Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU Acceleration of MIP Intra Prediction in VVC

The Versatile Video Coding (VVC) standard introduced several novel encoding tools for intra frame prediction, increasing the encoder complexity when compared to previous standards. Among these novelties is the MIP tool, whose acceleration has not been tackled in the literature. Therefore, an efficient parallelization of MIP prediction targeting GPU platforms is now proposed. The presented technique makes use of alternative reference samples and computes the distortion in an approximate manner to expose and potentiate massive parallelism. Moreover, the adopted prediction scheduling and memory communication were tailored by considering the GPUs' architecture and memory hierarchy. When compared with a CPU execution, this work is capable to accelerate the MIP prediction up to 105 times at the cost of a negligible coding efficiency loss of 0.284 % BD-BR.
Introducing Stochastic Functional Link Polynomial Filters

The class of Functional Link Polynomials (FLiP) filters is very broad and includes many popular nonlinear filters, as the well-known Volterra and the Wiener nonlinear filters. They are linear in the parameters and can approximate arbitrarily well any discrete-time, time invariant, finite memory, continuous nonlinear system. This work extends the approximation capability of FLiP filters to systems that together with the previous properties have random parameters. This is achieved by extending the FLiP representation to random coefficients and applying the Kosambi-Karhunen-Loève theorem to the coefficients.
Infrared Detection Technologies in Smart Agriculture: A Review

Technology is advancing at an unprecedented rate, and it’s changing the world in ways that were once unimaginable. IoT stands for the ‘‘Internet of Things which refers to the interconnectivity of everyday objects via the internet. With the advent of artificial intelligence and machine learning, we are now seeing the emergence of new technologies that can learn, reason, and make decisions like humans. Smart agriculture is an approach to farming that uses modern technologies such as sensors, data analytics, machine learning, robotics, IoT to improve the efficiency and productivity of agriculture. Infrared detection technologies can be used for smart agricultural fields. These technologies use sensors that detect infrared radiation emitted from plants and soil, which can provide useful information about plant health, water stress, and other factors that can affect crop growth and yield. The present review paper aims to discuss and summarize spectral dedector technologies and its applications in smart agriculture for optimizing resources, reducing waste and increasing efficiency. This review has a circumference of the IR sensors used in agriculture and in the scope of the review, recent findings from 2017 to 2023 are focused and analyzed for the possible future opportunities and prospects to develop a current reference for researchers. The main implications of using IR sensing technologies for agricultural applications include precision and data-driven decision making, improved resource efficiency, early detection of diseases and pests, optimization of crop yields, and promotion of sustainability and reduced environmental impact. While IR detection technologies have numerous applications in agriculture, this review paper specifically examines four primary areas: detection of health of agricultural products; food plucking, sorting and grading; monitoring and mapping of agricultural fields; weed detection in agricultural fields. In conclusion, the utilization of IR detection technology offers farmers valuable insights to maximize crop yields and enhance the overall well-being of agricultural land through greenhouse solutions.
AI-based MultiModal to Identify State-linked Social Media Accounts in the Middle East: A Study on Twitter

State-linked propaganda on Social Media poses a new challenge at the geopolitical level for the United States and other countries. The widespread of social media platforms makes it easier for adversaries to spread disinformation, conspiracy theories and social-cyber attacks at a scale that was not possible without such networks. Not only English content on social media represents such a challenge since some of those cyber-mediated attacks are initiated by agents who target other languages. In this paper, we proposed a Multimodal AI approach to detect statelinked accounts on twitter. As opposed to previous efforts, we focus our research on the Middle East and the Anti-USA content on Twitter. We trained AI Multimodal approaches on data with categorical, textual and numerical features. The study utilized experimental Twitter data connected to numerous suspected state-linked accounts on the platform. Additionally, we collected data to represent the negative samples. The findings indicate that the significance of textual modalities and AI language models in identifying state-linked accounts was limited. Our study demonstrated the crucial significance of account metadata and other modalities to detect state-linked propaganda and the associated accounts effectively.
Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Model

With the rapid growth of software and networks, the rate of cyber-attacks has increased rapidly. As a result, the demand for a dependable and suitable Intrusion Detection System (IDS) solution for safeguarding devices and networks has become essential. Nevertheless, in order to accurately detect the activities of new kinds of crimes, particularly tasking-step incidents, an effective IDS requires an accurate and up-to-date dataset. In this study, MSCAD is used which comes with multi-step attack tasking: the initial attack is for password-cracking type, and the subsequent one is a volume-driven Distributed Denial of Service (DDoS) attack. The dataset used (MSCAD) contains five types of internet attacks including Port Scan Traffic, App-based DDoS, Volume-based DDoS, Web Crawling, and Password Cracking (Brute Force). Nine algorithms including Gaussian Naive Bayes, Bournuli Naive Bayes, Decision Tree, K-Nearest Neighbors, Catboost, XGB, and Random Forest were employed as classifiers. The accuracy rate reached over 99.9% in terms of accuracy and AUC-ROC.
Sleep Apnea Events Classification from a Dual Accelerometry System Using Deep Learning Models

Sleep apnea syndrome (SAS) is a very common chronic disease characterized by the repetition of abnormal and frequent respiratory events, including central and obstructive apneas or hypopneas. Facing this public health problem, the challenge is twofold: improving early detection with minimally intrusive devices, and helping the diagnosis with an automatic scoring of recordings. In this context, we propose to exploit a dual thoracic and abdominal accelerometry system combined with electrocardiography (ECG) to classify sleep apnea events. Several common deep learning architectures were applied and performances were compared against several configurations of signals including nasal airflow from reference polysomnography (PSG), and according to manual expert annotations from PSG recordings. A Gated Recurrent Unit network was found to be the most efficient model and the configuration including ECG and abdominal and thoracic respiratory efforts from accelerometers as input channels provided very promising classification performances of normal and abnormal respiratory events. Thus, the simple technological proposition of dual accelerometry offers the possibility of an automatic identification of SAS events, very close to the expert annotations established from PSG multi-sensors.
Research and Development of Corporate-feed Waveguide Slot Array Antennas in 120GHz and 350GHz Bands

This talk presents the research and development of the corporate-feed waveguide slot array antennas in 120GHz and 350GHz bands. The full corporate-feed gives wideband characteristics. Low-loss electrical contact is achieved by laminating several metal plates with different waveguide structures and bonding with mechanical pressure and high temperature in a vacuum. The 120GHz-band antenna has fabricated by diffusion bonding of laminated thin metal plates. The 350GHz-band antenna introduces gold-coating silicon wafers for high precision in the fabrication. The perpendicular corporate-feed is introduced by removing the side walls of the cavities to simplify the radiating layer. A two-dimensional monopulse circuit is integrated with the antenna to achieve polarity orthogonal multiplexing in the rectangular coordinate system for non-far field region to increase the transmission capacity.
Synchronized Measurements for Monitoring Power Quality in DC Systems: A Proposal

Dc grids and microgrids represent attractive solutions for the integration of renewable energy sources, storage systems and chargers for electric vehicles, all requiring power electronic interface converters. However, there is a severe lack of measurement tools to deal with dc grids, both from a scientific and normative point of view. In this scenario, the present paper proposes to extend remote synchronized measurements, commonly adopted in ac power systems, also to dc distribution. This would enable observing the state of the grid, tracking dynamics and perform power quality monitoring, in particular detecting and measuring the most significant ripple components. In this respect, a possible algorithm, based on a suitably modified Taylor-Fourier Multifrequency model, has been developed. Achieved performance is assessed through numerical simulations that mimic a realistic situation, namely a dc load supplied by a 12-pulse rectifier operating under both steady-state and dynamic scenarios. Results show how the proposed method clearly outperform a conventional DFT-based approach when off-nominal conditions are considered.
Inferring Subsurface Compaction from InSAR Land Subsidence Measurements Using The Mogi's Model

Interferometric synthetic aperture radar (InSAR) is widely used in land subsidence monitoring. However, how to infer the subsidence mechanism and subsurface soil compaction remains to be resolved. In this paper, we tried to apply the Mogi's model to infer the subsurface compaction from InSAR measurements, assuming that the subsurface compaction is integration of distributed void points. First an array of void points with certain depths representing the groundwater layer were initially set up with random volumetric values. The volumes of each spherical cavity points were iteratively calculated using the Mogi's model to simulate the surface dislocation measured by the InSAR technique. In this way the underground soil compaction volume was inferred finally. Cangzhou City in the Northern China was selected as the test area, and the Sentinel-1 SAR data was used for InSAR measurements. The preliminary result shows that the compression volume of underground soil in Cangzhou City is about 7.5 mm - 11.5 mm in the vertical direction in 2020.
Performance Analysis of a Backscatter-assisted Full-Duplex Wireless-Powered Cognitive Radio Network

To prolong the lifespan of low-power cognitive sensors, we consider a backscatter-assisted wireless-powered cognitive radio network (WPCRN) in which the sensors must harvest sufficient energy to support conventional data transmission. When the sensors experience low energy levels, they switch to the backscatter communication mode to reduce the latency prior to transmitting sensed data. Additionally, we use the full-duplex model to achieve improved spectral and time efficiency. However, due to channel uncertainty, designing robust conventional and backscatter data transmission is challenging. Thus, the goal is to design a robust data combining vector against channel inaccuracies and, consequently, maximize the sum-throughput of all the sensors. We propose an efficient and low-complexity algorithm named Joint Optimal Time and Energy Allocation with Robust Beamforming (JOTEA-R). Numerical results show that the JOTEA-R algorithm improves the sum-throughput by approximately 15% compared to the benchmark equal time allocation algorithm with robust beamforming. The proposed system model can be used in a remote environmental monitoring network in which the sensors must continuously monitor and transmit information such as temperature, humidity, air quality, and pollution levels data to a cloud data center.
A micrometeorite record in Ordovician Durness Group limestones, Isle of Skye

ABSTRACT Samples of mid-Ordovician Durness Group limestone from Skye yield micrometeorites in the magnetic fraction after dissolution in acid. The micrometeorites are concentrated in the upper part of the sequence, in the Arenig Balnakeil and Croisaphuill formations. This finding is consistent with their stratigraphic distribution determined previously in the Durness district. A single sample of contemporaneous limestones from the Highland Border Fault Zone also yielded a micrometeorite. These observations suggest an elevated flux of meteoritic matter, shortly before the greatest concentration of meteorites in the geological record in the mid-Ordovician of Sweden.
PC-SSL: Peer-Coordinated Sequential Split Learning for Intelligent Traffic Analysis in mmWave 5G Networks

Fifth Generation (5G) networks operating on mmWave frequency bands are anticipated to provide an ultrahigh capacity with low latency to serve mobile users requiring high-end cellular services and emerging metaverse applications. Managing and coordinating the high data rate and throughput among the mmWave 5G Base Stations (BSs) is a challenging task, and it requires intelligent network traffic analysis. While BSs coordination has been traditionally treated as a centralized task, this involves higher latency that may adversely impact the user’s Quality of Service (QoS). In this paper, we address this issue by considering the need for distributed coordination among BSs to maximize spectral efficiency and improve the data rate provided to their users via embedded AI. We present Peer-Coordinated Sequential Split Learning dubbed PC-SSL, which is a distributed learning approach whereby multiple 5G BSs collaborate to train and update deep learning models without disclosing their associated mobile users data, i.e., without privacy leakage. Our proposed PC-SSL minimizes the data transmitted between the client BSs and a server by processing data locally on the clients. This results in low latency and computation overhead in making handoff decisions and other networking operations. We evaluate the performance of our proposed PC-SSL in the mmWave 5G throughput prediction use-case based on a real dataset. The results demonstrate that our proposal outperforms conventional approaches and achieves a comparable performance to centralized, vanilla split learning.
The Latest Developments in Software Defined Networking: Adoption Rate and Challenges

Software Defined Networking (SDN) is a computer network paradigm that has the potential to simplify network management and configuration by increasing network programmability and abstraction. In contrast to traditional networks, SDN separates the control plane, which decides how to route traffic, from the data plane, which delivers traffic to specific destinations. This makes the network control more programmable, dynamic, and centralized (through the SDN controller). SDN enables network managers to easily design network services and control traffic flows because of SDN's greater degree of abstraction, which eliminates the need to set up many individual network devices (switches and routers). The SDN technique cut costs, enables flexibility in configuration, decreases deployment time, provides automation, and simplifies network architecture without requiring knowledge of vendor-specific software/hardware. This paper seeks to review the current state of SDN adoption in South Africa, the factors impeding adoption, the potential application of SDN. Finally, the best migration strategy that operators can adopt to capitalize on the benefits offered by the SDN.
Association Mapping For Drought Tolerance In Wheat (Triticum Aestivum L.)

Fluctuating climatic conditions and increasing dearth of water resources are severely affecting crop yields.The present study was conducted to explore the genetic diversity for drought tolerance in bread wheat (Triticum aestivum L.) which can be used in future breeding programs.For this purpose, 200 genotypes of bread wheat (Triticum aestivum L.) comprising current cultivars, land races and improved lines were phenotyped over two consecutive seasons (2009-2010 and 2010-2011). Analysis of variance showed significant variation in several morphological and physiological traits including peduncle length (PL), extrusion length (EL), awn length (AL), plant height (PH), leaf rolling (LR), waxiness (WAX), relative water content (RWC), accumulation of proline (Pro), seed size (SZ), grains per spike (gr/sp) and yield/plant (Y) under normal and drought conditions during both years. Using multivariate analysis, two data sets C&S (control and stressed) and C-S/C (relative performance) were prepared to examine plant responses to drought stress.1st principle component (PC) accounted 24.97% variation for the 1st year and 43.85% variation for the 2nd in C&S dataset.For C-S/C dataset, 18.12% and 15.58% variation was observed in the 1st and 2nd years, respectively.For association mapping, 108 diverse wheat accessions were selected to tag molecular markers for drought tolerance using 25 SSR loci located on chromosome 2A.To eliminate spurious associations, population structure and kinship were taken into account using 30 unlinked markers covering all 21 chromosomes.A total of 11 markers were found associated using MLM (mixed linear model) approach with phenotypic variability ranging from 6.56 to 17.8%. The marker wmc455 was found associated with seed size (r2 7.6%) and plant height (r2 7.3%) under drought conditions.The relative water content was found associated with gwm312 under drought conditions for both yearsâ€™ data.The marker barc124 showed association with three different traits under different treatments.The study detected novel QTLs for drought adaptive traits and can be used for marker assisted breeding to enhance wheat performance under drought conditions.
Critical Review: How Well Do House Plants Perform as Indoor Air Cleaners?

SUMMARY In the late 1980’s, research indicated that plants had the capability to remove volatile organic compounds (VOC) from indoor air. The findings were based upon chamber studies involving injection of a pollutant into a small, sealed chamber and following the pollutant decay, with and without plants present. The results were striking with removal rates up to 90% in 24 hr. Other studies examining this effect followed. Today, even a casual search of the internet will find many articles extolling the benefits of using plants as indoor air cleaners. However, there has been little critical analysis of the application of plants to actual indoor environments and only a few field studies have been conducted. A critical review of results of both laboratory chamber studies and field studies leads to the conclusion that indoor plants have little, if any, benefit for removing indoor air of VOC in residential and commercial buildings. Finally, recommendations for improving future studies are presented.
Seasonal Variation of Liquid and Ice Water Path in Nonprecipitating Clouds over Oceans

Abstract Seasonal variations of liquid and ice water paths (LWP and IWP) in nonprecipitating clouds over oceans are estimated for 4 months by combining the International Satellite Cloud Climatology Project (ISCCP) and Special Sensor Microwave/Imager (SSM/I) data. The ISCCP data are used to separate clear/cloudy skies and warm/cold clouds and to determine cloud optical thickness, cloud-top temperature, and sea surface temperature. SSM/I data are used to separate precipitating and nonprecipitating clouds and to determine LWP. About 93% of all clouds are nonprecipitating clouds, and about half of nonprecipitating clouds are warm (cloud-top temperature > 0°C). The average LWP for warm nonprecipitating clouds is about 6 mg cm−2. The values of total water path obtained from the ISCCP values of optical thickness for cold nonprecipitating clouds are larger than the LWP values from SSM/I, which the authors explain in terms of IWP. The average IWP for cold nonprecipitating clouds is about 7 mg cm−2, with LWP being .
Ridge extraction to identify activated motor unit on heatmap generated by source localization of HD-sEMG signals

This research explores the utilization of ridge extraction techniques for the identification of activated motor units from heat map images generated by source localization of high density surface electromyogram (HD-sEMG) signals. The primary objective of this research is to accurately determine the number of activated motor units in these images, facilitating enhanced understanding of neuromuscular activities. The study involves the application of two ridge detection approaches: the first employs Otsu's thresholding in conjunction with the watershed algorithm, while the second utilizes the Hessian matrix. By applying these methods, the aim is to enhance the accuracy of identifying ridges associated with activated motor units in the heat map images. Following ridge extraction, two methods are employed to estimate the number of motor units within the images. The first method employs the K-Nearest Neighbors (KNN) algorithm, demonstrating promising accuracy in determining the count of motor units. The second method involves the utilization of K-Means clustering; however, its performance is observed to be less consistent, especially when confronted with images containing more than two motor units. These findings underscore the importance of selecting appropriate analysis methods when working with complex neuromuscular data.
Sentiment Analysis in social media: Handling Noisy Data and Detecting Sarcasm Using a Deep Learning Approach

Social media is a platform that is highly developed with the features offered so that the use of deep learning will in the process have an artificial neural network architecture in completing tasks in certain cases. Sentiment analysis will do positive, negative and neutral assistance. The problem in this study is that the data contains in the form of slang sentences, slang and writing errors contained in social media data so that literary dictionaries and manual annotations will be applied later. can handle noisy data and can do sarcasm detection. The results obtained in this study by approaching the deep learning model with the LSTM algorithm in sentiment analysis yielded an accuracy of 0.92, a precision value of 0.83, a recall value of 0.76 and an F1 value of 0.78 while the model from RNN in detecting sarcasm has a value 0.94, precision value 0.77, recall value 0.80 and F1 value 0.85.