Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Nanoscale Oxide Thin-Film Transistors for Emerging Display and Memory Applications

Oxide thin-film transistor (TFT) technology is fast and well developed since its first invention, where it is now widely used in flat panel displays. Scaling down the size of oxide TFT to the nanometer regime brings benefits such as higher density integration, faster switching speed and lower operating voltage. This miniaturization allows for very small pixel sizes, crucial for active-matrix micro light-emitting diode displays, and in particular, holographic displays. With the development of submicron manufacturing technologies for oxide TFTs, we will witness new, high-performance applications emerging. In this review paper, most of the commonly used TFT device structures at the nanoscale will be introduced, including the lateral and vertical architectures. Then, selected applications utilizing oxide nanoscale TFTs will be presented. Challenges in scaling down, including reliability, short channel effects and high contact resistance, will be presented along with possible solutions.
Multi-stage Information Processing Systems with VM Migration for Maximum Accuracy

This paper outlines a Multi-stage Information Processing system and a VM migration method to improve the performance of the system. To process applications that require responsiveness and accuracy at the same time, such as sensor data processing in automated driving, a Multi-stage Information Processing system has been proposed in which the edge computer and the data center computer process information simultaneously to obtain responsiveness from the edge computer and accuracy from the data center. This paper describes the system and explains the VM migration method to obtain highly accurate responsiveness in such a system.
A Hybrid CNN and Random Forest Approach for Wheat Disease Detection

The abstract provides a thorough assessment of an elaborate classification framework built for impact-level forecasting with an emphasis on ten separate classes. Precision ratings that vary between 92.79% to 95.07% indicate how accurate the model is at properly recognizing instances for every effect level. Recall rates ranging from 90.91% to 94.37% demonstrate the model's capacity to catch a substantial amount of true positive cases. The F1-Score, which balances precision and recall, regularly falls between 93.38% and 94.43%, indicating an equilibrium of performance through all classes. Support values fluctuate between 1096 to 1175 and provide information into the total quantity of instances every class, allowing for a more nuanced view of the way the model performs across different impact levels. The support proportion, set at 0.10 for each class, guarantees that the dataset has a balanced representation. The total accuracy of 83.9979% demonstrates the model's ability to make accurate predictions spanning all impact levels. Macro Average precision, recall, & F1-Score values of roughly 93.66% indicate the model's uniformity concerning accuracy across all classes. The Weighted Average confirms this, indicating a wellbalanced total performance. At 93.66%, the Micro Average, which provides aggregate metrics across every case, attests to the strategy's uniform accuracy. The presented model for classification performs well in estimating impact levels, providing a good combination of accuracy and recall for all classes. The numerical numbers highlight the model's dependability and adaptability for the task in conjunction, demonstrating its capacity to generalize well and produce accurate predictions for varied impact levels in the current dataset.
Critical Time of Weed Competition and Evaluation of WeedManagement Techniques on Ginger ( Zengeber Officinale ) at Tepi in South West Ethiopia

Ginger (Zengeber officinale) is one of the leading exported spice crop in Ethiopia. Weeds are a major constraint in ginger production. Ginger was found very poor competitor of weeds that as weeding was delayed tremendous yield loss was incurred. When weeding was totally ignored yield loss amounted to 100%. Understanding the critical period for weed competition and weed management techniques can be a tool for effective weed control and reducing the impacts of weeds. The experiment was conducted during 2010 to 2012 using randomized complete block design with three replications to determine the critical period and evaluation of weed management techniques at Tepi in south west Ethiopia. Timing of weed removal was based on the number of days after ginger planted. To determine critical time of weed competition, weeds allowed competing with ginger for 15, 30, 45, and 60 days after planting. In addition, season long weedy check and weed-free check were included as control. For weed management study five treatments (mulching at planting + weeding at 45, and 75 days; mulching at planting + weeding at 60 and 90 days; weeding at 30 + mulching + two hand weeding as needed; weeding at 30, 60 + mulching + one hand weeding as needed; weeding at 45, 75 + mulching + one hand weeding as needed) were applied which contain both hand weeding and mulching at different periods. All noxious and important weed species were abundantly grown in the experimental site and the surrounding, the important weeds were Poaceae and Asteraceae. Number of tiller, number of leaf per tiller, plant height, leaf width and length, rhizome width and length, number of fingers per rhizome and yield were affected significantly at p<0.001. Maximum yield loss (47%) was recorded when weeding applied at 60 days after planting (DAP) but minimum yield loss (9%) at 30 DAP. Yield advantage of 41% and 34% obtained when first hand weeding was applied at 30 and 45 DAP respectively as compared to 60 DAP. Sever ginger-weed competition was taken place between 30 and 60 days. Mulching of ginger after one or two hand weddings at 30 and 45 DAP found as good agronomic practice. plant height, leaf length, leaf width and weed biomass were do have a significant negative association with that of ginger yield where as number of ginger tiller per plant, number of leaf per tiller, rhizome length and width, number of fingers per rhizome, and stand count were found a positive significant association with yield. Hence, it would be wise to apply the first hand weeding between 30 and 45 DAP in order to avoid sever competition for maximum yield of the crop.
A Deep Learning Approach for the Optical Estimation of Particle Size Distributions of Bulk Materials

In energy-intensive industries, such as cement manufacturing, steal production, glass factories or in power plants classical fossil fuels, such as coal or gas, are replaced by more sustainable fuel sources derived from biomass and/or waste streams. However, these fuels tend to be quite volatile according to their main mechanical and chemical properties, such as particle size distribution, bulk density, etc. Therefore, real-time characterization methods need to be developed in order to allow an optimized utilization of such fuels in industry. This paper presents a visual approach for the self-acting estimation of the particle size distribution of a given fuel based on monocular image streams. The suggested methodology follows an image segmentation approach based on deep learning techniques. Due to the lack of ground truth data, an associated methodology for the automatic generation of a synthetic training data set is also presented. Different state-of-the-art segmentation models are implemented and trained on a training database for two different sustainable fuel sources. The performance is carefully validated by using different evaluation metrics. The implementation results showed that the deep learning approach produced more accurate segmented images compared to conventional image processing techniques.
Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-time and secure access to critical healthcare information.
Performance of Wheat Varieties under Late and Very Late Sowing Conditions

Wheat has a prominent position among cereals. It is high source of protein, good source of fibre and good in manganese and magnesium in unrefined state. Its area and productivity is increasing rapidly across the globe, due to its wider adaptability and sustainability under diverse agro climatic conditions (Kumar et al., 2014). There are various factors, which are responsible for low yield of wheat crop in the country but among these sowing time and and varietal selection are of primary importance. Wheat is the main crop of winter season and it has its own definite requirements for temperature and light for emergence, growth and flowering (Dabre et al., 1993). Selection of suitable crop varieties according to the agroclimatic conditions may play crucial role in realizing the optimum production of any crop commodity (Singh et al., 2008). Delay in sowing results in poor tillering and crop growth is generally slow due to low temperature. In late planting the wheat variety should be of short duration that may escape from high temperature at the grain filling stage (Phadnawis and Saini, 1992). Late sowing results in reduction of yield contributing characters like number of tillers and number of grains per spike (Ansary et al., 1989). The release of new varieties is a continuous process and different varieties perform differently under different sowing International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 9 (2017) pp. 3488-3492 Journal homepage: http://www.ijcmas. com
Task-oriented and Semantics-aware Communications for Augmented Reality

Upon the advent of the emerging metaverse and its related applications in Augmented Reality (AR), the current bit-oriented network struggles to support real-time changes for the vast amount of associated information, creating a significant bottleneck in its development. To address the above problem, we present a novel task-oriented and semantics-aware communication framework for augmented reality (TSAR) to enhance communication efficiency and effectiveness significantly. We first present an analysis of traditional wireless AR point cloud communication framework, followed by a detailed summary of our proposed semantic information extraction within the end-to-end communication. Then, we detail the components of the TSAR framework, incorporating semantics extraction with deep learning, task-oriented base knowledge selection, and avatar pose recovery. Through rigorous experimentation, we demonstrate that our proposed TSAR framework considerably outperforms traditional point cloud communication framework, reducing wireless AR application transmission latency by 95.6% and improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.
Determination of ABO Blood Grouping from Dentin and Pulp from Freshly Extracted Teeth and Teeth Stored in Sea Water Using Absorption Elution and Absorption Inhibition Method

Aim: We studied ABO blood grouping from dentin and pulp in freshly extracted teeth and also from the teeth stored in sea water. Materials and Methods: A total of 60 samples were selected & divided into 3 groups with 20 samples each. Group I analyzed within period of a week without any storage medium, group II, III were analyzed after 1 and 2 month of storage in sea water. Results: Statistical analysis was done using chi square test. By Absorption –Elution, pulp in group I, II, III, gave 90%,75%,75% of positivity. In dentin, Group I, II, III showed 55%, 45%, 20% of positivity. By Absorption -Inhibition method pulp in group I, II, III showed 45%, 20%, 0% of positivity. In dentin, group I, II, III showed 20%,5%,5% of positivity. Conclusion: This study concluded that pulp is most reliable than dentin even in sea water storage and absorption elution is most effective method in blood group identification in teeth than absorption inhibition method.
Mitochondrial genomes infer phylogenetic relationships among the 2 oldest extant winged insects (Palaeoptera)

21 Background: The relationships among the oldest winged insects (Palaeoptera), including the 22 Ephemeroptera (mayflies) and Odonata (dragonflies and damselflies), remain unclear. The 23 Palaeoptera together with the Neoptera have evolved as result of a rapid divergence from a common 24 ancestor in the distant past. Thus they are thought to be more susceptible to systematic inadequacies, 25 including taxon sampling, choice of outgroup, marker selection, and phylogenetic methods. Here we 26 reconstruct their phylogenetic relationship using newly sequenced mitochondrial genomes in 27 combination with 90 additional insect mitochondrial genomes. In particular, we investigate the 28 impact of the increased mayfly taxon sampling, the effect of rogue taxa, and the used phylogenetic 29 framework (Bayesian inference (BI) vs. maximum likelihood (ML)) approach. 30 Results: We found support for the clustering of the Odonata as most ancient, extant winged 31 insects, using BI based on an optimized data matrix. Overall, we found no support for the basal 32 Ephemeroptera clustering and the sister relationship between the Ephemeroptera and Odonata. Our 33 newly sequenced mitochondrial genomes of Baetis rutilocylindratus , Cloeon dipterum , and 34 Habrophlebiodes zijinensis showed the complete set of 13 protein coding genes and a conserved 35 gene orientation with the exception of two inverted tRNAs for H. zijinensis. 36 Conclusions: The increase of palaeopteran taxon sampling in combination with a Bayesian 37 phylogenetic framework was crucial to infer phylogenetic relationships within the three ancient 38 insect lineages of Odonata, Ephemeroptera, and Neoptera. Pruning of rogue taxa improved the 39 number of supported nodes in all phylogenetic trees. It remains to be tested weather an increased 40 taxon sampling might also reveal the elusive phylogenetic positions of other insect orders.
Conditional Generative Adversarial Networks for Rx-to-Tx Translation in Wireless Communication Systems

Wireless communication systems rely on channel estimation and equalization to ensure reliable and efficient data transmission. However, with the increasing demand for high connectivity in massive IoT networks, these processes are facing significant challenges. The complexity and intensive computation required for channel estimation and equalization results in high communication latency and power consumption, which can ultimately prevent the transceiver from restoring the originally transmitted signal. In this paper, we propose a novel approach to simplify wireless communication systems by using a conditional generative adversarial network (cGAN) model to replace both channel estimation and equalization blocks. We formulate the data recovery task as a translation from received data to the corresponding transmitted signal and introduce the concept of Rx-to-Tx translation based on a cGAN, which was initially developed for image-to-image translation. Our preliminary results demonstrate the feasibility and effectiveness of this approach, particularly for digital modulations. By carefully tuning the model's hyperparameters, we achieve the theoretical symbol error rate (SER) of QAMs in a Rayleigh propagation channel. Our proposed approach has the potential to significantly reduce the computational complexity and overhead typically associated with traditional channel estimation and equalization blocks. This can lead to more efficient and cost-effective wireless communication systems.
Serially Concatenated Systematic Coding for Wireless Steganography with OFDM Signaling

We propose a practical covert communication technique for IoT networks based on serially concatenated systematic coded (SCSC) wireless steganography, where the secret information is transmitted by orthogonal frequency division multiplexing (OFDM) signal. Instead of sending the Gaussian artificial noise (AN) so as to prevent the detection by the warden, the secret OFDM signal is hidden into the cover DFT-spread OFDM (DFT-s-OFDM) signal as a public signal since it is suitable for low-cost IoT devices due to its low peak-to-average power ratio (PAPR) property. In our proposed system, the public signal is generated from the parity bits of the secret information by utilizing systematic encoding. As a result, the legitimate receiver improves the error rate performance by concatenating the likelihood corresponding to both secret and public signals. On the other hand, since the parity bits are broadcast as the public signal, there is a risk of the secret information being decoded by the unintended receiver, warden. Therefore, we derive the conditions for systematic codes so as to achieve secure covert communications. Through computer simulation, we demonstrate that the coded bit error rate (BER) performance of our proposed scheme significantly outperforms that of the conventional system, and this enhancement can be achieved without the expense of the secrecy evaluated in terms of the probability of event detection error at the warden.
Centralized Power Control In Wireless Interference Channel With Imperfect CSI

The open problem of finding the highest possible sum rate of a K-user interference channel in the presence of fading when the transmitters have imperfect channel state information has been solved in this paper by using deep learning technique. Towards that end, we use a fully connected feed forward neural network for finding the transmit powers from K users. We use an ensemble of M fully connected neural networks and train each network of the ensemble independently. The best solution from this ensemble of solutions is treated as the final solution. The interesting observation is that the ensemble size need not be high. The degradation in sum capacity caused by imperfect channel state information is shown for various levels of imperfection and various levels of noise.
Fast Algorithm for HEVC using Frequency-based Convolutional Neural Networks

Compared to the previous H.264 video encoding standard, High Efficiency Video Coding (HEVC) offers a remarkable 50% reduction in bitrate while maintaining perceptual video quality. However, this enhancement comes at the cost of significantly increased encoding complexity. In HEVC, the quad-tree partitioning of coding units (CUs) is a major contributor to this complexity, as it involves an exhaustive search for rate-distortion optimization (RDO). Many existing approaches address this issue by utilizing neural networks to predict block partition decisions, primarily focusing on the spatial domain. In our work, we take a novel approach by introducing a frequency-based network to tackle this problem from a different angle. In comparison to the original HM16.5 encoding, the proposed method of learning in the frequency domain average time reductions about 64.74%, 67.21%, 70.8% and 79.52% on average at four QPs respectively.
Multi-Class Sentiment Analysis of Hindi Textual Data

Sentiment analysis plays a crucial role in natural language processing, especially when it comes to grasping and examining the emotional aspects within textual content. Categorizing text into multiple sentiment categories is a complex task due to the intricate and subjective nature of human emotions and expressions. In this paper, we present an extensive study on multi-class sentiment analysis of Hindi textual data, where the dataset is categorized into five different sentiments. To attain high accuracy in sentiment analysis, three different methods, namely Long Short Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM) have been employed. This is accomplished by fine-tuning hyperparameters and preprocessing the data, which significantly improves the accuracy of these models. The models have been trained and evaluated using the Hindi text dataset to determine the sentiment conveyed in the text. The experimental findings reveal that the SVM model outperforms the others in terms of accuracy. This study underscores the effectiveness of optimizing hyperparameters and improving data preprocessing to achieve superior accuracy. The insights gained from this research can be valuable for the development of sentiment analysis systems for Hindi text data, with applications ranging from social media analysis and customer feedback evaluation to market research.
Enhancing The User Interface and User Experience of SiKeren Presence Mobile Application With User Centered Design Method

SiKeren is a mobile-based application designed to facilitate attendance processes for civil servants within the scope of Jember District Government. Based on the initial evaluation, the SUS scores were found to be below the average parameter, scoring 60.9. Therefore, there is a potential for continuous enhancement in the SiKeren application. This is further supported by observations and in-depth interviews indicating shortcomings in the User Interface and User Experience (UI/UX) aspects of the SiKeren application. Given these conditions, this study aims to enhancing the UI/UX of SiKeren application to optimally meet user needs and preferences through redesigning the UI/UX using the User-Centered Design (UCD) methodology. The redesign will be evaluated using the System Usability Scale (SUS) method, with a focus on enhancing the quality of user experience. As a result, the overall usability level of the redesigned UI/UX for the SiKeren application, particularly in terms of user acceptance, performance, quality, and potential for user recommendation, has significantly enhanced and surpassed the usability level of the SiKeren application prior to the redesign. The redesigned UI/UX also successfully addresses user needs and preferences, as indicated by positive feedback from respondents during the final iteration interviews.
Shield Wires for Reducing the Lightning-Induced Overvoltages on Overhead Lines

The estimation of lightning-induced over voltages (LIOV) is essential for recognising the lightning threat to power transmission networks and also helps in analyzing the technique to mitigate the same. In the present work, effectiveness of the shield wire on reducing the lightning induced overvoltage is studied and it is found that shielding wires depending on their relative placement with respect to the phase conductor can marginally reduce the LIOV. In order to compute LIOV, the lightning channel is modelled as thin wire and modified transmission line with exponential (MTLE) decay engineering model is adopted to evaluate the lightning electromagnetic fields (LEMF), The coupling equations are then solved using the finite difference time domain (FDTD) technique.