Periodic Reset Research Articles

Trustworthy fog: A reputation-based consensus method for IoT with blockchain and fog computing

This paper proposes Trustworthy Fog, a novel reputation-based consensus method for Internet of Things (IoT) systems that leverages blockchain and fog computing technologies. By integrating fog computing’s near-end data processing capabilities with blockchain’s immutability and transparency, the proposed method addresses challenges related to latency, device load, and the adaptability of traditional consensus algorithms to resource-constrained environments. A reputation management module evaluates device and node behaviors, facilitating rapid authentication and consensus processes. Distinct reputation calculation schemes for physical devices and fog nodes aim to prevent reputation centralization through periodic resets of reputation values. Based on these values, a lightweight consensus algorithm balances computational capacity and reputation to select leader nodes. Simulations demonstrate the method’s effectiveness in dynamically reflecting device trustworthiness and ensuring fair consensus participation. This research advances IoT blockchain technology, offering a robust solution for the scalability and security challenges inherent in IoT networks.

Integrating human learning and reinforcement learning: A novel approach to agent training

Off-policy reinforcement learning (RL) algorithms are known for improving sample efficiency by employing prior experiences in experience replay memory. However, most existing off-policy RL algorithms are bottlenecked by the slow convergence speed and demand of a large number of interaction samples. In contrast, on-policy RL algorithms converge fast and continuously generate new samples. However, their success heavily depends on the accuracy of the generated samples. To address these challenges, a novel RL framework called HI-FER (Human-learning Inspired Frequent Experience Replay) is proposed by mimicking the process of human learning. HI-FER employs a parallelized experience replay and repetitive training framework to expedite the convergence rate of off-policy algorithms, which imitates the function of the human brain’s parallel information processing and repetitive learning. Additionally, a periodic network reset strategy and dynamic memory updating are leveraged by imitating the forgetting mechanism of humans to prevent overfitting triggered by repetitive updating on limited experiences. Extensive comparison experiments and ablation studies are performed on benchmark environments to evaluate the proposed method. The empirical results demonstrate that HI-FER outperforms the baselines in terms of sample efficiency on state-based (14% improvements) and image-based (51% improvements) tasks from DMControl. Project website and code: https://github.com/Arya87/HI-FER.

Active particle in a harmonic trap driven by a resetting noise: an approach via Kesten variables

We consider the statics and dynamics of a single particle trapped in a one-dimensional harmonic potential, and subjected to a driving noise with memory, that is represented by a resetting stochastic process. The finite memory of this driving noise makes the dynamics of this particle ‘active’. At some chosen times (deterministic or random), the noise is reset to an arbitrary position and restarts its motion. We focus on two resetting protocols: periodic resetting, where the period is deterministic, and Poissonian resetting, where times between resets are exponentially distributed with a rate r. Between the different resetting epochs, we can express recursively the position of the particle. The random relation obtained takes a simple Kesten form that can be used to derive an integral equation for the stationary distribution of the position. We provide a detailed analysis of the distribution when the noise is a resetting Brownian motion (rBM). In this particular instance, we also derive a renewal equation for the full time dependent distribution of the position that we extensively study. These methods are quite general and can be used to study any process harmonically trapped when the noise is reset at random times.

Comparison of Brownian jump and Brownian bridge resetting in search for Gaussian target on the line and in space

We consider a Brownian searcher with diffusion coefficient D in d-dimensions, for , that starts from the origin and searches for a random target with a centered-Gaussian distribution. The searcher is also equipped with a resetting mechanism that resets the searcher back to the origin. We consider three different resetting mechanisms. One is a Poissonian reset with rate r whereby at the reset time the searcher instantaneously jumps back to the origin, one is a periodic reset with period T whereby at the reset time the searcher instantaneously jumps back to the origin, and one is a Brownian bridge reset with period T, whereby the Brownian motion is conditioned to return to the origin at time T. Unlike the first two search processes, this last one has continuous paths. For d = 1 and d = 3, we obtain analytic formulas for the expected time to locate the random target, and minimize them over r or T, as the case may be. In one dimension, this expected time scales as , which is not surprising, but in three dimensions we obtain the anomalous scaling . We compare the relative efficiencies of the three search processes. In two dimensions, we show that the expected time scales as for the Poissonian reset mechanism.

Approximate waiting times for queuing systems with variable long-term correlated arrival rates

We consider waiting times in queuing systems with variable arrival rates in the presence of long-term correlations and periodic trends. We focus on a simplified model where the contributions of periodic and stochastic components could be analyzed separately, leading to queue lengths exhibiting periodic and stochastic resetting, respectively, with their effects summarized additively. We provide an approximate analytical solution that is based on the universal scaling of return interval statistics between level crossing events in long-term correlated data series. The accuracy of our results is validated explicitly by computer modeling, using both simulated data series and empirical traffic data from a network cluster hosting the World Cup ’98 web services characterized by extremely variable traffic intensity. We believe that the proposed approach could be useful to characterize the impact of long-term correlations and periodic trends in various complex systems, with prominent examples ranging from information, communication, logistic, transportation networks to climate, hydrological, as well as other natural, social and engineering systems.

Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers

Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To demonstrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.

Dissipative Floquet Dynamics: from Steady State to Measurement Induced Criticality in Trapped-ion Chains

Quantum systems evolving unitarily and subject to quantum measurements exhibit various types of non-equilibrium phase transitions, arising from the competition between unitary evolution and measurements. Dissipative phase transitions in steady states of time-independent Liouvillians and measurement induced phase transitions at the level of quantum trajectories are two primary examples of such transitions. Investigating a many-body spin system subject to periodic resetting measurements, we argue that many-body dissipative Floquet dynamics provides a natural framework to analyze both types of transitions. We show that a dissipative phase transition between a ferromagnetic ordered phase and a paramagnetic disordered phase emerges for long-range systems as a function of measurement probabilities. A measurement induced transition of the entanglement entropy between volume law scaling and sub-volume law scaling is also present, and is distinct from the ordering transition. The two phases correspond to an error-correcting and a quantum-Zeno regimes, respectively. The ferromagnetic phase is lost for short range interactions, while the volume law phase of the entanglement is enhanced. An analysis of multifractal properties of wave function in Hilbert space provides a common perspective on both types of transitions in the system. Our findings are immediately relevant to trapped ion experiments, for which we detail a blueprint proposal based on currently available platforms.

Accelerated Continuous-Time Approximate Dynamic Programming via Data-Assisted Hybrid Control

We introduce a new closed-loop architecture for the online solution of approximate optimal control problems in the context of continuous-time systems. Specifically, we introduce the first algorithm that incorporates dynamic momentum in actor-critic structures to control continuous-time dynamic plants with an affine structure in the input. By incorporating dynamic momentum in our algorithm, we are able to accelerate the convergence properties of the closed-loop system, achieving superior transient performance compared to traditional gradient-descent based techniques. In addition, by leveraging the existence of past recorded data with sufficiently rich information properties, we dispense with the persistence of excitation condition traditionally imposed on the regressors of the critic and the actor. Given that our continuous-time momentum-based dynamics also incorporate periodic discrete-time resets that emulate restarting techniques used in the machine learning literature, we leverage tools from hybrid dynamical systems theory to establish asymptotic stability properties for the closed-loop system. We illustrate our results with a numerical example.

Driving Waveform Design of Electrowetting Displays Based on a Reset Signal for Suppressing Charge Trapping Effect

Electrowetting display (EWD) device is a new type of reflective optoelectronic equipment with paper-like display performance. Due to the oil backflow phenomenon, it is difficult for pixels to be maintained a stable aperture ratio, so the grayscale of EWDs cannot be stabilized. To reduce the oil backflow in EWDs, a driving waveform composed of a driving signal and a periodic reset signal was proposed in this paper. A direct current (DC) signal was designed as the driving signal for driving pixels. The aperture ratio of pixels was determined by the amplitude of the DC signal. The periodic reset signal was divided into a charge release phase and a driving recovery phase. During the charge release phase, the driving voltage was abruptly dropped to 0 V for a period to release trapped charges. In the driving recovery phase, the driving voltage was rapidly increased from 0 V to a maximum value. To reach the same grayscale of EWDs, the driving waveform was returned to the driving signal at the end of the driving recovery phase. Experimental results showed that the aperture ratio of EWDs was unchanged when the driving waveform was applied. However, the aperture ratio of pixels was gradually decreased with the conventional driving waveform. It was indicated that the charge trapping effect and the oil backflow phenomenon can be effectively inhibited by the proposed driving waveform. Compared with the conventional driving waveform, the speed of oil backflow was reduced by 90.4%. The results demonstrated that the proposed driving waveform is beneficial for the achievement of stable grayscale in EWDs.

Design and Implementation of a Farrow-Interpolator-Based Digital Front-End in LTE Receivers for Carrier Aggregation

A digital front-end decimation chain based on both Farrow interpolator for fractional sample-rate conversion and a digital mixer is proposed in order to comply with the long-term evolution standards in radio receivers with ten frequency modes. Design requirement specifications with adjacent channel selectivity, inband blockers, and narrowband blockers are all satisfied so that the proposed digital front-end is 3GPP-compliant. Furthermore, the proposed digital front-end addresses carrier aggregation in the standards via appropriate frequency translations. The digital front-end has a cascaded integrator comb filter prior to Farrow interpolator and also has a per-carrier carrier aggregation filter and channel selection filter following the digital mixer. A Farrow interpolator with an integrate-and-dump circuitry controlled by a condition signal is proposed and also a digital mixer with periodic reset to prevent phase error accumulation is proposed. From the standpoint of design methodology, three models are all developed for the overall digital front-end, namely, functional models, cycle-accurate models, and bit-accurate models. Performance is verified by means of the cycle-accurate model and subsequently, by means of a special C++ class, the bitwidths are minimized in a methodic manner for area minimization. For system-level performance verification, the orthogonal frequency division multiplexing receiver is also modeled. The critical path delay of each building block is analyzed and the spectral-domain view is obtained for each building block of the digital front-end circuitry. The proposed digital front-end circuitry is simulated, designed, and both synthesized in a 180 nm CMOS application-specific integrated circuit technology and implemented in the Xilinx XC6VLX550T field-programmable gate array (Xilinx, San Jose, CA, USA).

Priorities for Patient Safety Improvement

Priority means precedence in right or rank and is set for resource allocation in patient safety targeting efforts to obtain the maximum benefit for the patients and the public. This research aims to understand Korean patient safety priorities in researches and activities supporting better social efforts for patient safety improvement. When Korean priority of patient safety research has been done using WHO’s global patient safety research priorities with patient safety experts, the result shows the mixed priorities of developed country type and developing country type. ‘Lack of communication and coordination’, ‘poor patient safety culture’ etc were similar to developed countries high ranks. But ‘health care associated infections’ and ‘extent and nature of the problem of patient safety’ were get high ranks similar to developing countries. ‘Effect of work pressure on safety’ ranks the highest in Korea compare to any other country types. This priority results were similar to another priority research for patient safety standards and indicators development which showed high ranks for ‘manpower levels’ and ‘patient identification and communication supporting technologies’. ‘Adverse drug events’ related incidents showed commonly high in priorities, incident rates, and reporting rates through studies and Korean Patient Safety reporting and learning system. But there was discrepancy between the patient safety priorities and governmental support. Therefore, it will be needed for patient safety improvement not only investment from hospitals and government focusing on high priority areas including manpower shortage & workload, communication technologies, and adverse drug events but also priority information sharing and periodic priority re-setting.

Documenting change with the Canadian Occupational Performance Measure for children with cerebral palsy.

To assess the Canadian Occupational Performance Measure's (COPM) ability to document change over 3years in children with cerebral palsy (CP). This was a prospective study with ambulatory children with CP, aged 2 to 6years. Caregivers set one to three COPM goals which were rescored annually over 3years. A ceiling effect for performance goals was operationalized as a score of 8. A Wald χ2 generalized estimating equations model adjusted for age, sex, and Gross Motor Function Classification System (GMFCS) level, evaluated change over time. In total, 124 children (47 [37.9%] females, 77 [62.1%] males; mean age 3y 11mo [SD 1y 1mo]; GMFCS level I [n=78, 62.9%], II [n=21, 16.9%], and III [n=25, 20.2%]) were set 345 COPM goals at baseline. By Year 3, 106 participants (85.5%) rescored 287 of the goals (83.2%). Performance scores increased between baseline mean (SD) 2.93 (0.56), Year 1 5.98 (0.58) with 34.8% at ceiling; Year 2 6.74 (0.60) 48.3% at ceiling; and Year 3 7.37 (0.60) 59.6% at ceiling (Wald χ2 [3]=607.18, p<0.001). Satisfaction scores increased between baseline 4.42 (0.59), Year 1 6.82 (0.60) with 48% at ceiling; Year 2 7.53 (0.60) with 62.2% at ceiling (Wald χ2 [3]=208.48, p<0.001); with no significant increase by Year 3 7.82 (0.62) with 66.9% at ceiling. COPM performance scores increased steadily over 3years. By Year 2, a ceiling effect was seen in about half of the goals. The COPM may have utility to measure change over 3years; periodic resetting of the descriptors of goal success are required to minimize ceiling.

A spatial cognition model based on the selection mechanism of hippocampus place cells

Biological studies show that place cells are the main basis for rats to know their current location in space. Since grid cells are the main input source of place cells, a mapping model from grid cells to place cells needs to be constructed. To solve this problem, a neural network mapping model of back propagation error from grid cells to place cells is proposed in this paper, which can accurately express the location in a given region. According to the physiological characteristics of border cells' specific discharge to the environment, the periodic resetting of the grid field phase by border cells is realized, and the position recognition in any space is completed by this model. In this paper, we designed a simulation experiment to compare the activity of the theoretical place cell plate, and then compared the time consumption of the competitive neural network model and the positioning error of RatSLAM pose cells plate. The experimental results showed that the proposed model could obtain a single place field, and the algorithm efficiency was improved by 85.94% compared with the competitive neural network model in the time-consuming experiment. In the localization experiment, the mean localization error was 41.35% lower than that of RatSLAM pose cells plate. Therefore, the location cognition model proposed in this paper can not only realize the efficient transfer of information between grid cells and place cells, but also realize the accurate location of its own location in any spatial area.

An Error Resilience and Concealment Method for Line-based Wavelet Image Compressions

Recently, some wavelet-based line compression methods for low-cost high-resolution image transmission have been studied. With these line compression methods, which are a combination of the multilevel discrete wavelet transform, predictive coding, and entropy coding, communications errors on the transmission channel can cause an image quality–degradation phenomenon of various line shapes. In this paper, we analyze image degradation patterns of wavelet-based hybrid line compression techniques caused by communications errors. Then, we propose an image quality–improvement method including a low-cost resynchronization technique by using a periodic reset to prevent the error propagation phenomenon of predictive coding, as well as an error concealment technique that uses only one additional line memory in the variable reference line–based decoder to improve degraded image quality. Experimental results showed that the proposed method achieved better image quality than the conventional techniques at the small expense of compression ratio while satisfying low-cost design conditions pursued by existing line compression systems.

Entanglement protection via periodic environment resetting in continuous-time quantum-dynamical processes

The temporal evolution of entanglement between a noisy system and an ancillary system is analyzed in the context of continuous time open quantum system dynamics. Focusing on a couple of analytically solvable models for qubit systems, we study how Markovian and non-Markovian characteristics influence the problem, discussing in particular their associated entanglement-breaking regimes. These performances are compared with those one could achieve when the environment of the system is forced to return to its input configuration via periodic instantaneous resetting procedures.

34.3 fJ/conv.-step 8-MHz Bandwidth Fourth-Order Pseudo-Differential Ring-Amplifier-Based Continuous-Time Delta–Sigma ADC in 65 nm

This letter presents two pseudo-differential ring amplifiers (RAs) suitable for a continuous-time (CT) operation as an alternative to traditional amplifiers. The designs retain the advantages of RAs, scale with process technology, and do not require a periodic reset. The RAs are designed to operate in an integrator configuration and use different methods to achieve stability in continuous time. A prototype was fabricated in a 65-nm CMOS containing two versions of a CT delta–sigma ADC using the two RAs presented. The ADCs consist of a fourth-order loop filter with optimized zeros, a single-bit quantizer that operates at a sampling frequency of 320 MHz, and a digital-to-analog converter. The best design proposed achieves a measured peak signal-to-noise and distortion ratio of 50.6 dB for an 8-MHz bandwidth, a dynamic range of 53.2 dB, and consumes 152 ${\mu }\text{W}$ at a supply of 1.1 V. The obtained figure of merit is 34.3 fJ/conv.-step which outperforms state-of-the-art delta–sigma ADCs in that specification range and is 77% superior to its traditional operational transconductance amplifier-based ADC counterpart.

Nanoscale Tipping Bucket Effect in a Quantum Dot Transistor-Based Counter.

Electronic circuits composed of one or more elements with inherent memory, that is, memristors, memcapacitors, and meminductors, offer lower circuit complexity and enhanced functionality for certain computational tasks. Networks of these elements are proposed for novel computational paradigms that rely on information processing and storage on the same physical platform. We show a nanoscaled memdevice able to act as an electronic analogue of tipping buckets that allows reducing the dimensionality and complexity of a sensing problem by transforming it into a counting problem. The device offers a well adjustable, tunable, and reliable periodic reset that is controlled by the amounts of transferred quantum dot charges per gate voltage sweep. When subjected to periodic voltage sweeps, the quantum dot (bucket) may require up to several sweeps before a rapid full discharge occurs thus displaying period doubling, period tripling, and so on between self-governing reset operations.

A high performance, low power computational platform for complex sensing operations in smart cities

Abstract This paper presents a new wireless platform designed for an integrated traffic/flash flood monitoring system. The sensor platform is built around a 32-bit ARM Cortex M4 microcontroller and a 2.4 GHz 802.15.4 ISM compliant radio module. It can be interfaced with fixed traffic sensors, or receive data from vehicle transponders. This platform is specifically designed for solar-powered, low bandwidth, high computational performance wireless sensor network applications. A self-recovering unit is designed to increase reliability and allow periodic hard resets, an essential requirement for sensor networks. A radio monitoring circuitry is proposed to monitor incoming and outgoing transmissions, simplifying software debugging. We illustrate the performance of this wireless sensor platform on complex problems arising in smart cities, such as traffic flow monitoring, machine-learning-based flash flood monitoring or Kalman-filter based vehicle trajectory estimation. All design files have been uploaded and shared in an open science framework, and can be accessed from https://osf.io/fuyqd/ . The hardware design is under CERN Open Hardware License v1.2.

Theta-Gamma Coding Meets Communication-through-Coherence: Neuronal Oscillatory Multiplexing Theories Reconciled.

Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.

Protrecting Retirement Wealth: A Survey of Australian Products

We survey the long-term derivative instruments (warrants) offered by Australian institutions to elderly Australian investors. Our focus is on products other than plain-vanilla life annuities. There are currently four active products. They incorporate a strike price which is either constant or eligible for periodic upward resets. The guarantee terms vary in length from 5 years to the lifetime of a second person. The products typically have hybrid European-American features, as early exercise is typically permissible yet subject to penalties. Benefits are mostly lump-sum but can be income streams. Fees are typically expressed as a percentage of some combination of the initial investment and the current balance.