The Van der Pol Fourier representation of the Riemann Zeta function in the critical strip is evaluated using the Fast Fourier Transform (FFT) algorithm and the Method of Stationary Phase. The Method of Stationary Phase approximation allows us to introduce a new representation for the zeta function. The new representation is used to propose new analytic predictions about non-trivial zeros.

In this letter, we propose the designs of 1-bit and 2-bit digitally coded metasurfaces, which achieve anomalous reflection-based beam-steering. First, we demonstrate the systematic design flow of coded metasurface using phase gradient digital unit cells to achieve anomalous reflection in the preferred direction for a normally incident plane wave. Initially, the design procedure is analytically implemented  to get the phase profiles for 1-bit and 2-bit digitally coded metasurfaces having equal sizes of $10\lambda_0 \times 10\lambda_0$ (where $\lambda_0$ is the free space wavelength at $5.9$ GHz) for a specific reflection angle, $(\theta_r=30^0,\phi_r=0^0)$, and subsequently, the far-field plots are generated. Furthermore, these phase profiles are used to generate 3D-CAD models of the 1-bit and 2-bit metasurfaces using CST, and the desired far-field patterns are obtained having half-power beamwidth (HPBW) of $5^0$. It is observed that the 1-bit coded metasurface produces additional side-lobe levels, which are minimized by use of 2-bit coding. Finally, the proposed 1-bit and 2-bit coded metasurfaces are fabricated, and the anomalously reflected far-field beam is detected by the received power at desired reflection angle and comparing the results with a perfect electric conductor (PEC). The proposed design is suitable for V2X communications and can be scaled to other frequencies.

Abstract—This paper proposes a new technique to identify the mismatch error in time-interleaved analog-to-digital converters. This is done by injecting an estimation signal comprising of two parts; one section for identifying gain mismatch error and another one for timing mismatch error. Passing through the TI-ADC’s channels, the estimation signal is influenced by the non-ideal sampling, which distorts the signal by adding some spurs in the output. This technique detects the mismatch error by extracting the spurs attached to the estimation signal with no interruption in data conversion. Since this method does not depend on feedback has high speed convergence. A simple gain and derivative filter are employed to compensate the identified mismatch error. Simulation results show that SFDR is improved by 23.2 dB in a two-channel TI-ADC. Moreover, the gain and timing mismatches are identified with just 265 samples after simulation running. The ripples on the identified error are maximally 0.4 % of the precise mismatch error of the converter.

**Note:** This preprint has been accepted for publication in [ IEEE Transactions on Circuits and Systems--I: Regular Papers ]. The published version is available on IEEE Xplore: [DOI link](  https://ieeexplore.ieee.org/document/10607897 ). The paper presents a design for a high-speed data acquisition (DAQ) system for electrical impedance tomography (EIT). The proposed solution involves using a high-speed analog-to-digital converter (ADC) to digitize the analog signals corresponding to multiple pairs of electrodes within the same cycle in a time-multiplexed manner. The extracted samples are fed to an artificial neural network (ANN) to estimate the peak amplitudes of the signals for every channel, which are then used for image reconstruction. Various ANN models with customized loss functions were assessed, and the optimal model selection approach using the grid search technique is presented. Unlike other multi-frequency based techniques, the suggested approach does not require a multi-frequency current source, thus simplifying the data acquisition system by not requiring high-quality narrow-band pass-band filters for different frequencies. The proposed approach allows EIT systems to operate at a very high throughput that can exceed 2,800 frames per second for a 50 kHz excitation signal using 32 or more electrodes. Extensive experimental testing showed that peak estimation accuracy can be achieved with more than 98%, even for signals with 40 dB SNRs. The suggested approach has thus promising potential for EIT applications requiring high SNR and fast data acquisition.

A document by zahid fazal fazal . Click on the document to view its contents.

Formation tracking (FT) control aims at handling cooperative tasks in multi-agent systems (MASs) to achieve desired performance. In these tasks, the leader’s input is generally non-zero and  unknown to all followers, i.e., its trajectory can be arbitrary and non-repetitive. In this paper, the additive property of linear systems is exploited to develop a unified framework for FT tasks of MASs, consisting of adaptive observer-based control (AOC) and iterative learning control (ILC). This framework employs an AOC controller to guarantee a fixed-shape formation between the leader and followers during the whole process, which reserves the initial condition for ILC. Also, it employs ILC to improve the FT performance of certain repetitive tasks (followers rotating around the leader) over the trials. This gives rise to a fully distributed algorithm working for a directed communication graph containing a spanning tree without requiring any eigenvalue information from the Laplacian matrix of the graph, which enables its application to MASs with a large number of agents. Comparisons are provided via a numerical simulation to show that the proposed combined AOC-ILC algorithm has less FT error than pure AOC (without ILC), which validates the feasibility and efficacy of this algorithm.

Recently, sentiment analysis is actively researched in the field of natural language processing because of the expansion of computer science and social media. However, sentiment analysis has a wide range of applications and it might be challenging for individual researchers to review and comprehend the trends of the studies. Therefore, this study suggests trends in sentiment analysis research to help sentiment analysis researchers. The sources of data collection are Google Scholar and Scopus. As a result, studies using various modalities, researching the change from a particular event and based on language itself have been explored actively in recent years.

The Internet of Things and location-based services rely heavily on indoor pedestrian navigation. It is important yet costly to create a map for indoor navigation. Recent studies examine the creation of radio maps using crowdsourcing (such as WiFi and BLE). In situations when there are insufficient WiFi or BLE nodes, the magnetic field map is the sole way to deliver indoor locating services without infrastructure. Previous research on magnetic map construction using crowdsourcing do not fulfill the requirements of the real scenario. In this work, we provide a summary of the real-world requirements for the crowdsourcing- based magnetic map-building method. Furthermore, we proposed an efficient crowdsourcing-based magnetic map creation approach that is resilient to the proposed requirements: short- period trajectories, uncalibrated magnetometer sensors, different pedestrian motion patterns, and irregular trajectory shapes. In the field test, the proposed method consumed 60.8 seconds to generate a map from a 12-hour dataset that met the proposed requirements. When the duration of each trajectory is 90 seconds, the mean position error is 1.48m (with scale correction) and 2.53m (without scale correction).

A nonlinear parameterized voltage - current mathematical model, with quantum probabilities to modulate deterministic functions, is discussed. Different possibilities for the model and two electric circuits applications are illustrated with simulations.

This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life (RUL). It discusses challenges and open problems faced in PdM. This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals. We propose the future direction for each challenge discussed in this article.

The chirp sweep is a go-to wide-band impedance measurement technique when speed and simplicity are of main concern. Additionally, the time-frequency trait of chirp scans provide unique benefits for systems exhibiting frequency couplings - a phenomenon often encountered in single-phase VSCs. Unfortunately, time domain interpretation of chirp responses are inaccurate if the VSC-based system exhibits weak damping, as the free response induced by the chirp cannot be safely neglected. We quantify this error for linear(ised) time-invariant and time-periodic systems, with impedance representations of scalar transfer functions and harmonic transfer functions, respectively. An observer termed the Multiple Chirp Reference Frame Filter is proposed which enables real-time estimation of the harmonic transfer functions and their corresponding chirp errors. By controlling the relative errors to negligible values, the chirp exhibits low rate around resonances and high rate elsewhere. An experimental admittance sweep of a single-phase STATCOM operated by dispatchable Virtual Oscillator Control, consolidates the proposed approach as a simple frequency scan technique for weakly damped and frequency coupled systems.

All-optical signal processing based on nonlinear optical devices is promising for ultrafast information processing in optical communication systems. Recent advances in two-dimensional (2D) layered materials with unique structures and distinctive properties have opened up new avenues for nonlinear optics and the fabrication of related devices with high performance. This paper reviews the recent advances in research on third-order optical nonlinearities of 2D materials, focusing on all-optical processing applications in the optical telecommunications band near 1550 nm. First, we provide an overview of the material properties of different 2D materials. Next, we review different methods for characterizing the third-order optical nonlinearities of 2D materials, including the Z-scan technique, third-harmonic generation (THG) measurement, and hybrid device characterization, together with a summary of the measured n2 values in the telecommunications band. Finally, the current challenges and future perspectives are discussed.

Energy Storage Systems (ESS) have increasingly come to occupy more space with the changing global energy landscape. One of the most important components of an ESS is the Energy Management System (EMS) or the Battery Management System (BMS). A Battery Energy Storage System (BESS) can store a significant amount of energy for long periods of time. The BMS is responsible for the operational safety of the battery modules in the battery pack. The primary responsibility is to safeguard the battery. This is done essentially from the safety aspect as it is very important to ensure cell balancing and End-Of-Life (EOL). Also, since the system temperature has a direct bearing on the power consumption profile, it is imperative for the BMS to regulate the system temperature. The BMS condition monitoring of the battery pack can be summarized as State Of Charge (SOC), State Of Health (SOH), State Of Power (SOP), and State Of Energy (SOE).

A document by Torstein Grav Aakre . Click on the document to view its contents.

This paper introduced the development of the ESP Module-based pill reminder, which will enable patients to obtain a prescription at the right time and time. Assisted automation is another technology that can bring great benefits to humans. Today, however, only 2 out of 50 people use assistive automation due to its high cost, lack of information on the subject, and availability. By 2050, one in 20 households will need a support product and most adults will need 2 or more of her products. I also found that people pay more attention to their work than their health. This procedure aims to alleviate the problem by reminding you to take your medication anywhere within the designated time and to contact the appropriate contacts. It is a combination of a patient’s physical and digital memory, which is important for communities of all ages, but especially effective for physicians with disabilities who forget to take their medications. The main goal is to keep the system useful and cost effective. The presented system uses ESP Module, AC motor, PIR sensor, Buzzer with alarm used to bring victims closer to receive the required doses according to the doctor’s note at the right hour. This practical and economic program will treat older patients, especially illiterate patients. This smart pill box can alleviate the burden on elderly family members by reminding them to take their medication on time, especially with infections like Covid-19

This is a preprint. Blockchain technology (BCT) is emerging as a gamechanger for many industries due to its unique benefits like ability to conduct peer-to-peer (P2P) transactions in trustless environment with increased data security and transparency in real-time. It is employed in Blockchain Enabled Interconnected Smart Microgrids (BSMGs) to automate local energy markets, facilitate energy trading, and manage microgrid operations. However, with the increasing adoption of BSMGs, several challenges regarding interoperability between BSMGs may arise. Different BCT platforms may use their indigenous tokens or specially designed energy tokens for financial transactions. Also, cross-border international energy trades can become P2P which may involve real-time transactions using the countries’ digital currencies. Thus, in order to overcome the geographical and technological barriers impeding the widespread usage of BSMGs and access to energy for all, decentralized exchanges (DEX) must be incorporated. DEX are used in decentralized finance to exchange digital tokens and cryptocurrencies on blockchains. In this paper, an Order Book DEX is implemented on Cosmos which exchanges tokens between different chains using the Inter Blockchain Communication protocol (IBC). The working logic of the DEX is discussed and implemented using Ignite CLI. The financial exchanges and recording using buy and sell orders are demonstrated for the first time in energy domain.

Brain Inspired Computing (BIC) is an emerging research field that aims to build fundamental theories, models, hardware architectures, and application systems toward more general Artificial Intelligence (AI) by learning from the information processing mechanisms or structures/functions of biological nervous systems. It is regarded as one of the most promising research directions for future intelligent computing in the post-Moore era. In the past few years, various new schemes in this field have sprung up to explore more general AI. These works are quite divergent in the aspects of modeling/algorithm, software tool, hardware platform, and benchmark data, since BIC is an interdisciplinary field that consists of many different domains, including computational neuroscience, artificial intelligence, computer science, statistical physics, material science, microelectronics and so forth. This situation greatly impedes researchers from obtaining a clear picture and getting started in the right way. Hence, there is an urgent requirement to do a comprehensive survey in this field to help correctly recognize and analyze such bewildering methodologies. What are the key issues to enhance the development of BIC? What roles do the current mainstream technologies play in the general framework of BIC? Which techniques are truly useful in real-world applications? These questions largely remain open. To address the above issues, in this survey we first clarify the biggest challenge of BIC: how can AI models benefit from the recent advancements in computational neuroscience? With this challenge in mind, we will focus on discussing the concept of BIC and summarize four components of BIC infrastructure development: 1) modeling/algorithm; 2) hardware platform; 3) software tool; and 4) benchmark data. For each component, we will summarize its recent progress, main challenges to resolve, and future trends. On the basis of these studies, we present a general framework for the real-world applications of BIC systems, which is promising to benefit both AI and brain science. Finally, we claim that it is extremely important to build a research ecology to promote prosperity continuously in this field.

Surface defect detection is an essential topic in the industrial inspection field. Many methods based on machine vision (MV) have been applied. However, it is still a challenging task due to the complexity of defects, including low-contrast, small objects, and irregular geometric boundaries. To deal with these problems, this paper proposes a novel multi-level feature fusion network (MFNet) with  the multi-branch structure for surface defect detection. Firstly, we extract low- and high-level features via the encoder based on ResNet34. Secondly, an improved atrous spatial pyramid pooling module is adapted to expand the receptive field of low-level features. Then, the decoder adopts a multi-branch structure to fuse multi-level features for details, and a global attention module is introduced to strengthen the effectiveness of feature fusion and detection accuracy. Finally, the optimal result from multiple outputs can be obtained by multi-branch. Extensive experiments are carried out on three representative defect datasets: CrackForest, Kolektor, and RSDDs. The quantitatively contrastive experiments prove that our method enjoys a better semantic segmentation performance in industrial defect detection, outperforming four excellent semantic segmentation networks (CrackForest: Accuracy-98.54%, F1-Score-74.71%, IoU-60.22%; Kolektor: Accuracy-99.82%, F1-Score-83.09%, IoU-71.38%; Type-Ⅰ RSDDs: Accuracy-99.79\%, F1-Score-85.09%, IoU-74.84%).