Design and Implementation of High Frequency and Low-Power Phase-locked Loop

Phase-locked loop (PLL) operates at a high frequency and due to the increased switching rate of the circuits, the power consumption is high. Designing a PLL which consumes less power without compromising the frequency of operation is essential. The sub-components of PLL such as the phase frequency detector, charge pump, loop filter, voltage-controlled oscillator, and the frequency divider have to be designed for reduced power consumption. The proposed PLL along with its sub-components have been designed using the CMOS 180nm technology library in the Cadence Virtuoso and simulated using Cadence Spectre with a supply voltage of 1.8V resulting in a 20% reduction in power with a higher frequency of operation compared to the reference PLL architecture. The capture range and lock range of the proposed PLL are 2.09 to 2.14 GHz and 1 to 3.5GHz, respectively. The designed PLL consumes less power and operates at a higher frequency.

How to Train Your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning

We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance, custom-built quadrotor platform. A common but often under-studied problem in developing RL agents for continuous control is that the control policies developed are not always smooth. This lack of smoothness can be a major problem when learning controllers as it can result in control instability and hardware failure. Issues of noisy control are further accentuated when training RL agents in simulation due to simulators ultimately being imperfect representations of reality—what is known as the reality gap . To combat issues of instability in RL agents, we propose a systematic framework, REinforcement-based transferable Agents through Learning (RE+AL), for designing simulated training environments that preserve the quality of trained agents when transferred to real platforms. RE+AL is an evolution of the Neuroflight infrastructure detailed in technical reports prepared by members of our research group. Neuroflight is a state-of-the-art framework for training RL agents for low-level attitude control. RE+AL improves and completes Neuroflight by solving a number of important limitations that hindered the deployment of Neuroflight to real hardware. We benchmark RE+AL on the NF1 racing quadrotor developed as part of Neuroflight. We demonstrate that RE+AL significantly mitigates the previously observed issues of smoothness in RL agents. Additionally, RE+AL is shown to consistently train agents that are flight capable and with minimal degradation in controller quality upon transfer. RE+AL agents also learn to perform better than a tuned PID controller, with better tracking errors, smoother control, and reduced power consumption. To the best of our knowledge, RE+AL agents are the first RL-based controllers trained in simulation to outperform a well-tuned PID controller on a real-world controls problem that is solvable with classical control.

HTNURL: Design of a High-Performance Low-Cost Triple-Node Upset Self-Recoverable Latch

A high-performance and low power consumption triple-node upset self-recoverable latch (HTNURL) is proposed. It can effectively tolerate single-node upset (SNU), double-node upset (DNU), and triple-node upset (TNU). This latch uses the C-element to construct a feedback loop, which reduces the delay and power consumption by fast path and clock gating techniques. Compared with the TNU-recoverable latches, HTNURL has a lower delay, reduced power consumption, and full self-recoverability. The delay, power consumption, area overhead, and area-power-delay product (APDP) of the HTNURL is reduced by 33.87%, 63.34%, 21.13%, and 81.71% on average.

Analytical Performance Estimation for Large-Scale Reconfigurable Dataflow Platforms

Next-generation high-performance computing platforms will handle extreme data- and compute-intensive problems that are intractable with today’s technology. A promising path in achieving the next leap in high-performance computing is to embrace heterogeneity and specialised computing in the form of reconfigurable accelerators such as FPGAs, which have been shown to speed up compute-intensive tasks with reduced power consumption. However, assessing the feasibility of large-scale heterogeneous systems requires fast and accurate performance prediction. This article proposes Performance Estimation for Reconfigurable Kernels and Systems (PERKS), a novel performance estimation framework for reconfigurable dataflow platforms. PERKS makes use of an analytical model with machine and application parameters for predicting the performance of multi-accelerator systems and detecting their bottlenecks. Model calibration is automatic, making the model flexible and usable for different machine configurations and applications, including hypothetical ones. Our experimental results show that PERKS can predict the performance of current workloads on reconfigurable dataflow platforms with an accuracy above 91%. The results also illustrate how the modelling scales to large workloads, and how performance impact of architectural features can be estimated in seconds.

Stress-Testing MQTT Brokers: A Comparative Analysis of Performance Measurements

Presently, Internet of Things (IoT) protocols are at the heart of Machine-to-Machine (M2M) communication. Irrespective of the radio technologies used for deploying an IoT/M2M network, all independent data generated by IoT devices (sensors and actuators) rely heavily on the special messaging protocols used for M2M communication in IoT applications. As the demand for IoT services is growing, the need for reduced power consumption of IoT devices and services is also growing to ensure a sustainable environment for future generations. The Message-Queuing Telemetry Transport or in short MQTT is a widely used IoT protocol. It is a low-resource-consuming messaging solution based on the publish–subscribe type communication model. This paper aims to assess the performance of several MQTT broker implementations (also known as MQTT servers) using stress testing, and to analyze their relationship with system design. The evaluation of the brokers is performed by a realistic test scenario, and the analysis of the test results is done with three different metrics: CPU usage, latency, and message rate. As the main contribution of this work, we analyzed six MQTT brokers (Mosquitto, Active-MQ, Hivemq, Bevywise, VerneMQ, and EMQ X) in detail, and classified them using their main properties. Our results showed that Mosquitto outperforms the other considered solutions in most metrics; however, ActiveMQ is the best performing one in terms of scalability due to its multi-threaded implementation, while Bevywise has promising results for resource-constrained scenarios.

Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks

The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.

Perovskite@Graphene Nanohybrids for Breath Analysis: A Proof-of-Concept

Nanohybrids comprising graphene loaded with perovskite nanocrystals have been demonstrated as a potential option for sensing applications. Specifically, their combination presents an interesting synergistic effect owing to greater sensitivity when bare graphene is decorated with perovskites. In addition, since the main drawback of perovskites is their instability towards ambient moisture, the hydrophobic properties of graphene can protect them, enabling their use for ambient monitoring, as previously reported. However not limited to this, the present work provides a proof-of-concept to likewise employ them in a potential application as breath analysis for the detection of health-related biomarkers. There is a growing demand for sensitive, non-invasive, miniaturized, and inexpensive devices able to detect specific gas molecules in human breath. Sensors gathering these requirements may be employed as a screening tool for reliable and fast detection of potential health issues. Moreover, perovskite@graphene nanohybrids present additional properties highly desirable as the capability to be operated at room temperature (i.e., reduced power consumption), reversible interaction with gases (i.e., reusability), and long-term stability. Within this perspective, the combination of both nanomaterials, perovskite nanocrystals and graphene, possibly includes the main requirements needed, being a promising option to be employed in the next generation of sensing devices.

Design and Preliminary Testing of a Magnetic Spring as an Energy-Storing System for Reduced Power Consumption of a Humanoid Arm

The increasing use of robots in the industry, the growing energy prices, and higher environmental awareness have driven research to find new solutions for reducing energy consumption. In additional, in most robotic tasks, energy is used to overcome the forces of gravity, but in a few industrial applications, the force of gravity is used as a source of energy. For this reason, the use of magnetic springs with actuators may reduce the energy consumption of robots performing trajectories due their high-hardness magnetic properties of energy storage. Accordingly, this paper proposes a magnetic spring configuration as an energy-storing system for a two DoF humanoid arm. Thus, an integration of the magnetic spring system in the robot is described. A control strategy is proposed to enable autonomous use. In this paper, the proposed device is modeled and analyzed with simulations as: mechanical energy consumption and kinetic energy rotational and multibody dynamics. Furthermore, a prototype was manufactured and validated experimentally. A preliminary test to check the interaction between the magnetic spring system with the mechanism and the trajectory performance was carried out. Finally, an energy consumption comparison with and without the magnetic spring is also presented.