Miracle-dz/UniFAR-FTUs huggingface dataset:
Collecting Digital Biomarkers on Cognitive Health Through Computer Vision and Gameplay: an Image Processing Toolkit for Card Games

Individuals suffering from cognitive impairment require early diagnosis and frequent follow-up. However, as the healthcare system is overburdened, contact moments with patients are sparse. Digital biomarkers are a form of non-intrusive continuous measurements of cognitive health, found in daily activities. They can help in the diagnosis and follow-up of cognitive impairments. Playing videogames is an example of these daily activities which requires cognitive effort, hence carrying cognitive information. However, extracting these digital biomarkers from commercial games tends to be challenging. To this end, we explored capturing digital biomarkers from existing card games through computer vision. A toolkit was designed for the standard Microsoft Solitaire Collection, allowing for unobtrusively measuring digital biomarkers. Results show that this technique allows for real-time processing of cognitive digital biomarkers. Digital biomarkers were captured from 44 participants in three age groups. An initial data exploration supports the promise of these digital biomarkers as bearers of cognitive information. Differences were seen amongst the age groups caused by age-related cognitive changes. These results suggest that digital biomarkers in commercial games can be used for unobtrusive long-term cognitive monitoring with minimal burden on patient and physician, potentially leading to a more complete, clear cognitive profile.
Live-UMLRT: A Tool for Live Modeling of UML-RT Models

In the context of Model-driven Development (MDD) models can be executed by interpretation or by the translation of models into existing programming languages, often by code generation. This work presents Live-UMLRT, a tool that supports live modeling of UML-RT models when they are executed by code generation. Live-UMLRT is entirely independent of any live programming support offered by the target language. This independence is achieved with the help of a model transformation which equips the model with support for, e.g., debugging and state transfer both of which are required for live modeling. A subsequent code generation then produces a self-reflective program that allows changes to the model elements at runtime (through synchronization of design and runtime models). We have evaluated Live-UMLRT on several use cases. The evaluation shows that (1) code generation, transformation, and state transfer can be carried out with reasonable performance, and (2) our approach can apply model changes to the running execution faster than the standard approach that depends on the live programming support of the target language. A demonstration video: https://youtu.be/6GrR-Y9je7Y
Analysis of Spectrum Aware Routing Algorithms in CR Based IoT Devices

Current research and development works have created a new concept in information and communication technologies: Internet of Things (IoT). IoT is a network of interconnected objects based on standard communication protocols. Today's wireless networks are characterized by a fixed spectrum assignment policy. In accordance with country policies and existing laws, wireless networks are configured to use the assigned frequency band. The limited available spectrum and inefficiency of spectrum utilization have created the paradigm of opportunistic spectrum idles in existing wireless networks. This new network paradigm has been called “x New Generation Networks (xG Networks)”, “Dynamic Spectrum Access (DSA)” and “Cognitive Radio Networks (CRN)”. The Internet of cognitive radio-based objects incorporates new methods in order to solve some problems such as spectrum usage, bandwidth and usage area of the objects using the internet, using the characteristics of cognitive radio networks. In this study, routing algorithms, which can be used for Cognitive Radio Based IoT objects, will be examined by considering channel switching cost, end to end delay cost, energy efficiency and bandwidth dependent cost.
Assessment of Cognitive Performance in Elderly Life via Meaningful Play

This extended abstract discusses the ongoing research on the “Dr. Solitaire” project. Together with the geriatrics department of the university hospital of Leuven, the possibilities of detecting dementia through tablet games are being explored. A tablet implementation of the popular Klondike Solitaire game was made, capturing digital biomarkers of cognitive performance. At this moment, a study is being designed to discern older adults with Mild Cognitive Impairment from their healthy counterparts.
On the Relation Between DOA-Vector Eigenbeam ESPRIT and Subspace Pseudointensity-Vector

Eigenbeam ESPRIT (EB-ESPRIT) is a subspace based method to estimate directions-of-arrival (DOAs) of sound sources from a spherical microphone array recording in the spherical harmonics domain (SHD). In recent works, nonsingular EB-ESPRIT methods have been proposed which can estimate the source DOA-vectors without ambiguities. In another recent publication, a subspace based pseudointensity-vector method (SS-PIV) has been proposed for DOA estimation. In this work, we derive the mathematical relation between the DOA-vector EB-ESPRIT and the SS-PIV method. We show that the SS-PIV can be seen as a special case of the DOA-vector EB-ESPRIT. Using this relation, we propose a novel DOA-estimator denoted as extended pseudointensity-vector (PIV). In the evaluation, we compare the DOA-vector EB-ESPRIT and the extended PIV with the SS-PIV and PIV under noisy and reverberant conditions.
Complementary Methods for Human Visual Perception of Visual Weather Lore Sky Objects Using Machine Learning Methods

Recent research indicate a surge in the use of machine learning and artificial intelligence to compliment the processes of human visual perception. In particular, applying closeness measures of digital objects is of great significance in the attempts to account for the correspondence between digitized sky objects and some human identifiable object. The scoring of computerized objects can be based on testing a combination of well-known features humans use for visual perception, with a consideration that the human visual cognition system is well tailored for discriminating structural information from visual objects. This way, benchmark tests can be used to compute some proximity of detected objects to the specified object’s reality. Apart from producing outputs for use in the predictions, object similarity tests can also act as a mechanism for quality assessment process for the results of computer object detectors. One assumption here is that similar objects cannot qualify as perfect matches to their real objects but may contain some acceptable divergence in their closeness. In this paper, algorithms for extracting shape, color and texture information in visual sky (specific to traditional weather lore) objects are investigated as candidates for visual sky objects benchmarking, and their performances compared using a collection of positive/negative instances of visual sky objects. The rationale for testing both positive/negative instances was due to the fact that while the sky objects detectors can be expected to generate positive detections, the number of false positives detectable should be negligible.
A Review on Various Methods for Classification of Massive Noisy Image

In image processing there are significant research areas to work on the image but classification of image is one of the most complex area. If the image contains blurred and noisy content, it will make the image classification process as a more complex and challenging task. Irrespective of the tediousness of such image, there are still various techniques to classify the images. If the input image contains high amount of noisy content and blurriness level, then these methods shows some limitations. The image classification results are not satisfactory and the accuracy of the methods are low. Generally, the classification methods are categorized in two main types, they are supervised and unsupervised classification. Both the methods have their own advantages as well as limitations. The main challenge is to obtain a proper and desired result from a noisy image as compared to that of the normal image. In this study we present an analytical review on various image classification techniques.
A Single-Controller-Four-Output Analog-Assisted Digital LDO with Adaptive-Time-Multiplexing Control in 65-nm CMOS

This paper presents a single-controller-four-output analog-assisted digital LDO which can regulate four output voltage domains by sharing only one digital controller with an adaptive-time-multiplexing control scheme. The area of the digital controller is 62% smaller compared to the sum of the digital controller area of four independent LDOs. An analog-assisted loop and a push-pull auxiliary loop are used to take over the control in steady state to save quiescent power, reduce output ripple and enhance the response speed. A prototype is fabricated in a 65nm CMOS process. An undershoot voltage of 100mV is measured with a 47mA/20ns load step, resulting a figure-of-merit as low as 0.12ps.
P4-NetFPGA-based network slicing solution for 5G MEC architectures

Network Slicing is one of the fundamental capabilities of the new Fifth-generation (5G)networks. It is defined as several logical networks that are created to fulfil specific Quality of Service (QoS)and Quality of Experience (QoE)requirements and are available over the same physical infrastructure. This paper proposes a novel extension to P4-NetFPGA framework to achieve network slicing between different 5G users in the edge-to-core network segment. This solution provides hardware-isolation of the performance in terms of bandwidth, latency and packet loss of 5G network traffic. The work proposed has been validated in a real 5G infrastructure.
Tensor-Train Modeling for Mimo-OFDM Tensor Coding-and-Forwarding Relay Systems

In this paper, we consider a new one-way two-hop amplify-and-forward (AF) relaying scheme with a tensor space-time coding under frequency-selective fading channels. The signals received at the destination of the multi-input multi-output (MIMO) system define a 6-order tensor which satisfies a tensor-train decomposition (TTD). We propose a new TTD based receiver for a joint channel and symbol estimation. The proposed receiver avoids the use of long training sequences and resort to very few pilots to provide unique estimates of the individual channel matrices and the symbol matrix. Numerical simulations show the performance of the new proposed TTD-based semi-blind receiver.
Downlink Throughput Statistics in interference-limited cellular systems based on Monte-Carlo Simulation Method

This paper considers the application of Monte-Carlo Simulation Method to quantify mean throughputs in interference-limited cellular systems along antenna pattern and frequency re-use factor. Firstly, we calculate the path loss between center cell to victim link receiver and all co-channel cells to victim link receiver to determine desired and undesired signals respectively. Next, AWGN and Shannon’s capacity have been used as a comparison to analyze the performance of throughputs in interference-limited cellular systems. The simulation results show that a re-use factor of N=1, directional antenna has improvement in the Shannon capacity by 242.2 % over Omnidirectional antennas whereas a frequency re-use factor of N=7 has increased to Shannon capacity by114% for N=7 over N=1.
BEV Object Tracking for LIDAR-based Ground Truth Generation

Building ADAS (Advanced Driver Assistance Systems) or AD (Autonomous Driving) vehicles implies the acquisition of large volumes of data and a costly annotation process to create labeled metadata. Labels are then used for either ground truth composition (for test and validation of algorithms) or to set-up training datasets for machine learning processes. In this paper we present a 3D object tracking mechanism that operates on detections from point cloud sequences. It works in two steps: first an online phase which runs a Branch and Bound algorithm (BBA) to solve the association between detections and tracks, and a second filtering step which adds the required temporal smoothness. Results on KITTI dataset show the produced tracks are accurate and robust against noisy and missing detections, as produced by state-of-the-art deep learning detectors.
Products exchange in a multi-level distribution network

This study addresses the problem of inventory management in a multi-level retailers' network of the company Lit Mag that manufactures and distributes mattresses. Its distribution network constitutes a central warehouse and several distribution centers located in Algerian cities, and each center delivers vendors to the area and each vendor is connected to set of customers. The network studied is composed of several levels: the starting point is the warehouse, the first level of the distributors, the second level for sellers and the last level for customers. each retailer in the network handles its order locally and independently of other retailers. In this work, we propose a collaboration between the different actors of the same level, by the exchange of products between them, in order to satisfy the demand of a seller by the residual stock of another neighbor seller and this is applied to all stages of the network. The purpose of this study in to see the impact of product exchanges in the distribution networks and their influence on the total costs of the logistics chain from the central warehouse to the final customer
Contribution of Advanced Demand Side Management to Angular Stability of Interconnected Transmission Networks

The implementation of Demand Side Management (DSM) in order to enhance power system operational flexibility has gained more and more attention during the past decade. However, DSM could endanger the system stability because it changes the characteristics of system demands. This paper establishes the overall impacts of DSM on angular stability of power system with high penetration of Renewable Energy Sources (RES) and high system operational uncertainty. The overall effects of DSM are assessed by considering the distance (in terms of Euclidean Norm) between stability manifolds of the system before and after applying DSM. The results show that changes of Euclidean Norm with pre-defined positive direction can be adopted to indicate the overall effect of DSM on system angular stability. They further indicate that depending on the system inertia, RES penetration levels and DSM application levels, the effects of DSM on power system angular stability could be very different. A modified version of the 68-bus NETS-NYPS test system in DigSilent/PowerFactory environment has been adopted to perform dynamic simulations.
Performance Analysis of User Scheduling in Massive MIMO with Fast Moving Users

This paper analyzes the effect of fast fading Doppler channel on user scheduling algorithms. Considering a millimeter wave (mmWave) downlink massive multiple-input multiple-output (M-MIMO) system, we adopt a two-stage precoding method to cancel the interference from users. Improved K-mean clustering method with weighted likelihood (WLD) similarity measure is employed to group the active users within a given region. After grouping the users, different scheduling methods, semi-orthogonal user selection (SUS) algorithm and greedy algorithm, are implemented. Initially, grouping and scheduling are performed based on the static channels. Then, the scheduled users are simultaneously served for the next time frames without rescheduling while the users are moving. With this approach, this paper reveals how static and time-varying user grouping and scheduling methods perform in terms of system sum-rate when the fast moving users experience the Doppler effect. Different scenarios of number of active users and time frames are simulated. It is observed that rescheduling is required when greedy algorithm is used in order to maintain high sum-rate, while SUS algorithm does not achieve high sum-rate compared to greedy algorithm.
Multi-Level Dual-Attention Based CNN for Macular Optical Coherence Tomography Classification

In this letter, we propose a multi-level dual-attention model to classify two common macular diseases, age-related macular degeneration (AMD) and diabetic macular edema (DME) from normal macular eye conditions using optical coherence tomography (OCT) imaging technique. Our approach unifies the dual-attention mechanism at multi-levels of the pre-trained deep convolutional neural network (CNN). It provides a focused learning mechanism by taking into account both multi-level features based attention focusing on the salient coarser features and self-attention mechanism attending higher entropy regions of the finer features. Our proposed method enables the network to automatically focus on the relevant parts of the input images at different levels of feature subspaces. This leads to a more locally deformation-aware feature generation and classification. The proposed approach does not require pre-processing steps such as extraction of region of interest, denoising, and retinal flattening, making the network more robust and fully automatic. Experimental results on two macular OCT databases show the superior performance of our proposed approach as compared to the current state-of-the-art methodologies.
Big Data for Small Business

Big data has brought a notable shift in the relationship between technology and business and the ways in which businesses operate. However, the opportunities that big data offer to businesses, come with potential adoption challenges that might have a negative impact on the adoption of big data analysis, which can result in the resistance of organizations to adopt big data analysis. This study aims to understand the reasons behind resistance of adopting big data analysis for small business in South Africa. Literature has been reviewed on changes concerns and factors influencing small business adopting big data analysis. A case study of South African small business was taken; three themes were used to theoretically analyse the collected data (decision making, challenges and technology). Data has been collected through individuals’ interviews with business owners. The study represents some of the different reasons why small South African businesses are resistant to adopt big data analysis. The findings of the study found that lack of knowledge about the real potential of big data analysis is what is stopping small businesses from adopting big data analysis. Additionally, more small business could adopt big data analysis had they been informed about the affordable analysis tools.
On-Chip Switch and Add/Drop Multiplexer Design With Left-Handed Behavior in Photonic Crystals

An integrated optical switch consisting of a Mach-Zehnder Interferometer with photonic crystals-based phase shifters is designed for telecom wavelength range. The phase modulation along the two arms of the interferometer comes from the photonic band-to-band transition on the photonic crystals that leads to a large effective index difference and results in a switching/modulation with a low-voltage, small optical interaction length, and a low insertion loss. Moreover, shifting along the same photonic band is utilized for constructing a fully reconfigurable add-drop multiplexer for tunable wavelength range with a broadband response.
An Integrated Index Towards Sustainable Digital Transformation

Emerging technologies are currently being explored globally. Governments in many countries have established related policies to update both service and manufacturing sectors. Conversely, corporate activities related to sustainable development have become active since the establishment of the Sustainable Development Goals in Rio + 20, 2012. What is of concern within these points of view is whether society is moving toward a sustainable digital transformation. This study aims to create an integrated index towards measuring sustainable digital society. As a working hypothesis, this study assumes that humans, businesses and governments are increasingly having more iterative interactions, especially with business playing a key role among Asian countries. This study therefore, focuses on the business sector to develop the analytical framework. Text-mining analysis, which is used as a tool to detect common words that represent the index's concepts. The analysis shows that headline indices in sustainable digital index concepts are composed of organizational management, technology management, environmental economy, and digital transformation. Target indices are composed of natural resource management, digital marketing and adoption, digital ethics, sustainable consumption and production, digital transition, and organizational and human resource management.
Critical analysis of instruments and measurement techniques of the shape of trees: Terresrial Laser scanner and Structured Light scanner

In this paper is described the starting of a research activity to define measurement tecniques and instruments for analyzing the shape of an olive tree in order to obtain the most interesting geometric characteristics, such as stem diameter, branch direction and crown volume. After an introduction to possible non-contact measurement systems applied till now to trees, we have choose two of them: one based on laser scanner and the other based on structured light. Both as been used to performed measurements on a test case: an olive tree. Data processing and comparision is illustrated.
Discrete Cosine Basis Oriented Motion Modeling for Fisheye and 360 Degree Video Coding

Motion modeling plays a central role in video compression. This role is even more critical in fisheye video sequences since the wide-angle fisheye imagery has special characteristics as in exhibiting radial distortion. While the translational motion model employed by modern video coding standards, such as HEVC, is sufficient in most cases, using higher order models is beneficial; for this reason, the upcoming video coding standard, VVC, employs a 4-parameter affine model. Discrete cosine basis has the ability to efficiently model complex motion fields. In this work, we investigate the motion modeling behaviour of the discrete cosine basis equipped with higher frequency cosine vectors. In particular, the developed discrete cosine basis is used as a single high-order model to describe a fisheye frame's motion; we employ this motion to produce an extra prediction reference, which is added to the HEVC list of references. Experimental results show an increase in delta bit rate, over conventional HEVC, when higher frequency cosine vectors are added in the motion modeling process. Then leveraging on this modified discrete cosine basis, we propose to employ it for predicting the motion in 360 degree video frames because of their resemblance with fisheye images. In this case, a delta bit rate of 2% is achieved, over conventional HEVC.
CRYPTOCOCCOSIS IN ADVANCE STATE OF AIDS AND ITS CHALLENGING THERAPEUTIC REGIMEN: IS IT REALLY CURABLE?

Cryptococcosis followed by Cryptococcal meningitis (CM) is a deadly disease in an immuno-compromised patient with the complication of advance human immunodeficiency virus/AIDS, which comes through an air born virus Cryptococcus neoformans, from bird dropping. Immune reconstitution inflammatory syndrome is also often associated with this disease. A 10 years study showed a 17% prevalence of CM in a tertiary care hospital, among which 53.1% were infected with AIDS. Treatment guideline is amphotericin B (AmB) and 5-flucytosine or AmB and fluconazole, but the iatrogenic effect, cost and scarcity of availability of these drugs are the leading cause of mortality due to this disease. Anyhow, various novel therapeutic approaches have been taken to reduce and/or to prevent the pathogenesis of CM for the sake of mankind. Keywords: Antiretroviral therapy, Seizures, Hypokalemia, Hypomagnisemia.