A new approach is proposed to deal with the metastability errors in asynchronous SAR ADCs. This proposal avoids the disadvantages of conventional metastability detectors (additional metastability events due to the detector itself,  lowness from additional comparator loading, additional logic delays, need to perform detection on each bit), but provides a significant decrease in metastability errors and their amplitudes. The approach uses both comparators’ outputs to detect whether metastability occurred at all during conversion (without determining in which particular bit). Theoretical examination and Octave simulation show that this information suffices to correct half of metastability errors.

With the development of wireless LANs, they are expected to have wide applications not only in traditional home but also in industrial applications. Therefore, the next generation of IEEE 802.11 wireless LAN standard demands higher throughput and lower latency. Full duplex (FD) technology, which allows a device to simultaneously transmit and receive signals on the same channel by cancelling self-interference, is one of the promising solutions to meet those requirements. Feasibility studies for applying FD technology to wireless LAN systems started in IEEE 802.11 standardization. In this article, we provide an overview of the FD mechanism and the technical problems for the realization. Furthermore, we provide the direction of the FD system for wireless LANs in IEEE 802.11 standardization. In addition, we discuss the expected low latency effect of the FD mechanism on the wireless LAN systems based on the direction in IEEE 802.11 standardization.

Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their brain activity. In this study, we develop machine learning models to detect moments of distraction (due to mind wandering or drowsiness) during meditation practice using EEG signals. We use EEG data of 24 participants while performing a breath focus meditation with experience sampling and extract twelve linear and non-linear EEG features. Features are fed to ten supervised machine learning models to classify (i) Breath Focus Awake (BFA) vs Breath Focus Sleepy (BFS), and (ii) BFA vs Mind Wandering (MW). We observe that the linear features achieve a maximum accuracy of 86% for classifying awake (BFA) and sleepy (BFS), whereas non-linear features have more predictive ability for classifying between BFA and MW with a maximum accuracy of nearly 78% . In addition, visualization of unsupervised t-SNE lower embeddings supports the evidence of distinct clusters for each condition. Overall our results show that machine learning algorithms can successfully identify periods of distraction during meditation practice in novice meditators based on linear and non-linear features of the EEG signal. Consequently, our results have important implications for the development of mobile EEG neurofeedback protocols aimed at facilitating meditation practice.

The exponential growth of cloud computing within the last decade has led to a severe consideration about the power requirement of the cloud data center. However, variations in the computing resource demands and fixed size of virtual machines (VMs) lead to a poor resource utilization, load imbalance, and excessive energy consumption within the data center. Dynamic VM consolidation effectively handles these problems, where VMs are placed on as few physical machines (PMs) as possible. Concurrently, VM allocation must be done strategically by considering various factors of the computing resources for minimal exploitation of them. The high frequency of VM consolidation may lead to the degradation of the system’s reliability by assigning VMs to unreliable PMs. A novel Quantum Machine learning driven Reliable Resource Allocation (QM-RRA) model is proposed to address these problems and enhance the data center’s reliability. We handle the problem by applying an Evolutionary Quantum C-Not Neural Network (QCNN) unit to forecast future resource usage at the cloud data center. In addition, Quantum Evolutionary Adaptive Algorithm (QEAA) is utilized to optimize qubit network weights. It offers energy saving by minimizing the number of active PMs, maximizing resource utilization, and enhancing the reliability of the overall data center. The QM-RRA model is evaluated by performing experiments on the Google Cluster Data set (GCD). The observed results are compared with the state-of-art methods and shows its supremacy in terms of different performance parameters like resource utilization, power consumption, and the number of active PMs. There is a significant reduction in power consumption and the number of active PMs up to 31.38%  and 44.93%, respectively. The enhancement in resource utilization is up to 25.61%, and overall system reliability is 63.35%.

Linear fractals associated with weights are investigated. Such fractals are important from a conservation law perspective that is relevant in a variety of physical systems such as materials science, sand dune fractals, barred galaxies, as well as in temporal processes like the EEG.  Weighted fractals lead to distributions that are consistent with the ubiquitous power law and the first digit phenomenon. Two linear fractal algorithms that are near optimal in the information theoretic sense are described. It is proposed that one mechanism for the emergence of these fractals is the indistinguishability amongst the particles in the evolution and transformation of physical systems.

Deep convolutional neural networks (CNNs) generate intensive inter-layer data during inference, which results in substantial on-chip memory size and off-chip bandwidth. To solve the memory constraint, this paper proposes an accelerator adopted with a compression technique that can reduce the inter-layer data by removing both intra- and inter-channel redundant information. Principal component analysis (PCA) is utilized in the compression process to concentrate inter-channel information. The spatial differences, truncation, and reconfigurable bit-width coding are implemented inside every feature map to eliminate the intra-channel data redundancy. Moreover, a particular data arrangement is introduced to enhance data continuity to optimize PCA analysis and improve compression performance. A CNN accelerator with the proposed compression technique is designed to support the on-the-fly compression process by pipelining the reconstruction, CNN computation, and compression operation. The prototype accelerator is implemented using 28-nm CMOS technology. It achieves 819.2GOPS peak throughput and 3.75TOPS/W energy efficiency with 218.5mW. Experiments show that the proposed compression technique achieves a compression ratio of 21.5%~43.0% (8-bit mode) and 9.8%~19.3% (16-bit mode) on state-of-the-art CNNs with a negligible accuracy loss.

Multiple longest common subsequence (MLCS) mining (a classical NP-hard problem) is an important task in many fields. Numerous applications in these fields can generate very long sequences (i.e., the length of the sequences ≥ 10^4), called big sequences. Such big sequences present a serious challenge to existing MLCS algorithms. Although significant efforts have been made to tackle the challenge, both existing exact and approximate MLCS algorithms fail to deal with big sequences as their problem-solving model MLCS-DAG (Directed Acyclic Graph) is too large to be calculated due to the memory explosion. To bridge the gap, this paper first proposes a new identification and deletion strategy of different classes of non-critical points, which are the points that do not contribute to the solution on the MLCS-DAG. It then proposes a new MLCS problem-solving graph model, called KP-MLCS-DAG (Key Point based MLCS-DAG). A novel parallel MLCS algorithm, called KP-MLCS (Key Point based MLCS), is also presented, which can mine and compress all MLCSs of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithm KP-MLCS drastically outperforms the existing state-of-the-art algorithms in terms of efficiency and effectiveness. The source code of KP-MLCS and related test datasets, etc., can be found online: https://github.com/kp-mlcs/KP-MLCS.

A document by Tushar Bose . Click on the document to view its contents.

This work provided a study of multiple polarization for use in a MIMO system. Currently, only 2 polarization MIMO system model is in use. However, there are multiple antennas in Tx and Rx sides, therefore, increasing the number of polarization equivalent to the number of antennas can improve MIMO channel capacity. We experimented with different combinations for polarization for 4T4R MIMO system. The 4 suitable polarization obtained is further studied for higher capacity with Transmitter Beam Angular Diversity. Tranmistter Beam Angular Diversity reduce the correlation among non orthogonal polarization channel and improve MIMO system capacity.

Multiscale problems with fine structure, involving electrically small fine details and relatively uniform areas, are posing significant challenges to any single algorithm in computational electromagnetics. Thus, a hybrid method combing the finite-difference time-domain (FDTD) method with the preciseintegration time-domain (PITD) method is proposed to handle such problems in this letter. For the hybrid method, the field values in the FDTD and PITD domains are calculated separately and independently. The information exchange between two solution domains is achieved by correcting the interface field components via the upwind condition. Several numerical experiments demonstrate the advantage of the hybrid method.

Designing a autonomus moblie robot for the implementation in disease detection in watermelon farming. PID controller is used as a closed loop system and extended Kalman filter for error correction. Visual SLAM for localization and mapping.

Flexible characteristics, tunable parameters, and improved power density are the desired attributes that have led to widespread research on active capacitors and inductors (ACI) in power conversion systems. However, the two-terminal realisation of ACI is challenging due to different trade-offs between power density, device stress, and reliability of the added components. Moreover, the requirement of handling second-harmonic ripple makes bulk electrolytic capacitors nearly indispensable in two-terminal ACIs. Thus, the scope of power density improvement becomes constrained. Hence, active capacitors requiring minimum or no bulk dc-link capacitance are seen as an attractive technology in recent literatures. However, they can mimic either inductive or capacitive behaviour with a single converter unit, restricting their adaptability across different applications. This limitation is addressed in this work with the proposed unified active capacitor and inductor (UACI), realised without any bulk dc capacitor. It emulates variable capacitance and inductance while offering a smooth transition from one characteristic to another. High effective impedance due to a small susceptance at transition duty prevents undesirable current overshoot during the transition. A laboratory-scale prototype of the proposed ACI is fabricated, and the hardware is verified up to +/-450 VAR.

We propose a method to efficiently compute the scattered electric field of a reconfigurable intelligent surface (RIS) for multiple configurations. In contrast to most existing methods that assume that each unit cell scatters an incident wave individually instead of collectively, our method accounts for the mutual coupling of unit cells. This allows us to estimate the scattered fields in the main scattering direction of an RIS, at an accuracy that is comparable to full-wave analysis. Furthermore, combined with ray-tracing, the computed scattered fields can be used to model wave propagation in realistic, multipath radio environments with RISs. Hence, our method efficiently addresses three critical considerations for the analysis of RIS-enabled links: mutual coupling between unit cells of an RIS, mutlipath effects in the channel due to the RIS acting as a diffuse scatterer, and the variability of the RIS scattering properties that requires extensive computational effort to account for.

Technical debt refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although often discussed informally, technical debt has not crystalized into a consistently applied type and remains invisible when describing issues in issue trackers that are used for coordinating work among software developers. In this study, we manually labeled references to technical debt for 1,934 tickets in the Chromium issue tracker. We used these labels to train a classifier to estimate labels for an additional 475,000 tickets. Our classifier significantly outperforms key phrase search, and we conclude that discussion of technical debt appears in about 16% of the tracked Chromium issues. This application of machine learning to locate technical debt can not only make it visible but also help in its timely resolution.

This work has been submitted to the IEEE Transactions on Biomedical Engineering for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Objective: Fetal heart rate (FHR) is critical for perinatal fetal monitoring. However, motions, contractions and other dynamics may substantially degrade the quality of acquired signals, hindering robust tracking of FHR. We aim to demonstrate how use of multiple sensors can help overcome these challenges, and improve measurement of desired signal. Methods: We develop KUBAI, a novel stochastic sensor fusion algorithm, to improve FHR monitoring accuracy. To demonstrate the efficacy of our approach, we evaluate it on data collected from gold standard large pregnant animal models, using a novel non-invasive fetal pulse oximeter. Results: The accuracy of the proposed method is evaluated against invasive ground-truth measurements. We obtained below 6 beats-per-minute (BPM) root-mean-square error (RMSE) with KUBAI, on five different more than 30-minutes long datasets. KUBAI’s performance is also compared against a single-sensor version of the algorithm to demonstrate the improvement in accuracy due to sensor fusion. KUBAI’s multi-sensor estimates are found to give 23.5% to 84% lower RMSE than single-sensor FHR estimates. Furthermore, KUBAI is shown to have 84% lower RMSE and ∼3 times higher R2 correlation with reference compared to another multi-sensor FHR tracking method found in literature. Conclusion: The results support the effectiveness of KUBAI, the proposed sensor fusion algorithm, to non-invasively and accurately estimate fetal heart rate with varying levels of noise in the measurements. Significance: The presented method can benefit other multi-sensor measurement setups, which may be challenged by low measurement frequency, low signal- to-noise ratio, or intermittent loss of signal in measurements.

The sixth generation (6G) of wireless networks are envisioned to support a plethora of human-centric applications and offer connectivity to a massive number of devices with diverse requirements, thus enabling massive Machine Type Communications. Nevertheless, with the rapid growth of the number of connected devices as well as the ever-increasing network traffic, network energy consumption has become a major challenge. Additionally, 6G is expected to catalyze the emergence of new applications that are characterized by their harsh environmental conditions, with ultra-small and low-cost wireless devices. Therefore, there is a pressing need for developing sustainable solutions that take into consideration all these requirements in order to realize the full potential of 6G networks. Within this context, zero-energy devices (ZEDs) have emerged as a prominent solution for the next generation green communication architecture. Such devices eliminate the need for recharging plugins and replacing batteries by integrating disruptive technologies, such as radio frequency energy harvesting, backscatter communications, low power computing, and ultra-low power receivers. Motivated by this, this article provides an in-depth review of the existing literature on the newly emerging ZEDs for future networks. We further identify different relevant use cases and provide an extensive overview on the key enabling technologies and their requirements for realizing ZED-empowered networks. Finally, we discuss potential future research directions and challenges that are envisioned to enhance the performance and efficiency of ZED-based networks.

The South African rail industry is seeking a critical measure to monitor and save energy usage in the freight rail sector. The rail industry is experiencing increasing operational costs and high energy consuming driving records  along various coal lines. Eco-driving is a modern and efficient way of driving that emphasizes fuel efficiency, speed, and safety. This study provides an algorithm to find the optimal trajectory for a freight train hauling a load over a specific distance. Optimised speed profile is composed of optimal acceleration, coasting, and deceleration. The Freight Eco-Driving Energy Optimiser (FEDEO) solution is not yet applied to freight trains globally, especially in Africa. In this study, the eco-driving strategy of a freight train is formulated as an optimisation problem, whose objective function is the energy cost. The decision variables are the tractive and braking effort notches, and the speed, acceleration and distance limits are formulated as constraints. The formulated eco-driving problem is solved by Mixed-Integer Non-Linear Programming (MINLP) from the Opti-Solver toolbox. The FEDEO algorithm is applied to a train consisting of eight 19E locomotives with two-hundred CCR-9 wagons, over a distance of 90.64 km. The results show up to 34.76% reduction in energy costs.

This work discusses the polarization of nonuniform plane waves (NPWs). A NPW can be created when a uniform plane wave is transmitted from a lossless medium into a lossy medium at an oblique angle. When a linearly polarized wave is transmitted into a lossy medium, the in-plane field transitions to elliptical polarization while the transverse field remains linearly polarized. This is a completely different behavior of a polarized wave compared to those that are created by introducing a phase difference between two perpendicularly polarized waves, where the polarization of both electric and magnetic fields changes together. The zero-divergence restriction determines the rotation direction of the NPW’s elliptical polarization.