Miracle-dz/UniFAR-FTUs huggingface dataset:
A Repetitive Motion Obstacle Avoidance Planning Scheme for Omnidirectional Mobile Manipulators with Noise Suppression Capability

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. To make OMRA accomplish repetitive motion, obstacle avoidance, and trajectory tracking tasks in complex dynamic environments with the presence of obstacles and external disturbances, a new noise-suppressed repetitive motion obstacle avoidance (NSRMOA) scheme is proposed. As established previously many OMRA motion planning assumes that the simulation is noise free. However, noise interference may prevent OMRA from accomplishing repetitive motion, obstacle avoidance, and trajectory tracking tasks, which may lead to task failure. The proposed the NSRMOA scheme with intrinsic noise suppression can solve external noise interference, so that the OMRA can accomplish the repetitive motion, obstacle avoidance and trajectory tracking tasks in the presence of external noise interference. Finally, the optimal solution of the NSRMOA scheme can be effectively obtained by using recurrent neural network (RNN). The effectiveness and accuracy of the scheme are verified by examples and comparisons.
Deep Learning Based Multi-Modal Data Fusion for Car Crash Detection

This research introduces a deep learning model approach to enhance predictive modeling by fusing tabular and image data to improve car crash detection performance. Effectively integrating these diverse data types can significantly improve model accuracy. Using an ensemble technique, we propose an architecture that blends convolutional neural networks for images and recurrent neural networks for tabular data. Analysis performance on a real dataset showcasing its superiority over conventional single-modal models.
Advancements in Text Classification, A Comprehensive Review

This paper aims to provide a comprehensive analysis of various text classification methods that are widely used to solve text-based classification, while highlighting their strengths and understanding the limitations that can hinder the performance of classifiers. By doing so, this paper intends to enhance the awareness of text extraction possibilities in the field of machine learning (ML) and artificial intelligence (AI). Text classification is an essential aspect of data understanding, as it enables the automated categorization and organization of unstructured text data. There are different approaches used for text classification problems, such as machine learning algorithms, rule-based logics, and deep learning architectures. Each of them offers different advantages and disadvantages depending on the specific application and dataset. By analyzing these techniques in depth, we aim to provide a better understanding of their respective capabilities and limitations, allowing researchers and practitioners to select the most appropriate technique according to their problem statements. This can ultimately lead to more effective and efficient knowledge understanding from textual data in various domains, including healthcare. Overall, this review paper makes an important contribution to the field of ML and AI by providing insights into cutting-edge text categorization approaches and their practical applications.
Student Stress Patterns: A Data Driven Exploration using Machine Learning Amidst Academic Challenges and Health Impacts

Student performance is most often hampered by mental health difficulties. Students' motivation, attention, and social ties can all be impacted by mental illness, all of which are key factors in their academic achievement. Despite the widespread use of emergency remote learning (ERL) by educational institutes during the COVID-19 pandemic, little is known about the elements that influence student satisfaction and stress levels in this innovative learning environment in a crisis. Our research uses machine learning algorithms to predict the stress faced by students based on their academic routines, thus providing a timely assessment of the pandemic's impact on students' stress levels. Data collected through student surveys relating to factors such as time spent on studying, social media, health and fitness etc. provide a strong basis to determine students' stress levels. Via supervised machine learning algorithms such as Naive Bayes, Random Forest, Artificial Neural Networks (ANN) etc., predictions are made on academic stress levels by analyzing the prime factors affecting the issue at hand, and a comprehensive comparison is performed with the proposal of the most optimum algorithm for the prediction of stress level.
A low power Class D audio amplifier with discrete time loop filter compensation

This brief describes a new Class D amplifier topology with digital input and a digital compensation loop filter to improve linearity. The core of the system is a high resolution hybrid multi-bit ΣΔ-Pulse Width Modulation (PWM) modulator. The modulator does not require dynamic element matching nor signal processing to correct for distortion arising from the discrete PWM process. A system prototype based on a FPGA and commercial 16b analog to digital converter (ADC) validate the system design before implementation in 0. 13µm CMOS. Measurement results at 1kHz yield a Total Harmonic Distortion (THD) better than 0.03% and greater than 30dB of noise rejection and linearity enhancement. The system is inherently scalable to deep submicron processes due to its predominately digital architecture.
Design and Implementation of Fractal Antenna for Wireless Applications, Analysis using Machine Learning

This paper proposes a wideband fractal antenna with circle and triangle together forming a first order fractal for wireless applications from 1 GHz to 6.5GHz which includes a C-Band. , Low power Trans-receiver etc., with a minimum return loss of -55.6658dB obtained at 2.5675GHz frequency. , using line feed technique and a gain of 4.1dB. The antenna has partial ground structure and FR4 Epoxy is used as substrate material. The fractal designed resonates 4 times. Machine Learning is introduced for the analysis of antenna and it is observed that quadratic linear regression has obtained a good correlation coefficient of 0.124.
Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection.
Beverage Consumption among U.S. Children Aged 0–24 Months: National Health and Nutrition Examination Survey (NHANES)

Data on beverage consumption patterns in early life are limited. The aim of this study was to describe beverage consumption by sociodemographic characteristics, along with water intake and sources of water among U.S. children aged 0–24 months. Data from 2740 children in the 2005–2012 NHANES were analysed. Food intake was determined via one 24-h dietary recall. Beverages were categorised according to What We Eat In America groups. Poverty–Income ratio was used to define household income. During infancy (0–5.9 months and 6–11.9 months) infant formulas were the most commonly consumed beverage, 74.1% and 78.6% of children consuming, respectively. Comparatively fewer children, 41.6% and 24.3%, consumed breast milk. In toddlers (12–24 months), the most commonly consumed beverages were plain milk (83.6% of children consuming), water (68.6%), 100% fruit juice (51.8%) and sweetened beverages (31.2%). Non-Hispanic black and Mexican-American children were more likely to consume sweetened beverages, 100% fruit juice and infant formula than Non-Hispanic white children. Children from lower income households were more likely to consume sweetened beverages and 100% fruit juice and less likely to consume breast milk than children from higher income households. Total water intake increased with age and the contribution of water from food and beverage sources was ~20% and ~80% for all children, respectively. Disparities in beverage consumption by race/ethnicity and income level are apparent in early life.
Analyzing Some Efficient Deep Learners for Face Pose Estimation

An efficient face recognition system is essential for security and authentication-based applications. However, realtime face recognition systems have a few significant concerns, including face pose orientations. In the last decade, numerous solutions have been introduced to estimate distinct face pose orientations. Nevertheless, these solutions still need to be adequately addressed for the three main face pose orientations: Yaw, Pitch, and Roll. This paper proposed a novel deep transfer learning-based multitasking approach for solving three integrated tasks, i.e., face detection, landmarks detection, and face pose estimation. The face poses variation vulnerability has been intensely investigated here underlying three modules: image preprocessing, feature extraction module through deep transfer learning, and regression module for estimating the face poses. The experiments are performed on the well-known benchmark dataset Annotated Faces in the Wild (AFW). We evaluate the outcomes of the experiments to reveal that our proposed approach is superior to other recently available solutions.
Collaborative Filtering-based Movie Recommendation Services Using Opinion Mining

This article explores the fusion of collaborative filtering (CF) techniques with opinion mining methodologies to enhance movie recommendation systems. The proposed approach harnesses user-item interaction data to employ CF algorithms for generating initial movie recommendations. Furthermore, opinion mining techniques are integrated to analyze textual reviews and extract implicit sentiment signals associated with movies. Leveraging sentiment-aware adjustments, the model refines CF-based recommendations by incorporating user sentiments expressed in reviews. In specifically, conducting aspect-based sentiment analysis on movie reviews leveraging a Long Short-Term Memory (LSTM) neural network architecture. The methodology involves applying LSTM-based sentiment analysis to extract sentiments from movie reviews, followed by aspect identification and sentiment assignment to specific movie elements or aspects. This aspect-focused sentiment analysis approach using LSTM-based sentiment analysis contributes to a comprehensive understanding of movie reviews. The fusion of CF with opinion mining techniques in movie recommendation systems represents a significant advancement toward enhancing recommendation accuracy and personalization.
Enhancing Smart Grid Security through Battery Energy Storage Integration: A Security-Constrained Unit Commitment Approach

Security-constrained unit commitment (SCUC) is a main study for optimizing power system operations while ensuring security. This article presents an innovative SCUC model that incorporates battery energy storage systems (BESS) to manage the variability introduced by renewable energy sources like wind and solar. The model accounts for BESS charging, discharging, and system security implications. This study employs the Performance Index of the Line's Flow to screen and rank contingencies, identifying critical generators and transmission lines to be included in the SCUC problem. Linear sensitivity analysis coefficients, including Power Transfer Distribution Factor (PTDF) and Line Outage Distribution Factor (LODF), are calculated for these elements. Four operational scenarios are analyzed using 118-bus system, revealing that BESS integration can reduce operational costs, particularly when cost differentials between peak and off-peak periods are substantial. However, in cases with minimal cost differences, relying solely on BESS for peak load power generation may not yield significant savings. These findings offer valuable insights for optimizing power systems, enhancing grid security, and maximizing storage technology utilization in pursuit of cleaner and more efficient electrical grids.
Freezing-induced uptake of disaccharides for preservation of chromatin in freeze-dried stallion sperm during accelerated aging†

Abstract Nonviable freeze-dried sperm have intact chromatin and can be used for fertilization via intracytoplasmic sperm injection. Freeze-dried sperm preferably should be stored at 4°C or lower, because DNA damage accumulates during storage at room temperature. Disaccharides are known to protect biomolecules both during freezing and drying, by forming a glassy state. Their use is challenging because cellular membranes are normally impermeable for disaccharides. In the current study, we demonstrate that membrane impermeable compounds, including lucifer yellow and trehalose, are taken up by stallion sperm when exposed to freezing. Trehalose uptake likely occurs during freezing-induced membrane phase transitions. Stallion sperm was freeze-dried in various formulations consisting of reducing or nonreducing sugars combined with albumin as bulking agent. Chromatin stability was studied during storage at 37°C, using the flow cytometric sperm chromatin structure assay and microscopic assessment of chromatin dispersion and DNA fragmentation after electrophoresis. Freeze-drying did not affect sperm chromatin, irrespective of the formulation that was used. DNA fragmentation index (DFI) values ranged from 5 to 8%. If sperm was freeze-dried without protectants or in a combination of glucose and proteins, DNA damage rapidly accumulated during storage at 37°C, reaching DFI values of respectively 95 ± 4 and 64 ± 42% after 1 month. DFI values of sperm freeze-dried with sucrose or trehalose ranged between 9–11% and 33–52% after 1 and 3 months storage, respectively. In conclusion, freeze-drying sperm with disaccharides results in uptake during freezing, which greatly reduces chromatin degradation during dried storage. Summary Sentence Subjecting cells to freezing facilitates uptake of membrane impermeable compounds, which can be applied to load sperm with disaccharides and to increase stability of chromatin in freeze-dried sperm during storage at ambient conditions.
A Novel Sparse Array for Non-Gaussian and Quasi-Stationary Sources

In this paper, we propose a novel super sparse nested array with larger virtual array aperture. Specifically, the number of consecutive lags, the number of different lags and virtual array aperture are derived with quantitative evaluation and comparison, and the corresponding array composition parameters for optimized array structures are also provided. In the non-Gaussian and quasi-stationary scheme, the subspace method and the cumulant method are used for source characteristics. Some simulation results prove that the proposed array exhibits a superior performance.
For geometric inference from images, what kind of statistical model is necessary?

In order to facilitate smooth communications with researchers in other fields including statistics, this paper investigates the meaning of "statistical methods" for geometric inference based on image feature points, We point out that statistical analysis does not make sense unless the underlying "statistical ensemble" is clearly defined. We trace back the origin of feature uncertainty to image processing operations for computer vision in general and discuss the implications of asymptotic analysis for performance evaluation in reference to "geometric fitting", "geometric model selection", the "geometric AIC", and the "geometric MDL". Referring to such statistical concepts as "nuisance parameters", the "Neyman-Scott problem", and "semiparametric models", we point out that simulation experiments for performance evaluation will lose meaning without carefully considering the assumptions involved and intended applications.
Smoothing polyhedra using implicit algebraic splines

Polyhedron “smoothing” is an efficient construction scheme for generating complex boundary models of solid physical objects. This paper presents efficient algorithms for generating families of curved solid objects with boundaty topology related to an input polyhedron. Individual faces of a polyhedron are replaced by low degree implicit algebraic surface patches with local support. These quintic patches replace the @ contacts of planar facets with C’ continuity along all irtterpatch boundaries. Selection of suitable instances of implicit surfaces as well as local control of the individual surface patches are achieved via simultaneouss interpolation and weighted least-squares approximation.
Visage: a user interface environment for exploring information

Visage is a prototype user interface environment for exploring and analyzing information. It represents an approach to coordinating multiple visualizations, analysis and presentation tools in data-intensive domains. Visage is based on an information-centric approach to user interface design which strives to eliminate impediments to direct user access to information objects across applications and visualizations. Visage consists of a set of data manipulation operations, an intelligent system for generating a wide variety of data visualizations (SAGE) and a briefing tool that supports the conversion of visual displays used during exploration into interactive presentation slides. This paper presents the user interface components and styles of interaction that are central to Visage's information-centric approach.
Effect of heart motion on the solutions of forward and inverse electrocardiographic problem - a simulation study

Solving the forward problem of electrocardiography provides a better understanding of electrical activities in the heart. The inverse problem of electrocardiography enables a direct view of cardiac sources without catheter interventions. Today the forward and inverse computation is most often performed in a static model, which doesnpsilat take into account the heart motion and may result in considerable errors in both forward and inverse solutions. In this work a dynamic heart model is developed. With this model the effect of the heart motion on the forward and inverse solutions is investigated.
Audio de-noising by spectral subtraction technique implemented on reconfigurable hardware

This paper proposes an efficient hardware architecture for the spectral subtraction algorithm applied to speech enhancement. Spectral subtraction algorithm is widely used in audio de-noising applications. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. After estimating the noise from the input speech the noise samples are subtracted, making it noise free. In this design we have two principal blocks, the noise estimation-subtraction block and the phase block, which are executed concurrently exploiting the parallel logic blocks of field programmable gate array (FPGA). We have implemented our design on Spartan6 LX45 FPGA, which also meets the high speed requirements. Resource utilization and delay information for the different blocks in our design are presented. Our proposed hardware implementation shows a better SNR value compared to the original software implementation. To the best of our knowledge, this work is the first of its kind of implementation in regards to FPGA based hardware design for adaptive noise filtering in speech.
Automated Addressing Failure Detection for Multiplexer Systems

Multiplexers are electronic devices which select between several input signals and deliver an output signal. Also known as data selectors or mux, multiplexers are commonly used in circuit design to provide signal switching, simplify hardware and manufacturing, and decrease cost. Systems which use a digital mux as a switch or selector are vulnerable to events called addressing failures, arising in hardware or software. An addressing failure results in the system requesting, or the mux delivering, a data signal other than the one intended. The unintended use of this erroneous signal may lead to systemlevel failures. This paper discusses a novel method of automatic detection for addressing failures, utilizing multiplexer channelization and a monitoring algorithm.
OS-multicast: On-demand Situation-aware Multicasting in Disruption Tolerant Networks

Disruption tolerant networks (DTNs) are emerging solutions to networks that experience frequent network partitions and large end-to-end delays. In this paper, we study how to provide high-performance multicasting service in DTNs. We develop a multicasting mechanism based on on-demand path discovery and overall situation awareness of link availability (OS-multicast) to address the challenges of opportunistic link connectivities in DTNs. Simulation results show that OS-multicast can achieve a better message delivery ratio than existing approaches, e.g. DTBR (a dynamic tree-based routing), with similar delay performance. OS-multicast also achieves better efficiency performance when the probability of link unavailability is high and the duration of link downtime is large
Efficient Object Localization for Unseen Object 6D Pose Estimation

Object localization is utilized as the first step in standard 6D object pose estimation methods to obtain the position information of the objects. However, these object localization methods cannot be directly applied to unseen objects, which is the focus of recent research on 6D object pose estimation. In this paper, an accurate and efficient localization method for unseen object is proposed, based on a template matching strategy. The Hybrid Channel-Spatial Attention Model (HCSAM) is designed to focus on the target object by enhancing the contextual differences between the target object and background. Additionally, The Multi-Scale Integration Transformer (MSIT) module is designed to eliminate noise interference and enhance semantic information in low-dimensional features by integrating multidimensional information. Our method outperforms existing approaches on the complicated occluded dataset LINEMOD, as well as on the challenging generalized pose estimation dataset GenMOP.