Online beat tracking (OBT) has always been a challenging task. Due to the inaccessibility of future data and the need to make inference in real-time. We propose Don’t Look back! (DLB), a novel approach optimized for efficiency when performing OBT. DLB feeds the activations of a unidirectional RNN into an enhanced Monte-Carlo localization model to infer beat positions. Most preexisting OBT methods either apply some offline approaches to a moving window containing past data to make predictions about future beat positions or must be primed with past data at startup to initialize. Meanwhile, our proposed method only uses activation of the current time frame to infer beat positions. As such, without waiting at the beginning to receive a chunk, it provides an immediate beat tracking response, which is critical for many OBT applications. DLB significantly improves beat tracking accuracy over state-of-the-art OBT methods, yielding a similar performance to offline methods.

Inferring music time structures has a broad range of applications in music production, processing and analysis. Scholars have proposed various methods to analyze different aspects of time structures, such as beat, downbeat, tempo and meter. Many state-of-the-art (SOFA) methods, however, are computationally expensive. This makes them inapplicable in real-world industrial settings where the scale of the music collections can be millions. This paper proposes a new state space and a semi-Markov model for music time structure analysis. The proposed approach turns the commonly used 2D state spaces into a 1D model through a jump-back reward strategy. It reduces the state spaces size drastically. We then utilize the proposed method for causal, joint beat, downbeat, tempo, and meter tracking, and compare it against several previous methods. The proposed method delivers similar performance with the SOFA joint causal models with a much smaller state space and a more than 30 times speedup.

The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time-critical, ultra-reliable, and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This paper first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.

Neurological and orthopedic chronic diseases are proven to numerous complications and related dysfunction for patients, and are leading factors of death and disability. Conventional methods of diagnosis and rehabilitation are conducted at hospitals. However, due to the aging society and controversial privacy protection, traditional methods implemented in medical institutions have faced numerous challenges. Hence, internet of healthcare things (IoHT) based techniques have recently begun to be implemented. In order to provide flexible utilization and secure privacy for patients, wearable devices, like whole body devices based on electromyography (EMG) and radio frequency identification (RFID) insole systems, based on gait-analysis were applied. Among these techniques, insole systems for medical analysis are more suitable, because the root cause of neurologic and orthopedic chronic diseases are directly related to several gait characteristics. Neurologic and orthopedic chronic diseases are caused by lesions of the central nervous system (CNS), peripheral nerves (PN) and orthopedic limbs, similarly gait features are comprehensive results generated by the coordination of CNS, PN and lower limbs However, owing to lack of standards for instrumentation and the knowledge gap between engineering and pathogenic mechanism, the medical application of insole systems and the development of IoHT are hindered. This article is presented to address these issues. In this context, five plantar sensing mechanisms and their related products will be overviewed. Next, pathogenic mechanisms and pathological gait features of six representative chronic neurologic diseases will be displayed. Likewise, the development of corresponding insole systems for supervision and recovery for these diseases will be reviewed as well. Finally, challenges and future trends for current work will be discussed. This article will not only provide guidelines for the design of insole systems targeted for specific diseases but will also inspire the innovation of novel ideas.

Smart contract is an important building block of blockchain. Automated market makers are working without an order book, and they determine the price of assets automatically. It is reported that he automated market makers have the impermanent loss, which causes financial damage to liquidity providers. Impermanent loss makes the liquidity providers hesitant to deposit assets in the liquidity pool. Therefore, their participation incentive from liquidity provision should be anticipated by automatic market makers inherently. However, the existence of impermanent gain has never been reported. Impermanent gain is important to attract liquidity providers without giving compensation incentives. This study shows that for some automated market makers, impermanent gain coexists with impermanent loss. Examples showing the coexistence and conditions are provided.

This manuscript was submitted to IEEE Power Engineering Letters. We propose an extension of the Dissipating Energy Flow (DEF) method for locating sources of forced oscillations (FOs), based on an energy function constructed by the complex integral approach, which we call Complex DEF (CDEF). To trace sources of FO, we study the scalar projection of CDEF in a specific direction on the complex plane. We show by simulated cases that this alternative has a good performance in cases where the original DEF fails.

Power distribution networks are experiencing large deployment of behind-the-meter distributed generation and storage along with electrified transport and heat, requiring fundamental changes in planning and operation. Synthetic representative networks may not be sufficient to fully address the needs of researchers, operators and decision-makers. There are increasing efforts to directly model large-scale real networks and conduct detailed analysis beyond conventional practices. This paper presents a large-scale medium voltage (MV)-low voltage (LV) integrated network model building, scenario determination and analysis approach for distribution networks. Data with different formats from several databases were used in model building. The model is validated using benchmark scenarios based on historical network measurements from the field. In collaboration with the national distribution system operator in Ireland (ESB Networks), a pilot study was conducted for a rural network in the Southwest of Ireland, highlighting possible challenges and offering suitable solutions for country-scale implementation.

Change detection and diagnosis based on information-based features is considered. As benchmark case study, the faults of bearings are selected. Information from the working states, including those generated by faults in bearings, are transformed and carried by the vibration's signals, which are further processed by advanced techniques of information and signal processing, as e.g., statistical models, Renyi and Tsallis entropies, and other complexity measures.

ElectrodeNet, a deep learning based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the Fast Fourier Transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC) and Spearman’s rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in CI coding strategy.

This paper presents architecture and design of a Twitch-based audience participation fighting game, where the strengths of game AI characters are dynamically adjusted based on audience cheering and jeering. Limitations underlying existing audience participation games (APGs) are discussed, and followed by solutions as demonstrated in our game. Our study includes AI tests on 500000 one-minute game rounds and a user experiment with 82 participants. We investigated user experience (UX), comparing two different kinds of gameplays (collaborative vs competitive), as well as two different kinds of interaction (cheering vs jeering). A pairwise preference, four-alternative forced choice (4-AFC), version of the Game User Experience Satisfaction Scale (GUESS) is used for UX assessment. At the end, we provided guidelines and hypotheses for future APG research.

In this paper, we study channel estimation at a uniform linear array (ULA) with $N$ antennas, where the channel at the ULA is composed of $L$ scatterers incoming from $L$ different angles of arrival (AoAs). It is assumed that Discrete-Time Fourier Transform (DTFT) beams (also known as \emph{analog} beams) are applied at the ULA to project the incoming signal onto a single (or multiple) RF chain(s), after which the signal is sampled and measured in baseband domain. This measurement procedure arises in various communication systems, such as the receive beam sweeping phase in 5G NR, where DTFT beams are used due to their simple implementation as linear phase shifts on analog antennas. A fundamental question about this procedure is the number of DTFT measurements necessary to recover the $L$ AoAs. Previous work on this problem showed (by applying compressed sensing theory) that $L\mathcal{O}(\log(N/L))$ measurements are sufficient for recovering the AoAs, which grows large for large $N$. Instead, by using properties of DTFT beam projections, we are able to show that if $N \geq 2L$, then $3L$ arbitrary DTFT measurements are enough; hence, dependency on $N$ is completely removed. The main reason for this drastic improvement is that sparsity is found in a \emph{non-linear} mapping of unknowns to observations, while compressed sensing deals with \emph{linear} mappings of unknowns to observations. Furthermore, if the DTFT beams are centered at harmonic frequencies (resulting in DFT beams) with period $N$, then $2L$ beam measurements are enough. With these results, an AoA estimation algorithm is formulated which has enormous complexity savings compared to $L$-dimensional AoA search such as maximum likelihood (ML) estimation. Numerical simulations demonstrate the algorithm's superior performance over a conventional algorithm.

In this paper, we propose a multi-purpose controllable electrochemical impedance spectroscopy (MP-cEIS) device for online and offline battery monitoring and testing during charge and discharge processes.

This article presents a description of the process of creating a model of the EZ-17 (Model 02) current probe by Rohde & Schwarz, which is extensively used in electromagnetic compatibility (EMC) research. A methodology for evaluating each component of the model is outlined step by step. However, the goal is not to physically disassemble an EZ-17 sensor, but to use only the parameters provided in the technical specifications of the device in creating the model. A comparison of the simulation results and the characteristics in the documentation shows a good fit between the model and the real device. An attempt was made to construct a doit-yourself (DIY) analog of the EZ-17 sensor using inexpensive parts and experimental comparisons were made between the analog and the actual device.

The paper evaluates the ability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) Network to develop its own set of inverse kinematics (IK) equations when trained with forward kinematics data. The forward kinematics equation is developed using the Denavit-Hartenberg (D-H) approach while the inverse kinematics equations (which is used to test the ANFIS Network) is developed analytically using geometry. This evaluation is carried out on a four degree of freedom (4DOF) robotic arm.

With the rapid development of big data and cloud computing, data management has become increasingly challenging. Over the years, a number of frameworks for data management and storage with various characteristics and features have become available. Most of these are highly efficient, but ultimately create data silos. It becomes difficult to move and work coherently with data as new requirements emerge as no single framework can efficiently fulfill the data management needs of diverse applications. A possible solution is to design smart and efficient hierarchical (multi-tier) storage solutions. A hierarchical storage system (HSS) is a meta solution that consists of different storage frameworks organized as a jointly constructed large storage pool. It brings a number of benefits including better utilization of the storage, cost-efficiency, and use of different features provided by the underlying storage frameworks. In order to maximize the gains of hierarchical storage solutions, it is important that they include intelligent and autonomous mechanisms for data management grounded in the features of the different underlying frameworks. These decisions should be made according to the characteristics of the dataset, tier status, and access patterns. These are highly dynamic parameters and defining a policy based on the mentioned parameters is a non-trivial task. This paper presents an open-source hierarchical storage framework with a dynamic migration policy based on reinforcement learning (RL). We present a mathematical model, a software architecture, and an implementation based on both simulations and a live cloud-based environment. We compare the proposed RL-based strategy to a baseline of three rule-based policies, showing that the RL-based policy achieves significantly higher efficiency and optimal data distribution in different scenarios compared to the dynamic rule-based policies.

In the Fibonacci series we have two numbers by adding them we get a series consisting of even and odd numbers in this it goes up to infinity we can track any n th number by the Binet’s formula. I have just thought of the multiplication of the first two terms and continued till where I can go, it means that the first two terms in the form (a, b) we will continue the multiplication as we do the addition in the Fibonacci series. As a result we will get the big integers from the 7th term approximately which is obvious by multiplying to its previous one it will come to an a very big integer which cannot be accountable by some range. If we do the multiplication the first two terms will be the same however from the third term it can be written as the power of that integers in which the powers will be following the Fibonacci series in this we can also find the n th term for the multiplicative series. Here the first two terms will in the same order as it will be given to find the series by changing the order it will violates the rule of restricted term. The meaning of the restricted here is that the order of (a, b) will be the same throughout the calculation of whole series we cannot alter that if we do so them it will not be more restricted term. So there are two concept in the multiplicative series restricted and non-restricted series. If the (a, b) is there and the operation is going on then it can be said as the restricted series if it is given (a, b) and asked for the (b, a) series then it is said as non-restricted series. I have considered 4 possible criteria to check the pairing of the variables (a, b). We will get to know about the series and also the n th term value of that series for all possible solutions

We present a decentralized advantage actor-critic algorithm that utilizes learning agents in parallel environments with synchronous gradient descent. This approach decorrelates agents' experiences, stabilizing observations and eliminating the need for a replay buffer, requires no knowledge of the other agents' internal state during training or execution, and runs on a single multi-core CPU.

By pushing resources to far-edge servers located in the proximity of users, edge computing can greatly reduce end-to-end transmission delays. Task offloading in multi-tier networks refers to the optimization of which tasks should be offloaded from the far-edge to the edge and the cloud. Moreover, the containerization of applications can further reduce resource and time consumption and, in turn, the latency of such applications. Even though Kubernetes has become the de facto container orchestrator, not many works have considered the offloading of containerized applications in Kubernetes clusters spanning from cloud to far-edge. In this work, the problem of offloading Kubernetes tasks (or pods) in three-tier networks is formulated and optimized. First, a utility function is presented in terms of the cumulative weighted pod response time, and a utility minimization problem with central processing unit (CPU) constraints is presented. Based on the optimal theoretical solution to this problem, a three-tier offloading decision algorithm (TTODA) is developed. Vertical scaling is considered, and specific hardware capabilities of each node are taken into account by setting specific SLAs that are fed back to the algorithm. Numerical results show that TTODA outperforms a typical Kubernetes QoS model based on first-in, first-served algorithm (FIFSA) in terms of utility, average pod response time, and usage of far-edge CPU. Further, TTODA achieves an excellent trade-off between performance and computational complexity, and thus it can help achieve the requirements of latency-sensitive applications. Moreover, TTODA can easily be extended to scenarios with joint memory and CPU constraints.