Real Hardware Research Articles

A review of virtual reality technology

Virtual reality technology (VR) is a computer simulation system that can create and experience virtual worlds. It utilizes computers to generate a simulated environment, allowing users to immerse themselves in the environment. Virtual reality technology has been one of the fastest-developing information technologies in recent years. It, along with multimedia technology and network technology, is known as the three most promising computer technologies. As an emerging science and technology, it has been less than 100 years since its emergence, and there is still great room for development in its theory and practical application. This article focuses on virtual reality technology and its applications, based on existing literature and statistical data. The main content includes its advantages, characteristics, technical composition, development history, and applications in different directions. The development process of virtual reality technology is not long, but it has unique advantages and technologies specifically serving it. It does not require much physical participation and has positive implications in applications such as education, entertainment, and healthcare. Its future direction will be towards replacing real hardware with virtual hardware, becoming more practical, intelligent, and refined.

LoRaSync: Energy efficient synchronization for scalable LoRaWAN

AbstractLow‐power wide‐area networks connect a large number of battery‐powered wireless devices over long distances. Among them, long range wide area networks (LoRaWAN) implement a pure ALOHA medium access scheme to save device energy by minimizing the radio usage. However, frame collisions restrain the network scalability when the traffic load increases. In this context, synchronization can be used to exploit the available bandwidth more efficiently by controlling the timing of frame transmissions and reducing the collision probability. Such strategy allows to increase the network throughput at the cost of an extra energy demand due to the inherent overhead. In that, the entailed network scenario is still supposed to address low power applications. Therefore this article timely presents LoRaSync, an energy‐efficient synchronization scheme designed for LoRa networks of any size. An accurate clock drift model was established based on measurements made on real cheap devices, and leveraged to support the design of LoRaSync. Our mechanism has been used to evaluate the same ALOHA‐based random access but on a time‐slotted basis, thus increasing the maximum achievable throughput compared to the legacy access. Throughput and energy efficiency models are established to evaluate the performances of a LoRaSync‐operated network. These models are validated with a simulation environment mimicking large‐scale deployments, and then used to determine the most energy efficient slot size for any traffic load. As a final proof of concept, LoRaSync has been implemented and tested on a LoRa testbed to demonstrate the feasibility of our solution on real hardware.

On the effects of biased quantum random numbers on the initialization of artificial neural networks

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware.

Effects of noise on information corruption in the quantum teleportation algorithm

In the quickly growing field of quantum computing, interference called “noise” causes discrepancies within the results of a quantum experiment. We aimed to observe the effects of noise on the quantum teleportation algorithm, which is used to transport the quantum state of one qubit to another qubit over distances. When the information in the teleportation algorithm is passed through noisy channels, the data can easily become corrupted. We conducted this study in order to observe the effects of noise on the information that travels through these channels. We aimed to understand the nature of the data corruption to help create improved and more faithful methods of data communication. Prior to conducting this study, we expected that the noise would have the most effect on the real and simulated backends and the least effect on the perfect backend. Through this study, we ran teleportation circuits on three different backends (a perfect simulation, a noisy simulation, and the real hardware) and compared the final results with our expected states. We observed that the predicted effects of noise on the information is accurate in some respects but that noise has the potential to drastically alter and corrupt the data. Moreover, the real hardware we used was far more susceptible to noise interference than the simulations. Our findings indicate that, to prevent the information from being compromised, other methods of noise reduction must be employed in conjunction with the quantum teleportation algorithm.

Deep Reinforcement Learning for Path-Following Control of an Autonomous Surface Vehicle using Domain Randomization

In this paper, we propose a path-following controller for an autonomous surface vehicle (ASV) that is based on model-free deep reinforcement learning. To make the learning agent more robust, we investigate domain randomization for sim-to-real transfer. We provide a comparison between three different algorithms: Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO). The trained models are evaluated on the small-scale ASV Altus-LSA Niriis in the maritime test basin at DFKI RIC, Germany. Our results show that applying domain randomization leads to a significant performance improvement compared to no domain randomization, when tested on real hardware.

Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community

The efficient management of energy communities relies on the solution of the “prosumer problem”, i.e., the problem of scheduling the household loads on the basis of the user needs, the electricity prices, and the availability of local renewable energy, with the aim of reducing costs and energy waste. Quantum computers can offer a significant breakthrough in treating this problem thanks to the intrinsic parallel nature of quantum operations. The most promising approach is to devise variational hybrid algorithms, in which quantum computation is driven by parameters that are optimized classically, in a cycle that aims at finding the best solution with a significant speed-up with respect to classical approaches. This paper provides a reformulation of the prosumer problem, allowing to address it with a hybrid quantum algorithm, namely, Quantum Approximate Optimization Algorithm (QAOA), and with a recent variant, the Recursive QAOA. We report on an extensive set of experiments, on simulators and real quantum hardware, for different problem sizes. Results are encouraging in that Recursive QAOA is able, for problems involving up to 10 qubits, to provide optimal and admissible solutions with good probabilities, while the computation time is nearly independent of the system size.

A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms.

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking; the action cell module determines the direction of subsequent movement; and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios.

Transfer of Logistics Optimizations to Material Flow Resource Optimizations using Quantum Computing

The complexity of industrial logistics and manufacturing processes increases constantly. As a key enabling technology of the upcoming decades, quantum computing is expected to play a crucial role in solving arising combinatorial optimization problems superior to traditional approaches. This study analyzes the current progress of quantum optimization applications in the logistics sector and aims to transfer an existing vehicle routing use case to a newly conceptualized matrix production use case regarding resource-efficient material flows. The simulation of the originating simple model is executed on a local circuit-based quantum simulator that emulates the behavior of real quantum hardware. Using a QAOA algorithm for problem-solving, optimal results have been achieved for all simulated scenarios. The theoretical material flow model is based on multiple assumptions and was created for testing reasons exclusively. For a realistic practical application, the model must therefore first be adapted and extended to include additional features.

Precise Quantum Angle Generator Designed for Noisy Quantum Devices

Precise Quantum Angle Generator Designed for Noisy Quantum Devices

Non-Serial Quantization-Aware Deep Optics for Snapshot Hyperspectral Imaging.

Deep optics has been endeavoring to capture hyperspectral images of dynamic scenes, where the optical encoder plays an essential role in deciding the imaging performance. Our key insight is that the optical encoder of a deep optics system is expected to keep fabrication-friendliness and decoder-friendliness, to be faithfully realized in the implementation phase and fully interacted with the decoder in the design phase, respectively. In this paper, we propose the non-serial quantization-aware deep optics (NSQDO), which consists of the fabrication-friendly quantization-aware model (QAM) and the decoder-friendly non-serial manner (NSM). The QAM integrates the quantization process into the optimization and adaptively adjusts the physical height of each quantization level, reducing the deviation of the physical encoder from the numerical simulation through the awareness of and adaptation to the quantization operation of the DOE physical structure. The NSM bridges the encoder and the decoder with full interaction through bidirectional hint connections and flexibilize the connections with a gating mechanism, boosting the power of joint optimization in deep optics. The proposed NSQDO improves the fabrication-friendliness and decoder-friendliness of the encoder and develops the deep optics framework to be more practical and powerful. Extensive synthetic simulation and real hardware experiments demonstrate the superior performance of the proposed method.

Load Recognition in Hardware-Based Low Voltage Distribution Grids Using Convolutional Neural Networks

Due to climate targets of the German government, the share of renewable energy in the power grid will be increased and the number of grid participants connected to the low voltage level of the power grid will rise. This leads to new requirements in voltage control, especially in low voltage distribution grids. In order to achieve a stable power grid in future, further development of control strategies is necessary. In this paper, a load recognition concept, which was tested on simulative data in previous work, is further developed to reduce simulation effort. Additionally, the concept is adapted for real hardware influences and active grid participants complicating the recognition task. Thus, the main contribution of this study is the successful application of the methodology within a hardware-based test grid containing a charging electric vehicle. Using a convolutional neural network in a time series classification setting, the recognition rates in this use-case exceeded 99 % while benefiting from an asymmetric charging behavior. Due to these promising results, future voltage control strategies could be supported based on gained information through integration of the presented concept.

CYBERSECURITY: RESEARCH ON METHODS FOR DETECTING DDOS ATTACKS

This article describes the problem of DDoS attacks, analyzing their nature and consequences. The paper covers common DDoS attack types, such as SYN flood, ICMP flood, UDP flood. Existing methods for detecting attacks from literature are reviewed, including machine learning approaches, including artificial neural networks, support vector machines and decision trees. The paper introduces a decision tree-based machine learning model for the detection of DDoS attacks. The model is trained and tested on a publicly available dataset. The dataset consists of 1,04,345 rows of data, where every row includes 23 features, such as source IP, destination IP, port number, number of bytes transferred from the switch port, etc. A similar set of characteristics can be obtained on a real network hardware using simple calculations, which makes it possible to approximate the model evaluation to real operating conditions. SYN flood, ICMP flood and UDP flood attack types are present in the data, as well as legitimate traffic. To avoid overfitting, only some columns were used, and columns such as IP addresses were discarded. The field “label” in each row of the dataset contains either 0 or 1 where 0 corresponds to legitimate traffic and 1 to malicious one. The problem of DDoS attack detection is therefore formally reduced to the task of binary classification of each row from the dataset. The constructed model achieves an average classification accuracy of 0.94 with a standard deviation at the level of 0.06 in detecting the above mentioned types of attacks. To objectively assess the effectiveness of the model and avoid distortion of the results, stratified 5-fold cross-validation was used. The developed model can be applied in the real world network hardware to filter malicious packets or as a tool for warning the administrator about an attack. This research advances cybersecurity by enhancing DDoS attack detection.

Extending the variational quantum eigensolver to finite temperatures

We present a variational quantum thermalizer (VQT), called quantum-VQT (qVQT), which extends the variational quantum eigensolver to finite temperatures. The qVQT makes use of an intermediate measurement between two variational circuits to encode a density matrix on a quantum device. A classical optimization provides the thermal state and, simultaneously, all associated excited states of a quantum mechanical system. We demonstrate the capabilities of the qVQT for two different spin systems. First, we analyze the performance of qVQT as a function of the circuit depth and the temperature for a one-dimensional Heisenberg chain. Second, we use the excited states to map the complete, temperature dependent phase diagram of a two-dimensional J1–J2 Heisenberg model. Numerical experiments on both quantum simulators and real quantum hardware demonstrate the efficiency of our approach, which can be readily applied to study various quantum many-body systems at finite temperatures on currently available noisy intermediate-scale quantum devices.

Biomimetic Small Intestinal Peristalsis Simulator Using Circumferential Pneumatic Artificial Muscles (cirPAM)

AbstractSurgical simulators offer clinicians an environment to train techniques before transitioning to on‐patient procedures. The realism with which a certain procedure can be mimicked is limited by the used simulator model. While for certain procedures surgical simulators are well established, other procedures lack a sufficiently realistic hardware model. The field of soft robotics is especially suited to act as a platform for developing soft tissue surgical simulators, matching both stiffness and extent of deformation of biological soft tissue. Here, an integrated, pneumatically actuated intestinal peristalsis simulator is developed that mimics a section of the small intestines. This simulator is able the mimic both the peristalsis functionality and the intestinal morphology. The simulator incorporates novel circumferential pneumatic artificial muscles (cirPAM) that radially contract and expand when exposed to respectively negative and positive pressures while keeping their cross‐sectional thickness constant. By sequencing the inflation of these cirPAM actuators, a global peristaltic motion is created with a peristaltic wave propagating at 20 mm s −1, which is in accordance with human physiology. Finally, the simulator is shown to pump viscous liquids, confirming that besides the morphological semblance, it also functionally resembles human small intestine, and can offer a platform for medical device testing and surgical training.

Quantitative measurement of link failure reaction time for devices with P4-programmable data planes

Quick handling of link failures remains a challenging issue in current communication networks, although it is crucial to many routing algorithms. Link failures are the leading cause of packet losses and delays, therefore, failure recovery is tied to stringent requirements for certain services, such as the sub-50 millisecond completion time for carrier-grade networks, which is sometimes difficult to achieve in traditional routing schemes. For this reason, fast recovery strategies are key pillars of modern communication networks. In this paper, we demonstrate the benefits of the devices with Programmable Data Planes (PDP) for fast reacting to link failures. We first review the link failure detection, reaction and recovery procedures and then we discuss the main fast failure recovery mechanisms employed by different types of devices in current communication networks. In addition, we present a novel method to measure the link failure reaction time of an Intel Tofino switch with PDP, as well as the results obtained when measuring such time using real hardware equipment. Our results show that such hardware devices provide a failure reaction time in the order of microseconds, with an average of 472.88μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}s, which poses PDP as a key technology to achieve zero packet loss and zero delay failure recovery.

An algorithm for fast multiplication of elements in 2-groups based on the Zhegalkin polynomials

Network design for a multiprocessor computing system or data center is an important problem where the search for graph models that have attractive topological properties and allow the use of efficient routing algorithms is carried out. Cayley graphs have the indicated properties, in particular such as high symmetry, hierarchical structure, recursive design, high connectivity and fault tolerance. The definition of the Cayley graph implies that the vertices of the graph are elements of some algebraic group. Selecting a group and its generating elements allows us to obtain a graph that meets the necessary requirements for diameter, degree of vertices, number of nodes, etc. A large number of scientific articles and monographs are devoted to solving this problem. The goal of this work is to create an algorithm for fast multiplication of elements in finite 2-groups whose exponent is 2n. The first section of the article provides a theoretical justification for the algorithm for fast multiplication in finite 2-groups. It is shown that elements of these groups can be represented in the form of bit strings, and their multiplication is carried out based on the Zhegalkin polynomials. The second section presents the pseudocode of the algorithm on the basis of which the Zhegalkin polynomials are calculated. The third section demonstrates an example of obtaining the Zhegalkin polynomials for a two-generated group of exponent 4. In conclusion, the prospects for using the algorithm on the real hardware are discussed.

Quantum Fourier Transform‐Based Arithmetic Logic Unit on a Quantum Processor

AbstractThis study proposes and construct a primitive quantum arithmetic logic unit (qALU) based on the quantum Fourier transform (QFT). The qALU is capable of performing arithmetic ADD (addition) and logic NAND gate operations. It designs a scalable quantum circuit and presents the circuits for driving ADD and NAND operations on two‐input and four‐input quantum channels, respectively. By comparing the required number of quantum gates for serial and parallel architectures in executing arithmetic addition, it evaluates the performance. It also execute the proposed quantum Fourier transform‐based qALU design on real quantum processor hardware provided by IBM. The results demonstrate that the proposed circuit can perform arithmetic and logic operations with a high success rate. Furthermore, it discusses in detail the potential implementations of the qALU circuit in the field of computer science, highlighting the possibility of constructing a soft‐core processor on a quantum processing unit.

Deep Neural Network Assisted Quantum Chemistry Calculations on Quantum Computers.

The variational quantum eigensolver (VQE) is a widely employed method to solve electronic structure problems in the current noisy intermediate-scale quantum (NISQ) devices. However, due to inherent noise in the NISQ devices, VQE results on NISQ devices often deviate significantly from the results obtained on noiseless statevector simulators or traditional classical computers. The iterative nature of VQE further amplifies the errors in each loop. Recent works have explored ways to integrate deep neural networks (DNN) with VQE to mitigate iterative errors, albeit primarily limited to the noiseless statevector simulators. In this work, we trained DNN models across various quantum circuits and examined the potential of two DNN-VQE approaches, DNN1 and DNNF, for predicting the ground state energies of small molecules in the presence of device noise. We carefully examined the accuracy of the DNN1, DNNF, and VQE methods on both noisy simulators and real quantum devices by considering different ansatzes of varying qubit counts and circuit depths. Our results illustrate the advantages and limitations of both VQE and DNN-VQE approaches. Notably, both DNN1 and DNNF methods consistently outperform the standard VQE method in providing more accurate ground state energies in noisy environments. However, despite being more accurate than VQE, the energies predicted using these methods on real quantum hardware remain meaningful only at reasonable circuit depths (depth = 15, gates = 21). At higher depths (depth = 83, gates = 112), they deviate significantly from the exact results. Additionally, we find that DNNF does not offer any notable advantage over VQE in terms of speed. Consequently, our study recommends DNN1 as the preferred method for obtaining quick and accurate ground state energies of molecules on current quantum hardware, particularly for quantum circuits with lower depth and fewer qubits.

Scalable and Interactive Simulation for IoT Applications With TinySim

Recent years, the rapid development of IoT (Internet of Things) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications, i.e., device heterogeneity, long-range communication and cloud/edge-device integration. Difficulties are raised by the above features towards IoT application developers, e.g., predicting and evaluating the performance of the entire IoT application system. To deal with the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and seamless transplantation. TinySim takes advantage of the hardware-independent features of TinyLink programming language. Hence, a similar code can be used for both simulation and execution on real hardware platforms. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the number of simulation events, greatly speeding up the simulation process. We carefully evaluate TinySim using benchmarks and two concrete case studies. TinySim can simulate representative IoT applications such as smart flowerspot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4,000 devices within 11.2 physical-minutes for 10 simulation-minutes, which is about 3× faster than the state-of-art approach.

Low Harmonics Plug-in Home Charging Electric Vehicle Battery Charger Simulation Model of Zero Crossing

Harmonic currents are high in conventional rectifiers. The previous work proposed a new battery charger circuit to reduce harmonics effect and improve total harmonic distortion (THD). The design and development of a home charging electric vehicle battery charger circuit should anticipate potential issues. For this reason, the proposed battery charger, buck chopper, and zero cross circuits must be simulated before real hardware implementation. Simulation and simulation models are needed to describe system behaviour because experimentation might be dangerous, expensive, and time-consuming. This paper describes a simulation model of a zero-crossing circuit and its control algorithm, that provide control signals to a five-level single phase Multi Level Rectifier (MLR)) to draw the MLR for a clean sinusoidal line current with low harmonics. The proposed circuit and the algorithms perform as expected when tested using MATLAB/Simulink and PROTEUS software.