ethylene oxide)-Like Plasma Polymer Films Used for the Controlled Release of Nisin
Authors: Kousal Jaroslav , Sedlaříková Jana , Kolářová-Rašková Zuzana , Krtouš Zdeněk , Kučerová Liliana , Hurajová Anna , Vaidulych Mykhailo , Hanuš Jan , Lehocký Marián
Published: June 2020

Poly(ethylene oxide) (PEO)-like thin films were successfully prepared by plasma-assisted vapor thermal deposition (PAVTD). PEO powders with a molar weight (Mw) between 1500 g/mol and 600,000 g/mol were used as bulk precursors. The effect of Mw on the structural and surface properties was analyzed for PEO films prepared at a lower plasma power. Fourier transform (FTIR-ATR) spectroscopy showed that the molecular structure was well preserved regardless of the Mw of the precursors. The stronger impact of the process conditions (the presence/absence of plasma) was proved. Molecular weight polydispersity, as well as wettability, increased in the samples prepared at 5 W. The influence of deposition plasma power (0–30 W) on solubility and permeation properties was evaluated for a bulk precursor of Mw 1500 g/mol. The rate of thickness loss after immersion in water was found to be tunable in this way, with the films prepared at the highest plasma power showing higher stability. The effect of plasma power deposition conditions was also shown during the permeability study. Prepared PEO films were used as a cover, and permeation layers for biologically active nisin molecule and a controlled release of this bacteriocin into water was achieved.
A Theoretical Multifractal Model for Assessing Urea Release from Chitosan Based Formulations
Authors: Iftime Manuela Maria , Irimiciuc Stefan Andrei , Agop Maricel , Angheloiu Marian , Ochiuz Lacramioara , Vasincu Decebal
Published: June 2020

This paper reports the calibration of a theoretical multifractal model based on empirical data on the urea release from a series of soil conditioner systems. To do this, a series of formulations was prepared by in situ hydrogelation of chitosan with salicylaldehyde in the presence of different urea amounts. The formulations were morphologically characterized by scanning electron microscopy and polarized light microscopy. The in vitro urea release was investigated in an environmentally simulated medium. The release data were fitted on five different mathematical models, Korsmeyer–Peppas, Zero order, First order, Higuchi and Hixson–Crowell, which allowed the establishment of a mechanism of urea release. Furthermore, a multifractal model, used for the fertilizer release for the first time, was calibrated using these empirical data. The resulting fit was in good agreement with the experimental data, validating the multifractal theoretical model.
Conformation of Pullulan in Aqueous Solution Studied by Small-Angle X-ray Scattering
Authors: Yang Jia , Sato Takahiro
Published: June 2020

Small-angle X-ray scattering functions were measured for six pullulan samples with molecular weights ranging from 2.3 × 104 to 7.4 × 105 in 0.05 M aqueous NaCl at 25 °C and fitted by the perturbed wormlike chain model, comprising touched-bead sub-bodies, to obtain wormlike chain parameters. The parameter values determined were consistent with those determined from previously reported dilute solution properties of aqueous pullulan. Because radii of gyration of not only pullulan polymers, but also pullulan oligomers were consistently explained by the touched-bead wormlike chain model perturbed by the excluded volume effect, the pullulan chain takes a local conformation considerably different from the amylose chain, although both polysaccharides are flexible polymers with an approximately same characteristic ratio.
Some Guidelines for the Synthesis and Melting Characterization of Azide Poly(ethylene glycol) Derivatives
Authors: González-Fernández Daniel , Torneiro Mercedes , Lazzari Massimo
Published: June 2020

We provide fundamental guidelines in the form of a tutorial to be taken into account for the preparation and characterization of a specific class of poly(ethylene glycol) (PEG) derivatives, namely azide-terminated PEGs. Special attention is given to the effect of these chain end groups and their precursors on properties affecting the PEGylation of proteins, nanoparticles and nanostructured surfaces. Notwithstanding the presence of 13C satellite peaks, we show that 1H NMR enables not only the routine quantitative determination of chain-end substitution, but is also a unique method to calculate the absolute number average molecular weight of PEG derivatives. In the use of size exclusion chromatography to get molecular weight distributions, we highlight the importance of distinguishing between eventual secondary reactions involving molecular weight changes and the formation of PEG complexes due to residual amounts of metal cations from reactants. Finally, we show that azide end groups affect PEG melting behavior. In contrast to oxygen-containing end groups, azides do not interact with PEG segments, thus inducing defect formation in the crystal lattice and the reduction of crystal sizes. Melting temperature and degree of crystallinity decrease become especially relevant for PEGs with very low molecular weight, and its comprehension is particularly important for solid-state applications.
Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production
Authors: Liu Yifeng , Zhang Wei , Du Wenhao
Published: June 2020

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.
A Hierarchical Clustering Method for Land Cover Change Detection and Identification
Authors: Häme Tuomas , Sirro Laura , Kilpi Jorma , Seitsonen Lauri , Andersson Kaj , Melkas Timo
Published: June 2020

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis.
Use of Fluorescence Sensing to Detect Nitrogen and Potassium Variability in Maize
Authors: Siqueira Rafael , Longchamps Louis , Dahal Subash , Khosla Raj
Published: June 2020

Real-time fluoro-sensing is a promising crop sensing technology to support variable-rate nutrient management for precision agricultural practices. The objective of this study was to evaluate the potential of fluoro-sensing to detect the variability of nitrogen (N) and potassium (K) in the crop canopy at the early growth stages of maize (before the V6 crop growth stage). This study was conducted under greenhouse conditions in pots filled with silica sand, and maize plants were supplied with modified Hoagland’s solution with different rates of N and K. Sensor readings were collected using a Multiplex®3 fluorescence sensor and analyzed using ANOVA (analysis of variance) to test differences in crop response to nutrient rates. Regression analysis was used to assess the ability of fluorescence sensor-based indices to estimate N and K in the crop canopy. The results of this study indicate that all fluorescence indices under consideration enabled the detection of N variability in the maize canopy prior to the V2 crop growth stage. The NBI_B (nitrogen balance index blue) index enabled N uptake detection (R2 = 0.99) as early as the V2 crop growth stage. However, the fluorescence indices failed to identify K deficiency, as the maize plants with K treatments showed little to no variability of this nutrient at early crop growth stages as measured by plant tissue analysis. The results present a tremendous opportunity to assess N uptake at early growth stages of maize for precision nitrogen application. We recommend using fluorescence sensor-based NBI_B or NBI_R (Nitrogen balance index red) for early detection of nitrogen uptake in maize for precision nitrogen management.
Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China
Authors: Huth Juliane , Gessner Ursula , Klein Igor , Yesou Hervé , Lai Xijun , Oppelt Natascha , Kuenzer Claudia
Published: June 2020

In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China’s second largest freshwater lake in the middle reaches of the Yangtze River—the Dongting Lake—and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA’s Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake’s wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km2, indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km2. Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km2, which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.
Technique for Concurrent Internal Calibration during Data Acquisition for SAR Systems
Authors: Reimann Jens , Schwerdt Marco
Published: June 2020

Nowadays, stringent requirements for the quality of SAR data products exist. The radiometric and image quality requirements can only be fulfilled by knowing the disturbances caused by the radar hardware itself. The internal calibration is a mechanism to estimate these disturbances from the real hardware. Unfortunately, most of the current internal calibration schemes interrupt the SAR data acquisition during internal calibration and hence cause a loss of azimuth samples and a degradation of the image quality. The calibration is thus often performed only before and after a data take. However, these two calibration events are hardly sufficient to estimate the instrument parameters during the whole acquisition especially for long data takes, which will become more common in future SAR system. In this paper, an internal calibration scheme is introduced which allows performing the internal calibration during nominal data acquisition by injecting a coded calibration signal into the receiver with very low power. This signal causes only minor degradation of the SAR image but can be retrieved from the data using adequate signal processing. This technique is described, the theoretical performance estimated, and finally demonstrated on real data from the TerraSAR-X mission. A selection of possible coding scheme is also analyzed with respect to their suitability for the proposed internal calibration scheme.
Exploring TanDEM-X Interferometric Products for Crop-Type Mapping
Authors: Busquier Mario , Lopez-Sanchez Juan M. , Mestre-Quereda Alejandro , Navarro Elena , González-Dugo María P. , Mateos Luciano
Published: June 2020

The application of satellite single-pass interferometric data to crop-type mapping is demonstrated for the first time in this work. A set of nine TanDEM-X dual-pol pairs of images acquired during its science phase, from June to August 2015, is exploited for this purpose. An agricultural site located in Sevilla (Spain), composed of fields of 13 different crop species, is employed for validation. Sets of input features formed by polarimetric and interferometric observables are tested for crop classification, including single-pass coherence and repeat-pass coherence formed by consecutive images. The backscattering coefficient at HH and VV channels and the correlation between channels form the set of polarimetric features employed as a reference set upon which the added value of interferometric coherence is evaluated. The inclusion of single-pass coherence as feature improves by 2% the overall accuracy (OA) with respect to the reference case, reaching 92%. More importantly, in single-pol configurations OA increases by 10% for the HH channel and by 8% for the VV channel, reaching 87% and 88%, respectively. Repeat-pass coherence also improves the classification performance, but with final scores slightly worse than with single-pass coherence. However, it improves the individual performance of the backscattering coefficient by 6–7%. Furthermore, in products evaluated at field level the dual-pol repeat-pass coherence features provide the same score as single-pass coherence features (overall accuracy above 94%). Consequently, the contribution of interferometry, both single-pass and repeat-pass, to crop-type mapping is proved.
Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning
Authors: Liu Yao , Gao Lianru , Xiao Chenchao , Qu Ying , Zheng Ke , Marinoni Andrea
Published: June 2020

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.
A Spatio-Temporal Analysis of Active Fires over China during 2003–2016
Authors: Wei Xikun , Wang Guojie , Chen Tiexi , Hagan Daniel Fiifi Tawia , Ullah Waheed
Published: June 2020

Fire is a common circumstance in the world. It causes direct casualties and economic losses, and also brings severe negative influences on the atmospheric environment. In the background of climate warming and rising population, it is important to understand the fire responses regarding the spatio-temporal changes. Thus, a long-term change analysis of fires is needed in China. We use the remote sensed MOD14A1/MYD14A1 fire products to analyze the seasonal variations and long-term trends, based on five main land cover types (forest, cropland, grassland, savannas and urban areas). The fires are found to have clear seasonal variations; there are more fires in spring and autumn in vegetated lands, which are related to the amount of dry biomass and temperature. The fire numbers have significantly increased during the study period, especially from spring to autumn, and those have decreased in winter. The long-term fire trends are different when delineated into different land cover types. There are significant increasing fire trends in grasslands and croplands in North, East and Northeast China during the study period. The urban fires also show increasing trends. On the contrary, there are significant decreasing fire trends in forests and savannas in South China where it is most densely vegetated. This study provides an overall analysis of the spatio-temporal fire changes from satellite products, and it may help to understand the fire risk in the changing climate for a better risk management.
An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting
Authors: Galiatsatos Nikolaos , Donoghue Daniel N.M. , Watt Pete , Bholanath Pradeepa , Pickering Jeffrey , Hansen Matthew C. , Mahmood Abu R.J.
Published: June 2020

Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001–2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R2 = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R2 = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001–2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer’s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer’s Accuracy. A year-by-year analysis of change from 2001–2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data.
Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans
Authors: Caulier-Cisterna Raúl , Blanco-Velasco Manuel , Goya-Esteban Rebeca , Muñoz-Romero Sergio , Sanromán-Junquera Margarita , García-Alberola Arcadi , Rojo-Álvarez José Luis
Published: June 2020

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial–temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial–temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial–temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial–temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.
Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods
Authors: Weng Shizhuang , Yu Shuan , Guo Binqing , Tang Peipei , Liang Dong
Published: June 2020

Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400–1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.
A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios
Authors: Dantas Silva Felipe S. , Silva Esau , Neto Emidio P. , Lemos Marcilio , Venancio Neto Augusto J. , Esposito Flavio
Published: June 2020

The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. One of the main reasons for this is the availability of techniques provided by this paradigm, such as environmental monitoring employing user data and everyday objects. The facilities provided by the IoT infrastructure allow the development of a wide range of new business models and applications (e.g., smart homes, smart cities, or e-health). However, there are still concerns over the security measures which need to be addressed to ensure a suitable deployment. Distributed Denial of Service (DDoS) attacks are among the most severe virtual threats at present and occur prominently in this scenario, which can be mainly owed to their ease of execution. In light of this, several research studies have been conducted to find new strategies as well as improve existing techniques and solutions. The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. However, the high granularity that characterizes the IoT scenarios and the wide range of techniques explored during the DDoS attacks make the task of finding and implementing new solutions quite challenging. This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios. To fill this knowledge gap, in this study we carry out an in-depth investigation of the state-of-the-art and create a taxonomy that describes and characterizes existing solutions and highlights their main limitations. Our taxonomy provides a comprehensive view of the reasons for the deployment of the solutions, and the scenario in which they operate. The results of this study demonstrate the main benefits and drawbacks of each solution set when applied to specific scenarios by examining current trends and future perspectives, for example, the adoption of emerging technologies based on Cloud and Edge (or Fog) Computing.
Enabling Medicine Reuse Using a Digital Time Temperature Humidity Sensor in an Internet of Pharmaceutical Things Concept
Authors: Hui Terence K. L. , Donyai Parastou , McCrindle Rachel , Sherratt R. Simon
Published: June 2020

Medicinal waste due to improper handling of unwanted medicines creates health and environmental risks. However, the re-dispensing of unused prescribed medicines from patients seems to be accepted by stakeholders when quality and safety requirements are met. Reusing dispensed medicines may help reduce waste, but a comprehensive validation method is not generally available. The design of a novel digital time temperature and humidity indicator based on an Internet of Pharmaceutical Things concept is proposed to facilitate the validation, and a prototype is presented using smart sensors with cloud connectivity acting as the key technology for verifying and enabling the reuse of returned medicines. Deficiency of existing technologies is evaluated based on the results of this development, and recommendations for future research are suggested.
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images
Authors: Brehar Raluca , Mitrea Delia-Alexandrina , Vancea Flaviu , Marita Tiberiu , Nedevschi Sergiu , Lupsor-Platon Monica , Rotaru Magda , Badea Radu Ioan
Published: June 2020

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.
Processing Fluorescence Spectra for Pollutants Detection Systems in Inland Waters
Authors: Arques-Orobon F. Jose , Prieto-Castrillo Francisco , Nuñez Neftali , Gonzalez-Posadas Vicente
Published: June 2020

Development of contaminant detection systems in various natural and industrial environments has been favored in recent years thanks to the evolution of processors and sensors. Our group works specifically on contaminant detection systems in inland waters: immediate and continuous detection is a fundamental requirement in this type of sensing. Regarding the sensors, the proposed system is based on fluorescence, since it offers a method in which there is no contact with water, which means less wear on the components and a great saving in cleaning and maintenance. On the other hand, the spectrum processing is of great importance, since it is used both in the generation of a library of fluorescence spectra taken in the laboratory and in the continuous analysis of the samples and in the comparison algorithm for identification. The validity of the system is based on the last process that is carried out in a very short time. This article describes a system to process spectra in a more accelerated way.
Recurrent GANs Password Cracker For IoT Password Security Enhancement
Authors: Nam Sungyup , Jeon Seungho , Kim Hongkyo , Moon Jongsub
Published: June 2020

Text-based passwords are a fundamental and popular means of authentication. Password authentication can be simply implemented because it does not require any equipment, unlike biometric authentication, and it relies only on the users’ memory. This reliance on memory is a weakness of passwords, and people therefore usually use easy-to-remember passwords, such as “iloveyou1234”. However, these sample passwords are not difficult to crack. The default passwords of IoT also are text-based passwords and are easy to crack. This weakness enables free password cracking tools such as Hashcat and JtR to execute millions of cracking attempts per second. Finally, this weakness creates a security hole in networks by giving hackers access to an IoT device easily. Research has been conducted to better exploit weak passwords to improve password-cracking performance. The Markov model and probabilistic context-free-grammar (PCFG) are representative research results, and PassGAN, which uses generative adversarial networks (GANs), was recently introduced. These advanced password cracking techniques contribute to the development of better password strength checkers. We studied some methods of improving the performance of PassGAN, and developed two approaches for better password cracking: the first was changing the convolutional neural network (CNN)-based improved Wasserstein GAN (IWGAN) cost function to an RNN-based cost function; the second was employing the dual-discriminator GAN structure. In the password cracking performance experiments, our models showed 10–15% better performance than PassGAN. Through additional performance experiments with PCFG, we identified the cracking performance advantages of PassGAN and our models over PCFG. Finally, we prove that our models enhanced password strength estimation through a comparison with zxcvbn.
Acoustic Energy Harvesting and Sensing via Electrospun PVDF Nanofiber Membrane
Authors: Shehata Nader , Hassanin Ahmed H. , Elnabawy Eman , Nair Remya , Bhat Sameer A. , Kandas Ishac
Published: June 2020

This paper introduces a new usage of piezoelectric poly (vinylidene fluoride) (PVDF) electrospun nanofiber (NF) membrane as a sensing unit for acoustic signals. In this work, an NF mat has been used as a transducer to convert acoustic signals into electric voltage outcomes. The detected voltage has been analyzed as a function of both frequency and amplitude of the excitation acoustic signal. Additionally, the detected AC signal can be retraced as a function of both frequency and amplitude with some wave distortion at relatively higher amplitudes and within a certain acoustic spectrum region. Meanwhile, the NFs have been characterized through piezoelectric responses, beta sheet calculations and surface morphology. This work is promising as a low-cost and innovative solution to harvest acoustic signals coming from wide resources of sound and noise.
Long-Distance Sub-Diffraction High-Resolution Imaging Using Sparse Sampling
Authors: Wang Duo , Fu Tianjiao , Bi Guoling , Jin Longxu , Zhang Xingxiang
Published: June 2020

How to perform imaging beyond the diffraction limit has always been an essential subject for the research of optical systems. One effective way to achieve this purpose is Fourier ptychography, which has been widely used in microscopic imaging. However, microscopic imaging measurement technology cannot be directly extended to imaging macro objects at long distances. In this paper, a reconstruction algorithm is proposed to solve the need for oversampling low-resolution images, and it is successfully applied to macroscopic imaging. Compared with the traditional FP technology, the proposed sub-sampling method can significantly reduce the number of iterations in reconstruction. Experiments prove that the proposed method can reconstruct low-resolution images captured by the camera and achieve high-resolution imaging of long-range macroscopic objects.
Development of a Portable, Ultra-Rapid and Ultra-Sensitive Cell-Based Biosensor for the Direct Detection of the SARS-CoV-2 S1 Spike Protein Antigen
Authors: Mavrikou Sophie , Moschopoulou Georgia , Tsekouras Vasileios , Kintzios Spyridon
Published: June 2020

One of the key challenges of the recent COVID-19 pandemic is the ability to accurately estimate the number of infected individuals, particularly asymptomatic and/or early-stage patients. We herewith report the proof-of-concept development of a biosensor able to detect the SARS-CoV-2 S1 spike protein expressed on the surface of the virus. The biosensor is based on membrane-engineered mammalian cells bearing the human chimeric spike S1 antibody. We demonstrate that the attachment of the protein to the membrane-bound antibodies resulted in a selective and considerable change in the cellular bioelectric properties measured by means of a Bioelectric Recognition Assay. The novel biosensor provided results in an ultra-rapid manner (3 min), with a detection limit of 1 fg/mL and a semi-linear range of response between 10 fg and 1 μg/mL. In addition, no cross-reactivity was observed against the SARS-CoV-2 nucleocapsid protein. Furthermore, the biosensor was configured as a ready-to-use platform, including a portable read-out device operated via smartphone/tablet. In this way, we demonstrate that the novel biosensor can be potentially applied for the mass screening of SARS-CoV-2 surface antigens without prior sample processing, therefore offering a possible solution for the timely monitoring and eventual control of the global coronavirus pandemic.
Spatiotemporal Interaction Residual Networks with Pseudo3D for Video Action Recognition
Authors: Chen Jianyu , Kong Jun , Sun Hui , Xu Hui , Liu Xiaoli , Lu Yinghua , Zheng Caixia
Published: June 2020

Action recognition is a significant and challenging topic in the field of sensor and computer vision. Two-stream convolutional neural networks (CNNs) and 3D CNNs are two mainstream deep learning architectures for video action recognition. To combine them into one framework to further improve performance, we proposed a novel deep network, named the spatiotemporal interaction residual network with pseudo3D (STINP). The STINP possesses three advantages. First, the STINP consists of two branches constructed based on residual networks (ResNets) to simultaneously learn the spatial and temporal information of the video. Second, the STINP integrates the pseudo3D block into residual units for building the spatial branch, which ensures that the spatial branch can not only learn the appearance feature of the objects and scene in the video, but also capture the potential interaction information among the consecutive frames. Finally, the STINP adopts a simple but effective multiplication operation to fuse the spatial branch and temporal branch, which guarantees that the learned spatial and temporal representation can interact with each other during the entire process of training the STINP. Experiments were implemented on two classic action recognition datasets, UCF101 and HMDB51. The experimental results show that our proposed STINP can provide better performance for video recognition than other state-of-the-art algorithms.
Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques
Authors: Chowdhury Moajjem Hossain , Shuzan Md Nazmul Islam , Chowdhury Muhammad E.H. , Mahbub Zaid B. , Uddin M. Monir , Khandakar Amith , Reaz Mamun Bin Ibne
Published: June 2020

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.
Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data
Authors: Carrera Berny , Kim Kwanho
Published: June 2020

Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.
DYNASTI—Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
Authors: Junior Sidnei , Riker André , Silvestre Bruno , Moreira Waldir , Oliveira-Jr Antonio , Borges Vinicius
Published: June 2020

Internet of Things (IoT) is evolving to multi-application scenarios in smart cities, which demand specific traffic patterns and requirements. Multi-applications share resources from a single multi-hop wireless networks, where smart devices collaborate to send collected data over a Low-Power and Lossy Networks (LLNs). Routing Protocol for LLNs (RPL) emerged as a routing protocol to be used in IoT scenarios where the devices have limited resources. Instances are RPL mechanisms that play a key role in order to support the IoT scenarios with multiple applications, but it is not standardized yet. Although there are related works proposing multiple instances in RPL on the same IoT network, those works still have limitations to support multiple applications. For instance, there is a lack of flexibility and dynamism in management of multiple instances and service differentiation for applications. In this context, the goal of this work is to develop a solution called DYNAmic multiple RPL instanceS for multiple ioT applicatIons (DYNASTI), which provides more dynamism and flexibility by managing multiple instances of RPL. As a result of this, the traffic performance of multiple applications is enhanced through the routing, taking into consideration the distinct requirements of the applications. In addition, DYNASTI enables the support of sporadic applications as well as the coexistence between regular and sporadic applications. DYNASTI achieved results that demonstrate a significant improvement in reducing the number of control messages, which resulted in increased packet received, decreased end-to-end delay, reduced energy consumption, and an improvement in service differentiation to multiple applications.
A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
Authors: Carrillo-Alarcón Juan Carlos , Morales-Rosales Luis Alberto , Rodríguez-Rángel Héctor , Lobato-Báez Mariana , Muñoz Antonio , Algredo-Badillo Ignacio
Published: June 2020

The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.
The Role of Agency in the Emergence and Development of Social Innovations in Rural Areas. Analysis of Two Cases of Social Farming in Italy and The Netherlands
Authors: Dalla Torre Cristina , Ravazzoli Elisa , Dijkshoorn-Dekker Marijke , Polman Nico , Melnykovych Mariana , Pisani Elena , Gori Francesca , Da Re Riccardo , Vicentini Kamini , Secco Laura
Published: June 2020

Social innovation is considered a relevant concept to tackle societal challenges and needs in rural areas and to promote smart, inclusive and sustainable growth. The characterising sector of rural areas is agriculture; therefore, the focus of this paper is on social innovation in the field of social farming. Among the many factors leading to the emergence and development of social innovation, agency has been considered relevant in the literature on transformability and transformative social innovation as it is the ability to turn contextual difficulties into opportunities for social innovation and for inclusive growth. This paper proposes an evaluation framework to assess the different dimensions of agency by triangulating quantitative with qualitative data and by using indicators. This paper adopts a case study approach, analysing two cases of social farming in Italy and the Netherlands. The results show that the social innovation idea and the resilience of the agency are among the most relevant dimensions for the emergence and development of social innovations. Finally, this paper discusses the three most relevant factors for agency to lead to social innovation: idea and embeddedness of the agency, transformability of the context through agency´s resilience, and agency as catalyst for empowerment.