Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Boswellia sacra in Dhofar Mountains, Sultanate of Oman: Economic Value and Environmental Role

Boswellia sacra from Burseraceae family, grows wild in dry hot lands of southern part of Oman (Dhofar), where the water is limited and the land unsuitable for traditional agriculture purpose. Boswellia sacra is a multipurpose tree, but more famous for its high value olibanum. The olibanum is used for ritual and in church ceremonies worldwide, traditional medicines, pharmaceutical, perfumery, cosmetics, chewing, painting, food flavouring. In addition to this, the species has other numerous environmental, historical, and socio-economical benefits. This study is based on literature reviews, and on the interview local people, as well as on personal field experiences. The objective is to introduce the importance of the species and to study the native habitats, the distribution, and the harvesting techniques. The soil is carbonitic, isohyperthermic, lithic torriothent, poor in mineral elements. The olibanum produces when the tree tapped by making a thin vertical slicing into the tree trunk. Initial size should be about 18 cm 2 (3 × 6 cm) and 1-2 mm in depth and the diameter tree trunk should be not less than 10 cm. Tree tapping should be stopped 2-3 weeks before the rain season. The method of tree tapping and olibanum collection need to be revised for long-term tree protection.
Generative AI-Powered Spark Cluster Recommendation Engine

Apache Spark is a data processing framework that performs complex processing tasks on very large data sets very smoothly. It distributes data processing tasks across multiple compute instances, either on its own or in tandem with other distributed computing tools. With the increasing amount of data and evolution of ML models, the need to accomplish complex feature engineering/pre-processing, Model training quickly has become optimum. As compared to a single compute instance, a cluster of compute instances shows a remarkable performance boost to achieve faster data processing. Since a cluster is a combination of multiple compute instances (Worker Nodes) managed by a Master Node, the total cost to utilize such cluster configuration is very high. But it is seen that there is significant cost reduction as these clusters like (Databricks, and EMR) are governed by the Cloud platform, there are the options of paying as you use. Also depending on workload, the cluster can be designed in such a way that it provides the best performance at lower costs. But this process is manual and needs sufficient technical skills and experience to design a cluster. In the presented approach, automation of the cluster selection process is demonstrated, by developing a GEN-AI-based recommendation engine. This work presents Conditional generative adversarial networks (cGANs) which are used to produce more samples from the joint distribution of sparse custom training data. Depending upon the data workload, expected usage time, and budget presented recommendation engine suggests instance type for master and worker nodes along with the number of worker nodes needed.
An Antenna Array Design Method Based on Residual Graph Neural Network

A learning method based on residual graph neural network was proposed to improve the efficiency of antenna array design. Firstly, the geometric parameters of the antenna array are processed into graph representation, which is used as the input features of the graph neural network. After the graph encoding process, the complex-valued fully connected network with residual connections is used to predict the radiation pattern of the antenna array. Based on the residual graph neural network, a seven-element linear array structure is designed to prove its effectiveness.
SANE – A Journey for Health: Prototype Game Design and a Cognitive Walkthrough Evaluation

The SARS-CoV-2 pandemic has forced many individuals to work from home in order to minimize the risk of contracting the virus. This extended period of isolation has had a significant impact on mental health, and there is a stigma surrounding mental health issues that can prevent individuals from seeking professional help. Games have been shown to be an effective tool for learning new concepts, and in this study, we present a proof-of-concept digital game prototype designed and developed during the pandemic in 2020. The prototype game provides opportunities for users to learn about mental health and the professionals who approach related disorders, with a focus on anxiety and depression. We also evaluate the prototype game's usability and effectiveness using the Cognitive Walkthrough method. Our findings suggest that the game has the potential to contribute to the promotion of mental health and well-being, especially in times of crisis such as the SARS-CoV-2 pandemic.
An Improved Equivalent Circuit Model of Single Photon Avalanche Photodiode with Voltage-Variable Junction Capacitance and Current-Controlled Avalanche Switch

In this paper, an improved single-photon avalanche photodiode (SPAD) equivalent circuit model is proposed. As a high-performance optoelectronic device, SPAD has a wide range of applications in optical communication, biomedical and quantum communication, etc. Establishing the SPAD equivalent circuit model is crucial for the analysis and use of SPAD performance because EDA tools can direct the design and optimization of photodetection systems. In order to make the simulation of photocurrent behavior more compatible with the actual scenario, this paper adopts segmented modeling of SPAD photocurrent and uses approximate fitting function to solve the problem of non-convergence in simulation analysis. Additionally, in this study, current control switches are used to replicate the SPAD's avalanche state in response to a change in photocurrent and to provide more accurate avalanche control. To address the problem that the junction capacitance of the SPAD varies with the voltage between the anode and cathode, this paper uses ABM devices to build an accurate and portable model of the voltage-variable junction capacitance, which can improve simulation accuracy.
An Efficient Method to Extract the Effective Permittivity of the Honeycomb Composite

The effective medium theory is proposed for analyzing the periodic honeycomb structure. The equivalent model and the effective permittivity are developed from reflection coefficient of the periodic unit. The accuracy of this method is then verified by comparing the reflection coefficients between the periodic honeycomb structure and the equivalent models, revealing a stronger agreement at different incidence angles than NRW method.
Unsupervised Network Traffic Anomaly Detection Method Based on Attribute Graph in Power Internet of Things

With the rapid development of computer and communication technology, the power Internet of Things has become an inevitable trend of intelligent and informatized power grid construction. However, the openness of the power Internet of Things makes it more vulnerable to cyber attacks and affects the normal operation of the power system. Therefore, in order to improve the stability of the power system, it is necessary to detect the abnormality of the traffic data generated by the network attack and give early warning of the attack in time. However, almost all existing network traffic anomaly detection methods are strongly dependent on labeled data, manually selected features, and balanced datasets. These methods are not only expensive, but also difficult to distinguish unknown abnormal types. This paper proposes a network traffic anomaly detection method based on autoencoder and attribute graph. This method is designed to learn generic abstract features by autoencoder and avoid the influence of manual features. Then the network traffic is abstracted into an attribute graph based on abstract features, and an anomaly detection model based on the attribute graph is designed to filter out anomalous traffic depending on topology and similarity. At last, the feasibility and effectiveness of the algorithm proposed is verified, on the two network traffic public datasets (NSL-KDD and CICIDS2017). Experimental result demonstrate that the model proposed in this paper has better detection performance compared with other state-of-the-art network traffic anomaly detection algorithms in unsupervised condition, which can be effectively used for imbalanced dataset.
Direct Solution of MoM Matrix Equation Using Sherman-Morrison-Woodbury Formula-Based Algorithm with Lagrange Interpolations

In this article, the Sherman-Morrison-Woodbury (SMW) formula is applied to accelerate the direct solution of electromagnetic scattering problem for three-dimensional (3D) perfect electric conducting (PEC) objects with the Lagrange interpolations. To speed up the direct solution of the matrix equation, the dense impedance matrix is transformed into products of several diagonal matrix blocks via the SMW formula. The Lagrange interpolation algorithm effectively compresses the matrices of far blocks interactions. The numerical results demonstrate the effectiveness and accuracy of the proposed algorithm.
Room Acoustic Characterization with Smartphone-Based Automated Speech Recognition

Characterizing and monitoring the acoustic quality of a room is important for maintaining effective speech communication. Noise and echoes make speech harder to perceive, especially for individuals with auditory disabilities or remote conference participants. Reverberation time and noise level are common performance measures, but these are static measures and can be difficult for non-specialists to measure and interpret. In this work we consider smartphone-based automated speech recognition (ASR) as a proxy for speech intelligibility to assess room acoustics. We evaluate the error rate of the on-device ASR transcription of recordings of real speech and noise in a reverberant conference room. We also model the spatial processing benefits of binaural hearing by comparing recordings from an omnidirectional microphone (remote participant) and a microphone array (local participant). In a quiet room with a nearby microphone the ASR systems achieve near-perfect recognition rates, and performance degrades gradually as noise level or microphone distance are increased. Array processing improves robustness, providing an effective SNR improvement of around 3 dB. The results demonstrate that smartphone-based ASR can provide a convenient and readily available real-time assessment of the effects of noise and reverberation, even when those distortions are not apparent to local listeners.
e-Activities: Undergraduate International Students Adaptation in Binus University

Higher education institution is expected to be able to understand the important elements in managing a college in the digital age. It is expected that Binus would help international students during their adaption time. The campus could make programs so that the culture shock is easier to traverse and eliminate the traumatic effects for students. It would create positive impressions for the Binus branding activities to bring more international students in. The methodology used was constructive methodology with focus group discussions among foreign students, consisted of 6 international students from Binus University. It was found that international students were doing e-activities to learnt Indonesian language as their target country during their time before the course started, using some apps/games. At the same time, using another apps, they learnt Indonesian culture from their friends as well. They adapted to Indonesian culture by learning Indonesian language. Since they are all digital natives, they are also very familiar with digital technology. They were doing their e-activities in their daily life, including the adaptation to Indonesian culture.
Studying the Effects of Clutter Using V-Band Radar for Drone Classification

Radars are all-weather instruments that operate in various frequency bands and therefore are useful for detecting and monitoring unmanned aerial vehicles (UAV) such as drones. Majority of the research in drone classification are carried out in controlled, clutter-free indoor environments. This work presents drone detection data collected using a 66 GHz research radar from aiRadar Inc in both indoor and outdoor environments. Time-frequency analysis techniques, namely short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are used to extract micro-Doppler signatures of the rotating propellers. A qualitative comparison was done for the radar returns of a quadcopter and an octocopter, both for indoors and outdoors environment. It was discovered that the spectrograms and scalograms are noisier outdoors compared to indoors. It was also determined that the scalograms from CWT produced more distinctive features compared to STFT.
HYDRA - High Yield Dense Robotic Arms

The integration of multiple robotic arms in industrial applications has traditionally been limited by the challenges of collision avoidance and the difficulty of coordinating the movements of different types of robots from different manufacturers. We propose HYDRA, a system based for efficient robot control utilizing a 5G Non Public Network to maintain flexibility while enabling connectivity. Our approach is based on enveloping spheres for collision-free multi-robot motion planning. This approach involves a central controller, providing all necessary state information for each robots decentralized controller. Those decentralized controllers controlling the robots by providing details to the robots in an sufficiently high frequency, by interpolating between the states received from the centralized one. In this work, we explore the use of enveloping spheres in the context of robot-robot and human-robot collaboration, specifically in situations where signal latency is a critical factor in system response time and safety distance. We also discuss the potential applications of this approach in various industries, such as the integration of robots from different manufacturers and the control of a wide range of kinematics, including industrial robots, automated guided vehicles, drones, doors, and cranes. Utilizing a 5G Non Public Network further simplifies installation and contributes to the adaptability of future industrial systems. Overall, HYDRA has the potential to revolutionize the field of multi-robot motion planning and enable safer, denser robot workspaces and more efficient human-robot collaboration.
Photogrammetric determination of body length and body mass in Galápagos sea lions (Zalophus wollebaeki)

Measuring body morphometrics of large mammals is difficult, time consuming and requires a lot of equipment. Photogrammetric measurements are less intrusive, faster and only a single researcher with a bag full of equipment is necessary. In the present study a photogrammetric method to determine the body length and mass of Galapagos sea lions (Zalophus wollebaeki) is developed and evaluated. A photogrammetric method developed for body length calculations of whales (Jaquet 2006) is used, extended and improved. Pictures of animals are taken and the distance between camera and animal is used for scaling. For calibration of the camera and validation of the method some animals are captured and body parameters are measured manually. Accuracy (R², s.d.) and precision (mean deviation of manual and photogrammetric values) of the method are evaluated. The application of this photogrammetric method derives highly accurate (adult length R² = 0.777, s.d. = 3.71; mass R² = 0.867, s.d. = 6.26) and precise (mean deviations: length = 2.02, mass = 5.29) calculations of body length and body mass for adult and offspring Galapagos sea lion. Correlations between manually measured and photogrammetrically calculated values are found to be highly significant (p-values: length: p = 0.001, mass: p = 0.0001). Additionally the impact of several factors (while taking the picture, while analyzing or position of the animal in the picture) influencing the accuracy of this method are analyzed. Based on these analyses, recommendations are given for the application of the photogrammetric method to determine body length and mass of sea lions.
Energy optimal attitude control of a satellite actuated by magnetorquers in minimum time*

In this article we combine a previously introduced energy optimal cost function based on net power with a minimum-time cost for attitude control of a spacecraft. The benefit of such an approach over the previous optimization scheme which used a fixed control horizon is that the control horizon does not have to be set in advance, which can be desirable if it is difficult to set the final time of the optimization. This time optimal objective is combined with a cost function based on net energy, which is the integral of the difference between the power the satellite gains from solar power and the power it spends performing the attitude maneuvers. The weighting between these two objectives can be exploited to let the optimal solver choose the final time in cases where the exact final time of the maneuver is not critical. Magnetorquers produce a magnetic torque that can be used to control the attitude of a spacecraft, but due to their physical limitations it is not trivial to predict when the spacecraft attitude will reach its reference value. For this reason, an approach such as this where the final time is set by the optimal solver can be beneficial. The difficulties related to the use of two terms in the cost function for the distance from the reference attitude and angular velocity, together with net power cost function and time, is discussed.
Optimizing the Incremental Update Mechanism by Inlaying File Indexes on Flash Storage

With the aim of reducing network traffic and up-dating costs, emerging mobile systems generally use incremental update algorithms to upgrade the image of mobile applications. Existing incremental update algorithms reuse the data in the local old image of the application. In this paper, we analyze the file accesses patterns during incremental updates of applications and find that most data in the new image is from the old image. Thus, we propose a new file access mechanism to optimize the memory consumption of incremental update algorithms by inlaying the file indexes in the storage device. We implement the proposed mechanism in Linux kernel 5.19. 1 and evaluate it with real-world images of applications. The experimental results show that the proposed mechanism can reduce 30% -80% memory footprints in comparison with HDiffPatch, the state-of-the-art incremental update algorithm.
Aabbir: A New Solution for children with speech and intellectual disabilities.

By enabling us to express our needs and experiences to one another, communication helps us connect with people in daily life and fosters the development of relationships. While some groups in our society have trouble communicating, many of them are unable to express themselves. the absence of instruments to facilitate successful communication among individuals with disabilities and ease both their own and their communicators' everyday lives. In this paper, we suggest a unique approach to helping kids with speech and intellectual challenges. Certain children and people with speech and intellectual disabilities who are unable to utilize speech to connect with their partners, such as parents, coworkers, or friends, can benefit from AAC (augmentative and alternative communication) technology. AAC software is user-friendly, high-quality, and open source, however, Arabic-speaking people do not commonly support it. We proposed a new solution for communication apps based on AAC technology using the Firebase for backend and the Flutter application builder for the front end. Our project strives to offer a dependable and trustworthy application in the sector of augmented and alternative technologies. Our project center on putting in place a system that makes it easier for persons with complex communication requirements to communicate. The program makes sure that it satisfies the requirements for accessibility, availability, and dependability. One of the research's shortcomings, though, is the lack of Arabic-supporting software.
Model-based Loss Minimization Control of a Squirrel cage Induction Motor Drive with shorted Rotor under Indirect Field Orientation

This paper presents the development of an energy saving scheme via indirect field oriented control (IFOC) at the optimum operating region. A model-based steady state power loss analysis is carried out to determine the optimal speed and the optimal rotor flux that will serve as reference for the speed and torque tracking control. IFOC ensures the decoupled control of the torque and flux components of the stator current in a similar version as in DC drives through rotor flux orientation. The need for multiple sensors is eliminated by the analytical loss model approach, leading to an appreciable cost reduction. PI controller is used for the independent control of the flux and the torque producing components of the stator current. This is done at steady state, using the optimal rotor flux and speed computed in a loss minimization algorithm. The gains for optimum performance of the PI controller is selected via genetic algorithm (GA) optimization technique. The developed scheme is validated via a realistic numerical simulation and, the shorted rotor induction machine shows a very efficient speed and torque tracking performance at various load conditions and speed settings.
Analysis on Influence of Flight Speed on Scattering Characteristics of RAM -CII Model

The Finite Volume Time Domain (FVTD) method can be used to effectively fit complex shaped scatterer. This method can be better combined with the flow field and has high computational accuracy, which is suitable for the calculation of electromagnetic scattering characteristics of plasma coating. The electromagnetic simulation model of RAM-CII aircraft is established, and the correctness of the algorithm is verified. FVTD method is used to calculate and analyze the effect on changes in flight speed at the same altitude on the RAM-CII model with plasma sheath.
Stochastic Based Clustering and Improved Whale Optimization Algorithm to Predict Sickle Cell Anemia in Tribes of Nilgris

Genetic disease which is known as sickle cell anaemia (SCA) affects red blood cells (RBCs). A change in HBB gene causes aberrant haemoglobin to be produced. Red blood cells (RBC) with this defective haemoglobin have a sickle shape, which can result in a range of health problems. In this research, a classification-based prediction model is suggested by implementing the data mining technique for predicting the SCA disease. For features selection, the Improved Whale Optimization (IWO) is used and for classification, a stochastic based Clustering algorithm is used. The data set used in this work is a collection of sickle cell anemia test results of the tribe peoples who lives in the different areas of Nilgiris region. This dataset contains the data of 300 patients with 14 features related to SCA which are acquired from NAWA-Nilgiri Adivasi Welfare Association in Nilgris. 60% of a dataset is used for testing, while 40% is used for training. Based on testing dataset, the suggested model is assessed in terms of specificity, accuracy, PPV, sensitivity, MCC and NPV. The suggested model has overtaken the other entire compared model in expressions of all the parameters.