Miracle-dz/UniFAR-FTUs huggingface dataset:
AI based Smart Parking System

A new venture has been endorsed to thrive a user-friendly smart parking system with minimal human interference. The proposed system scans for the vehicle type and vehicle number when a new vehicle enters with the help of a transfer-learning based model and a CNN model. A suitable parking spot is searched based on the vehicle type and assigned to the vehicle. A customer would be monitored with the help of webcams and number plate recognition model after entering the parking lot and would be alerted through message if he/she has parked his/her vehicle in a spot that is not assigned to him. The customer would then be displayed with a bill at the exit point with automated calculation of parking charges by the system. YOLOv3 and Tiny-Yolov3 models has been proposed for vehicle detection. The models have been compared by detection time per frame, detection accuracy and its F1 Score. On exploration, an EfficientNet based TL model, mobileNet Extended model and light-weight CNN model has been proposed for vehicle type classification. Testing is done to compare the classification accuracy and time. The EfficientNet based TL model performed extremely well in test data as well as in real-time data. For number plate detection, a pre-trained cascade classifier has been proposed. On the other hand, a CNN model, a ResNet50 extended model and a ResNet152 extended model has been proposed for the purpose of character recognition. The models have been compared based on their classification accuracy and average recognition time per character. The output system has been delivered as a website to the end-user with 2 separate portals for customer and parking lot owner. Each portal has its own features that satisfy its target user. The proposed system has been efficient in satisfying needs of the customer and the vehicle type classification model has given a good accuracy score.
A Simple Search Coil Based on Motor Winding Strands for Rotor Position Estimation

Conventional sensors for detecting the rotor position in permanent magnet synchronous motors can be influenced by environmental conditions and may pose reliability concerns during operation. Accordingly, this study proposes a search coil design in which a single strand of the motor winding from each phase terminal is used to derive the three-phase stator flux linkage. The flux linkage is transformed into the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> frame to determine the stator flux linkage angle, which is then used to interrogate a pre-established lookup table to determine the corresponding motor rotor angle. The effectiveness of the proposed search coil is evaluated via experimental trials using a commercial 3.7-kW motor. The position estimation results are found to be in good agreement with those obtained using a commercial resolver under various speed and load conditions. The proposed search coil offers a simple, noninvasive, and reliable means of estimating the rotor angle. Importantly, the proposed search coil can function as either a reliable backup in the event of motor position sensor failure or as a primary sensor. It, thus, provides a suitable choice for a wide range of motor types.
Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis

Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: the ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE). Our approaches employ a new symbolization process that considers both permutation relations and amplitude information. Additionally, the ensemble technique is utilized to reduce the dependence on parameter selection. We performed a comprehensive evaluation of the proposed methods using various synthetic and experimental signals. The results illustrate that EIPE is capable of distinguishing white, pink, and brown noise with a smaller number of samples compared to traditional entropy algorithms. Furthermore, EIPE displays the potential to discriminate between regular and non-regular dynamics. Notably, when compared to permutation entropy, weighted permutation entropy, and dispersion entropy, EIPE exhibits superior robustness against noise. In practical applications, such as RR interval data classification, bearing fault diagnosis, marine vessel identification, and electroencephalographic (EEG) signal classification, the proposed methods demonstrate better discriminating power compared to conventional entropy measures. These promising findings validate the effectiveness and potential of the algorithms proposed in this paper.
DNN-based CSI-RS Port Virtualization Matrix Design in Massive MIMO System

In massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) is needed to achieve high data rate. However, getting the CSI of the full antennas is hard since channel state information reference signal (CSI-RS) overhead is large. Therefore the CSI-RS port virtualization matrix which groups the antennas in a single CSI-RS port at the base station is used. In this paper, we propose a deep neural network (DNN) based CSI-RS port virtualization matrix design scheme for a time division duplex (TDD) massive MIMO system. The proposed scheme utilizes both the CSI-RS and sounding reference signal to get the CSI and estimates the CSI-RS port virtualization matrix in terms of codebook index. Simulation result shows that the proposed DNN based scheme can achieve up to 25.8% performance gain, particularly in high uplink signal-to-noise ratio regions where more accurate uplink channel information can be obtained.
Tactile Object Recognition Using Fluid-Type Sensor and Deep Learning

Tactile data are essential in object perception. Based on such data, various objects can be recognized and differentiated. The fluid-type tactile sensor employs a colored fluid captured between an elastic skin and a transparent plate where the color intensity of the fluid observed by a camera is directly related to the deformation of the skin. In this letter, the fluid-type sensor is restructured by employing an optimization method that is sensitive to skin deformation. Moreover, the implementation of nonopaque skin is proposed to reveal surface color features in addition to the shape of the object in tactile images. A convolutional neural network (CNN), a neural network based on transfer learning and dropout layer was implemented to classify objects based on their tactile images, which is a combination of shape of the objects and their surface color features. Although the number of training samples is small, an accuracy of 94.2% is achieved. The proposed sensor is 3-D printable and can be fabricated at a low cost.
Diversity of the Passiflora L. in the Serra do Mar ecoregion and the relationships with environmental gradients, South and Southeast, Brazil

The species of the Passiflora (Passifloraceae s.s. ) are distributed in tropical and subtropical regions and are of great ecological importance. The Serra do Mar is formed of large escarpments along Brazil’s east coast, between the States of Rio de Janeiro and Rio Grande do Sul. This study aims to point out occurrences of Passiflora species in herbaria along the Serra do Mar, in addition to evaluating the conditions of each specimen found. The richness and sample effort maps were prepared to establish the locations where the greatest numbers of species and records were encountered. We evaluated the relationship of species distribution through Canonical Correlation (CCA) and Quadratic Polynomial Analysis. We also evaluated the floristic composition using a cluster analysis. Calculations of the Extent of Occurrence (EOO) and Area of Occupancy (AOO) were performed for endemic species to the Atlantic Forest area occurring in the Serra do Mar. A total of 53 species, belonging to the Passiflora were found in the Serra do Mar. The States of Rio de Janeiro and São Paulo were the ones that recorded the greatest numbers of species and collection effort. The climatic variables Precipitation of the Driest Quarter and Annual Average Temperature are the most correlated to the distribution of Passiflora in the area. Twenty-two species are endemic to the Atlantic Forest area, and 21 are recorded as having some degree of threat on Official Lists. In this study, we present all the Passiflora species recorded for the Serra do Mar, the richness of species, including those endemics to the Atlantic Forest, demonstrates the importance of the area for conservation.
Hybrid SVPWM Strategy of Cascade H-Bridge Multilevel Converter for Battery Energy Storage System

A cascade H-bridge (CHB) stands out for its modular structure and high output voltage among various power converter schemes for battery energy storage systems. While space vector pulsewidth modulation (SVPWM) offers better utilization of the dc-link voltage, it is seldom employed in CHB designs due to the substantial computational burden associated with an increasing number of voltage levels in the converter. This article introduces a novel hybrid SVPWM approach in a multilevel CHB for battery energy storage systems. In this proposed system, the reference vector is decomposed into a low-frequency vector and a high-frequency vector. The low-frequency vector is generated by the low-frequency modules operating at the line frequency, while the high-frequency vector is produced by the high-frequency module working in the pulsewidth modulation mode. It compensates for the disparity between the low-frequency vector and the reference vector. To determine the low-frequency vector, an algorithm based on coordinate transformation is introduced. This significantly reduces the computational complexity, making it independent of the output voltage levels. The switching losses decrease as most power devices operate at the fundamental frequency. An experimental platform of the CHB is constructed to validate the advantages of the proposed modulation algorithm.
A Current-Mode Implementation of A Nearest Neighbor STDP Synapse

The Artificial Intelligence (AI) disruption continues unabated, albeit at extreme compute requirements. Neuromorphic circuits and systems offer a panacea for this extravagance. To this effect, event-based learning such as spike-timing-dependent plasticity (STDP) in spiking neural networks (SNNs) is an active area of research. Hebbian learning in SNNs fundamentally involves synaptic weight updates based on temporal correlations between pre-and post-synaptic neural activities. While there are broadly two approaches of realizing STDP, i.e. All-to-All versus Nearest Neighbor (NN), there exist strong arguments favoring the NN approach on the biologically plausibility front. In this paper, we present a novel current-mode implementation of a postsynaptic event-based NN STDP-based synapse. We leverage transistor subthreshold dynamics to generate exponential STDP traces using repurposed log-domain low-pass filter circuits. Synaptic weight operations involving addition and multiplications are achieved by the Kirchoff current law and the translinear principle respectively. Simulation results from the NCSU TSMC 180 nm technology are presented. Finally, the ideas presented here hold implications for engineering efficient hardware to meet the growing AI training and inference demands.
The Response of Geomagnetic H and Z Components During X-Class Solar Flare

This paper presents the study of solar flare effects (SFE) on H and Z components of the geomagnetic field. The raw data of H and Z components are extracted from Magnetic Data Acquisition System (MAGDAS) at Langkawi station (LKW:6.3°, 99.78°) and INTERMAGNET website for Lanzhou (LZH: 36.1°,103.84°) and Yakutsk (YAK: 61.96°,129.66°) stations. There are eight (8) events of X-class solar flares from 2011 to 2014 were selected to shows the changes and enhancement of H and Z components with different latitudes. The regression analysis is used to prove the correlation between the enhancement of geomagnetic field and the x-ray flux. The statistical results show the ratios of Δ HSFE/Δ H0 and Δ ZSFE/Δ Z0 is greater than zero for all stations and proven the SFE has affected the H and Z components and not just an augmentation of pre-flare ionospheric currents.
Political Participation and Socioeconomic Status (SES) the Governor Election in Kepulauan Riau

Political participation is an individual activity that may affect a country's or government's political policy. Political participation in democracy takes many forms, such as voting, establishing a political group, engaging in legal or illegal protests, campaign involvement, discussing politics offline or online, or becoming a political party member. The term political participation in this study refers to voting participation. Voter participation is important because it creates a political and democratic climate that encourages people to participate actively in state administration. To determine strategies and policies to increase voter participation, what factors can influence someone to vote can be seen. One factor that influences a person's desire to participate in politics is a person's socioeconomic status. Therefore, this study examines how Socioeconomic Status (SES) influences political participation. Political participation in this study is seen from whether the permanent voters (DPT) exercise their right to vote in the 2020 gubernatorial election. The SES consists of socioeconomic status, which is seen from: demographic background (social status, including age, ethnicity, educational background, and religion); economic status of the average income of voters per month. Krejcie Morgan sampling method, 384 respondents were processed with Eviews 11 software using logit regression model analysis. This study found three variables significantly affect the dependent variable: Ethnic, residential election participation, and trust level in the candidate. This study recommends that the strategies used in the efforts to enhance political participation of the Riau Islands community in the regional head election are: forming volunteers democracy, dividing four segments of socialization, improving the quality of the organizing committee resources by conducting technical guidance, aggressively achieving political socialization, political publications, and the delivery of the stages of the elections using various social media platform. Moreover, implementing Good Governance will produce output from society's most economical market mechanisms. Thus, it is expected that people's political awareness will be enhanced.
Geometry‐based flipper motion planning for articulated tracked robots traversing rough terrain in real‐time

Tracked robots operating on rough terrain are often equipped with controllable flippers to help themselves overcome large obstacles or gaps. How to automate the control of these auxiliary flippers to achieve autonomous traversal remains an open question, which still necessitates inefficient manual teleoperation in practice. To tackle this problem, this article presents a geometry‐based motion planning method for an articulated tracked robot to self‐control its flippers during autonomous or semiautonomous traversal over rough terrain in urban search and rescue environments. The proposed method is developed by combining dynamic programming with a novel geometry‐based pose prediction method of high computational efficiency, which is applicable for typical challenging rescue terrains, such as stairs, Stepfields, and rails. The efficient pose prediction method allows us to make thousands of predictions about the robot poses at future locations for given flipper configurations within the onboard sensor range. On the basis of such predictions, our method evaluates the entire discretized configuration space and thereby determines the optimal flipper motion online for a smooth traversal over the terrain. The overall planning algorithm is tested with both simulated and real‐world robots and compared with a reinforcement‐learning‐based method using the RoboCup Rescue Robot League standard testing scenarios. The experimental results show that our method enables the robots to automatically control the flippers, successfully go over challenging terrains, and outperform the baseline method in passing smoothness and robustness to different terrains.
Signal Preprocessing Towards IoT Acoustic Data for Farm Pest Detection

In Africa the realization of the Sustainable Development Goal 2 (SDG-2) of zero hunger by 2030 is threatened by agricultural pests. Acoustic technology offers one method for pest detection in agriculture. AudioMoth microphones were deployed in three farms in Rwanda. Band filters were applied to the files for listening to pest calls for labeling. The WAV files were processed using Scikit-maad package in Python to spectrograms, which allowed for visualization. The regions of interest were selected to be used for labeling. Results suggest that birds are the dominant pest during the morning sections of the day while frogs are active at night. The contribution of this work is to provide a roadmap towards labeling for detection of agricultural pests using machine learning at the edge.
Repeated low-dose caffeine ingestion during a night of total sleep deprivation improves psychological state without affecting oxidative stress status in young recreational runners

ABSTRACT The present study aimed to examine the effects of repeated low-dose caffeine consumption during a night of total sleep deprivation on psychological state and blood levels of oxidative stress markers after an exhaustive endurance exercise. Twelve recreational runners performed four test sessions in a double-blind randomized order following a baseline night (BN) or a night of total sleep deprivation (TSD). At each test session, blood samples were collected and psychological tests were performed before and after exercise. In comparison with placebo, caffeine ingestion decreased the feeling of sleepiness (FSL) by 28.1% (p < 0.05), reduced the feeling of muscle pain (FMP) by 10.5% (p < 0.05), enhanced the feeling of well-being (FW) by 31.6% (p < 0.05) after TSD, and did not affect levels of oxidative stress markers. Therefore, Caffeine is an effective strategy to counteract psychological adverse effects induced by TSD without being pro-oxidant during endurance exercise.
Wireless Detection Systems Using Matrix-Oriented Diffusion

The Matrix Oriented Diffusion (MOD) method proposed in the research aims to decrease the energy consumption of nodes in wireless detection systems by using a matrix-based approach for diffusion. By optimizing the use of energy in the nodes, the MOD method can improve the overall performance and prolong the lifespan of the system. In the MOD method, small clusters are formed based on a virtual matrix that is structured according to the routing fidelity. The virtual matrix allows for efficient and effective diffusion of information within the clusters, leading to the reduced energy consumption of the nodes. By organizing the nodes in this manner, the MOD method improves the efficiency of the wireless detection system and minimizes energy waste. In the MOD method, clusters are utilized to handle the large amounts of data generated by the sensing nodes, which can result in faster processing and reduced energy consumption. By grouping nodes together into clusters, the MOD method enables more efficient management of data floods and reduces the burden on individual nodes. This leads to improved overall performance and a longer lifespan of the system.
Performance Analysis of Underwater Images Enhancement Techniques

This paper presents the performance analysis of enhancement techniques for underwater images. This requires only image processing techniques without the use of specialized hardware. A scene's organizational pattern or the situation under the sea changes as depth of water increases. We use enhancement with fusion of two images; The first one has been contrast corrected and the second one is white balanced obtained from the original image. The two images are combined by the fusion using the weights that are associated with them. The aim of using weight maps is to improve the way the colors and borders are transferred to the enhanced image. A detailed weight map is used to avoid the low-frequency components, transitions and variations which may cause artifacts. It has the potential to enhance the image's current quality. To evaluate these results, PSNR, Mean Squared Error(MSE), the Structural Similarity Index (SSIM), in addition to the histogram analysis as quantitative metrics are used.
Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

In the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors.