Miracle-dz/UniFAR-FTUs huggingface dataset:
LPWAN Based IoT Surveillance System for Outdoor Fire Detection

Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical. Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision. Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak. This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts. For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors.
Low Cost Defect Detection Using a Deep Convolutional Neural Network

Starting with the late 1990s, more and more visual recognition tasks are being automated. The focus is around removing the human from the loop, thus increasing the overall process efficiency. Modern advancements in artificial intelligence allow us to start to automate tasks performed by specialized human beings, such as: visual inspection and defect detection. In this visual inspection process a deep convolutional neural network is used to fulfill the task of visual inspection of a bearing mounting process and classify it into 2 classes: good or faulty. This process is characteristic to the automotive sector. After training the CNN model the performances are evaluated by running the classification part of the algorithm on the validation dataset. No special training constraints are used and no special training method are used: everything is open source, as the goal of this research is to prove the viability for smaller companies to use AI in their everyday processes, in order to improve the overall business efficiency. The model can replace the human operator in the loop by having a mean accuracy of 90% and a mean processing time of 8 seconds. The overall cost of this application was well under the general market price for this kind of solutions. A path for further improvements is also demonstrated.
Power allocation for BER minimization in an uplink MUSA scenario

Multi-user shared access (MUSA) is one of the most promising non orthogonal multiple access (NOMA) schemes to accommodate the stringent requirements of massive machine type communications (mMTC). MUSA adopts a grant-free access strategy and uses non-orthogonal spreading sequences to accommodate a large number of users in the same radio resources. The inter-user interference is subsequently handled using successive interference cancellation (SIC) algorithm, which allows a low decoding complexity. However, sequential decoding can be affected by error propagation when the power difference between simultaneous transmissions is not significant. In this paper, we propose a power allocation algorithm in order to avoid the error propagation problem by guaranteeing sufficient power difference between concurrent received signals. We formulate an optimization problem which aims at minimizing the system average bit error rate. We show that the optimization problem has an optimal global solution and an algorithm is proposed for its resolution. Simulation results show the impact of performing power allocation for SIC decoding on the bit error rate.
AC stress reliability study of a new high voltage transistor for logic memory circuits

This paper presents a junction reliability study on a new architecture of high voltage transistor with a double poly gate for Non-Volatile Memory (NVM) Technology. The experiments results are supported by TCAD simulations. The drain-bulk AC stress was performed on the new architecture and compared with the conventional demonstrating an important improvement in terms of reliability, for different bias conditions, taking advantage of its design architecture. Finally, process and design optimizations of the new architecture are presented.
Three-dimensional Reconstruction and Measurement of Avian Eggs through Digital Imaging

This paper presents a computer vision-based method for the 3-D (three-dimensional) reconstruction and characterization of avian eggs. Two low-cost cameras are used to acquire images of eggs from top and side views, respectively. The image segmentation is performed using the image binarization technique. The contour-slice based method is employed for the 3-D reconstruction. The geometrical parameters of avian eggs, such as length, breadth, volume and surface area, are then computed based on the reconstructed model. The performance of the system is evaluated using eggs from different breeds and sizes. Comparative results between the physical measurement and the proposed approach suggest that the digital imaging approach has an overall accuracy of 98% for the geometrical parameter measurement of avian eggs.
A Novel Data-Driven Fault Feature Separation Method and Its Application on Intelligent Fault Diagnosis Under Variable Working Conditions

As mechanical fault diagnosis enters the era of big data, the traditional fault diagnosis methods under variable working condition are difficult to be applied because of the massive computation cost and excessive reliance on human labor. For the application of intelligent fault diagnosis under variable working conditions, the crucial difficulty is that the variable speed or load can cause smearing and skewing of classable feature. It is the key to break the predicaments by extracting the features which are irrelevant to the working conditions and contain fault information. This paper propose a new intelligent fault diagnosis framework under variable working conditions called Data-driven Fault feature Separation Method (DFSM) which can eliminate the working condition features from all the information and employ the rest fault information for diagnosis. In our DFSM, classification loss ensures the basic classified ability, first. Second, uncorrelation loss increases the discrepancy between fault features and working condition features. Then, reference loss guides the working condition encoder only extracting the working condition features. Finally, autoencoder loss ensures that all the information has been extracted. It should be noticed that our DFSM is trained only using the dataset under a certain working condition and can diagnose faults with high accuracy under variable even time-varying working condition, which means that it is easy to be applied. The experimental results of rolling bearing dataset show that the proposed DFSM can not only break the limitation of existing methods, but also achieve a superior performance comparing with related method. Besides, through the visual understanding, the proposed DFSM is certified that it is able to eliminate the working condition information and extract precise classable feature for fault diagnosis.
Monitoring of Nonlinear Processes With Multiple Operating Modes Through a Novel Gaussian Mixture Variational Autoencoder Model

Customized production, quality variation of raw materials and other factors make industrial processes work in multiple operating modes. In general, complex industrial processes have strong nonlinearity under each operating mode. In this paper, a Gaussian mixture variational autoencoder (GMVAE) model, which combines with Gaussian mixture and VAE, is proposed to monitor nonlinear processes with multiple operating modes. Due to the Gaussian mixture distribution limitation in latent variable space, GMVAE can not only automatically extract features of the nonlinear system, but also make these features follow Gaussian mixture distribution. Based on Gaussian mixture distribution in latent variable space and the reconstruction error, two probability monitoring indexes are constructed, whose control limits can be determined by $\chi ^{2}$ distribution. TE benchmark data and real hot strip mill process (HSMP) data have been used to verify the effectiveness of the proposed method.
Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar, and camera signals [1]. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations and can even be adversarial perturbations (APs).
Performance Evaluation of Nonlinear Effects in Frequency-Splitting SWIPT Signals

Simultaneous wireless information and power transmission (SWIPT) is one of the popular technique used in radio frequency energy harvesting (EH). Frequency splitting based SWIPT (FS-SWIPT) is a novel SWIPT technique that can be adopted with frequency domain multiple access signal transmission protocol for the near field EH application in biomedical sensors. In this paper, we implement FS-SWIPT in orthogonal frequency division multiplexed (OFDM) signal, the OFDM signal has separate sub carriers for the data symbols and the energy carrying symbols. At the receiver, the energy and information symbols are based on their sub carriers by using FS-SWIPT technique. The amplification of high energy OFDM signal at transmitter by using a high power amplifier are susceptible to non linear distortion (NLD), which degrades the bit error rate (BER) performance. We analyze the impact of NLD on the BER performance of FS-SWIPT and validate the analytical results by using simulations.
Path Tracing Denoising Based on SURE Adaptive Sampling and Neural Network

A novel reconstruction algorithm is presented to address the noise artifacts of path tracing. SURE (Stein’s unbiased risk estimator) is adopted to estimate the noise level per pixel that guides adaptive sampling process. Modified MLPs (multilayer perceptron) network is used to predict the optimal reconstruction parameters. In sampling stage, coarse samples are firstly generated. Then each noise level is estimated with SURE. Additional samples are distributed to the pixels with high noise level. Next, we extract a few features from the results of adaptive sampling used for the subsequent reconstruction stage. In reconstruction stage, modified MLPs network is adopted to model a complex relationship between extracted features and optimal reconstruction parameters. An anisotropic filter is used to reconstruct the final images with the parameters predicted by neural networks. Compared to the state-of-the-art methods, experiment results demonstrate that our algorithm performs better than other methods in numerical error and visual image quality.
Towards Realisation of a Non-Invasive Blood Glucose Sensor Using Microstripline

In this paper, the design of a microwave based noninvasive glucose sensor at the operating frequency of 19 GHz is presented. Worldwide, about 451 million adults suffer from diabetes. For optimal therapy, people with diabetes should monitor their blood glucose level continuously over the day. For that purpose non-invasive microwave based glucose sensors are under research. Non-invasive microwave based glucose sensors must automatically perform continuous measurements with a high and stable accuracy. However, there exists no reliable noninvasive microwave based glucose sensor up to now.The proposed sensor is designed based on a two-port microstripline (MSL) network. Water with various glucose concentration from 0 to 500 mg/dl is placed in a slot between the two ports. The simulation results show that the phase change in the reflection coefficient for the various glucose concentration depends on the matching. The measurement of the glucose concentration 10 mg/dl can be realized by applying a taper ground plane as a matching technique decreasing reflections at the interface between the microstripline substrate and the water.The proposed sensor achieves a sensitivity of 2 ° phase change per 10 mg/dl glucose concentration variation. In the future, the simulation results should be validated in an experimental setup.
Reduced Ectomycorrhizae on Oak Near Invasive Garlic Mustard

Abstract Invasive plants may disrupt symbioses between plants and soil biota. We tested whether ectomycorrhizal fungi (ECM) associating with Quercus rubra (Northern Red Oak) seedlings was lower near invasive Alliaria petiolata (Garlic Mustard). We quantified ECM colonization, identified morphotypes, and compared composition in forests with contrasting Garlic Mustard densities. Seedlings had lower ECM colonization and diversity in a stand with dense Garlic Mustard than in a stand without Garlic Mustard. ECM composition also differed between stands. Seedlings in a stand with moderate density Garlic Mustard had marginally less ECM than those at the no Garlic Mustard site. These findings suggest that ECM association is negatively correlated with Garlic Mustard invasion. This association may result in lasting changes to forest communities and hinder site restoration following Garlic Mustard removal.
El terrorismo visto desde las teorías sobre movimientos sociales

Resumen Estas reflexiones pasan revista a algunas de las enseñanzas que pueden ser incorporadas al análisis de los fenómenos terroristas. Se repasa brevemente las contribuciones y limitaciones que cada uno de los principales enfoques teóricos ofrecen de cara a su aplicación a los estudios sobre terrorismo. A este respecto, se subrayan sobre todo los problemas planteados por las aproximaciones socioestructurales, las aportaciones ofrecidas por los modelos racionales y su necesaria complementación con las teorías de la identidad social con la aproximación construccionista.
Identification of Vehicle Obstruction Scenario Based on Machine Learning in Vehicle-to-vehicle Communications

Vehicle obstruction is a special scenario in vehicle-to-vehicle (V2V) communications. In this case, channel characteristics including path loss and spatial distributions are obviously different from other typical vehicular communication scenarios. However, the vehicle obstruction scenario is difficult to be identified by global navigation satellite systems (GNSS) or radars, so it is difficult for V2V communication systems to respond to sudden changes in channel characteristics due to vehicle obstructions. Therefore, by correctly identifying the vehicle obstruction scenarios, V2V communication systems can select appropriate propagation channel models to maintain an effective and reliable operating state. For this reason, this paper presents a machine-learning-based vehicle obstruction scenario identification approach for V2V communications. Channel characteristics extracted from measurements form the datasets used to training, then the back-propagation neural network (BPNN) is trained and a scenario identification model is obtained. Subsequently, identification accuracy is verified by using validation data. The results show that the identification accuracy for vehicle obstruction scenarios is more than 97%, which indicates that the approach proposed in this paper shows good performance in vehicle obstruction scenario identification in V2V communications.
Flower Extracts and Their Essential Oils as Potential Antimicrobial Agents for Food Uses and Pharmaceutical Applications

Plants with potential therapeutic value have been used from time immemorial to cure various ailments and infectious diseases. Secondary metabolites or the bioactive compounds (phytochemicals) present in plants have been reported to be accountable for various observed biological activities. Consumer awareness of the possible side effects of using chemical-based antimicrobial agents has forced researchers to identify and explore natural plant-based antimicrobial agents (or preservatives) that are toxicologically safe, especially when used in food applications. Of late, scientific evidence has been provided on the potential antimicrobial activities exhibited by certain traditionally used flower extracts or their essential oils (edible and wild). This review focuses on providing and updating available information on the antimicrobial activities exhibited by flowers, which are envisaged to find potential applications as natural preservatives for foods or applications in the pharmaceutical industries to develop new and economical herbal-based products for treating various diseases.
AN OVERVIEW ON PHARMACOLOGICAL ACTIVITIES OF MUSA SAPIENTUM AND

Banana plant (Musa sapientum, Musa paradisiaca) is a herb, in the genus Musa. It is cultivated mainly for its fruit. The plants are mostly large perennial herbs with rhizomes, with stout and unbranched stem. Leaves are simple large with stem clasping sheaths. The fruit is a woody capsule or leathery or fleshy soft berry. The fruit average 125 gm, of which approximately 75% is water and 25% dry matter content. Bananas are valuable source of vitamin A, vitamin C and potassium. The plant is reported to have antiulcerogenic, antilithiatic, antimicrobial, analgesic, antihypertensive, antidiarrhoeal, antiallergic, antioxidant, diuretic, hypolipidemic, hypoglycaemic, hair growth promoting, haemostatic, muscle relaxant, mutagenic, wound healing and vasodilatory activities. A vast majority of our population depend largely on herbal medicines. This review gives an overview
Enhanced resistance against a fungal pathogen Sphaerotheca humuli in transgenic strawberry expressing a rice chitinase gene

Fragaria×Ananassa Duch. cv. Toyonoka is a main variety of strawberry in Japan, but it is susceptible to a pathogenic fungus, Sphaerotheca humuli. Rice chitinase gene under the control of cauliflower mosaic virus (CaMV) 35S promoter was introduced into the strawberry plants using Agrobacterium tumefaciens. The transgenic plants showed an increased resistance to the powdery mildew, S. humuli.
Extracting Microservices’ Candidates from Monolithic Applications: Interface Analysis and Evaluation Metrics Approach

There is a migration trend toward microservices architecture coming from the monolithic applications. This research proposes a decomposition method that extracts microservices’ candidates through analyzing the application programming interface in order to extract the operations and the parameters. Then the operation names are converted into word representations using word embedding models. Next, semantically similar operations are clustered together to provide a microservice’ candidate. Additional step is to evaluate the proposed candidate using cohesion and complexity metrics. The proposed algorithm improved the decomposition approach for big applications but did not affect the decomposition of smaller applications.
Funtional Domain Structure of Human Heterochromatin Protein HP1 Hsa: Involvement of Internal DNA-Building DNA-Binding and C-Terminasl Self-Association Domains in the Formation of Discrete Dots in Interphase Nuclei

Human heterochromatin protein HP1(Hsalpha) possesses two evolutionarily conserved regions in the N- and C-terminal halves, so-called chromo and chromo-shadow domains, and DNA-binding domain in the internal non-conserved region. Here, to examine its in vivo properties, we expressed HP1(Hsalpha) as a fusion product with green fluorescent protein in human cells. HP1(Hsalpha) was observed to form discrete dots in interphase nuclei and to localize in the centromeric region of metaphase chromosomes by fluorescence microscopy. Interestingly, this dot-forming activity was also found in the N-terminal half retaining the chromo and DNA-binding domains and in the C-terminal chromo-shadow domain. However, the chromo domain alone stained nuclei homogeneously. To correlate this dot-forming activity with self-associating activity in vitro, the chromo and chromo-shadow domain peptides were independently expressed in Escherichia coli, affinity purified, and chemically cross-linked with glutaraldehyde. In a SDS-polyacrylamide gel, the former mainly produced a dimer, while the latter produced a ladder of bands up to a tetramer. When passed through a gel filtration column in a native state, these peptides were exclusively separated as a dimer and a tetramer, respectively. These results suggested that the internal DNA-binding and C-terminal chromo-shadow domains are both involved in heterochromatin formation in vivo.
Bulletin of the International Neuro-Psychoanalysis Society

During the first year of the Istanbul Group, the aim has been to introduce the field of neuropsychoanalysis to specialists within various disciplines in Istanbul, in order to build a common ground for those willing to integrate their theoretical and research findings for the enhancement of neuropsychoanalysis. Therefore, regardless of the differences in terminologies used in neuroscience and psychoanalysis, the exploration of conceptual overlappings and agreements among these disciplines will be the core philosophy of our group. The group considers neuropsychoanalysis to be a scientific method able to resolve the splitting in science that dates back to the famous “mind–body” problem. During the previous century, the scientific split between the study of the brain on the one hand, and the study of the psyche on the other, enabled scientists to enlarge the data bank, making crucial findings in both neurology and psychology. However, mutual idealization and devaluation, respectively, prevented the specialists from accepting the validity of the findings from the discipline to which they did not themselves belong. This kind of a narcissistic denial led to (so to speak) “Part Sciences. ” During our new century, a synthesis of these split data is required in order to reach a phase of “Whole Science” in the study of mind and body. Therefore, our group conceptualizes neuropsychoanalysis as a method to reduce scientific splitting and to improve scientific maturation in our field. According to our approach, participants from several disciplines—psychology, psychiatry, neurology, psychoanalysis, philosophy, physics and sociology—have been invited to the group. Three study groups are being initiated, in order to elaborate three different themes:
Topology-Independent Robust Stability Conditions for Uncertain MIMO Networks

We give a sufficient and a necessary condition for the topology-independent robust stability of networked systems formed by uncertain MIMO systems. Both conditions involve constants associated with the nominal node dynamics and arc interconnection matrices, the uncertainty bounds, and the maximum connectivity degree of the network; they are scalable (they can be checked locally), independent of the network topology and even of the number of nodes and arcs, and hold for networks of heterogeneous MIMO systems and interconnection matrices, with heterogeneous uncertainties. The dual cases of 1-norm and $\infty $ -norm bounds are considered. In both cases, if the systems at the nodes are diagonal, we get a necessary and sufficient condition. We apply our results to the topology-independent robust stability analysis of a case-study from cancer biology.