Towards an Improved Model for 65-nm CMOS at Cryogenic Temperatures

Cryogenic CMOS is a crucial subcomponent of quantum-technological applications, particularly as control electronics for quantum computers. Simulation is an important first step in designing any CMOS circuit. However, the standard BSIM4.5 model is only applicable for temperatures between 230 K and 420 K. In this work, N-type MOSFETs with different dimensions in a 65-nm CMOS technology were characterized at room temperature and liquid helium temperature (4.2 K). These measurements were compared with corresponding simulations from the BSIM4.5 model. A model of drain current in the triode region was constructed, where key parameters, such as threshold voltage and effective mobility, were modified. By adjusting these temperature-dependent parameters, the modified model predicted the triode region currents with an error reduced to 7.6%. Thus, the modified model can be utilized to simulate transistor behavior in the triode region at cryogenic temperatures

Optimal design of measurements on queueing systems

We examine the optimal design of measurements on queues with particular reference to the M/M/1 queue. Using the statistical theory of design of experiments, we calculate numerically the Fisher information matrix for an estimator of the arrival rate and the service rate to find optimal times to measure the queue when the number of measurements are limited for both interfering and non-interfering measurements. We prove that in the non-interfering case, the optimal design is equally spaced. For the interfering case, optimal designs are not necessarily equally spaced. We compute optimal designs for a variety of queuing situations and give results obtained under the $D-$- and $D_s$-optimality criteria

Introduction to ATM Design And Performance

xii,190 hlm, Ilust : 23 c

A Novel Objective Video Quality Assessment Metric For Cloud Gaming Applications

This study introduces a unique method for evaluating the Quality of Experience (QoE) of cloud gaming applications objectively for various network conditions, involving different packet latency and packet loss ratio (PLR) parameters. We use a combination of a perceptual hash (pHash) and a structural similarity index measure (SSIM) to detect the duplication of packets with greater accuracy than either pHash or SSIM alone. We discovered that increased PLR causes more frame duplication in cloud gaming applications, which lowers the end-user QoE. Using the NetEm emulator and the Parsec streaming platform in a controlled network environment, we evaluated our model on three distinct games based on content complexity, genre, and popularity in cloud gaming applications. We observed that different content complexity of games is impacted differently by varying network conditions; for example, slow-moving games are more affected by PLR and frame duplication. We also carried out a subjective QoE evaluation of cloud gaming with 22 volunteers. The results obtained from subjective testing were used to extend our study to compare the accuracy of our model compared with existing Full-Reference (FR) and No-Reference (NR) techniques. We discovered that our model surpasses the majority of the currently available solutions in terms of predicted accuracy, many of which were based on Video on Demand (VoD) applications. This demonstrates how important it is to create a QoE model, particularly for cloud gaming given its distinctive utilisation of network parameters

Significance of Cross-Correlated QoS Configurations for Validating the Subjective and Objective QoE of Cloud Gaming Applications

In this paper, utilising real-internet traffic data, we modified a popular network emulator to better imitate real network traffic and studied its subjective and objective implications on QoE for cloud-gaming apps. Subjective QoE evaluation was then used to compare cross-correlated QoS metric with the default non-correlated emulator setup. Human test subjects showed different correlated versus non-correlated QoS parameters affects regarding cloud gaming QoE. Game-QoE is influenced more by network degradation than video QoE. To validate our subjective QoE study, we analysed the experiment’s video objectively. We tested how well Full-Reference VQA measures subjective QoE. The correlation between FR QoE and subjective MOS was greater in non-correlated QoS than in correlated QoS conditions. We also found that correlated scenarios had more stuttering events compared to non-correlated scenarios, resulting in lower game QoE

Subjective Quality Assessment for Cloud Gaming

Using subjective testing, we study the effect of the network parameters, delay and packet loss ratio, on the QoE of cloud gaming. We studied three different games, selected based on genre, popularity, content complexity and pace, and tested them in a controlled network environment, using a novel emulator to create realistic lognormal delay distributions instead of relying on a static mean delay, as used previously; we also used Parsec as a good representative of the state of the art. We captured user ratings on an ordinal Absolute Category Rating scale for three quality dimensions: Video QoE, Game-Playability QoE, and Overall QoE. We show that Mean Opinion Scores (MOS) for the game with the highest levels of content complexity and pace are most severely affected by network impairments. We also show that the QoE of interactive cloud applications rely more on the game playability than the video quality of the game. Unlike earlier studies, the differences in MOS are validated using the distributions of the underlying dimensions. A Wilcoxon Signed-Rank test showed that the distributions of Video QoE and Game Playability QoE are not significantly different

Variation in QoE of Passive Gaming Video Streaming for Different Packet Loss Ratios

The rise in popularity of the streaming platforms for gaming videos like Twitch.tv results in more users watching and streaming gaming videos passively. These streaming videos show great dependence on Quality of Service (QoS) parameters like latency and packet loss. This motivates us to evaluate the Quality of Experience (QoE) of three popular streaming game videos for different Packet Loss Ratios (PLR). In this paper, using streaming gaming videos, we demonstrated the limitations of using Mean Opinion Score (MOS) to evaluate QoE. Using a novel method of data representation, we presented our results as distributions of user-rating at different PLR configuration and compared them with other subjective QoE evaluation methods like MOS and standard deviation of Opinion score (SOS). Our results show that evaluating QoE using MOS loses information about user diversity. Our findings also include SOS retaining user diversity but posing challenges to quantifying the QoE at varying PLR values. For streaming video games we show that QoE distributions evaluate user perception accurately. Our work in this paper also studies how different games with different content complexity, genre and pace are affected by PLR. Our results show that slow-paced games like FIFA 20 get affected more significantly by packet loss