Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Autonomous Electro-Optic Platforms Enabling Innovative Brain Therapies

The future of brain research lies in the application of new technologies drawing from the latest developments in biology, physics and engineering to advance our understanding of how this complex organ processes, integrates and transfers information. Among these, optogenetics is a groundbreaking technology that allows using light to selectively activate neurons in the cortex of transgenic animals, usually mice, to observe its effect in large biological networks. A new research paradigm drawing from these advances consists of synchronizing optogenetic stimulation with electrophysiology recordings, to close the loop and to regulate the neural microcircuits, or to repair them. Such an approach holds promise to accelerate the development of new therapeutics against brain diseases by enabling entirely new experimental research scenarios with freely behaving animal models. As a result, the development of advanced wireless microelectronic implantable systems to elicit, extract and process brain data in real time has become a source of significant interest. This paper reviews the design challenges and the state-of-the-art technology in this field. We present the design of a complete electro-optic device for preforming optogenetics and multichannel electrophysiology in a closed-loop (CL) system with live neurons. We cover the design of the different CMOS integrated building blocks involved in this system to perform photostimulation and multichannel neural recording in parallel. We describe advanced hardware strategies to perform action potential (AP) detection, neural data compression and AP sorting in real-time, over several parallel recording channels for enabling real-time CL neural control. Finally, we present CL experimental results obtained in vivo with an electro-optic prototype.
Homomorphic Sorting With Better Scalability

Homomorphic sorting is an operation that blindly sorts a given set of encrypted numbers without decrypting them (thus, there is no need for the secret key). In this article, we propose a new, efficient, and scalable method for homomorphic sorting of numbers: <italic>polynomial rank sort</italic> algorithm. To put the new algorithm in a comparative perspective, we provide an extensive survey of classical sorting algorithms and networks that are not directly suitable for homomorphic computation. We also include, in our discussions, two of our previous algorithms specifically designed for homomorphic sorting operation: <italic>direct and greedy sort</italic>, and explain how they evolve from classical sorting networks. We theoretically show that the new algorithm is superior in terms of multiplicative depth when compared with all other algorithms. When batched implementation is used, the number of comparisons is reduced from <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="sunar-ieq1-3030748.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="sunar-ieq2-3030748.gif"/></alternatives></inline-formula> provided that the number of slots is larger than or equal to the number of elements in the set. Our software implementation results confirm that the new algorithm is several orders of magnitude faster than many methods in the literature. Also, the polynomial sort algorithm scales better than the fastest algorithm in the literature to the best our knowledge although for small sets the execution times are comparable. The proposed algorithm is amenable to parallel implementation as most time consuming operations in the algorithm can naturally be performed concurrently.
In memory of Professor Michael Aaronovich Golub

The article briefly describes the life and scientific activity of Michael Aaronovich Golub, Doctor of Physical and Mathematical Sciences, Professor at the University of Tel Aviv (Israel), a well-known specialist in the field of diffractive optics, micro-optics, and digital holography, one of the scientists whose pioneering work became a foundation for a new scientific direction – computer optics.
RESEAP: An ECC-Based Authentication and Key Agreement Scheme for IoT Applications

Although the Internet of Things (IoT) provides many benefits for our life but it also raises many security threats. The main risk is the security of the transferred data comprising very critical information that its leakage compromises our privacy. In this regard, many security protocols have been introduced in literature, among which multi factor authentication protocols have been received considerable attention. in this paper, in the first step, the first third party security analysis of the newly proposed scheme denoted as ESEAP (designed by Kumari et al.) is presented. The provided analysis shows that this protocol has a number of security flaws including vulnerability to off-line password guessing attack, traceability attack, impersonation attack, insider attack and also desynchronization attack. For the second step, an enhanced protocol denoted as $RESEAP$ is proposed in which we use physically unclonable function to improve its security. We prove the security of RESEAP informally and also formally in real or random model, which is a widely accepted security model to prove the security of a cryptographic protocol. While the security analysis confirms that RESEAP protocol has better security, its comparison with ESEAP also shows its higher efficiency.
Model-Driven Development of Web APIs to Access Integrated Tabular Open Data

More and more governments around the world are publishing tabular open data, mainly in formats such as CSV or XLS(X). These datasets are mostly individually published, i.e. each publisher exposes its data on the Web without considering potential relationships with other datasets (from its own or from other publishers). As a result, reusing several open datasets together is not a trivial task, thus requiring mechanisms that allow data consumers (as software developers or data scientists) to integrate and access tabular open data published on the Web. In this paper, we propose a model-driven approach to automatically generate Web APIs that homogeneously access multiple integrated tabular open datasets. This work focuses on data that can be integrated by means of join and union operations. As a first step, our approach detects unionable and joinable tabular open data by using a table similarity measure based on word embeddings. Then, an APIfication process is developed to create APIs that access the previously integrated datasets through a single endpoint. A running example is presented throughout the article, as well as a set of experiments for performance evaluation to show the feasibility of our approach.
High-speed flexible arm to reduce the effect of spinning ball in table tennis

Table-tennis robots struggle to return a spinning ball to the opponent' s court because the direction of bound depends on the direction and magnitude of the spin. We aim to solve this problem using a swing motion that is robust over a large range of spin patterns without accurate recognition of the spin. Specifically, we examine the relation between the swing speed and direction of return. When the racket speed is 7 m/s, the difference in the return direction is only 20 % of that when the racket is not swung for the ball spinning in the range from -20 to 20 rev/s. In addition, we investigate the increase in swing speed of the racket due to flexibility of the swinging arm. We find that a flexible arm swings more rapidly than a rigid arm throughout the range of motion when exploiting the joint angle limit.
Deep Convolutional Architecture for Block-Based Classification of Small Pulmonary Nodules

A pulmonary nodule is a small round or oval-shaped growth in the lung. Pulmonary nodules are detected in Computed Tomography (CT) lung scans. Early and accurate detection of such nodules could help in successful diagnosis and treatment of lung cancer. In recent years, the demand for CT scans has increased substantially, thus increasing the workload on radiologists who need to spend hours reading through CT-scanned images. Computer-Aided Detection (CAD) systems are designed to assist radiologists in the reading process and thus making the screening more effective. Recently, applying deep learning to medical images has gained attraction due to its high potential. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a detection system based on DCNNs which is able to detect pulmonary nodules in CT images. In addition, this system does not use image segmentation or post-classification false-positive r eduction t echniques which are commonly used in other detection systems. The system achieved an accuracy of 63.49% on the publicly available Lung Image Database Consortium (LIDC) dataset which contains 1018 thoracic CT scans with pulmonary nodules of different shapes and sizes.
Medium Access Probability Model Based on CSMA/CA for a DSRC Network Driven by Poisson Line Process

Vehicular networks are gaining a huge momentum as a mean for an intelligent transportation system that can address numerous problems related to traffic monitoring, accidents, autonomous driving, etc. However, the deployment of vehicular networks is a big challenge for vehicles manufacturers and governments in terms of responsibility and cost. Compared to related research efforts that assume a fixed medium access probability (MAP) of nodes, this paper presents an analytical model using the stochastic geometry tool where we derive the MAP of DSRC nodes in a network composed of vehicles (DSRC nodes) that are located on a system of roads. We then model the roads using a Poisson line process (PLP) and the locations of nodes on each road as a homogeneous 1D Poisson point process (PPP). Also, we model the MAP based on the IEEE 802. 11p standard, where we consider the effect of interference and the carrier sense multiple access with collision avoidance (CSMA/CA). We validated the analytical model against Monte Carlo simulations to prove its accuracy and reliability. The importance of our work stems from the fact that the developed MAP model contributes to the understanding of future DSRC network deployments.
Computer Aided System (CAS) Of Lymphoblast Classification For Acute Lymphoblastic Leukemia (ALL) Detection Using Various Pre-Trained Models

Computer Aided System (CAS) is an automated, fast and accurate approach for detection and classification purposes. It is used to help experts or medical practitioner as a second opinion to analyze the blood smear image. It is done manually by some practitioners but it is time consuming and creates confusion as different pathologists give different observations and results as it is highly dependent on the experts’ skills. Other than that, it is also challenging to analyze it manually as there are thousands of images. Some researchers used CAS by applying the machine learning to classify the data. However, significant features must be known before proceeding with classification process. In this paper, Convolutional Neural Network (CNN) is applied to classify the WBC types to identify Acute Lymphoblastic Leukemia (ALL). It is a better approach as no complex features need to be designed and it is a fast response program. Pre-trained models of deep learning which are AlexNet, GoogleNet and VGG-16 are compared to each other to find the model that can classify better. There are 260 images in IDB-2 database and 242 images in LISC database. Five types of WBC are classified for LISC database while for IDB-2 database, Lymphoblast and Non-Lymphoblast is classified specifically. As a result, for both database, AlexNet achieve the best result in terms of the training and testing accuracy for each class. Training accuracy for IDB-2 is 96.15% while testing accuracy for Lymphoblast and Non-Lymphoblast is 97.74% and 95.29% respectively. Training accuracy by AlexNet for LISC is 80.82% and testing accuracy is the highest for each class except Monocyte. Overall, AlexNet works better than the other two models for classification for both databases.
Data Analytics, Machine Learning and Risk Assessment for Surveillance and Situation Awareness

Modern surveillance networks are able to provide trajectories of all kinds for aircrafts and vessels worldwide or at least in extended areas of the airspace or earth surface. Best known are Automatic Dependent Surveillance – Broadcast (ADS-B) and (Satellite-) Automatic Identification System (AIS) used in air and maritime surveillance. Both of them are cooperative systems. Besides these sources, sensors based on ground installations or mounted on airborne and space-based platforms deliver object trajectories independently of any transponders. This is done by advanced tracking and fusion algorithms generating trajectories out of sensor measurements. Examples include GMTI radar-based systems operating on UAV platforms or imaging systems based on high altitude pseudo satellites (HAPS) and satellites. These surveillance systems enable a continuous extraction of mid- and long-term trajectories of objects. Besides the trajectory generation, the challenge will be to place them into the right context and to provide situational awareness. This includes the estimation of the intents of the tracked objects, activity-based intelligence, and the determination of patterns of life. Otherwise, even modern surveillance systems are not able to take a real advantage of the gathered data. Therefore, trajectories are further processed by data analytics and machine learning. Unsupervised machine learning offers techniques to cluster and to partition trajectories, extract highly frequented routes and points of interest, predict object movement and identify anomalous behaviour. On the other hand, transponder and broadcast systems provide additional attributes of the tracked trajectories. These labels pave the way for numerous supervised machine learning methods. The derived predictors realise the determination of object types and activities. Finally, these new data analytic techniques have to be integrated in existing near real time surveillance systems. This requires specific system architectures as well as a completely new software and hardware landscape. In summary, trajectory-based data analytics, machine learning and risk assessment are embedded on local or global clouds and use dedicated mechanisms for distributed and parallel processing
Brain Tumor Segmentation and Classification approach for MR Images Based on Convolutional Neural Networks

Brain tumor considers one of the most dangerous types of cancers as it affects the main nervous system of the human body. Therefore, several techniques using computer vision has been proposed for early diagnoses and avoiding surgical intervention. However, these techniques face challenges in terms of segmentation and classification processes to detect the brain tumor within Magnetic resonance images (MRI). This paper proposes an automated system for detecting and classifying the brain tumor. This system composed of three phases is including enhancement, segmentation, and classification. The enhancement phase utilizes the Adaptive Histogram Equalization (AHE) in order to adjust the MRI images. The segmentation process was achieved using U-NET to segment the abnormal cells from normal brain tissue. The classification phase was conducted using 3D-CNN to classify the brain tumor into a High-Grade Glioma (HGG) and a Low-Grade-Glioma (LGG). Several experiments were conducted to validate the developed system using Brats-2015 dataset. The system achieved 99.7% (Dice Similarity Coefficient) DSC as an accurate rate for segmentation, and 96% and 98.5% an accurate rate using 5-fold and 10-fold, respectively.
Etude des propriétés structurales et modélisations de poudres nanostructurées de fer obtenues par broyage mécanique

Ce travail est consacre a l’etude des proprietes structucturales et magnetiques de poudres nanostructurees de fer pur obtenues par broyage haute energie. L’analyse par spectrometrie Mossbauer et par diffraction de rayons X, en fonction du temps de broyage, nous a permis de rendre compte de la nature structurale et microstructurale de ces poudres. L’etude par diffraction de rayons X met en evidence une taille moyenne de grains cristallins (type CC) de 13 nm separes par des joints de grains dont la proportion atomique permet d’estimer leur epaisseur ~ 0. 7 nm. La structure hyperfine observee sur les spectres Mossbauer met en evidence la presence d’une seule composante magnetique a raies fines, attribuee aux grains cristallins avec un champ hyperfin typique de la phase CC. Contrairement aux mesures magnetiques de FC et ZFC, la presence de joints de grains est indispensable pour comprendre les resultats. Afin de comprendre la structure de ces poudres nanostructurees de fer et le role des joints de grains, nous avons applique la methode de Monte-Carlo basee sur l’utilisation d’un potentiel de type «Embedded Atom Method». Cette methode nous a permis de decrire une structure granulaire realiste composee de grains et de joints de grains resultant de la proximite de grains orientes differemment. La technique consiste a effectuer un recuit simule a l’aide d’une methode du thermostat modifiee pour rendre compte de l’histoire de l’echantillon (structures metastables obtenues par broyage haute energie). L’approche numerique predit une configuration structurale a l’interface qui n’est, ni de type CC, ni de type CFC mais en accord avec les resultats experimentaux.
Characterization of 3D-knitted Substrates

The paper deals with the relative permittivity measurement of the selected types of 3D-knitted fabrics by the two-line method combined with the matrix-pencil method and by the transmission/reflection method. The measured relative permittivity of the fabrics is in the range from 1.17 to 1.23. Further, the fabrics exhibit slight anisotropic behavior. The obtained results can be used for the design of the microwave devices where the 3D knitted fabrics play the role of the substrate.
Toward Practical Digital Phase Alignment for Coherent Beam Combining in Multi-Aperture Free Space Coherent Optical Receivers

In free space optical communication systems, multi-aperture coherent optical receivers based on digital coherent beam combining (D-CBC) technique can provide exceptionally high sensitivity and are more robust to the atmosphere turbulence compared with the single aperture receivers with the same collection area. D-CBC relies on the digital phase alignment algorithm (PAA) to align the different versions of signals in phase. However, due to the limited working frequency and space of the digital signal processing (DSP) circuits, the main obstacle to realizing real-time phase alignment of multiple high-speed optical signals is the computation complexity. Therefore, we need to minimize the computation complexity while guaranteeing a satisfactory performance. In this paper, we investigate the relationship between the computation complexity and the combining loss (CL) for both maximum ratio combining (MRC) and equal gain combining (EGC) based D-CBC. Universal analytical expressions are deduced that allow easy minimization of the computation complexity for both MRC and EGC based receivers according to the prescribed CL and input optical signal-to-noise ratios (OSNRs). The analytical expressions are validated by extensive numerical simulations. It is demonstrated that the computation complexity is mainly determined by the quality of the signal with a larger OSNR in MRC, while it is determined by the overall quality of the signals in EGC. When EGC is replaced with MRC, the computation complexity can be reduced by more than 55% at the same CL when the OSNR difference between the signals to be combined is above 10dB. The maximum computation complexity increases exponentially with decreasing input OSNR lower limit and the smaller the CL, the steeper the slope. Furthermore, when the prescribed CL is relaxed from 0.1 to 0.5dB, the maximum computation complexity can be reduced by about 80%. The results provide useful guidelines toward practical phase alignment on a real-time platform.
On the Fairness of the Coexisting LTE-U and WiFi Networks Sharing Multiple Unlicensed Channels

In this paper, we are the first to analyse the performance of the carrier sense adaptive transmission (CSAT) scheme in the large-scale coexisting LTE-U and WiFi networks (CLWNets) sharing multiple unlicensed channels (UCs). As the capability of idle channel detection (ICD) has significant effect on the medium access probability (MAP) of WiFi access points (WAP), the closed-from MAP of the WAP and the explicit expressions of downlink successful transmission probabilities (STPs) of LTE-U-base-station (LBS) users and WAP users are derived with/without equipping ICD capability in WAPs, where the positions of LBSs and WAPs are modelled following the Matern hard core process. Based on the derived STPs, the fairness between the LTE-U network and the WiFi network, which is defined as the minimum weighted area spectral efficiency (ASE) of LBS and WAP users, is analysed versus the number of UCs, the fairness weighting factor and the densities of LBSs and WAPs. Furthermore, the optimal off-duty-cycle (DC) ratio to maximize the minimum weighted ASE is obtained based on the derived STPs in the on-DC and off-DC durations. Our results show that the fairness between the LTE-U and WiFi networks can be achieved by adjusting the off-DC ratio under low weighting factors. The increase of UC number leads to a decrease of the optimal off-DC ratio, enhancing the transmission duration of LBSs, and the shrink of UC bandwidth only causes negligible reduction in the CLWNets ASE. In addition, the WAP medium access mechanism mode with ICD capability is preferable as it can provide a maximum of $70\%$ ASE enhancement in the CLWNets.
Research on the Factors Influencing the Intention to Use Intelligent Teaching Tools under the Integrated Model

Under the trend of information teaching on the mobile Internet, intelligent teaching tools promote the effectiveness of intelligent teaching tools in teaching design and teaching interaction, and enrich the diversification of classroom teaching. Optimize the depth of teaching interaction and the accuracy of classroom effective evaluation and feedback. This study constructs an integrated technology acceptance model based on six dimensions: performance expectation, information technology level, improvement of academic performance, technology satisfaction teaching-task adaptation, and external factors (school, society, teachers) to explore the influencing factors of teacher’ intention to use intelligent teaching tool. The results show that the six dimensions indicators of the model have a significant positive impact on the use intention. The research results play a positive role in guiding teachers to make effective use of teaching tools to carry out teaching.