The design of ultra-low power, highly linear and low noise Delta-Sigma modulators (DSMs) is an active topic in research and industrial application to realize high-resolution analog-to-digital converters (ADCs) for applications such as audio, sensor or IoT digitization. In such DSMs, the first integrator stage usually consumes most of the power, which is why innovative amplifier architectures have been proposed. This paper presents a case study of different state of the art power-efficient operational amplifier (OpAmp) topologies in a 180 nm CMOS technology using a standard 1.8V supply. A detailed comparison is made in terms of power, noise, area, linearity, and design effort, which can serve as a guide for designers when choosing the most suitable amplifier architecture for such application.

The theory of incremental Delta-Sigma (I-DS) converters relies on memoryless operation between consecutive Nyquist samples. This is achieved by resetting the analog loop-filter and digital reconstruction filter every Nyquist period. It is shown that the use of reconstruction filters with negligible first-sample weights opens the possibility of removing the periodic reset from the analog loop filter of incremental Delta-Sigma modulator (I-DSM) while maintaining memoryless operation. The periodic reset can further be removed from reconstruction filters whose impulse response is inherently finite. Combining the two features enables free-running Delta-Sigma (DS) converters to be used in time-domain multiplexing applications and without inter-channel interference.

Continuous-time (CT) Delta-Sigma (DS) ADCs are a popular choice for high resolution, medium bandwidth applications. A critical challenge is the excess-loop-delay (ELD) in the feedback path of the modulator, which causes performance degradation and instability. For free-running Delta-Sigma modulators (DSMs), performance can be fully recovered by adjusting the loop filter to restore the original noise transfer function (NTF) without ELD. For CT incremental Delta-Sigma (I-DS) ADCs, this has not been specifically investigated, but it is worth noting that the process of ELD compensation is slightly different. In this paper, we investigate the effect of ELD on I-DS ADCs and show that the performance cannot be fully recovered by NTF engineering as in the free-running case, but requires signal transfer function (STF) engineering as well. We also present solutions on how to engineer the STF and how to fully recover the performance even in the presence of ELD in CT I-DS ADCs.

The maximum stable amplitude (MSA) is an important characteristic of a Delta-Sigma modulator (DSM) for high resolution and power efficiency. In free-running DSMs it is known that the MSA usually corresponds to a similar value as the amplitude with the maximum signal-to-quantization-noise ratio (SQNR) and that it depends on several factors including: loopfilter architecture, loopfilter scaling and aggressiveness, internal quantizer bit-width, signal transfer function (STF) and input frequency. Commonly, incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are derived from free-running prototypes enhanced by a periodic reset, and the assumption is that the underlying characteristics remain the same. In contrast, we elaborate in this work that the MSA of I-DS ADCs doesn’t correspond to the amplitude with the maximum SQNR anymore and that this amplitude is not exclusively determined by the DSM, but also depends on the chosen reconstruction filter. Furthermore, the reasons for this interdependency of the reconstruction filter and the amplitude with maximum SQNR are highlighted and solutions to solve this problem are presented.

The increasing complexity of automated production systems, especially in industries like aircraft manufacturing, calls for a methodological framework to efficiently manage and validate control program changes. This paper proposes a novel approach, Continuous Virtual Commissioning in Operation (CVCO), designed to detect and validate such changes during operational phases. CVCO enhances system efficiency and reliability by integrating virtual commissioning, which focuses on validating only the identified changes, thereby minimizing downtime. This structured framework ensures operational integrity through the identification and validation of program changes. Validation through a scenario involving a single robotic system illustrates the feasibility and effectiveness of the approach, indicating its potential applicability in various manufacturing domains involving complex scenarios.

Element mismatch error shaping had been thoroughly studied in free-running Delta-Sigma (DS) ADCs, which enabled using multi-bit quantizer in the loopfilter while shaping the non-linearity errors of the feedback DAC unit element mismatch. A popular shaping technique in free-running DS ADCs is the simple rotational data-weighted averaging (DWA), which was shown to be ineffective in state of the art (SoA) higher order incremental Delta-Sigma (I-DS) ADCs. Hence, prior SoA proposed smart shaping techniques tailored for I-DS ADCs, though adding significant area and power complexity compared to DWA. In this work it is shown that by applying periodic reset to the DWA’s rotation index renders it very feasible and effective for DAC mismatch shaping even in higher order I-DS ADCs without the need of changed algorithms.

The offset cancellation in incremental Delta-Sigma (IDS) ADCs has to be treated differently compared to the cancellation in their freerunning counterparts. In the state of the art (SoA), there exists a lack of theory about this topic. Therefore, in this paper offset propagation and cancellation in I-DS ADCs is analysed. Thereby the idea of fractal sequences to cancel the offset is revised and extended to a more general description to cancel the offset at the output of the I-DS ADC. The derived theory is further explained and backed up by simulations for an exemplary modulator architecture. The intention is to give a deeper understanding on the offset propagation and cancellation in I-DS ADCs and close this gap in the SoA.

This paper introduces an Artificial Neural Network (ANN) to predict the stability of Delta-Sigma modulators (DSMs) and, furthermore, shows its beneficial employment in a genetic optimization algorithm. Since a DSM is a non-linear system, its stability often can’t be predicted by simple algebra. Therefore, a new approach predicting the stability of DSMs using an ANN is presented in this work. It is shown how the data generation and training of such a network can be done. Furthermore, the derivation of high-level coefficients for DSMs is a tedious task, which is often solved by time consuming simulations. The application of the derived ANN in a genetic algorithm to find these high-level coefficients leads to the significant time savings of close to 50%.

In the aircraft manufacturing industry, the process plans created for tasks such as automated drilling and riveting are lengthy and require prior testing and validation using Virtual Commissioning (VC), which can be time-consuming. The introduction of a new process plan requires the complete plan to be tested and validated again, exacerbating the challenge of testing and validating several plans or sequences in advance using VC. This research paper proposes using process mining and footprints conformance checking to reduce VC time for robotic systems. By integrating these techniques, differences in process plans can be detected, and only the new sequences or changes from an old to a new process plan need to be tested, significantly reducing the time needed to perform VC. This research implies that the developed approach can improve the efficiency of VC and ultimately enhance the performance of automated manufacturing systems.

In this paper, the linear-exponential incremental Delta-Sigma (I-DS) ADC is analyzed as a dynamic reconfiguration technique. The influence of the parameters of the exponential phase on the performance of the ADC will be analysed and further investigated under the presence of coefficient mismatch. Furthermore, higher order linear-exponential I-DS ADCs will be introduced and the analysis is extended from first to higher order modulators, giving valuable insights and showing the benefits and drawbacks of higher order linear-exponential I-DS ADCs compared to first order ones. The intention is to give an insight and understanding of the performance improvements, limiting factors and trade-offs achieved by the exponential phase.

In this paper, analysis of linear time-variant systems is applied to incremental Delta-Sigma (I-DS) ADCs with periodic architectural reconfiguration in the frequency domain. The analysis will then be applied to the example of linear-exponential I-DS ADCs as a state-of-the-art dynamic reconfiguration technique. It is shown how a matched reconstruction filter of the reconfigured linear-exponential incremental Delta-Sigma modulator (I-DSM) can be mathematically derived. Using the calculated overall transfer functions of the linear-exponential I-DS ADC, accurate performance predictions can be given. The proposed method allows the accurate prediction of performances and gives insight and understanding of the performance improvements and trade-offs achieved by reconfiguration techniques in I-DS ADCs in general and the exponential phase in particular.

Regular exercise has immediate and long-term benefits for people of all ages. Maintaining an adequate amount of daily exercise is important to overall health and wellbeing. Our research focuses on the development of a socially assistive robot, Salt, to facilitate different upper body exercises. During the exercises, the robot is uniquely able to autonomously detect a user's affect and engagement as well as measure their heart rate to prevent overexertion. A robot emotion model using an n th order Markov Chain is used to determine the robot's appropriate emotions during interactions based on user affect and engagement, and its own emotion history. Human-robot interaction experiments were conducted to investigate perceived usefulness and acceptance. The results showed that most users were engaged and had positive valence towards the robot during the interactions. Post-experiment questionnaire results also showed they were able to detect the robot's emotions and enjoyed interacting with it.

The most important step in an empirical computer scientist's research is gathering sufficient real-world data to validate a system. Unfortunately, it is also one of the most time-consuming and expensive tasks: placing measurement tools in remote networks or end-clients requires one to marshal resources from different administrative domains, devices, populations, and countries. Often such efforts culminate in a trace that is deficient in multiple ways: a small set of test subjects, a short time frame, missing ground truth for device IDs, networking environments lacking in diversity and geographic spread, or highly biased sampling. We present a method of addressing these challenges by leveraging the most open and globally accessible test and measurement platform: digital advertising. Digital advertising instantly provides a window into 7 billion devices spanning every county for an extremely low cost. We propose Advertising as a Platform (AaaP), an ad-based system to perform massive-scale mobile measurement studies. In contrast with measurements made by large media companies who own platforms, ad networks, and apps, we concentrate on the opportunities and challenges for researchers that are end-users of advertising systems. We evaluate a prototype system, discuss ethical guidelines, and demonstrate its use in four scenarios: IP2Geo databases, bandwidth measurement, energy management, and the identifiability of mobile users. We show the efficacy and ease-of-use of AaaP, and illuminate key challenges and the great promise of using AaaP to study a wide variety of mobile phenomena.

Cross-site scripting (XSS) attacks target Web sites with cookie-based session management, resulting in the leakage of privacy information. Although several server-side countermeasures for XSS attacks do exist, such techniques have not been applied in a universal manner, because of their deployment overhead and the poor understanding of XSS problems. This paper proposes a client-side system that automatically detects XSS vulnerability by manipulating either request or server response. The system also shares the indication of vulnerability via a central repository. The purpose of the proposed system is twofold: to protect users from XSS attacks, and to warn the Web servers with XSS vulnerabilities.