Performance analysis and design optimization of parallel-type slew-rate enhancers for switched-capacitor applications

The design of single-stage OTAs for accurate switched-capacitor circuits involves challenging trade-offs between speed and power consumption. The addition of a Slew-Rate Enhancer (SRE) circuit placed in parallel to the main OTA (parallel-type SRE) constitutes a viable solution to reduce the settling time, at the cost of low-power overhead and no modifications of the main OTA. In this work, a practical analytical model has been developed to predict the settling time reduction achievable with OTA/SRE systems and to show the effect of the various design parameters. The model has been applied to a real case, consisting of the combination of a standard folded-cascode OTA with an existing parallel-type SRE solution. Simulations performed on a circuit designed with a commercial 180-nm CMOS technology revealed that the actual settling-time reduction was significantly smaller than predicted by the model. This discrepancy was explained by taking into account the internal delays of the SRE, which is exacerbated when a high output current gain is combined with high power efficiency. To overcome this problem, we propose a simple modification of the original SRE circuit, consisting in the addition of a single capacitor which temporarily boosts the OTA/SRE currents reducing the internal turn-on delay. With the proposed approach a settling-time reduction of 57% has been demonstrated with an SRE that introduces only a 10% power-overhead with respect of the single OTA solution. The robustness of the results have been validated by means of Monte-Carlo simulations

Low-cost IoT-based sensor system: A case study on harsh environmental monitoring

Wireless Sensor Networks (WSNs) are promising technologies for exploiting in harsh environments such as can be found in the nuclear industry. Nuclear storage facilities can be considered harsh environments in that, amongst other variables, they can be dark, congested, and have high gamma radiation levels, which preclude operator access. These conditions represent significant challenges to sensor reliability, data acquisition and communications, power supplies, and longevity. Installed monitoring of parameters such as temperature, pressure, radiation, humidity, and hydrogen content within a nuclear facility may offer significant advantages over current baseline measurement options. This paper explores Commercial Off-The-Shelf (COTS) components to comprise an installed Internet of Things (IoT)-based multipurpose monitoring system for a specific nuclear storage situation measuring hydrogen concentration and temperature. This work addresses two major challenges of developing an installed remote sensing monitor for a typical nuclear storage scenario to detect both hydrogen concentrations and temperature: (1) development of a compact, cost-effective, and robust multisensor system from COTS components, and (2) validation of the sensor system for detecting temperature and hydrogen gas release. The proof of concept system developed in this study not only demonstrates the cost reduction of regular monitoring but also enables intelligent data management through the IoT by using ThingSpeak in a harsh environment

Switched Capacitor Circuit Techniques for High Performance CMOS Analog Circuits

Switched capacitor (SC) circuits are the main building blocks in many structures such as filters, data converters, sampling circuits and sampled-data amplifiers. The key challenge is to design such circuits which are the prime components of any IoT system with low power consumption without compromising on the performance. In this dissertation, power efficient novel switched capacitor techniques have been explored to suppress noise and mitigate the slewing in switched capacitor-based analog to digital converters (ADCs). First, a two-step incremental ADC(IADC) with pseudo-pseudo differential (PPD) structure is presented. PPD integrators are implemented with added correlated level shifting (CLS) technique to relax the output swing and gain requirements of the operational transconductance amplifier (OTA). The two-step IADC is implemented with the first step configured as a single-bit first-order IADC and the second step, using a two-capacitor SAR-assisted extended counting to further enhances the accuracy. The design is demonstrated by implementing the prototype in 65nm CMOS technology. Second, new passive charge compensation techniques are described. The proposed techniques mitigate the slewing in the OTA by providing a controlled amount of charge to the output of the OTA. The effectiveness of the proposed charge compensation technique in a switched-capacitor integrator is demonstrated in a second-order DSM. Circuit-level simulations including the effects of process, voltage and temperature variations are also presented. The extracted simulation results show a 12 dB improvement in SNDR using the proposed technique compared to conventional DSM without charge compensation

Remote Laboratory for E-Learning of Systems on Chip and Their Applications to Nuclear and Scientific Instrumentation

Configuring and setting up a remote access laboratory for an advanced online school on fully programmable System-on-Chip (SoC) proved to be an outstanding challenge. The school, jointly organized by the International Centre for Theoretical Physics (ICTP) and the International Atomic Energy Agency (IAEA), focused on SoC and its applications to nuclear and scientific instrumentation and was mainly addressed to physicists, computer scientists and engineers from developing countries. The use of e-learning tools, which some of them adopted and others developed, allowed the school participants to directly access both integrated development environment software and programmable SoC platforms. This facilitated the follow-up of all proposed exercises and the final project. During the four weeks of the training activity, we faced and overcame different technology and communication challenges, whose solutions we describe in detail together with dedicated tools and design methodology. We finally present a summary of the gained experience and an assessment of the results we achieved, addressed to those who foresee to organize similar initiatives using e-learning for advanced training with remote access to SoC platforms

Self-inductance of the circular coils of the rectangular cross-section with the radial and azimuthal current densities

ABSTRACT: In this paper, we give new formulas for calculating the self-inductance for circular coils of the rectangular cross-sections with the radial and the azimuthal current densities. These formulas are given by the single integration of the elementary functions which are integrable on the interval of the integration. From these new expressions, we can obtain the special cases for the self-inductance of the thin-disk pancake and the thin-wall solenoids that confirm the validity of this approach. For the asymptotic cases, the new formula for the self-inductance of the thin-wall solenoid is obtained for the first time in the literature. In this paper, we do not use special functions such as the elliptical integrals of the first, second and third kind, nor Struve and Bessel functions because that is very tedious work. The results of this work are compared with already different known methods and all results are in excellent agreement. We consider this approach novel because of its simplicity in the self-inductance calculation of the previously-mentioned configurations

Active SLAM: A Review On Last Decade

This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation.Comment: 34 pages, 8 figures, 6 table

5th-Order Elliptic LPF Using Passive Charge Compensation Technique (PCC) Design

The passive charge compensation (PCC) technique was introduced for switched capacitor (SC) circuit to increase the slew rate and enhance the linearity performance, as PCC techniques are used on the Delta-Sigma modulator (DSM) in ADC circuitry. The PCC technique of the project was applied to the design of a SC filter to explore how PCC can relax the constraints in each of the stages. The filter designed in the study consisted of a conventional cascaded low-pass 5th order elliptic filter with bilinear, high-Q, low-Q sections and PCC switched cap branches, which were added at output of the first integrator of each section. When implemented in a TSMC 180nm CMOS process, this architecture showed a significant noise reduction of 12.3dB at the 3rd harmonic distortion and 13.1dB at the 5th harmonic distortion as compared to the conventional scheme. In additional, a rail-to-rail input and output operational amplifier was designed in a 180nm process with the complementary input pair and class-AB output stage

Adversarial Gaussian Denoiser for Multiple-Level Image Denoising

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios

Self-Healing in Cyber–Physical Systems Using Machine Learning:A Critical Analysis of Theories and Tools

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems