Miracle-dz/UniFAR-FTUs huggingface dataset:
OFDM Radar With Subcarrier Aliasing—Reducing the ADC Sampling Frequency Without Losing Range Resolution

Many radar applications require a high range resolution, entailing the necessity for a large signal bandwidth. When employing an orthogonal frequency-division multiplexing (OFDM) radar system in its standard implementation, the radar system must operate at a high sampling frequency to ensure covering the entire signal bandwidth, thus imposing significant challenges on its implementation. Therefore, methods for reducing the sampling frequency are topic of recent research. In this work, a method for reducing the analog-to-digital converter (ADC) sampling frequency at the receiver without losing range resolution is proposed. This is achieved by activating only a fraction of the subcarriers at the transmitter, while simultaneously allowing for systematic aliasing due to undersampling in the receiver. This aliasing is designed such that the active subcarriers do not interfere with each other, which ensures a simple separation of them in the frequency domain. The proposed concept, denoted as subcarrier aliasing (SA), allows reducing the ADC sampling frequency in the receiver, but lacks in reducing the digital-to-analog converter (DAC) sampling frequency in the transmitter. However, the key asset of SA-OFDM lies in the properties of its waveform, making it especially suitable for joint radar and communication applications over other waveforms, e.g., stepped-carrier OFDM.
Generalized multiple importance sampling for Monte-Carlo global illumination

The global illumination or transport problems can be considered as a sequence of integrals, while their Monte-Carlo solutions - as different sampling techniques. Multiple importance sampling takes advantage of different sampling strategies, and combines the results obtained with them to establish a quasi-optimal sampling for Monte-Carlo quadratures. This offers a combination of totally different global illumination algorithms which preserves their strengths. However, implementation of this general concept poses two problems. The solution of the global illumination problem does not contain a single integral, but a sequence of integrals that are approximated simultaneously. One objective of this paper is to generalize the fundamental theory of multiple importance sampling to sequences of integrals. The cost of the computation and the contribution of a single integral to the final result vary. On the other hand, different methods compute a sample with different computational burdens, which should also be taken into account. This paper attacks this problem and introduces the concept of computational cost in multiple importance sampling. The theoretical results are used to combine bi-directional path tracing and ray-bundle based stochastic iteration. We conclude that the combined method preserves the high initial speed of stochastic iteration, but can also accurately compute the specular light paths through bi-directional path tracing.
Short Circuit Behavior of Dual Three-phase Permanent Magnet Synchronous Motors with Different Mutual Inductance in Electric Propulsion Application

The dual three-phase permanent magnet synchronous motors (DTP-PMSMs) are suitable in aircraft electric propulsion unit (EPU) application because of their good reliability and fault-tolerant capability. However, this fault-tolerant capability can get weakened or even eliminated in short circuit fault due to the influence of flux coupling between the two isolated three-phase sub-systems. In this paper, a mathematical model of DTP-PMSMs with different mutual inductance between the two sub-systems is developed and analyzed. Based on the model, different short circuit protection methods are discussed and compared. Finally, a simulation of an EPU consisting of a DTP-PMSM, and a simplified constant speed propeller (CSP), is implemented to verify the short circuit behaviors.
Improved speech emotion recognition based on music-related audio features

Emotions are essential for human communication as they reflect our inner states and influence our actions. Today, emotions provide crucial information to many applications, from virtual assistants to security systems, mood-tracking wearable devices, and autism robots. The speech emotion recognition (SER) model must be lightweight to run on varying devices with limited computational power. This research investigates the performance of music-related features for SER based on the auditory and neuropsychology evidence about the connection of emotional speech and music in human perception. Unlike prior works on low-level descriptors that primarily focus on differentiating human speech production, our method employs features ex-tracted directly from raw speech signals through Discrete Fourier Transform and Constant-Q Transform. These features represent the perceptual pitches and timbre characteristics of the human voice. The 10-fold cross-validation results show that our method improves the accuracy of the audio feature-based approach on RAVDESS, CREMA-D and IEMOCAP datasets. Findings from the ablation study imply the significance of perceptual pitch, the perceptual loudness and the combination of pitch and timbre features in building a robust SER model. Compared to pretrained deep learning embeddings, our method demonstrates its generalizability and high efficiency despite a much smaller model size.
Model Predictive Control-enabled Fault Ride Through Operation Strategy for High Power Wind Turbine

The increasing penetration of wind power poses new requirements in terms of ancillary services to support the stable operation of power system. Based on the flexible positive and negative sequence control (FPNSC) method for current reference generation, this paper presents a model predictive control (MPC)-enabled fault ride through operation strategy for high power wind turbine complying with next-generation grid code requirements. The proposed strategy calculates the current references considering the converter current constraint, and further exploits the dynamic response of MPC to optimize the voltage support capability during balanced and unbalanced grid faults. Simulation results are given to validate the operation performance of the proposed fault ride through operation strategy.
Performing multiple cognitive tasks through dynamics in spiking neural networks

In this work, we consider recurrent spiking neural networks trained to perform multiple target tasks inspired by cognitive neuroscience experiments. The networks consist of the adaptive exponential integrate-and-fire neurons, the connectivity is modified during supervised training by input-output examples, and resulting networks are reverse engineered to infer dynamic mechanisms of performing the target tasks. Several features are found that are qualitatively consistent with experimental findings observed in task-performing animals including the emergence of task-specific functional clusters and neurons with high mixed selectivity.
Comparing severe gas transport situations through the network: Similarity or reduction methods

A number of severe entry-exit combinations is selected, to investigate whether or not all possible future gas transport situations through the gas transport network of Gasunie Transport Services can be met. These severe entry-exit combinations are selected, such that if the transport capacity is manageable regarding the pipeline network for these entry-exit combinations, then it is possible to satisfy all possible entry-exit combinations. In short, these severe entry-exit combinations represent the most severe transport situations through the gas network within contractual limits, describing realistic market behavior, and determining the size and shape of the gas transport network. These are often called shipping variants or stress tests. These stress tests are denoted by vectors which components represent the gas capacities at entry and exit points. If a large set of stress tests is generated, then it takes a relatively large amount of time to evaluatethesestresstests. The aim of this project is, therefore, to reduce this generated set as much as possible. When reducing this set, we have to make sure that we derive a minimal set, which is still a complete set. So, our goal is to delete identical stress tests from the generated set, but also less severe transport situations. This report focuses on the quadratic form distance, which can measure similarities between vectors for which the individual components are correlated. In addition, some generated sets of stress tests are reduced according to reducing criteria.
A Unified Perspective on Time-Domain and Frequency-Domain Kalman Filters for Acoustic System Identification

Time-domain and frequency-domain Kalman filters are applied as adaptive filters to many audio signal processing problems, especially to acoustic system identification. As the formal relationship between these two Kalman filters is not obvious, we connect them using a unified perspective. To this end, we show the equivalence of both filters as a special case and transfer concepts from one domain to the other. Specifically, the concept of considering multiple past observations is transferred to the frequency-domain Kalman filters, and the concept of block-based processing is transferred to the time-domain Kalman filters. Transferring these concepts yields new Kalman filter variants that allow for a different trade-off between convergence speed, steady-state performance and computational complexity.
Current Advances in Hyperspectral Remote Sensing in Urban Planning

Hyperspectral cameras collect loads of spectral bands of notably slender bandwidths simultaneously thereby to extract the spectral signatures of items and substances which offers a better recognition and identification of items on city areas. In this paper the precise specific application of hyperspectral remote sensing to city planning is examined. In recent times more than 55% of the population can stay in towns and the non-stop increase in urban vicinity causes severe damage to biodiversity such as pollution, global warming and many more. Consequently city planning has turned out to be vital to make life secure in towns. Hyperspectral imaging is beneficial to detect, discriminate, and recognize city buildings, sealed soils, vegetation, water surfaces, roads, etc and to assess their real locations and areas to create a better environment. This paper additionally gives an outline of hyperspectral image processing strategies and suggestions for proper urban making plans using hyperspectral technology.
A State-Space Recurrent Neural Network Model for Dynamical Loudspeaker System Identification

A majority of adaptive systems in audio and acoustic signal processing, for instance in acoustic echo control, relies on finite-impulse response (FIR) modeling for the sake of simplicity and inherent stability. In this paper we consider the problem of dynamical loudspeaker modeling and use a contemporary machine-learning framework for infinite-impulse response (IIR) acoustic system identification. Specifically, we convert a physically-motivated parametric dynamical loudspeaker model to a multivariate state-space representation and solve the parameter identification problem via an equivalent recurrent neural network (RNN). With an appropriate training procedure, we obtain a stable computational system with a correspondence of its few parameters to the physical loudspeaker model. Besides the verification on synthetic data provided by the physical model, we also apply the procedure to a set of real loudspeaker recordings. With appropriate model order, the neural network delivers a simple yet accurate recursive loudspeaker representation.
AutoPipe: A Fast Pipeline Parallelism Approach with Balanced Partitioning and Micro-batch Slicing

Recently, pipeline parallelism has been widely used in training large DNN models. However, there are still two main challenges for efficient pipeline parallelism: i) a balanced model partition is crucial for pipeline efficiency, whereas prior works lack a sound solution to generate a balanced partition automatically. ii) the startup overhead is inevitable and especially significant for deep pipelines, which is an essential source of pipeline bubbles and severely affects pipeline scalability. We propose AutoPipe to solve these two problems, which contains i) a planner for automatically and quickly generating a balanced pipeline partition scheme with a fine-grained partitioner. This partitioner groups DNN in the sub-layer granularity and finds the balanced scheme with a heuristic search algorithm; and ii) a micro-batch slicer that reduces pipeline startup overhead according to the planner results by splitting the micro-batch evenly. This slicer automatically solves an appropriate number of micro-batches to split. The experimental results show that AutoPipe can accelerate training by up to 1.30x over the state-of-the-art distributed training framework Megatron-LM, with a 50% reduction in startup overhead and an order-of-magnitude reduction in pipeline planning time. Furthermore, AutoPipe Planner improves the partition balance by 2.73x-12.7x compared to DAPPLE Planner and Piper.
Dynamic Parameter Identification of Six-axis Industrial Robot Based on Improved Genetic Algorithm

Focusing on the problems that the traditional genetic algorithm optimization excitation trajectory cannot meet the constraints in the robot parameter identification, a dynamic parameter identification method based on the improved genetic algorithm is proposed. Firstly, a linearized industrial robot dynamic model is built. Secondly, the excitation trajectory is obtained by optimizing the improved genetic algorithm. Finally, calculate the robot dynamic parameters by the least square method. The experimental results show that the optimal excitation trajectory obtained by this method can meet the constraints, shorten the optimization time and effectively improve the efficiency and effect of dynamic parameter identification.
Impact of aluminum casing on high-frequency transformer leakage inductance and AC resistance

High-Frequency (HF) transformer is a central part of isolated HF power converters. Its parameters, in particular leakage inductance and losses, have a significant impact on the overall converter performances. With the increase of switching frequencies linked to the use of SiC and GaN based active devices, the control of the HF transformer parameters becomes essential. A HF transformer is usually designed without considering its surrounding. However, the latter can have a significant impact on the transformer's performance. In this paper, the effect of an aluminum casing surrounding the transformer is studied and quantified for two parameters: The transformer leakage inductance and the supplementary losses. The goal is to consider the casing effect in the early design stage of power converters.
Restoring Phase Linearity of Uniform Linear Array With Certain Decoupling Techniques

This paper studies the influence of mutual coupling effect on the phase relationship between elements of uniform linear array when incident by incoming waves at certain angles. For an ideal array, the phases of the received signals obeys a certain linear relationship when the wave is incident at a certain angle. By comparing the phase relationships of the signal received by each element before and after decoupling, we come to the conclusion that the phase linearity can be improved by proper decoupling.
6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness.
Classification of Trust in Social Networks using Machine Learning Algorithms

Many real-world complex phenomena have dynamic network architectures with nodes and linkages that are added and withdrawn over time. The definition and explanation of network dynamics is a major scientific task, with the classification of short and long-term changes being a vital test. Users connect with one another in these networks, discuss their interests in resources, express their thoughts on those resources, and spread their information . Because each user only has a limited understanding of other users and the majority of them are anonymous, the trust factor is critical in identifying a suitable product or specific individual. In this paper, Advogato and epinion datasets are taken, the various features are calculated for each pair of nodes, and the trust value is prepared. The trust values are classified using the Machine Learning Techniques: Support Vector Machine(SVM), K-Nearest Neighbors(KNN), Logistic Regression, Random Forest, and Light GBM
Experiments with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments

We propose an SDN-based Non-IP multi-protocol platform (REWIRE) that mixes and matches, on-demand, multi-ple Non-IP protocol strategies with real rapidly-detected network conditions and loT data communication patterns. The REWIRE solution supports centralized monitoring of Wireless Mesh Net-works (WMN) and manages alternative Non-IP protocols stacks (i.e., NDN, DTN & NoD) in an adaptable manner based on change-point analysis & clustering mechanisms. To this end, our proposal grafts flexibility and adaptability capabilities to the WMN providing the communication backbone in real Smart-City environments. Our platform was implemented in real WMNs environments over Fed4FIRE+ test-beds considering an loT sce-nario using traffic patterns based on real sensor measurements.
M-tree index for music search based on similarity of cosine contours and tags

The similarity between songs is an important part of the MIR (Music Information Retrieval), but the definition of the similarity is very subjective. Similarity can be used, for example, in recommendation systems. These systems recommend similar songs based on the user history from a database. To find similar songs in a fast way we have to index the data. Most database indexes are created for exact item searches. GiST (Generalized Search Tree) gives us the possibility to create an index with a distance function between items. These distances can be used for similarity measures. In this paper, we show how can use music similarity for distance in M-tree, which is a distance-based index. Two similarity metrics are used to create an index of music data: song tags and cosine contour.
Fast and Accurate Song Recognition: an Approach Based on Multi-Index Hashing

An activity of wide interest for researchers and companies working in the field of audio signal processing is the capability to automatically recognize in real-time short excerpts of broadcast or played commercial songs. It appears quite difficult to obtain a robust approach able to generate a fast algorithm in order to analyze several audio flows at the same time. In this paper, we compare the results obtained using a specific improvement of an algorithm we recently proposed against several baseline approaches. Specifically, we introduced an approach based on Multi-Index Hashing which permits to improve noticeably speed in fingerprints searching also on very large datasets. Experimental results, performed using the MTG-Jamendo dataset, containing more then 50, 000 songs, show our approach outperform the others jointly considering performance parameters: accuracy, precision and query time.
Adaptive PID Trajectory Tracking Algorithm Using Q-Learning for Mobile Robots

Classical PID controllers usually rely on some prior knowledge to manually adjust the gains of the controller and determine them. However, when the mobile robot works in a complex and changeable environment, the fixed PID gains may be difficult to meet the needs of the robot trajectory tracking accuracy. Therefore, this paper proposes a Q-learning-based adaptive PID trajectory tracking algorithm. Firstly, we construct a trajectory tracking Q-PID controller based on the error model of mobile robot. Then, the Q-learning algorithm is used to adaptively adjust the gains of the PID controller online. Meanwhile, the incremental active learning exploration method is used to improve learning efficiency and adaptability of agent. Finally, we use simulation experiments to verify the high performance of our algorithm.
Using a virtual reality headset in the simulation of the control room of a nuclear power plant

Simulators for operator training have long been in use for nuclear power plants. These are traditionally full-scope simulators with an expensive physical replica of the control room. Recent advances in virtual reality headsets provide affordable means for presenting a stereoscopic view of a virtual model of the control room. While the commercially available headsets are still not perfect, they offer more realism than flat screens, showing a stereoscopic view. The paper provides some details on how a virtual reality headset has been utilized for viewing the virtual control room of the Paks Nuclear Power Plant, modeled with the Blender Game Engine, with an added autofocus feature based on a pragmatic method. Some aspects of the solution are outlined, and, partially based on the experiences gained in the project, current problems and future trends of virtual reality headsets are discussed.
A Comprehensive Study on Review of AI Techniques to Provide Security in the Digital World

Technology the usage of technology is part in our daily life. One cannot imagine the life without smart gadgets, personal computers, laptops and the most important is INTERNET. Without usage of Internet, one cannot sustain in this busy life. Internet became a part and parcel in everyone’s life. The technology makes the human life easier but with the advancement in the technology leads to the jeopardizing consequences in the form of threats. Threats are mainly the security threats, malware attacks, malicious users, viruses and many more. In various cases these effects cause psychological or financial damage to the individual either directly or indirectly. To combat with the present situation researchers are integrating sophisticated and hybrid techniques to boost the devices security mainly the cyber devices. The paper gives a brief overview of techniques implemented for the security using the fields if Artificial Intelligence (AI). In short AI for security. In this paper the main focus is to cover the wide range of algorithms to solve the problem of security theft. AI have many subfields in this paper discussed about the Artificial Neural Networks (ANN), Fuzzy systems, Deep learning, Machine Learning (ML).