Miracle-dz/UniFAR-FTUs huggingface dataset:
Let's Identify the Similar and Confusing Raags of Hindustani Classical Music

The identification of different raags in Indian classical music is a challenging task, and distinguishing between Raag Bhoopali and Raag Deshkar is no exception. The reason why this is a difficult task is both the raags share similar melodic structures, including the use of a pentatonic scale (“Audav” scale) with five notes (Sa, Re, Ga, Pa, and Dha). This makes it challenging to differentiate between the two raags, especially for those who are not familiar with the nuances of Indian classical music. The present research work addresses this issue of identification between two raags. We have purposed a method based on attention employing a Neural Network (NN) architecture, for identifying the two. To assess the proposed technique, we curated a dataset of Hindustani Classical music comprising these two distinct raags. Before proceeding with the analysis, we performed multiple pre-processing steps, audio data augmentation techniques, and feature extraction methods on the dataset, which yielded several feature values. Next, we employed the proposed architecture that utilized a classification algorithm. Additionally, to gain further insights into the proposed approach we conducted a counterfactual analysis, which involved identifying the minimum changes required in the input data to change the classification outcome. The attention-based neural network architecture plays a significant role in extracting pertinent information about the raags from the pre-processed data. The developed technique was evaluated based on key performance parameters such as accuracy, precision, recall, and F1 score, yielding impressive scores of 0.949, 0.949, 0.949, and 0.949 respectively. This indicates the clear superiority of our proposed approach over previously published methods in the literature.
Agrobacterium-Mediated Transformation of

Wheat is the world’s second largest crop, supplying 19% of human calories; the largest volume crop traded internationally and grown on approximately 17% of the world’s cultivatable land (over 200 million hectares) (Jones, 2005; Atchison et al., 2010). However, probably due to climate change, some adverse environmental conditions have caused a downward trend in world wheat production (FAO, 2003; 2011). In this context, developing new higher yielding wheat varieties more tolerant or resistant to abiotic and/or biotic stress, using all available plant biotechnology technologies available, should be considered as the major challenge.
EchoImage: User Authentication on Smart Speakers Using Acoustic Signals

The user authentication has drawn increasingly attention as the smart speaker becomes more prevalent. For example, smart speakers that can verify who is sending voice commands can mitigate various types of attacks such as replay attack or impersonation attack. Existing user authentication solutions either cannot be applicable to smart speakers directly or require certain additional user-device interaction or pre-installed infrastructure, which may severely affect the user experience and create extra burdens to users. In this work, we propose a user authentication system EchoImage utilizing acoustic images, which are derived from the smart speaker by emitting beep signals and sensing echoes from the user's body with its microphone array, as the proof for user authentication. Given the acoustic samplings of the reflected beep signal, our system designs a distance estimation component by applying a correlation based technique on the beamformed signal to estimate the distance between the user and microphone array. Our image construction component then constructs a virtual imaging plane using the estimated distance and steers the array towards each grid of the plane to generate an acoustic image of the user. Moreover, we propose a transfer learning-based method to derive efficient features from the constructed images, and employ SVM classifiers for accurate user authentication. Our extensive experiments demonstrate that our system is robust and accurate across various scenarios.
Power System Control Method Based on Fuzzy Theory and Lawson Algorithm

The role of system control in the power system is very important, but there is a problem of inaccurate result testing. The conventional mode cannot solve the power system control problem in Lawson's algorithm, and the calculation is unreasonable. Therefore, this paper proposes a fuzzy theory to analyze innovative power system control. Firstly, the Lawson algorithm is used to evaluate the power system control, and the indicators are divided according to the requirements of the power system control method to reduce the power system control in the interfering factor. Then, the fuzzy theory forms a power system control scheme for the power system control, and synthesizes the power system control results Analyze. MATLAB simulation shows that the accuracy and time of Lawson's algorithm for power system control are better than those of conventional power system control under certain evaluation criteria Mode.
Steering Angle Prediction Algorithm Performance Comparison in Different Simulators for Autonomous Driving

The development of autonomous driving (AD) algorithms and their testing would be very expensive when real-world vehicles and environments would be used from the very beginning of development. That is why in the initial development phases simulators are often used, in which it is possible to achieve realistic simulations of the real world. Therefore, many different simulators for AD have been developed. One of the main tasks of a fully autonomous vehicle is steering angle prediction (SAP). This paper explores SAP using a PilotNet CNN-based algorithm that recognizes essential features from input images to predict vehicle trajectory. After training on real-world images, the algorithm’s performance is evaluated in both CARLA and AirSim simulators, achieving 91.65% autonomy in CARLA and 71.87% in AirSim. The algorithm is further trained on the images obtained from the simulator, resulting in improved performance of 96.34% in CARLA and 78.99% in AirSim in terms of total autonomy. The study demonstrates the benefits of simulators for the development of AD algorithms and highlights the importance of testing in multiple simulators to achieve reliable performance across different environments and scenarios, including different lighting and weather conditions, types of road markings, and track configurations. The paper also highlights the advantages and disadvantages of individual simulators, which can be useful for the development and testing of future SAP algorithms.
Hierarchical, Distributed and Brain-Inspired Learning for Internet of Things Systems

In this paper, we propose EdgeHD, a hierarchy-aware learning solution that performs online training and inference in a highly distributed, cost-effective way. We use brain-inspired hyperdimensional (HD) computing as the key enabler. HD computing performs the computation tasks on a high-dimensional space to emulate functionalities of the human memory, such as inter-data relationship reasoning and information aggregation. EdgeHD exploits HD computing to effectively learn the classification models on individual devices and combine the models through the hierarchical IoT nodes without high communication costs. We also propose a hardware design that accelerates EdgeHD on low-power FPGA platforms. We evaluated EdgeHD for a wide range of real-world classification applications. The evaluation shows that EdgeHD provides highly efficient computation with reduced communication. For example, EdgeHD achieves on average $3.4\times$ and $11.7\times (1.9\times$ and $7.8\times$) speedup and energy efficiency improvement during the training (inference) as compared to the centralized learning approach. It reduces the communication costs by 85% for the training and 78% for the inference.
Analyzing Spatial Distribution Patterns of Wheat Ears

The management of wheat breeding and cultivation can be launched accurately through the assistance of the visualization of wheat ears counting and spatial distribution. This study aimed to develop an automatic and accurate counting method, and a visualization way of spatial distribution of wheat ears from it. Based on unmanned aerial vehicle (UAV) acquisition and Global Wheat Head Detection (GWHD) dataset, the wheat ears dataset was constructed. Five typical deep learning target detection models, including Faster R-CNN, SSD, RetinaNet, YOLOv3 SPP and YOLOv5s, were selected for training, verification, and testing. Among them, Faster R-CNN and YOLOv5s models showed the most satisfying counting effect, with the accuracy of wheat ears recognition above 95%. After the improvement of the two models, the ears recognition accuracy of Faster R-CNN model exceeded 96%, while the ears recognition accuracy of YOLOv5s model exceeded 98%. In order to provide decision-making basis for the precise management of wheat farmland, this study explored and realized the density and size distribution visualization of wheat ears by using image processing technology. In addition, the shape distribution visualization of wheat ears was studied and accomplished with the foundation of U-net model. In particular, the number distribution visualization of wheat ears in the whole target wheat field in video data was performed effectively by adopting the combination of YOLOv5s+DeepSort and Python program.
Methodology for Reliability Assessment of Industrial Control Systems

The article covers issues related to the development of methodological provisions for the assessment of the reliability of industrial control systems (ICSs) and the assessment of the effectiveness of ICSs applications by the reliability parameter. The distinctive feature of the suggested methodology is its simplicity, which allows ICS personnel who are not specialists in ICS reliability theory to master it. The author formally set the task and defined the input and output data required to solve that task. The proposed methodological provisions include the range of reliability parameters, the definition of ICS failures and malfunctions, the development of the reliability scheme, and the development of the mathematical model for reliability assessment. The main actions of the suggested methodology are the development of rules for statistical data collection and calculation and the development of rules for the assessment of effectiveness, i.e., the correspondence between projected reliability assessment and reliability assessment implemented during ICS operation. This methodology was tested during scientific and methodological support of works for the modernization of the Russian Federation State Automated System "Vybory" and for the design of the industrial control system Skolkovo "Smart city", in a number of projects on ICS development for different organizations, and it was brought up to the operational method. In further studies, we plan to refine the suggested mathematical model for reliability assessment and develop algorithms for the use of the proposed data collection and calculation rules, enabling integration with built-in control systems for hardware and operating systems.
An Energy Efficient Routing Algorithm for WSN Using Q-Learning Based Data Aggregation Method

Q-Iearning is an energy efficient routing algorithm for wireless sensor networks (WSN s) that uses the reinforcement learning technique for routing decisions. The algorithm is expected to reduce energy consumption in routing by dynamic route selection and maintenance. In Q-Iearning, the energy efficiency of routes is evaluated using a reward system based on the dynamic network properties. Based on these rewards and various learned states, a dynamic Q-table is maintained which is used by the nodes to make routing decisions. To further enhance the energy efficiency, Q-Iearning also supports power saving techniques such as sleep mode that allow the nodes to temporarily reduce their energy consumption. Additionally, Q-Iearning is fault-tolerance aware and utilizes conflict-free transmission techniques to reduce the energy cost and minimize packet loss. The algorithm has been evaluated using the IEEE 802.15. 4 standard, and simulation results show that it can reduce energy consumption by up to 53 %, with minimal packet loss.
Flat and broadband Continuum source based on similariton spectrum generated in germanium-doped photonic crystal fiber for DWDM

We investigate the generation of flat and broadband Continuum based on similariton spectrum in germanium-doped photonic crystal fiber via Raman amplification. The proposed fiber is analyzed numerically using vector Finite Element Method (VFEM) with a circular perfectly matched layer (PML). By using 30 mol% of germanium concentration, we obtain ultra-high nonlinearity about 61.71w-1km-1, low confinement loss about 2.42*10-8 dB/km and near zero flattened normal chromatic dispersion of -2.55 ps/nm/km from 1450 to 1650nm. The continuum generated into the 3.5-m-long proposed fiber has flat and broadband spectrum of 130 nm and centered at 1550nm. This continuum allows generating at least 100 channels separated by 100 GHz for DWDM.
Algorithm of UAV Trajectory Creation for Data Collecting from Seismological Sensors

Modern technologies and scientific research play a key role in the study and monitoring of earthquakes. One of the most effective methods of collecting data on seismic activity is the use of seismological sensors that allow you to register and analyze the fluctuations of the Earth’s crust. However, collecting data from seismological sensors in an open area can present certain difficulties, especially in remote and hard-to-reach areas for ground-based monitoring tools. In such cases, the use of unmanned aerial vehicles (UAVs) becomes a promising solution. The main restriction on the use of UAVs is the presence of natural obstacles in the form of mountain peaks and hills that UAVs are not able to overcome without a correctly planned flight path. In this connection, the purpose of this article is to present an UAV flight trajectory planning algorithm for collecting data from seismological sensors in an open area, taking into account the terrain. In this paper, the advantages that UAVs provide for seismological studies in difficult terrain conditions are considered. An algorithm for constructing the trajectory of the UAV using cell decomposition for the task of full coverage of the terrain is proposed, and the results of testing the algorithm in the Gazebo simulator are also presented. Based on the results of the simulation, the proposed trajectory construction algorithm made it possible to successfully collect data from 95.8% of sensors, spending an average of 19 minutes on the mission.
Polar Coded Distribution Matching for Probabilistic Shaping and Stealth Communication

We present a polar coded one-to-many distribution matching scheme. The scheme uses a randomized encoding approach to approximate, in an invertible manner, binary discrete memoryless sources (B-DMSs) using a binary symmetric source. This is accomplished by using polar codes as lossy source codes, and by leveraging their linear structure. Due to the special recursive structure of polar codes, the encoding and decoding complexity of the scheme is of order $\mathcal{O}(N \log N)$, where N denotes the output blocklength. Leveraging the rate-distortion optimality of polar codes, the scheme is shown to be asymptotically optimal for probabilistic shaping and stealth communication over binary output alphabets. Namely, in the limit of large blocklength N, the polar coded scheme is shown to approximate any B-DMS with vanishing Kullback–Leibler divergence and with rate approaching the entropy of the B-DMS.
Re‐evaluation of late Holocene fire histories of three boreal bogs suggest a link between bog fire and climate

We re‐evaluated mid‐ and late Holocene fire records at three bog sites in southern Finland and Estonia through quantitative and qualitative analysis of sedimentary charcoal records. Our analysis suggests synchrony amongst the Kontolanrahka, Männikjärve and Lakkasuo bog records. Over the last 5000 years, three episodes of elevated fire occurrence were evident in these bog records, taking place from 4500 to 4000, 3500 to 2000 and 1000 to 500 cal. a BP. These episodes were probably influenced primarily by climate, but also possibly by expansion/retreat of (pre)historic human populations. Although previous studies have proposed that during the late Holocene only weak fire−climate linkages exist, the presented analysis suggests millennial‐ and centennial‐scale synchronous burning episodes.
Temporal Convolutional Network and its Application in Various Sectors

Artificial Intelligence has been working to meet the needs of humans for a very long time. There have been many progressive evolutions along the way. Neural Networks have emerged to be the best in the field of recognition and data analysis. There have been myriad developments and preparation of models over decades. There is CNN, RNN, FNN, DNN, TCN and many more. In this paper, we aim to compare the working of TCN which stands for Temporal Convolutional Network with the workings of above-mentioned models to figure out the best use of technology for catering the human needs. This paper is a deep analysis of the erstwhile models that are being used and how the temporal convolutional network finds its applications in the present scenario, what are the emerging fields that it can be implemented in and what were the positives of TCN over other neural network models. Through a careful study we have come to a conclusion that TCN could be the basic starting point of all the neural networks as it has proven to have less training time, less memory utilization and gives better results compared to other models. The functioning of TCN models is designed in such a way that no information can be drained from future to the past. Furthermore, the input image can be analyzed irrespective of the length as the same can be mapped to the output layer. TCN finds its application in various multilateral domains that includes healthcare, sequential forecasting, preventing cyber attacks, speech enhancement and many more. It has the capability to outperform the other models most of the time by a major difference and sometimes by a significant margin. Therefore, the applications of TCN can vary in accordance to the needs of the user.
Reliable Data Delivery in Underwater Optical Wireless Sensor Networks

Underwater Optical Wireless Sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promoting the performance of UOWSNs, while the reliability issue has not been fully addressed. In this paper, we propose a novel reliable data delivery scheme based on a cluster structure. First, we determine the orientation of each sensor for directional optical communication, which aims to establish reliable next-hop links among sensors. We formalize such an orientation problem into a submodular function maximization problem and propose a greedy method with an approximation ratio guarantee to solve it. Then, a cluster head designation scheme is developed to improve the data delivery success rate while minimizing the number of cluster heads. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme. The results reveal that compared with other algorithms, the proposed scheme can ensure a data delivery success rate of over 98.5 % while only keeping 45.3% fewer cluster heads. Furthermore, test-bed experiments are carried out to verify the applicability of the proposed scheme in practical applications.
Indirect Testing of Specifications in Analog Integrated Circuits Based on Black-Box Modeling

Conventional testing method of post-manufactures analog integrated circuits (ICs) is mainly relied on expensive automatic test equipment (ATE), which measure the specifications that included in the datasheet according to customized testing circuits and stimulus. In this paper, we proposed an indirect testing method based on modeling the mapping relationship between specifications and responses of much simpler circuits as black box. The model with good fitting ability could map the responses to specifications. Taking results obtained by standard approach as references, we verified the accuracy and effectiveness of this kind of indirect method by simulation experiments, the results shows that all errors of selected specifications obtained by standard and indirect method test are maintained in an acceptable level for post-manufacture testing.
Smart Stethoscope for Remote Healthcare Monitoring Using IoT: A Cost-Effective Solution

Worldwide, the major cause of death is Cardiac Dysfunction, which can be detected through heart sounds. Acoustic stethoscopes are one of the oldest methods of auscultation. An ordinary acoustics-based stethoscope is limited in providing high-quality sound in noisy environments. This paper addresses the problem of cost-effectively making a smart stethoscope using modern electronics and computing technologies for cardiac auscultation. Along with that, to have telemedicine involved with the utmost accuracy possible. A signal conditioning circuit is designed to process the auscultation signals measured by a condenser microphone using a Preamplifier, a High pass filter, a Low Pass Filter, and an Op-Amp Subtractor. An ESP32 development board is used to convert the signal to digital format and transmit the signal through Bluetooth to an Android application to visualize signals. The smart stethoscope is designed to record and transmit real-time heart sounds to healthcare professionals while considering cost-effectiveness and portability. In the future, we can use the recorded signals to predict cardiac disorders using Deep learning models.
Higher Order Dual Sliding Mode Controller for Position Tracking of Permanent Magnet Synchronous Motor

Higher order dual sliding mode controller is a hybrid of Terminal sliding mode controller for field oriented current control and sliding mode super twisting controller for Permanent Magnet Synchronous Motor (PMSM). Sliding mode control (SMC) is a popular technique used for the control of Permanent Magnet Synchronous Motors (PMSMs). It is a robust control method that ensures high performance in the presence of uncertainties and disturbances. This work presents the position tracking of permanent magnet synchronous motor based on higher order sliding mode control. To enhance the robustness of the control system against various environmental conditions, parameter uncertainties, and nonlinear dynamics in the stator current, a sliding mode based current controller is introduced for field-oriented control. The sliding surface is defined based on the stator current error. The control input is then designed to regulate the stator current to slide along the surface, leading to accurate control of the PMSM.
Reduction of Bit Rate Error by Mapping Optimization Technique for Digital Signal Processing

Bit rate error is an important factor that affects the performance of digital signal processing. In order to reduce this error, it is important to optimize the signal mapping process. This paper presents a mapping optimization technique that is used to minimize bit rate error in digital signal processing systems. The optimization process involves the selection of mapping functions with optimal properties that minimize the bit rate error. The optimization technique takes into account the signal constraints and the hardware-specific characteristics of the system, and uses an iterative search process to find the optimal mapping functions that minimize the bit rate error. The results show that the optimization technique reduces the bit rate error significantly, and can be applied to a wide range of digital signal processing systems. Furthermore, the optimization technique offers significant improvement in performance compared to the traditional methods.