Miracle-dz/UniFAR-FTUs huggingface dataset:
MSFFAR-Net: A Multi-Scale Feature Fusion and Attention-Guided Residual Network for Bangladeshi Medicinal Plants Classification

The classification of plant species, particularly medicinal ones, has gained much research attention due to challenges in manual identification and the urgency to protect endangered species. The growing need for automated identification of medicinal plants has led to a focus on using leaf images, which provide abundant information and are easily accessible. The importance of medicinal plants in healthcare underscores the need for efficient identification methods, given the time-consuming reliance on experts. Vision-based systems offer fast and accurate recognition, benefiting researchers and the public in identifying these essential plant species. This work proposed a Multi-Scale Feature Fusion and Attention-Guided Residual Network (MSFFAR-Net) that incorporates pre-trained MobileNetv2 as feature extractor and a series of newly introduced Attention Residual Convolution Module to classify Bangladeshi Medicinal plants. Our model, validated through a 4-fold cross-validation, achieved high classification mean accuracy of 98.12% for BDMediLeaves and 96.18% for the Bangladeshi Medicinal Plant Dataset.
Advancements in Video Tracking Technology: A Deep Learning Approach with SiamMask

Object tracking is a crucial research focus within computer vision and holds significant value. Nonetheless, many existing trackers exhibit suboptimal tracking performance when confronted with object deformations or disappearances. To address this issue, we have introduced an attention mechanism into the original twin network, creating an attention-based dual-branch twin network. This innovation aims to tackle deformations and object disappearance issues in tracking problems and has demonstrated promising results in experimental trials. The incorporation of the attention mechanism substantially enhances the tracking effectiveness of the tracker, with a notable boost in accuracy.
Improving Skin Cancer Diagnosis: A Comprehensive Analysis of Transfer Learning and Ensemble Models

Skin cancer has become a major global health issue, necessitating improved diagnostic methods for better clinical outcomes. This study thoroughly evaluates deep learning methods for classifying skin cancer using dermoscopic images. We compare the performance of popular pre-trained models, including VGG16, ResNet50, Xception, and MobileNet, against a natively trained SqueezeNet model. The results show that VGG16 and ResNet50 models have achieved state-of-the-art accuracy of 94%, with SqueezeNet following closely at 93%. This validates the effectiveness of transfer learning. We suggest using ensemble models that combine pre-trained networks (VGG16, MobileNet, ResNet, and Xception) to achieve a maximum accuracy of 97% using model averaging. Beyond accuracy, our ensemble models excel across Precision, Recall, and F1-score metrics, emphasizing their potential for real-world usage. Additionally, we present ROC curve analysis and confusion matrices to provide clinical insights. This study highlights the capabilities of transfer learning for skin cancer classification while introducing novel ensemble techniques that can profoundly impact computer-aided diagnosis through improved accuracy and robustness.
Energy and Computational Efficient Precoding for LEO Satellite Communications

This paper focuses on energy efficiency (EE) pre-coding design and computational-efficient precoding updating strategy for low earth orbit (LEO) satellite communications. Firstly, we formulate the EE precoding problem, which aims to maximize the EE metric under the quality of service (QoS) constraint and per-antenna power constraint (PAPC). By intro-ducing semidefinite relaxation, first-order Taylor approximation, and quadratic transformation, the problem is transferred into a convex one that can be efficiently solved. Moreover, due to the continuous movement of LEO satellites, precoding is performed frequently to maintain the high EE performance, leading to high computational complexity. Consequently, we consider prolonging precoding intervals to reduce complexity while alleviating severe performance degradation during the intervals. To this end, a computational-efficient beam direction change (BDC) algorithm is proposed to update pre coding vectors, which makes the main lobes of beams always point toward users. Furthermore, an adaptive method is proposed to adjust the precoding interval flexibly. Simulation results have indicated the effectiveness of the EE precoding algorithm and the BDC algorithm.
One Automaton to Rule Them All: Beyond Multiple Regular Expressions Execution

Regular Expressions (REs) matching is crucial to identify strings exhibiting certain morphological properties in a data stream, resulting paramount in contexts such as deep packet inspection in computer security and genome analysis in bioinformatics. Yet, due to their intrinsic data-dependence characteristics, REs represent a complex computational kernel, and numerous solutions investigate pattern-matching efficiency in different directions. However, most of them lack a comprehensive ruleset optimization approach to truly push the pattern matching performance when considering multiple REs together. Thus, exploiting REs morphological similarities within the same dataset allows memory reduction when storing the patterns and drastically improves the dataset-matching throughput. Based on this observation, we propose the Multi-RE Finite State Automata (MFSA) that extends the Finite State Automata (FSA) model to improve REs parallelization by leveraging similarities within a specific application ruleset. We design a multi-level compilation framework to manage REs merging and optimization to produce MFSA(s). Furthermore, we extend iNFAnt algorithm for MFSAs execution with the novel iMFAnt engine. Our evaluation investigates the MFSA size-reduction impact and the execution throughput compared with the one of multiple FSA in both single-and multi-threaded configurations. This approach shows an average 71.95% compression in terms of states, introducing limited compilation time overhead. Besides, best iMFAnt achieves a geomean $5.99\times$ throughput improvement and $4.05\times$ speedup against single and multiple parallel FSAs.
Secure Land Purchasing using Different Multi-Party Skyline Queries with Anonymous Information

A large number of available information causes data overload for users to choose the optimal options. In case of online land purchasing, it is challenging for a user to choose an optimal option from a vast number of lands with different areas and prices. Again, datasets of the seller (i. e. land owner) are sensitive since it is a business issue. Therefore, ensuring the privacy of the information, anonymity of the seller and their information etc. are crucial. This paper proposes a framework to find out the dominant (i. e. optimal) datasets using several skyline query algorithms namely block nested loop (BNL), bitmap, index, and nearest neighbour (NN). Besides, to ensure data privacy, separate decryption keys are allocated among multiple authorities, to ensure data anonymity, mixed network-based ElGamal cryptosystem with re-encryption and shuffling techniques are used, to ensure anonymity of the seller, an anonymous identity after registration phase is assigned. Lastly, The selection processes, privacy, result analyses etc. shows the efficiency of the framework and makes easier for land buyers to choose the optimal options. This paper is a preliminary report of the proposed system.
Classification of NFR based Importance Level of User Story in Agile Software Development

The fact that requirement engineering creates effective technology and prevents system failure is very important in the early phases of software development. The final implementation criteria and the selection of many alternative models both heavily depend on non-functional requirements. The term "non-functional requirements" (NFR) refers to a group of qualities that software must possess, such as security, dependability, performance, etc. Extracting and considering NFR from user stories in Agile Software Development (ASD) can help developers deliver quality software that meets the expectations of users completely. This paper proposed the classification of NFR based importance level of user story in ASD using machine learning models. At the beginning of this work, requirement converted into user stories, then the weight of NFR type and importance level of each user story calculated and classified using machine learning models. The outcome of this work shows that random forest classifier and XGBoost are achieved the best results with an accuracy of 96.39% and decision tree classifier accuracy is 96.13%, with comparisons of K-nearest neighbor, linear discriminant analysis, support vector classifier, logistic regression, and naïve bayes.
OrthZig: Concurrent Transmissions Based on Waveform Orthogonality in ZigBee

As one of the key technologies in the Internet of Things (IoT), Zigbee is widely used in industrial, agricultural, or medical scenarios due to its short distance, low delay, and high reliability of communications. However, the intensive deployment and concurrent transmissions of devices in ZigBee networks lead to severe collision and interference, reducing the throughput of the entire wireless network. Effective collision resolutions have been explored in the literature, but most of them use chip sequence as unit to decompose collision through waveform comparison and subtraction, which will cause issues such as error propagation, bit errors due to scarce samples, and high computational complexity. To make up for the above deficiencies, we propose a new physical layer design called OrthZig to achieve high-precision collision resolution in ZigBee networks without central controller. The key idea of OrthZig is to utilize the Direct Sequence Spread Spectrum adopted by ZigBee physical layer to achieve concurrent transmissions of multiple users based on the orthogonality of signal waveforms and distributed coordination. Additionally, we reduce the requirement for synchronization accuracy by partitioning the sync pair in the spread sequence, ensuring orthogonality for correct decoding. Extensive simulations are conducted from multiple dimensions to simulate the overall performance. Evaluation results demonstrate that OrthZig has lower average bit error rate and higher network throughput than the state-of-the-art.
Power Management of Fuel Cell Electric Vehicle Using Single Inductor Multi Input Converter

In fuel-cell electric vehicles, batteries compensate for the vehicle's poor power density, short response time, and limited ability to store energy. Two conventional converters will combine a fuel cell and batteries. One converter will serve as a unidirectional device, facilitating the connection between a fuel cell and a load. The second converter, on the other hand, will function as a bidirectional device, enabling the charging and discharging of a battery. This paper combines the two converters, removing the need for separate inductors and reducing the number of switches for each mode. Switches operating at lower voltages reduce voltage stress while increasing efficiency and power density. The proposed converter employs a fuzzy logic controller to transition between operating modes; this decreases the number of switches required, boosts the converter's power density, and mitigates the voltage stress it experiences.
Ensemble Model Based on Deep Learning for Forecasting Crypto Asset Futures in Markets

This paper investigates an ensemble convolutional and recurrent neural network architecture for cryptocurrency price forecasting. The inherent volatility and noise in cryptocurrency time series pose considerable modeling challenges. The proposed ensemble model integrates convolutional neural networks (CNN) and gated recurrent units (GRU) to jointly discern spatial patterns and temporal dynamics. The model is trained on an extensive dataset comprising daily historical prices for major cryptocurrencies spanning January 2015 to October 2023. The time series data is structured into rolling input sequences of historical prices and target outputs as future price values. Comprehensive hyperparameter tuning is conducted to optimize model performance. Rigorous validation on held-out test data enables analysis of multi-step prediction accuracy. Results demonstrate that the ensemble CNN-GRU model achieves high forecasting proficiency. Evaluation metrics including Root Mean Squared Error quantify the model's efficacy in learning the nuanced volatility signatures of cryptocurrencies. Additionally, the high R-squared scores attained, including 0.99 for Bitcoin and Ethereum and 0.98 for Ripple, underscore the model's exceptional capacity to explain cryptocurrency price fluctuations. This substantiates the model's utility for generating actionable insights for investors and analysts in the cryptocurrency domain.
Secure Cloud Storage Access Framework Using Blockchain Technology

Data is often stored as ciphertext on cloud servers to maintain privacy and security. A third-party access key and intervention are required if a data user wants to access encrypted data. A fraudulent third party, however, will jeopardize the system's security. A blockchain-based enhanced secure cloud storage access system was presented to address this issue. We achieve a novel technique by combining Ethereum blockchain technology and ciphertext-policy attribute-based encryption with a reduced attribute string. There is no reliance on a trusted third party for the suggested cloud storage architecture. Our plan contains three essential components. Our technique is superior and more viable based on processing cost, encryption, and decryption time.
Local Field Scaling for Point Surface Normal Estimation

In this paper, we propose a refined technique for point surface normal estimation utilizing local receptive field scaling. Traditional methods often struggle with artifact patterns like gradients and stripes, compromising accuracy. To address this issue, the proposed method integrates relative positioning for streamlined weighted surface approximation and local farthest point distance normalization to address artifact density and sparsity challenges. In addition, our approach enhances network training efficiency. Experimental results on public datasets confirm that our method outperforms existing techniques in the sense of both accuracy and robustness.
Performance Comparison of CMOS Based Voltage Reference Circuits in 180nm Technology

The accurate and stable generation of reference voltages is vital in low power VLSI circuit applications. In this paper, a performance comparison of CMOS-based voltage reference circuits implemented in 180nm technology node is evaluated. The study’s goal is to assess and compare the efficiency metrics of several voltage reference circuits in order to help circuit designers choose the best appropriate circuit for their specific requirements. The findings of the study have the potential to impact circuit design practices and improve the accuracy and stability of voltage references in analog and mixed signal integrated circuits. The result also highlights the potential trade-offs for further optimization in future circuit designs.
Liver Tumor Synthesis and Segmentation Based on Generative Adversarial Networks

Tumor segmentation in liver computed tomography (CT) imaging is clinically valuable to assist physicians in diagnosis and prognosis. However, limited labeled data limits segmentation accuracy. In this study, to enrich the training data, we used a label-free method to generate a mask containing tumor regions for liver CT images, then superimposed the tumor regions with liver region masks and synthesized liver CT images with tumor regions using the superimposed masks with generative adversarial networks (GANs). Finally, we verified the effect of image synthesis using manual discrimination, deep learning model segmentation and other methods. The experimental results show that the tumors synthesized by GANs have realistic shapes and textures, which can be confused with real tumors even by professional doctors. In addition, the synthesized tumor images enhance the effectiveness of segmentation models trained on real tumor images, and the proposed tumor synthesis method outperforms other studies. These results demonstrate the effectiveness of the proposed image synthesis method.
Passive Ranging with a Team of Aircraft using Angle Only Tracks

This paper presents a passive-ranging algorithm with a team of aircraft operating with infrared search and track (IRST) systems. The angle-only track output from each aircraft is shared across other aircraft, and the passive ranging algorithm is developed. A nonlinear least squares problem is formulated with the associated angle-only tracks from each aircraft to estimate the target range. A heterogeneous angle-only track-to-track association algorithm is presented and a detailed fusion architecture is presented for multi-target angle-only track-to-track association. Simulations are carried out to check the robustness of the proposed algorithm for passive ranging.
Joint Placement, Routing and Dimensioning at the Network Edge for Energy Minimization

Thanks to resource virtualization, Physical Network Operators (PNOs) can share their 5G network to multiple Mobile Virtual Network Operators (MVNOs) which can leverage the shared physical infrastructure to deploy their services up to the edge. This allows much more flexibility with respect to the previous generation of cellular networks: MVNO software components can be placed at different locations, can be allocated a certain amount of virtual resources (e.g., bandwidth, CPU cycles), and be reachable via different paths. To the best of our knowledge, strategies to minimize energy consumption while satisfying Service Level Agreements (SLAs) between the PNO and the MVNOs are still largely missing, particularly if it is required to take the nonlinearity of delays into account. To fill this gap, we formulate the problem of joint placement of software components, routing of user requests and resource dimensioning. SLAs are represented in terms of latency and reliability constraints. Via Column Generation, we obtain exact solutions in real-sized networks. Our numerical results show that we can save up to 50% energy in networks with up to 30 nodes compared to the state-of-the-art algorithms, which are focused on placement or resource minimization.
A Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications

Neural recording systems are essential for understanding the brain and developing treatments for neurological disorders. Analog-to-digital converter (ADC) is among the required building blocks of neural recording systems, as they convert the brain’s electrical signals into digital data that can be processed and analyzed by processing units. In this paper, a new nonlinear $A D C$ for spike sorting in biomedical applications has been introduced. The $A D C$ is implemented with MOSFET varactors and voltage-controlled oscillators (VCO). By exploiting the nonlinear capacitance characteristics of MOSFET varactors, the ADC has a parabolic quantization function to suppress background noise in biomedical signals. Furthermore, nonlinear digitization gives an effective resolution that is almost 0.11 bit more than its physical number of bits. The resolution of neural signals can vary from 3.1 bits in the low amplitude range to 9.11 bits in the high amplitude range. The circuit was designed and simulated using a $180 \mathrm{~nm}$ CMOS process, taking up $0.102 \mathrm{~mm}^{2}$ of silicon area. While operating at the sampling frequency of $25 \mathrm{kS} / \mathrm{s}$ and a supply voltage of 1 Volt, this ADC dissipates $62.4 \mu \mathrm{W}$.
Enhancing Emergency Response Through Speech Emotion Recognition: A Machine Learning Approach

Applications of interaction between humans and computers, such as emergency response systems, heavily depend on emotion recognition. In this experiment, we present an investigation into Speech Emotion Recognition (SER) specifically in the context of emergency calls using a meticulously curated dataset. This dataset comprises audio recordings from 18 speakers, each expressing four distinct emotions (angry, drunk, painful, and stressful) while reading predefined emergency scenarios. We conducted comprehensive preprocessing, which involved feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), Chroma (Pitch Classes), and Mel Spectrogram Frequency. The performance of many machine learning models, such as the KNeighbors Classifier, MLP Classifier, Random Forest Classifier, Gradient Boost Classifier, SVM, and Logistic Regression, was then assessed by dividing the dataset into training and testing sets. Our results reveal that the KNeighbors Classifier outperforms other models, achieving an accuracy of 67.06% and maintaining balanced performance metrics. These results offer insightful information about whether SER is practical for emergency call applications. This research contributes to the understanding of emotion recognition in critical situations and can enhance the efficiency of emergency response systems by automating emotion assessment in distress calls. Our findings have practical implications for the development of intelligent systems that can better assist emergency service providers.
Wind Power Prediction Based on BiLSTM-Attention Modeling

This study presents a Bilateral Long Short-Term Memory with Attention (BiLSTM-Attention) based model for accurate prediction of multivariate time-series data. The model combines an attention mechanism with a bi-directional LSTM architecture to capture complex temporal dependencies and focus on highly relevant data features. By experimentally analyzing multi-year real data from an onshore wind farm in Bangalore, India, and comprehensively comparing it with traditional LSTM and other benchmark models, the results show that the BiLSTM-Attention model performs better, with an RMSE of 0.1880, an MAE of 0.1211, and a MAPE of 0.6143, which results in more accurate and interpretable data predictions. The interpretability of the model helps to understand the influencing factors in depth.
Effect of Petai Bark Extract Gel on Tyrosinase and TRP1 Gene Expression (In Vivo Experimental Study on Hyperpigmented Wistar Mice Exposed to UVB Model)

UVB irradiation can induce the formation of Reactive Oxygen Species (ROS) which causes activation of melanin synthesis through activation of tyrosinase and tyrosinase related protein-1 (TRP1). Secondary metabolites contained in petai peel extract are known to play a role in inhibiting ROS production due to exposure to UVB rays. This study aims to determine the effect of administering petai peel extract gel on the expression of the tyrosinase and TRp1 genes in mouse skin tissue exposed to UVB. The research design was posttest only control group with a completely randomized design method. The samples studied were 24 mice exposed to UVB light with a wavelength of 302 nm and an energy of 390mJ/cm2/day 3 times a week for 2 weeks. This research was carried out in four groups, namely the healthy group (K1), the negative control group (K2), treatment 1 (K3) with 10% petai peel extract gel and treatment 2 (K4) with 20% petai peel extract gel. Tyrosinase and TRP1 gene expression was analyzed using qRT-PCR. qRT-PCR analysis showed that there was a significant decrease in tyrosinase and TRP1 gene expression between groups K3 (tyrosinase 3,19±2,12 and TRP1 4,96±3,42) and K4 (tyrosinase 0,65±0,44 and TRP1 2,22±1,18) compared to group K2 (tyrosinase 17,92±3,77 and TRP1 35,91±4,52). Administration of petai peel extract gel can reduce the expression of tyrosinase and TRP1 genes in hyperpigmentation mice exposed to UVB light.