Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling Light to Optimize Growth and Added Value of the Green Macroalga Codium tomentosum

Codium tomentosum is a recently domesticated green macroalga, being currently cultured as an extractive species in integrated multi-trophic aquaculture (IMTA). Optimization of light requirements in outdoor systems must be achieved to increase the market value of cultivated algal biomass. The present study addresses the seasonal effects of light intensity and wavelength on productivity, pigment composition and epiphyte overgrowth in C. tomentosum cultured in a land-based IMTA system. Exposure to high light (non-filtered sun light) lead to higher net productivities in spring. However, non-filtered sun light caused significantly reduced productivities during summer when compared to filtered sun light (~ 4x lower irradiance levels). Furthermore, lower photosynthetic capacity (Fv/Fm) was observed in macroalgae cultured under high light during summer, indicating photoinhibition. Treatments with filtered sun light (low and red light) showed intermediate and more stable productivities. Epiphyte biomass was higher under high light and the lowest epiphyte overgrowth was recorded under red light. Concentrations of light-harvesting pigments were lower in summer than in spring, indicating a seasonal photoacclimation of macroalgae. An opposite seasonal trend was observed for accessory xanthophylls, as the main role of these pigments is photoprotection. Higher all-trans-neoxanthin and violaxanthin concentrations were found in high light than in low or red light treatments, confirming the important role of these biomolecules in the photoprotection of C. tomentosum. This study underlines the importance of controlling light to optimize algal growth outdoors and enhance the production of high-value compounds (i.e., pigments). Additionally, this practice can also reduce epiphyte overgrowth, thus enhancing the valorization of macroalgal biomass derived from C. tomentosum aquaculture.
Assessing Student Understanding of Reflection in Engineering Education

The use of reflection in engineering education is often used but with little investigation to the utility or effectiveness in engineering education. Efforts made by the Consortium to Promote Reflection in Engineering Education (CPREE) have drawn attention to how reflection is used in the engineering curriculum. What is unknown is whether students recognize why their courses embed reflection activities. The following paper assessed student conceptions of what it means to reflect and why they are asked to reflect in their project-based design courses. Our intended goal was to see if students understood what reflection is and appreciate reflection as a lifelong skill rather than assignments to be graded or a waste of time. Our findings suggest that most students see reflection as an opportunity to look back at what they have done. A subset of students also saw reflection as a process or something that will impact future actions. Students mostly associated reflection with positive actions rather than focusing on mistakes and failures. They saw the main intent of reflection as being learner-centered, with some seeing reflection as having impact on a design project, their profession, life, or the particular class in which they were asked to reflect. This explicit look at student conceptions of reflection within a design context provides faculty with an understanding of what students bring to their class and how they can frame reflection activities to better compliment their students learning.
An FPGA Accelerator for Genome Variant Calling

In genome analysis, it is often important to identify variants from a reference genome. However, identifying variants that occur with low frequency can be challenging, as it is computationally intensive to do so accurately. LoFreq is a widely used program that is adept at identifying low frequency variants. This paper presents an FPGA-based accelerator for LoFreq. In particular, this accelerator is targeted at virus analysis, which is particularly challenging, compared to human genome analysis, as the characteristics of the data to be analyzed are fundamentally different. This accelerator can achieve up to 120× speedups on the core computation of LoFreq and speedups of up to 32.4× across the entire program.
Transfer Learning-Based Fully-Polarimetric Radar Image Classification with a Rejection Option

In this paper, convolutional neural networks based on transfer learning are employed for the classification of fully polarimetric radar images with a rejection option. In a conventional supervised classification problem, the network has to choose from one of the known classes. However, a classifier, in a real-world scenario, may deal with an open set recognition problem in which the target under test is not included in the classifier training set. The capability of a classifier to discriminate between known and unknown targets may enable the classifier to self-learn from the experience as well as to enrich the system memory, which may in turn open the door to cognitive classifiers. This paper aims at testing the capability of transfer learning approaches, using VGG16 and AlexNet, to recognize unknown target classes of ISAR images. A threshold-based scheme has been applied to the softmax scores to discriminate among known and unknown targets. Data augmentation techniques such as translation and Gaussian noise addition are also employed in order to expand the diversity of the training dataset.
Long-Range LoRaWan backscatter based sensors for medical and wearable applications

In this work, a system for long range and low data rate transmission based on backscattered Long Range Wide Area Network (LoRaWAN) packets is studied. Two prototypes have been developed. The first has been designed for deep implants that work at MICS (Medical Implant Communication Service) band. The other prototype is designed for the European 868 MHz ISM band. Both devices use a ceramic antenna with a matching network tuned to the desired frequency band, offering high radiation efficiency. Coverage measurements show that the backscatter system is feasible to read data from deep implants and wearable devices at a distance of several meters from the carrier due to the high sensitivity of commercial LoRa transceivers.
Wideband 6-Bit SiGe BiCMOS T/R Module Core-Chip for X-Band Phased-Arrays

In this paper, a 8-to-12 GHz 6-bit SiGe BiCMOS transmit/receive (T/R) module core-chip is presented. Its sub-blocks are designed by considering the 3-dB gain-bandwidth (BW) to provide a linearly increasing gain for the core-chip. The design achieves 21.95/23.86 dB of peak gain with a slope of +3.7/+3.86 dB/GHz from the receiver and transmitter chains, respectively. The receiver's measured NF is 12.8 dB, while the transmitter's output-referred compression point is measured as 13.6 dBm. The 18.61 mm2 core-chip achieved maximum 5%0.5 dB of RMS errors respectively, dissipating 400 mW of power. To the best of authors' knowledge, the presented SiGe BiCMOS 6-bit core-chip has the best gain and BW features without sacrificing from power dissipation and RF functionality.
Finding Anomalies in Radar Sea Clutter Using Radon Transforms

This paper shows an application of a technique for finding anomalies in an image. The technique is based on transforming the image in a way which reduces the ‘normative’ form into a compact region within the transform domain. This region can be eliminated and the transform inverted to leave only anomalous features. The technique is applied to range-time data from a millimetre-wave radar observing littoral sea clutter. The wavefronts are approximately straight in such images, so a Radon transform is used to compress straight lines into single points. Removing the brightest points in the transformed image leaves only the anomalies in the scene (i.e. features not corresponding to the waves on the sea).
Effects and Countermeasures at High Velocities for the Frequency Comb OFDM Radar Scheme

Conventional orthogonal frequency-divison multiplexing (OFDM) radar systems require high sampling rates to achieve fine range resolutions. To overcome this challenging requirement, the so-called frequency comb OFDM radar scheme has been proposed. It keeps the sampling rates constant and simultaneously increases the bandwidth in the radar channel and achieves very high unambiguous velocities. In the originally proposed scheme, however, ghost targets can occur at high velocities. Thus, this paper proposes and verifies an adaption of the subcarrier allocation scheme that overcomes this problem.
Real time data evaluation with wearable devices: An Impact of Artifact Calibration Method on Emotion Recognition

Smartwatch technology is transforming the environment of transmission and monitoring for stakeholders and research participants who want to provide real-time data for evaluation. A range of sensors are available in smartwatches for gathering physical activity and location data. Here, combining all of these elements allows the collected data to be sent to a remote computer, allowing for real-time monitoring of physical and perhaps emotional development. Photoplethysmography is an easy and economical optical sensing technology that is commonly used to assess heartbeats. PPG is a non-invasive device that measures the volumetric fluctuations of blood flow using a light source and a sensor at the top layer of skin. Models concerning HRV (Heart Rate Variability) analysis are being studied in various domains, including human emotion recognition (HER). Smartwatches as sensor-based devices play an essential role as photoplethysmographic (PPG) data are frequently evaluated for this assessment. However, the nature of these waves (in terms of additional interruptions) may not always be flawless, even though they are susceptible to many factors, such as motion artifacts, light sources, stress distribution, ethnic background, or circumstances. Here techniques for antique rectification play a significant role &, as a response, impact the outcome. This research proposes a novel data distortions mitigation strategy for improving emotion detection classification efficiency using photoplethysmography waves throughout auditory invitation & a Support Vector Machine (SVM) model. Compared to previously undertaken data using a conventional toolset, i.e.,48.81, the presented scheme provides an improved categorization in trigger sensing, i.e., 68.75 percent. An alternative indicator, such as electroencephalographic activity, could be used in conjunction with PPG for further improvement.
Joint Range-Angle Super-resolution Estimation for Frequency Division MIMO Radar

A new joint range-angle super-resolution estimation algorithm is proposed for Frequency Division Multiple-Input Multiple-Output (FD-MIMO) radar that transmits a group of narrow-band Frequency-Modulated Continuous-Wave (FMCW) signals. Firstly, the Range-Doppler maps of all transmit-receive antenna pairs are obtained, considering the range and Doppler walk corrections. Then the peak values of the Range-Doppler maps for all transmit-receive antenna pairs are arranged together to estimate the range and angle of the target by utilizing 2-Dimensional spectral estimation methods. Simulation results further verify the theoretical derivations and reveal the high range and angle resolution performance of the proposed system and algorithm.
SNCF workers detection in the railway environment based on improved YOLO v5

In nearest past years object detection techniques becomes the magic key to solving several problems in computer vision, in this work, we introduce our enhanced YOLO v5 detector for detecting SNCF (National Society of France Railroad) workers in the railway environment. Our contribution in this work is presented by creating a new dataset about SNCF workers to use for training our model detector and improving YOLO v5 by reducing the number of its parameters where we reduce the number of classes in YOLO layers to only one class, that ensure to augment the speed of detection and increase the accuracy of our detector. Finally, we apply the four versions of YOLO v5 (S, M, L, X) and compare them. We achieved a high speed in the detection of SNCF workers in YOLO v5-S with 0.1 ms and high precision in YOLO v5-X with a rate of 0.9731 %.
LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery

It has been observed that the existing convolutional neural network (CNN)-based ship detection models often result in high false detection rate in low-resolution optical satellite images. This problem arises from the following factors: 1) the current 8-b rescaling schemes make the images lose some important information about ships in low-resolution imagery; 2) the effective features of ships at low resolution are far fewer than those of ships at high resolution; and 3) the detection of low-resolution ships is more sensitive to object-background contrast variation. To solve these problems, a low-resolution marine object (LMO) detection YOLO model, called LMO-YOLO, is proposed in this article. First, a multiple linear rescaling scheme is developed to quantize the original satellite images into 8-b images; second, dilated convolutions are included in a YOLO network to extract object features and object-background features; finally, an adaptive weighting scheme is designed to balance the loss between easy-to-detect ships and hard-to-detect ships. The proposed method was validated by level 1 product images captured by the wide-field-of-view sensor on the GaoFen-1 satellite. The experimental results demonstrated that our method accurately detected ships from low-resolution images and outperformed state-of-the-art methods.
Man versus Microbe - Pre-Apocalyptic Phase Failed?

We are in the era of emerging pristine microbes and antibiotics with changing trends of urbanization and industrialization that could be both boons, as well as bane. The best exemplifi cation of growing microbial resistance could be quantifi ed to the thousands of new cases of multi-drug resistant tuberculosis emerging every year. Another concern is the incompliance of patients in taking the antibiotics for the prescribed time which again makes the antibiotics to act ineffectively in the subsequent visit. Biological warfare can be at a small scale where members of family getting hospitalized because of food poisoning to the whole nation being killed due to biological warfare. Modern techniques of bio-war attacks include a car could roam around the city emitting a contaminated aerosol through a fake tailpipe. Man-made biological warfare and battle of drug resistance is on-going threat globally. Stringent usage of multi-drug antibiotics against microbial defensive strategies and development of a coherent strategy to defend against bioterrorism are needed to be effectuated to prevent "apocalypse. "
Experimental Deep Learning Assisted Super-Resolution Radar Imaging

In recent radar applications, the demand for higher resolution imaging has been increased. Particularly, collision avoidance radars must recognize any objects of any size to prevent accidents and save lives. To address it, we design deep neural networks assisted MUSIC-based directional of arrival (DOA) estimators to enhance the resolution by increasing SNR of the radar array response. In fact, the SNR improvement is achieved by learning the noise structure using residual learning. To that end, we propose a modified residual deep network, which has higher performance than the conventional one. They are compared by the simulations in terms of eigenvalue distributions, SNR improvement, and the DOA resolution. Ultimately, in the experiment, we demonstrate the integration of the deep learning denoiser with the cumulant-based MUSIC giving more 3D details about the human shape as the radar target compared to the conventional MUSIC method.
Radar-Based Classification of Automotive-Related Scenarios using Temporal Information

Human gesture classification using radar sensors is becoming indispensable in the era of autonomous driving. Challenging automotive-related gestures need different techniques for accurate and quick classification. In this paper, classifications based on temporal radar data using CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and BRNNs (Bidirectional Recurrent Neural Networks) are going to be introduced and compared. RNNs have the advantage of getting the classification decision even before the arrival of the total data stream. Twelve challenging scenarios are presented, together with the signal processing chain. Classification is performed on successive range-velocity diagrams, which spares the step of time-frequency processing already used in other applications. Four different models are introduced which result in high classification accuracies. This shows a high potential of employing radar for gesture classification using temporal information.
Forecasting Methods and Computational Complexity for the Sport Result Prediction

Competition in gaming is one of the most widely audience watching team game in the ecosphere. Predicting the conclusion of a contest has developed so simple because to advancements in technology. We are utilising machine learning methods such as supervised learning to envisage the winner of a One Day International (ODI) cricket match. In order to train the models, we use each player's own career statistics as well as squad recitals such as flickering and speeding performances. The ability of players and many parameters, on the other hand, play an important role in determining the ultimate outcome of a match. As a result, we're employing supervised learning algorithms to forecast the match's outcome. It also aids the team's coaches in learning and analysing where the team's performance is lacking, so that the coaches may discover a solution to increase their team's strength. So, in this study, we use four different sorts of machine learning algorithms and compare them to each other to see which one is the most accurate and produces the greatest results.
Analysis, Design, and Implementation of a High-Performance Rectifier

A high-performance rectifier is introduced in this study. The proposed rectifier combines the conventional pulse width modulation, soft commutation, and instantaneously average line current control techniques to promote circuit performance. The voltage stresses of the main switches in the rectifier are lower than those in conventional rectifier topologies. Moreover, conduction losses of switches in the rectifier are certainly lower than those in conventional rectifier topologies because the power current flow path when the main switches are turned on includes two main power semiconductors and the power current flow path when the main switches are turned off includes one main power semiconductor. The rectifier also adopts a ZCS-PWM auxiliary circuit to derive the ZCS function for power semiconductors. Thus, the problem of switching losses and EMI can be improved. In the control strategy, the controller uses the average current control mode to achieve fixed-frequency current control with stability and low distortion. A prototype has been implemented in the laboratory to verify circuit theory.
Identification of voice pathology from temporal and cepstral features for vowel ‘a’ low intonation

In the sphere of medicine, voice pathology analysis is a very important field. Voice makes sound as it is made up of air and vibrations of vocal cords. The wind blows across the vocal cords, forcing them to come together and vibrate when a person speaks. This vibration generates the sound. Anything that causes a change in one's voice is referred to as a voice disorder. Around 25% of the world's population suffers from voice issues as a result of their jobs requiring them to talk much louder than normal. Voice assessment of vocal pathologies is an important technique for recognising voice disorders. As the voice signal has lot of unpredictability, feature extraction will lessen the variability present. In this work three different pathologies such as Laryngitis, Leukoplakia and dysphonia are considered. Pathological voice samples pronounced with sound of vowel ‘a’ uttered in /a/_l (low pitch) is chosen from Saarbrucken voice database. Different temporal features such as signal entropy, root-mean-square, energy, zero crossing rate and Mel-Frequency Cepstral Coefficients (MFCC) are extracted from the voice signals. Results shows that among the extracted features, MFCC is able to discriminate between different voice pathologies considered.
Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified. ” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT
Emulation of Non-Reciprocity applied in Load-Modulated Power Amplifier Architectures using Single Amplifier Load-Pull Measurements

An active load-pull measurement technique is exploited to test the performance of novel high-efficiency power amplifier (PA) concepts based on non-reciprocity. This technique allows new PA architectures, including its load-modulation, to be evaluated before any construction is necessary. Merely active load -pull measurements of a single representative device or PA, in combination with knowledge of the S-parameters of the output combiner, are needed. As a result of the technique, the load modulation on the branch PAs are exposed, thereby allowing PA designers to optimize PA performance. The technique is demonstrated by emulating a load-modulation PA, consisting of two branch PAs with their outputs combined using a non-reciprocal circulator. The resulting PA concept is designed and evaluated at 2.14 GHz.
Diagnosing Musculoskeletal Disorders from Shoulder Radiographs Using Deep Learning Models

In recent years, exploring how Artificial Intelligence, especially deep learning algorithms, may be used to analyse healthcare data has become crucial. Deep learning can be employed for assisting the doctor and supplementing the process of diagnosing diseases. Early diagnosing of the problems of the shoulder, a vital body part will be useful to manage or solve the shoulder pain with efficacy. Our study has utilized deep learning to diagnose abnormalities from radiographs of the upper extremity, specifically the shoulder. The abnormality in the shoulder has been detected from the radiographs of the shoulder using four pre-trained Convolutional neural networks (CNN) models such as VGG-16, DenseNet-201, ResNet-50, and EfficientNet-B1. We have also calculated the accuracy, precision, recall, specificity, sensitivity, Flscore, and area under the receiver operating characteristic (AUROC). Shoulder radiographs from the MURA dataset were used to test the four CNN models. From the experiments conducted, both ResNet-50 and VGG-16 had the maximum accuracy of 81%; ResNet50 had the best AUC score of 0.74 and it outperformed the other three models.