Real estate prices are an important reflection of the economy and their prices are great interest to both buyers and sellers. Hundreds of houses are sold every day and the buyer asks himself what is the reasonable price that this house deserves. In this paper, a new regression model is proposed for the accurate prediction of house prices. This model is based on the hybrid recurrent neural network where the Gated Recurrent Unit (GRU) is fused with the Long Short-Term Memory (LSTM) and applied to a particular dataset that characterizes houses in Boston. Massachusetts dataset from Scikit-learn is used in this research to train and evaluate this regression model using the data on Boston housing. Several experiments were conducted on the proposed algorithm and evaluated with the commonly used metrics. The results of these experiments showed that the proposed model has better performance when the networks are used in the fusion process than when they act individually. It also has better accuracy and lower Root Mean Square Error when compared to several states of art methodologies.

Objective: Orthostatic intolerance (OI) is common in Long Covid. Physical counterpressure manoeuvres (PCM) may improve OI in other disorders. We characterised the blood pressure-rising effect of PCM using surface electromyography (sEMG) and investigated its association with fatigue in adults with Long Covid. Methods: Participants performed an active stand with beat-to-beat hemodynamic monitoring and sEMG of both thighs, including PCM at 3-minutes post-stand. Multivariable linear regression investigated the association between change in systolic blood pressure (SBP) and change in normalised root mean square (RMS) of sEMG amplitude, controlling for confounders including the Chalder Fatigue Scale (CFQ). Results: In 90 participants (mean age 46), mean SBP rise with PCM was 13.7 (SD 9.0) mmHg. In regression, SBP change was significantly, directly associated with change in RMS sEMG (β=0.25, 95% CI 0.07–0.43, P=0.007); however, CFQ was not significant. Discussion/Conclusion: PCM measured by sEMG augmented SBP without the influence of fatigue.

Objective: In laparoscopic surgery, quality of haptic feedback is reduced compared to conventional surgery, leading to unintentional tissue damage during grasping. From the perspective of haptics, poor mechanical design of laparoscopic instrument joints induces friction and a nonlinear actuation -  tip force relation. In this study, a novel laparoscopic grasper using compliant joints and a magnetic balancer is presented, and the reduction in hysteresis and friction is evaluated. Methods & Results: The hysteresis loop of the novel compliant grasper and two conventional laparoscopic graspers (high quality Aesculap and low quality unbranded grasper) were measured. In order to assess quality of haptic feedback, the lowest grasper tip load perceivable by instrument users was measured with the novel and both the conventional laparoscopic graspers. The hysteresis loop measurement yielded a mechanical efficiency of 43% for the novel grasper, compared to- 25% and 23% for the Aesculap and the unbranded grasper, respectively. The forces perceivable by the user through the novel grasper were significantly lower (mean 1.37N, SD 0.44N) than those of conventional graspers (mean 2.15N, SD 0.71N and mean 2.65N, SD 1.20N, respectively). Conclusions: The balanced compliant grasper technology has the ability to improve the quality of haptic feedback compared to conventional laparoscopic graspers. Research is needed to relate these results to soft and delicate tissue grasping in a clinical setting, for which this instrument is intended.

Using computer simulations, we analyze the bit-error rate (BER) characteristics of the optical transmission system repeated by erbium-doped fiber amplifiers (EDFAs) and two-mode phase-sensitive optical parametric amplifiers (PSAs), where  phase-conjugated (PC) twin waves are co-propagating through the link. We focus on the 256 quadrature-amplitude-modulation (QAM) format, whose transmission characteristics are seriously affected by fiber nonlinearity because the required power is significantly high. Changing the repeater spacing and group-velocity dispersion (GVD) of fibers for transmission in the two-mode PSA-based system, we calculate the BERs as a function of the launched power. When the repeater spacing and  GVD value of fibers are properly designed, the two-mode PSA-based system can mitigate nonlinear impairments more effectively than the EDFA-based PC twin system.

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.

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.

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).

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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.

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.

In this paper, we investigate the motion of wheeled mobile robots on rough terrains modelled as noisy nonholonomic constraints. Such constraints are the natural extension of ideal nonholonomic constraints when the Stratonovich process is directly introduced in the constraint equations. The resulting stochastic model can capture motion on rough surfaces, random skip/uncertainty in the wheel-ground point of contact, or stochastic motion of the surface. We study a differential robot with ideal noisy and affine noisy constraints, where each case models a certain aspect of motion on rough terrains. We then qualitatively investigate their corresponding stochastic dynamics through Monte-Carlo simulations. The proposed stochastic model for roving rough terrains has the potential to serve as the process model in model-based motion estimators relying on measurements from an interoceptive suite of sensors. The challenge will be dealing with the nonlinear appearance of the noise in the equations of motion.

The battery is at the heart of the electric vehicle and determines many of its key performance features. Hybrid batteries, combining two battery cell chemistries, benefit from the particular strengths of each battery cell technology, such as high specific energy or high specific power. This paper proposes a novel hybrid battery configuration where the interfacing between the two sets of cells is accomplished through a bidirectional multilevel neutral-point-clamped dc-dc converter. The novel topology is presented and a suitable power converter modulation and control are developed. The feasibility and benefits of such configuration are demonstrated and illustrated. In particular, the proposed battery system features a lossless intermodule battery state-of-charge balancing within both sets of battery cells. Simulation and experimental results are provided in the case of the three-level internal battery interfacing to verify the good performance of the proposed hybrid battery configuration, modulation, and control.

Quantum computers with hundreds of noisy qubits are already available for the research community. They have the potential to run complex quantum computations well beyond the computational capacity of any classical device. It is natural to ask the question, what application these devices could be useful for? Land Use and Land Cover classification of multispectral Earth observation data collected from the earth observation satellite mission is one such problem that is hard for classical methods due to its unique characteristics. In this work, we compare the performance of several classical machine learning algorithms on the stilted re-labeled dataset of the Copernicus Sentinel-2 mission, when the algorithm has access to Projected Quantum Kernel (PQK) features. We show that the classification accuracy increases drastically when the model has access to PQK features. We then naively study the performance of these algorithms with and without access to PQK features on the original Copernicus Sentinel-2 mission data set. This study provides key evidence that shows the potential of quantum machine learning methods for Earth Observation data.

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where a BD with nonlinear energy harvesting model cooperates with decode-andforward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secure energy efficiency (SEE). Specifically, the closed-form and   asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmission power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks.  

A novel technique to accurately characterize interconnects with general, piecewise homogeneous material parameters and arbitrary polygonal cross-sections is presented. To compute the per-unit-of-length complex inductance and capacitance matrices of the considered structures, we apply a boundary integral equation framework, invoking a Dirichlet-to-Neumann formalism to recast the problem at hand. The pertinent operators are constructed by means of the numerically fast Fokas method. Numerical examples of various multiconductor transmission lines demonstrate that our proposed scheme is flexible and precise. Since our method is not limited to rectangular cross-sections, manufacturing effects such as etching can also be taken into account. Moreover, the examples are not restricted to RLGC-data as we also consider signal attenuation, slow-wave factors and cross-talk.