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Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that combines the powerful feature extraction capabilities of the Superpoint algorithm and the exact feature matching capabilities of the Lightglue algorithm with the image fusion algorithm of Unsupervised Deep Image Stitching (UDIS). Our proposed method effectively improves the situation where the linear structure is distorted and the resolution is low in the stitching results of the UDIS algorithm. On this basis, we make up for the shortcomings of the UDIS fusion algorithm. For stitching fractures of UDIS in some complex situations, we optimize the loss function of UDIS. We use a second-order differential Laplacian operator to replace the difference in the horizontal and vertical directions to emphasize the continuity of the structural edges during training. Combined with the above improvements, the Super Unsupervised Deep Image Stitching (SuperUDIS) algorithm is finally formed. SuperUDIS has better performance in both qualitative and quantitative evaluations compared to the UDIS algorithm, with the PSNR index increasing by 0.5 on average and the SSIM index increasing by 0.02 on average. Moreover, the proposed method is more robust in complex environments with large color differences or multi-linear structures.

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets.

Music has a great role and importance in human life since it has the ability to trigger or convey feelings. As recognizing music emotions is the subject of many studies conducted in many disciplines like science, psychology, musicology and art, it has attracted the attention of researchers as an up-to-date research topic in recent years. Many researchers extract acoustic features from music and investigate relations between emotional tags corresponding to these features. In recent studies, on the other hand, music types are classified emotionally by using deep learning through music spectrograms that involved both time and frequency domain information. In the present study, a new method is presented for music emotion recognition by employing pre-trained deep learning model with chroma spectrograms extracted from music recordings. The AlexNet architecture is used as the pre-trained network model. The conv5, Fc6, Fc7 and Fc8 layers of the AlexNet model are chosen as the feature extracting layer, and deep visual features are extracted from these layers. The extracted deep features are used to train and test the Support Vector Machines (SVM) and the Softmax classifiers. Besides, deep visual features are extracted from conv5_3, Fc6, Fc7 and Fc8 layers of the VGG-16 deep network model and the same experimental applications are made in order to find out the effective power of pre-trained deep networks in music emotion recognition. Several experiments are conducted on two datasets, and better results are obtained with the proposed method. The best result is obtained from the VGG-16 in the Fc7 layer as 89.2% on our dataset. According to the obtained results, it is observed that the presented method performs better.

In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.

Moisture variation is extremely relevant for the stability of ecosystems in Central Asia (CA). Therefore, moisture evolution and its potential driving mechanism over the region are always a hot research topic. Although much effort has been devoted to understanding the processes of moisture evolutions in CA during the Quaternary, particularly the Holocene, the associated underlying mechanisms remain in a state of persistent debate. In this study, the granulometry, clay mineral and chroma properties of a loess section (named ZSP section) in the westerlies-dominated Ili Basin, NW China are investigated. With the accelerator mass spectrometry radiocarbon dating (AMS 14C)-based Bayesian age–depth model, we provide a sensitive record of effective moisture evolution since the last glacial maximum (LGM) in the basin, and the results help enhance understanding of the possible driving mechanisms for westerly climate change. Comparisons of clay mineralogy indices shows that the study area is involved in the Northern Hemisphere dust cycle processes as a dust source, and the content of <2 μm grain size fraction in the ZSP section can thereby be used to reflect the westerlies’ intensity. After deducting the complicated influencing factors for lightness changes throughout the section, the calibrated lightness is adopted to indicate the regional effective moisture. Our findings show that effective moisture is relatively abundant during the LGM and the middle–late Holocene, with dry climate conditions during the last deglaciation and early Holocene. We argue that westerlies’ intensity was the main factor for driving the effective moisture evolution in the Ili Basin since the LGM. Local and source evaporation intensity and effective intra-annual control time of the westerlies over the study area exerted a minor influence on the moisture changes.

Extrapyramidal symptoms encompass features of Parkinsonism, including bradykinesia, cogwheel rigidity, and resting tremors, which contribute to motor impairments hindering handwriting and speech. In this study, we analyzed voice data captured using a voice sensor setup from 94 patients exhibiting varying levels of EPS and 30 unaffected controls. Each participant provided 13 recordings of repeated vowel and consonant sounds. The Drug-Induced Extrapyramidal Side Effect Scale and Glasgow Antipsychotic Side Effect Scales were used when grading patients into mild, moderate, and severe extrapyramidal symptoms, both administered by trained clinicians. To develop an objective assessment tool, we employed a transfer learning approach using a DenseNet architecture for feature extraction and classification. Its architecture enables the hierarchical concatenation of features at each layer. In this study, we identified that key acoustic features, MFCC, chroma, and spectral contrast vary significantly with the severity of extrapyramidal symptoms. Based on these findings, we developed a DenseNet-based model capable of predicting extrapyramidal symptoms from voice data. This model can classify with an accuracy of 81.9% and a precision of 82.0%. To the best of our knowledge, this is the first study to introduce a voice-based model for assessing the severity of extrapyramidal symptoms.

The analysis of quality characteristics of sweet potato is an important basis for the selection and utilization of sweet potato varieties. The nutritional indexes (sugar content, drying rate, amylose content, amylopectin content, anthocyanin content), structural indexes (hardness, elasticity, cohesion, adhesibility, chewiness, resilience) and chroma of 20 sweet potato varieties were measured and significant analysis was conducted. The nutritional value, texture quality and processing suitability were evaluated. The results showed that the dry weight rates of sweet potato varieties 197-4 (36.2%) and 196-16 (33.5%) were high and 193-3 was low compared with other varieties, indicating varieties 197-4 and 196-16 were more suitable for flour production, and 193-3 were more suitable for fresh food. The single potato weight of 193-16 was the largest (674 g), and that of 198-2 was the smallest (143 g). The acre yield of 193-16 was the highest, which was 42 000 kg/hm 2 , and the acre yield of 197-37 is the lowest, which was 25 500 kg/hm 2 . The variety 197-37 was suitable for pigment extract due to the highest anthocyanin content (38.3 µg/g), and the drying rate was 31.2%, which was suitable for the promotion of starch processing sweet potato varieties. The yield of 42 000 kg fresh sweet potato per hectare can process 560 kg of starch, with an output value of about 3930 US dollars per hectare. These findings can guide sweet potato breeding and processing decisions.

Music has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, Soundcloud or any other internet platform. Music Information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, Rock, Hip hop, Nazrul, Rabindra and Folk music. We have started with some traditional machine learning algorithms having K-Nearest Neighbor, Logistic Regression, Random Forest, Support Vector Machine and Decision Tree but ended up with a deep learning algorithm named Artificial Neural Network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and Mean Chroma Frequency Values of that raw amplitude data. But we found that music Tempo having Beats per Minute value with two previous features present better accuracy.

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

Objective: To evaluate and compered the effect of different temperatures (5°C, 37°C and room temp. ±23°C) pre- cured and post-cured for three universal- Chroma composite materials (Hybrid-Nano fillers, Supra-Nano, Nano filler) on the polymerization degree and micro-hardness.Materials: A seventy-five disc-samples-shaped were fabricated from (Omnichroma, Vittra APS, DenFil N), for each test in different temperatures (5°C, 37°C and room temp. ±23°C) were light cured according to manufacture instruction. The Fourier trans-form infrared spectroscopy was used to the polymerization degree measured for each sample while the micro-hardness was measured by the using of Vickers hardness test. Data were analyzed using One-Way-Analysis of Variance at level p < 0.05.Results: The analysis showed that there was significant difference in the polymerization degree and in the micro-hardness of the samples fabricated at the different temperatures when heated pre- and post- cured of all materials increase, in the polymerization degree and the micro-hardness of the samples.Conclusions: Increasing three universal- Chroma composite materials (Hybrid-Nano fillers, Supra-Nano, Nano filler) temperature whether pre-cured and post-cured allows for maintaining or increasing polymerization degree and hardness of three universal- Chroma composite materials especially DentfilN Nano- filler composite.