Miracle-dz/UniFAR-FTUs huggingface dataset:
Accident Factors Detection and Prediction for Indian states and district roads

The transportation systems are increasing day by day. The increased number of vehicles on the road leads to more accidents. In this situation, priority to road safety is must, and the government has made many attempts to predict and pinpoint accidents zone, roads etc. The usual cause for road accidents is rash driving; however, the accidents are increasing day by day due to other factors. This paper proposes the detection of factors for accidents using the prediction system for Indian states and districts of Karnataka. The proposed system, detected many factors such as seasons, classes of road and road types as the major cause for road accidents using machine learning algorithms apart from the regular factors. The results from the experiments were appreciable for the dataset collected from nearby police stations and hospitals of Bangalore and Tumakuru road and kaggle dataset. The testing results were found to be near to the manual analysis.
Swin-ResUNet: A Swin-Topology Module for Road Extraction from Remote Sensing Images

Road extraction from remote sensing images plays a crucial role in navigation, traffic management, urban construction, and other fields. With the development of deep learning in the field of computer vision, road extraction from remote sensing images using deep learning models has become a hot research topic. The convolution-based U-shaped road extraction models have some issues such as high extraction error rate and poor continuity on road topology. The Transformer-based road extraction methods also have issues such as low extraction accuracy and large GPU memory usage. In order to solve the above issues, we propose a Swin-ResUNet structure and use the new paradigm Swin Transformer to extract roads in remote sensing images. Specifically, we construct a Swin-Topology module by adding a Sobel layer based on residual connections to the Swin Transformer block. Based on the Swin-Topology module, we propose a Swin-ResUNet network structure in order to better capture the topology of roads. Experimental results show that the values of mIOU and mDC obtained on the Massachusetts dataset were 64.1% and 76.6% respectively, and the corresponding values on the DeepGlobe2018 dataset were 66.69% and 75.86% respectively. When the batch size is 8, the GPU memory usage with Swin-ResUNet is about 9 GB, which is significantly smaller than other Transformer-based networks. Compared with convolution-based U-shaped structures, the Swin-ResUNet can better capture the topology of roads in remote sensing images and improve road extraction accuracy. Compared with other Transformer-based road extraction methods, the Swin-ResUNet can improve the accuracy of road extraction and reduce GPU memory usage.
Stereo Saliency Detection by Modeling Concatenation Cost Volume Feature

RGB-D image pair based salient object detection models aim to localize the salient objects in an RGB image with extra depth information about the scene provided to guide the detection process. The conventional practice for this task involves explicitly using depth as input to achieve multi-modal learning. In this paper, we observe two main issues within existing RGB-D saliency detection frameworks. Firstly, we claim that it is better to define depth as extra prior information instead of as a part of the input for RGB-D saliency detection, as we can directly perform saliency detection based only on the appearance information from the RGB image, while we cannot perform saliency detection given only the depth data. Secondly, there exists a huge domain gap in terms of the source of depth between different benchmark testing datasets, e.g. depth from Kinect and stereo cameras. In this paper, we focus on the variant of stereo image pair based saliency detection, where the depth is “implicitly” encoded in the stereo image pair for effective RGB-D saliency detection. Experimental results illustrate the effectiveness of our solution.
Multi-channel output gain margin improvement using generalized sampled-data hold functions

This paper considers the multichannel gain margin problem for linear time-invariant continuous-time multi-input multi-output plants controlled by memoryless generalized sampled-data hold functions (GSHF). It is proved that arbitrarily large output gain margins can be achieved by suitable selection of the sampling time and the hold function. We use state space methods and give a constructive design procedure. The controller we use is memoryless in the sense that the discrete-time closed-loop system has the same McMillan degree as the open-loop continuous-time plant. >
The Healthcare Data Management Maturity Model (HDM3) for the assessment of healthcare data management in developing countries

Developing countries struggle with a range of healthcare data management challenges. The scope of the challenges is broad and it is difficult to determine on which components of healthcare data management to focus when endeavoring to improve healthcare data management systems in developing countries. This study develops, presents and evaluates a maturity model, the Healthcare Data Management Maturity Model (HDM3), that healthcare entities can incorporate to assist them in determining the as-is state of their healthcare data management. This identifies the components to be focused on during improvement endeavors. The conceptual HDM3 and its various components are presented in this study.
Cluster Analysis of the Northern Hemisphere Wintertime 500-hPa Height Field: Spatial Patterns

Abstract Hierarchical cluster analysis based on the method of Ward is performed on the Northern Hemisphere wintertime 10-day low-pass-filtered 500-hPa height field, using the NMC operational analyses for the period 1946–85. Input data are gridded fields at 5-day intervals, a total of 702 maps, each with 445 grid points. The measure of similarity between maps is the squared height difference, averaged over all grid points; that is, the squared “distance” between the maps in multidimensional phase space. The closest two of the 702 maps are merged to form a cluster that, in subsequent calculations, replaces the maps from which it was formed. This procedure (modified slightly, to deal with the differing numbers of maps in the clusters) is repeated 701 times until all the maps have been merged to form a single cluster whose centroid corresponds to the climatological mean map. The two clusters involved in the final merger, the pair of smaller clusters that merged to form each of them, and so on, are represented.
Design or Redesign Business Models' Innovation in the Digital Transformation Context

Organizations face the challenge of determining how they should shape and implement innovations driven by Digital Transformation. They face dilemmas about which innovations and changes are needed or put in place before designing or redesigning their business models. Previous research has helped advance the understanding of the digital transformation journey and the development of new business models based on emerging technologies. However, little research studied the key factors that affect an organization's ability to redesign or design business model innovations. This research investigates the key factors contributing to the design of business model innovations in digital transformation. Therefore, this paper presents a theoretical framework of the main factors related to business model innovation design to theoretically fill this research gap. Transformational maturity, resulting from the organization's dynamic capabilities and digital competencies, and corporate culture, seems to contribute to changes in business processes and models. We present research proposals and a framework to help research and practice consider the main factors related to potential business model design according to the level of transformational maturity and the intensity of interpreneurial culture.
Applications of Internet of Things(IoT) in Agriculture: The Need and Implementation

Humans are motivated to improve agricultural yields by implementing new technologies due to the world's growing population. Precision agriculture, which is expected to raise crops considerably, could be achieved via the Internet of Things (IoT). However, a significant investment in IoT systems for agriculture and farmers who are not computer aware make the large-scale implementation of IoT systems in agriculture problematic. As a result, it is advocated that the focus on the deployment of agriculture IoT systems be widened to include the entire life cycle of agriproducts. While adopting agricultural IoT systems, energy factor must be in consideration. Farmers' interest in IoT technologies will rise significantly if green IoT systems are used throughout the agri-product life cycle. Regarding IoT-based agricultural network technologies, several aspects, including network architecture and layers, network topologies, and network protocols, have been investigated. This research investigates the ways in which Internet of Things (IoT)-based agricultural systems may be combined with other important technologies, such as cloud computing, large amounts of data storage, and analytics. This study also discovers security issues in IoT agriculture. There have also been a few examples of successful IoT-based agriculture rules and policies that various countries have implemented. However, there are still many unanswered questions about IoT agriculture research.
High Rejection Highpass Filter for Wideband Applications Using Suspended Stripline Technology

This paper presents a high rejection and compact ultra-wideband (UWB) highpass filter using suspended stripline technology based on Generalized Chebyshev polynomial that operates between 3 GHz to 12 GHz for wideband application. Based on the Generalized Chebyshev polynomial, a 13th order lowpass filter with 5 pairs of finite transmission zeros (TZ) is synthesized. Under frequency transmission, the highpass filter network can be realized. Suspended stripline is used to realize the proposed highpass filter by introducing parallel coupled line placed between the ultra-thin Roger Duriod RO3003 substrate and the resonators to obtain higher coupling and achieve very wideband. With the unique passband, stopband elements and excellent reliability in rough working condition, the application of the filter ranging from military and aerospace to commercial communication system such as ultra-broadband receiver, 5G and others. The proposed filter is simulated, fabricated, and tested to verify the proposed design concept. The measured and simulated results are in agreement to each other. Therefore, the highpass filter in this paper could serve as a reference for high rejection and ultra-wideband application.
Group-based Sparse Coding with Adaptive Dictionary Learning for Image Denoising

Group sparse coding (GSC) is a powerful mechanism that has achieved great success in many low-level vision tasks, showing great potential in image denoising. Traditional group sparse coding generally uses overcomplete dictionaries and $l_{1}$-norm to regularize sparse coefficients. But this is only an estimate of the solution, which cannot obtain a sparse solution and has a high computational cost. In this paper, we use a GSC framework with adaptive dictionary learning for image denoising. In order to improve the accuracy of obtaining sparse coefficients, the dictionary used in this paper is learned from the input image, which can be obtained by applying SVD once for each patch group. Then use ADMM algorithm to solve the objective function. Experimental results show that the PSNR value of our approach not only is competitive with many advanced image denoising methods but also achieves better visual effects.
Hyperspectral Image Segmentation, Feature Reduction and Clustering using k-means

In this study, a novel clustering methodology is proposed, which utilizes k-means sequentially for performing feature reduction, segmentation, and clustering on hyperspectral imagery, to extract information. The proposed methodology is a multi-stage framework. Initially, k-means is utilized to perform feature reduction. In the next stage, k-means is again deployed to perform hyperspectral image segmentation, using new feature set obtained from the first stage. Finally, k-means clustering is carried out on segmented hyperspectral image by making use of reduced feature set. To assess the performance of the proposed methodology, experiments are conducted over three sets of hyperspectral images. Purity and Normalized Mutual Information (NMI) score are used for evaluation. The experimental results prove that the proposed methodology has an edge over the other compared clustering methodologies. Results show incorporation of feature reduction and image segmentation techniques leads to significant improvement in accuracies.
Supply Chain Delay Mitigation via Supplier Risk Index Assessment and Reinforcement Learning

Supply chains are vulnerable to unforeseen delays, which might adversely affect delivery performance. Quantifying the risk profiles of each supplier based on their historic delivery patterns and forecast deviations can help make superior decisions in multi-supplier scenarios. This problem has been previously approached from linear programming and qualitative assessment perspectives; however, application of machine learning and reinforcement learning-based methods are still in a nascent stage. This paper proposes a machine learning technique to classify a supplier into one of four risk indices accurately on real-world datasets from Ericsson's supply hub. A reinforcement learning agent is also trained in a custom-modeled environment to split an order among multiple suppliers while minimizing the delays. Additionally, a working web-based tool is developed to demonstrate these techniques, that may be extended to other domains.
An 8nm MOS and its Applications

An 8nm MOSFET and CMOSFET have been demonstrated in this paper. The present MOS is working with a minimal leakage current of 5.346 x 10-10 A/um having a minimal channel length 18.6nm at thermal voltage of 25.85mV. Indium gallium arsenide has been chosen as a semiconductor material and as an oxide agent hafnium oxide has been deployed, as the alternative candidates of silicon. It has been found that the 8nm MOS has achieved drive current 5.557 x 10-5 A/um, threshold voltage 0.358v at supplied drain voltage of 0.005V. Apart from that an inverter circuit has been simulated using the CMOS which has been designed using the present 8nm MOSFET by deploying the alternatives of Silicon and the performance of both the inverter circuit and the CMOS device along with the 8nm MOSFET has been showcased in this paper.
Two Step Recognition of Raags in Hindustani Classical Music Using Supervised Deep Learning

In the Indian Classical system of music, we have various raags which differ from one another based on the notes being used in them, their frequency bandwidth, the subtle intricacies of the progression of notes, etc. In [1], we have worked upon the classification of a sample of music into a thaat which is an upper-level classification simply based on the frequencies present in it. In this paper, we take the work one step ahead to recognize the raag after successful classification of the thaat. In this way, it forms a two-step process where we first identify the thaat under which the music falls, followed by which we recognize the raag which corresponds to the music in consideration.
Convolution-mix-Transformer Generator model to synthesize PET images from CT scans

PET/CT is a type of medical imaging that has been shown to be useful for both disease diagnosis and treatment monitoring. However, PET/CT imaging systems are still not widely used in hospitals because radioactive material injection is required and there aren't many PET scanners available, but CT scanners are widely used in health care facilities. In this paper, we introduce a new Convolution-mix-Transformer as a Generator network for translating CT images to PET images. The method has been tested on PET/CT images of 791 patients, CT images are trained as a full range image instead of just a small window, as systems often do to make it easier to see specific parts of the body. We generate PET images and analyze SUV values based on CT images. We use five common measures to evaluate results: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean structural similarity index measure (MSSIM), mean absolute error (MAE) and Fréchet inception distance (FID), and our result which are significantly higher than other models. Our suggested model successfully converts CT images to PET images by including a trained image-to-image translation approach. The generated PET images maintain both the regional and global features of medical imaging.
A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance

Compliance with one specified dosing strategy of assigned treatments is a common problem in randomized drug clinical trials. Recently, there has been much interest in methods used for analysing treatment effects in randomized clinical trials that are subject to non-compliance. In this paper, we estimate and compare treatment effects based on the Grizzle model (GM) (ignorable non-compliance) as the custom model and the generalized Grizzle model (GGM) (non-ignorable non-compliance) as the new model. A real data set based on the treatment of knee osteoarthritis is used to compare these models. The results based on the likelihood ratio statistics and simulation study show the advantage of the proposed model (GGM) over the custom model (GGM).
Failed Co-Creation: The Case of the BCNStreet-Lab Project

We present the results of the BCNStreet-lab project, a co-creation process of a prototype that "died before it even existed" and that has led us to reflect on the creative power that participatory design can have among experts. Examining the high variability that the design acquired with the co-creation sessions, we realized how an open co-creation process can generate overflows in the design. It makes innovation possible but can also lead to failure, as is this case, because of how risky participatory design of “fluid technologies” and how ineffective reproducing the Quadruple helix innovation model in small projects can be. We tried to account for the fruitful relationship between co-creation and the design of electronic technologies, which happens in the “lobby” of the technical design of the prototype. It generates negotiations and interactions that lead to progress in the democratization of design practices in electronic engineering, computing, and telecommunications. At this point, co-creation is conceived as a space in which institutional practices, social needs, technological imaginaries, economic interests, techno-scientific interests, etc., are brought together, translated and inscribed into the design of technologies.
Control laws for sliding mode speed control of variable speed drives

This paper presents an investigation of various control laws to study the characteristics of sliding mode speed control (SLMSC), and to overcome some disadvantages of SLMSC such as steady state speed chattering and error. It is demonstrated that, by the appropriate choice of a suitable control law, a robust, fast and accurate SLMSC performance, with reduced steady state chattering and error, can be achieved in a practical variable speed drive system. Digital simulation and experimental results based on a self-controlled synchronous motor (SCSM) are presented to substantiate the analysis.
Spectroscopic factors from direct proton capture

Spectroscopic factors derived from direct capture studies are systematically compared to those obtained from transfer reaction experiments and from shell model calculations for the A=16–32 target mass region. The direct proton capture and proton transfer spectroscopic factors are obtained in the present work from a reanalysis of literature data by using the same bound state potential parameters. Our direct capture spectroscopic factors differ significantly from the values originally reported in the literature. The sensitivity of the direct capture cross section to different choices for the scattering potential is explored. We find evidence that spectroscopic factors obtained from direct capture studies are as reliable as those extracted from transfer measurements if the (direct capture) radial scattering wave function is calculated with a zero nuclear scattering potential instead of the common choice of a hard-sphere potential.
Biomagnetic field detection using very high sensitivity magnetoimpedance sensors for medical applications

This paper demonstrates the performance of magnetoimpedance (MI) sensors for biomagnetic field recording. The rms noise of an amorphous wire MI sensor with 600 turns of pick‐up coil was estimated as approximately 3 pT/Hz1/2 at 1 Hz. Here, we demonstrate that this sensitivity is enough to clearly detect magnetic cardiogram signals. We also measured the biomagnetic field around a smooth muscle tissue sample taken from a guinea‐pig. We discovered that the magnetic‐field waveform of a small tissue sample is similar to the voltage waveform measured by a microelectrode array in a smooth muscle sheet sample. The results suggest that the localized biomagnetic field generated by smooth muscle cells can be detected by an MI sensor. We concluded that the MI sensor is a promising device for use as a noninvasive tool to test cell activity or function through magnetic‐field measurement. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Iterative image restoration for linearly degraded images. I. Basis

An image-restoration method applying the iterative method to solve simultaneous linear equations is described. The advantages of this method are that the memory capacity to be used is minimal, the computation time is very short, and the man-machine interaction in the course of processing is easily effected. Owing to these advantages, this method seems to be superior from a practical viewpoint to other recently proposed linear-algebraic approaches for image restoration. The mathematical basis of this iterative image-restoration method is described and the suitability of this method is presented. The characteristics of this method are clarified through analysis in frequency space. Nonlinear constraints can also be introduced in this method, which restrain occurrence of erratic results caused by noise amplification. Experimental results using a minicomputer-base digital image-processing system demonstrate that the method is very effective and applicable in practice.
On wavy instabilities of the Taylor-vortex flow between corotating cylinders

At least four wavy instabilities are found numerically by analysing the linear stability of Taylor-vortex flow (TVF) in the limit of a small gap between two concentric cylinders which rotate differentially in the same direction. Two of the wavy instabilities, including the one leading to conventional wavy vortex (WVF), have the same axial wavelength as TVF at the onset of instability, while the other two are characterized by subharmonic modes with axial wavelengths twice as long as those of TVF. The two subharmonic instabilities appear to correspond to the wavy-inflow-boundary flow (WIB) and the wavy-outflow-boundary flow (WOB) observed in the experiment of Andereck, Liu & Swinney (1986). The phase velocities, measured in the rotating frame of reference, of all the wavy instabilities are non-zero at the onset except that the phase velocity of WVF vanishes in the region where the average rotation rate Ω of the cylinders is small. By using this simple bifurcation property of WVF for small Ω, time-independent finite-amplitude non-axisymmetric solution branches bifurcating from TVF are followed numerically. The most interesting findings are that some of the solution branches cross the line Ω = 0, producing three-dimensional nonlinear solutions in plane Couette flow.