Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Width-Modulated SiN Long Period Grating Designed for Refractometric Applications

A novel integrated photonic structure based on Long Period Waveguide Gratings (LPWGs) relying on channel-width modulation is proposed and tested for refractometric applications. These LPWGs have been fabricated through a Silicon Nitride process and experimentally characterized in terms of both surface and bulk sensitivities. Surface sensing configuration is first achieved by propagating one of the coupling optical modes into an epoxy-based negative photoresist SU8 cladding region that is in contact with the analyte via its outer surface. We subsequently show that the proposed LPWGs cladding layer can be advantageously replaced by a gas-porous polymeric bulk layer such as Styrene-co-AcryloNitrile (SAN) as the cladding region to be directly sensed to anticipate future gas sensing applications. Here, bulk sensing is optimized by increasing the analyte's influence on the modal propagation constants as it is demonstrated to be currently the most promising solution to effectively enhance the figure of merit of long period gratings of given lengths. Using varying water-glycerol mixtures, the surface sensitivity of these LPWGs has been measured at up to 240 nm per RI unit (RIU) that is in agreement with simulation. In addition, the bulk sensitivity has been indirectly estimated to be $\sim$1900 nm/RIU via temperature measurements, which corroborates simulation results, thereby paving the way towards gas sensing applications.
Research on System Log Anomaly Detection Combining Two-way Slice GRU and GA-Attention Mechanism

A wide variety of logs are generated during the running of the system, which record the state of the system at runtime and the various operations performed by the system. They are good sources of information for online monitoring and exception detection. Therefore, it is important to detect the anomaly logs in the system quickly and accurately to maintain the security and stability of the system. This paper presents a log anomaly detection algorithm based on Bi-SSGRU-Ga-Attention, which has the advantages of simple parameters and fast convergence. It reduces the running time and achieves high accuracy. It has achieved good results in log analysis of large information systems.
LiDAR Simulation for Performance Evaluation of UAS Detect and Avoid

The solution to mitigating risks associated with beyond Visual Line of Sight (BVLOS) operations of Unmanned Aerial System (UAS) generally focuses on the use of advanced Unmanned Traffic Management (UTM) systems. However, this solution does not take into account other uncooperative objects in the airspace. A more robust approach is to have UTM integrations coupled with onboard machine vision which is tied to automated collision avoidance systems. Future BVLOS regulations in urban situations may require robust embedded software that is capable of detecting air collision hazards in realtime at near and far ranges as uncooperative small aircraft and other unpredictable small objects with fast-changing and unscheduled trajectories pose significant hazards to UAS. This work presents the concept and initial prototyping of a Digital Twin to evaluate the capability of UAS mounted LiDAR to detect small-object air collision risks. A Digital Twin of the Port of Hamburg is augmented with typical port and harbour aerial hazards such as birds, drones, helicopters, and low flying aircraft. The use case scenarios are created in Maya and Unity, with Optix ray tracing of typical LiDAR imaging configurations used to replicate the cause and effect relationship between different LiDAR specifications and their response to small flying objects. Our results demonstrate the inhomogeneous point clouds generated at different spatial-temporal parts of the LiDAR scanning cycle and field of view. These results confirm the challenges of detecting small uncooperative objects by LiDAR.
A Taxonomy of Web Security Vulnerabilities

the rapid evolution in web applications and software development has not witnessed a similar pace in development opposite what has taken place in the security and cybersecurity arenas. Web applications security has become a significant issue since insecure applications and software undermine various areas including finance, health care, defense, and other mission critical infrastructures. Web application vulnerability results from misconfiguration, flaws in the design, implementation, operation, or management at the different levels of a web application (input side, output side, or both sides). This taxonomy paper studies web vulnerabilities, their impact on users’ infrastructures, and their detection tools (static, dynamic, or hybrid) and mitigation mechanisms at different software architecture levels.
Prediction Alzheimer's disease from MRI images using deep learning

Alzheimer's is one of the diseases that are the most publicized type of dementia. Alzheimer's disease will be born every 3 second the world. Previous research shows that early prediction of AD in the medical field for reduced cost of treatment and time of it. To this end, construct an efficient prediction system for AD, which is the goal of this paper, often reduces time to treatment, medical errors, and overall healthcare cost. We used Deep Learning to predict and diagnose AD and for this reason using python code in Colaboratory as platform environments. In particular, we used 2D CNN and vgg16 to achieve the research goal, we used experiments conducted on MRI images from Kaggle dataset. Our experiment achieved accuracy of 67.5% for 2D CNN algorithm, while the vgg16 algorithm achieved accuracy of 70.3%. We conclude by showing that deep learning can improve the prediction AD and using algorithm vgg16 is better than 2D CNN.
Digital Marketing and Big Data: a bibliometric analysis of scientific production from the Scopus database

To identify the most frequently developed topics in the area of Big Data and Digital Marketing, a quantitative analysis was developed in December 2020. This analysis was focused on 750 publications and later on 67 publications on Big Data and Digital Marketing from the Scopus database. A bibliometric analysis was developed using the VOSviewer software and the technique of term co-occurrence and author co-authorship. Clusters were found for each of the analyzed situations.
Twitter Rumor Detection Technology Based on Hierarchical Capsule Network

The widespread use of social media enables people to quickly and conveniently obtain and share information, and it also brings about the rapid spread of false information. A large number of online rumors flooded the social network media, which had a serious impact on people’s lives and social stability. The research on the detection technology of network rumors has become a hot research topic in recent years. The capsule network can clearly view the hierarchical structure of the data by viewing the activated path. This paper proposes a rumor detection model based on a hierarchical capsule network. The model can use static routing and dynamic routing to obtain clear input signals and accurately determine data information; the model fully takes into account the spatial structure characteristics of the text, effectively identify the content of the text. Therefore, this model can effectively improve the accuracy of rumor detection. This experiment uses twitter public datasets, under different conditions of dynamic routing and static routing, compared with the existing typical rumor detection models, the accuracy of the proposed model is improved by 6.1% and 5.6%.
Circularly Polarized Self-oscillating Active Integrated Slot-Loop Antenna Using Cross-Coupled Pair

A circularly-polarized self-oscillating active integrated slot antenna, rarely discussed in literature, is proposed and demonstrated in this paper. The proposed design simply consists of a cross-coupled pair oscillator and a slot-loop antenna. The circular polarization characteristics of the antenna were fulfilled by using spatially dual-feed technique with a quadrature phase difference. The fabricated antenna shows a stable oscillation at 920 MHz for UHF RFID applications with an equivalent isotropic radiated power (EIRP) of 8.1 dBm. The polarization characteristics of the proposed antenna were verified by a simple polarization sensing system capable of capturing the amplitude ratio and phase difference between two orthogonal field components simultaneously. Good agreement between the simulated and measured results is observed.
Neighborhood Component Analysis and Artificial Neural Network for DDoS Attack Detection over IoT Networks

Recently, modern networks have been made up of connections of small devices that have less memory, small CPU capability, and limited resources. Such networks apparently known as Internet of Things networks. Devices in such network promising high standards of live for human, however, they increase the size of threats lead to bring more risks to network security. One of the most popular threats against such networks is known as Distributed Denial of Service (DDoS). Reports from security solution providers show that number of such attacks are in increase considerably. Therefore, more researches on detecting the DDoS attacks are necessary. Such works need monitoring network packets that move over Internet and networks and, through some intelligent techniques, monitored packets could be classified as benign or as DDoS attack. This work focuses on combining Neighborhood Component Analysis and Artificial Neural Network-Backpropagation to classify and identify packets as forward by attackers or as come from authorized and illegible users. This work utilized the activities of four type of the network protocols to distinguish five types of attacks from benign packets. The proposed model shows the ability of classifying packets to normal or to attack classes with an accuracy of 99.4%.
Cross-Band Scattering Suppression for MultiBand Base Station Antenna Arrays

This paper presents a dual-band dual-polarized interleaved base station antenna array unit based on a filtering antenna for cross-band de-scattering. The array is configured as two columns of antenna arrays operating at a higher band from 1.71 GHz to 2.30 GHz interleaved with one column of antenna array operating at a lower band from 0.80 GHz to 0.96 GHz. By inserting low-pass high-stop filters into the low-band dipole arms, a filtering antenna that can efficiently suppress the radiation at the higher band is achieved. On one hand, the obtained filtering antenna has a slightly reduced gain and narrower bandwidth, which is attributed to the filters. On the other hand, the obtained filtering antenna working at the low band has minimum negative effect on the high band antenna performance.
Plant disease detection based on lightweight CNN model

The human population keeps increasing over the last decades. It requires a significant increase in agricultural production. However, the agricultural production is greatly affected by various plant diseases. Timely and accurately identifying the types of leaf diseases is very important for plant diseases control. Convolutional neural network (CNN) is one of the most popular ways for image identification. It can automatically learn appropriate features from training data. In this paper, we propose a light-weight CNN model based on SqueezeNet. The proposed model is trained and tested using the open source PlantVillage dataset. Testing results show that the proposed model can achieve an accuracy of 98.46% while the memory requirements for this model is only 0.62 MB. This demonstrates the technical feasibility of light-weight CNNs in classifying plant diseases using embedded system.
Multi-tone ground truth estimation for ADC testing

During laboratory testing uncontrollable factors such as asynchronous clock, unknown path loss etc. can hamper the measurement process potentially leading to erroneous conclusions. In this paper we focus specifically on the problem of high speed ADC testing with one or more simultaneous sine wave input signals. Even a well constructed test environment can and will have difficulties in guaranteeing, with absolute certainly, the exact signal presented to the ADC’s input, i.e. it can be difficult to know the ground truth input signal accurately.In this paper we present 3 new algorithm to assist in this task. The first two are extensions to previous works on ground truth estimation for a single tone and DC offset with and without complete knowledge of the tone’s frequency respectively; we extend these works to cover multi-tone testing and introduce a very general and compact matrix formulation. The third algorithm extends this work again by considering the very common scenario where all the tones are frequency locked thereby reducing the size of the solution space and increasing accuracy of the ground truth estimation.
A Hybrid Communication Protocol for IoT Applications in Smart Domain

Internet connected objects also known as IoT is redefining our lifestyle and transforming the way we interact with the technology. The proposed hybrid communication protocol presents a secure way of validating the entities involved in a communication system using mutual authentication mechanism and a secure data transmission using encryption/decryption and MAC generation. In this protocol, key development for encipher/decipher and MAC creation is done using logistic system, a widely used disorderly system. This also has a decision enhancement system (DES) for noise detection and refining the decisions ought to be made by the gateway server and eventually stores the corrected data to the cloud where further behavioral pattern predictions are done using AI algorithms.
Cyber Intrusion Detection using Natural Language Processing on Windows Event Logs

This paper applies deep learning and natural language processing to Windows Event Logs for the purpose of detecting cyber attacks. Data is collected from an emulated network that models an enterprise network. The network experiences a cyber attack that uses a spear phishing email and the eternal blue exploit to spread botnet malware. A machine learning anomaly detection algorithm is constructed using the transformer model and self-supervised training. The model is able to detect both the compromised devices as well as attack timing with near perfect precision and recall. These results suggest that this approach could function as the detection portion of an autonomous end point defense system wherein each device is able to react independently to potential intrusions.
Semantic Segmentation of Brain Tumor from 3D Structural MRI Using U-Net Autoencoder

Automated semantic segmentation of brain tumors from 3D MRI images plays a significant role in medical image processing, monitoring and diagnosis. Early detection of these brain tumors is highly requisite for the treatment, diagnosis and surgical pre-planning of the anomalies. The physicians normally follow the manual way of delineation for diagnosis of tumors which is time consuming and requires too much knowledge of anatomy. To resolve these limitations, convolutional neural network (CNN) based U-Net autoencoder model is proposed which performs automated segmentation of brain tumors from 3D MRI brain images by extracting the key features of the tumor. Additionally, Image normalization, image augmentation, image binarization etc. are applied for data pre-processing. Later on, the model is applied to the new 3D MRI brain images to test the accuracy of it. Applying the proposed method, the accuracy is obtained upto 96.06% considering the 18 subjects. Finally, this approach is a well-structured model for segmenting the tumor region from MRI brain images as compare to the other existing models which may assist the physicians for better diagnosis and therefore, opening the door for more precise therapy and better treatment to the patient.
An Enhanced Controller for Four Leg Inverter-fed Loads in an Aircraft Power System

This paper proposes a novel controller for improving the output voltage regulation of a three phase four-leg inverter feeding balanced and unbalanced ac loads in an aircraft power system. Conventional autonomous three phase inverter control uses open loop reference phase angle to perform transformation from stationary to synchronously rotating reference frame and vice versa. However, this method can cause inaccuracy in output voltage tracking under heavy loads, due to phase delay caused by significant voltage drop in the output filter and long cable impedances from inverter to load end. To obtain accurate tracking and dynamic response of output voltage with respect to the commanded reference, control improvement based on forward transformation of the sensed feedback output voltage and current using PLL angle is proposed. The proposed strategy is verified through simulation, and also the Controller Hardware-in-Loop(C-HIL) results are provided to show the effectiveness of the proposed control improvement.
Numerical Analysis of Optical Wavelength Switching by Dispersive and Nonlinear Dielectric in 2D Photonic Crystal

For optical wavelength division multiplexing (WDM) signal, optical duplexer composed of square-lattice photonic crystal structure with nonlinear and dispersive material was analyzed numerically. The output power distribution in two output ports was demonstrated for duplexer by linear dielectric material. The result suggests that optical signal switching is available depending on signal wavelength. This is useful for all-optical signal switching for optical WDM telecommunication system.
Automatic Target Recognition on High Resolution SAR Images with Deep Learning Domain Adaptation

Synthetic Aperture Radar (SAR) images provide a good means of analyzing large areas despite fog, clouds, etc. High resolution SAR images enable the detection of a wide variety of relevant targets to detect potential threats and assess the situation. However, the vast amount of information and the peculiarities of SAR imagery require a huge amount of trained experts and/or time to comb through the data. To reduce this massive time and cost factor automatic target recognition is required to detect and classify potential targets. With the recent advances in the field of deep learning based domain adaptation, the automatic detection can now be achieved on an unprecedented level. We demonstrate, how simulated data can be used to almost eliminate the need for the rare and very cost intensive real target data. In particular, we show that even without any real training data, a very good classifier can be trained.
Reflector-backed Microstrip Patch Antenna For Medical Body Area Network

This paper presents reflector-backed microstrip patch antenna covering the medical body-area network (MBAN) and a 2.4 GHz industrial, scientific, medical band. The combination of electromagnetic coupled feed line and reflector is proposed to widen impedance bandwidth and reduce antenna sensitivity to human body effects. The proposed antenna radiates in unidirectional beam to meet the requirement of on- and off-body propagation. 3-layer tissue model is used to represent human body for on-body channel simulation. The antenna is built on substrate with permittivity of 3.3 and finite ground plane of 50mm × 50 mm. The antenna, when evaluated at fc=2.45 GHz, achieves -10 dB S11 bandwidth of 7.7% (on-body) and 8.5%(off-body), and realized gain of 6.6 dBi. Good on-body performance of the proposed antenna shows its prospect in MBAN.
A Deep Learning-Based Approach for Camera Motion Classification

The automatic estimation of the various types of camera motion (e.g., traveling, panning, rolling, zoom…) that are present in videos represents an important challenge for automatic video indexing. Previous research works are mainly based on optical flow estimation and analysis. In this paper, we propose a different, deep learning-based approach that makes it possible to classify the videos according to the type of camera motion. The proposed method is inspired from action recognition approaches and exploits 3D convolutional neural networks with residual blocks. The performances are objectively evaluated on challenging videos, involving blurry frames, fast/slow motion, poorly textured scenes. The accuracy rates obtained (with an average score of 94%) demonstrate the robustness of the proposed model.
Silicon nanoribbons based printed transistors for high-performance flexible electronics

In this work, we present a room-temperature (RT) processable p-type Si nanoribbons-based field effect transistor (NRFETs) as building blocks for high performance flexible electronics. The nanoribbons (NR) fabrication process was optimized for selective p-type doping for source/drain and then they were transfer printed on flexible substrate. Following this the high quality silicon nitride (SiNx) dielectric was deposited at RT through inductively coupled plasma chemical vapour deposition ICP-CVD method. The transfer and output characteristics of p-type NRFET are presented along with device stability evaluation for a total of 100 bending cycles. The extracted mobility (~85 cm2.V-1s-1) and current on/off ratio (>103) show excellent potential for using transfer printed p-type Si NRs as building blocks for high performance flexible CMOS circuits.
Capon's Beamformer and Minimum Mean Square Error Beamforming Techniques in Direction of Arrival Estimation

Direction of arrival (DoA) estimation is among the most common applications of array signal processing. Capon's beamformer is a commonly used technique that enables high DoA estimation accuracy under certain conditions. Minimum mean square error (MMSE) beamforming has not been studied as commonly as Capon's beamformer since it requires the signal characteristics of the targets, which are typically unknown. In this work, an MMSE beamforming based DoA estimation technique is presented and Capon's beamformer is derived by using the presented technique. Hence, a new perspective on Capon's beamformer is provided.
Study on Invulnerability Optimization of Optical Backbone Networks Based on Complex Networks

With the increasingly complex environment of optical backbone network, the threat it faces also increases. Network survivability optimization is one of the most important research fields of communication networks. For a given optical network, it is of great practical significance to build or backup links to reduce the influence caused by external disturbances. In this paper, combined with the resources of limited equipment, the appropriate network survivability evaluation system was selected, and the determined network attack mode was used to finally establish the survivability optimization model of optical backbone network. At the same time, combined with the specific case, the genetic algorithm was used to solve the established damage resistance optimization model, and the verification and analysis were carried out. Through the case simulation, when 10% link resources are invested, the network survivability increases by about 10%, and the cost of unit survivability is 2558.4. When 15% of the link resources are invested, the network survivability increases by about 25%, and the cost per unit survivability is about 3733.3. When 25% of the link resources are invested, the network survivability increases by about 30%, and the cost per unit survivability is about 5521.3. When 35% of the link resources are invested, the network survivability increases by about 60%, and the cost per unit survivability is about 3909.0. Therefore, according to the existing resources and the expected optimization degree, this paper can effectively provide a specific optimization scheme, which provides theoretical support and practical guidance for the destruction resistance optimization of optical networks.