Semiconductor manufacturing has been extensively exploiting machine-learning (ML) to process equipment sensory data (ESD) for near-real time anomaly detection (AD). ESD characteristics are highly diversified and data lengths vary among processing steps and cycles. Cleansing ESD with minimum distortion (CMD) to fit the fixed-length input requirement by ML-based AD is critical to AD effectiveness and is challenging. This paper presents a novel CMD method of four innovations: i) statistical mode-based equalization of step data lengths for the least number of step data length changes, ii) importance indicator value (IIV) of a data sample based on its relative difference with the subsequent sample, and iii) step data segmentation into groups based on samples of significant IIVs and  the least-entropy-group-to-cleanse-first rule, and iv) cleansing the least IIV sample(s) in the selected group for step data length equalization. CMD application to ESD demonstrates its characteristics preservation property. Simulation experiments are on an integration of data cleansing with an unsupervised ML-based AD system, STALAD. Comparisons with two benchmark methods over AD scenarios of small-scale drifts and shifts show that CMD not only is superior in facilitating accurate detection by STALAD but also helps detect anomaly much earlier than using the two benchmarks.

As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs.

This paper deals with predictive maintenance of infrastructure objects, focusing on the inspection of bridges. To achieve this goal, unmanned aerial vehicles place measuring units for data collection at different points of the bridge. This work deals with the question of how these measuring units can be found and removed by them later. For this purpose, a magnetic localization method based on the STAR method is presented. In contrast to the STAR method, which only detects the center of a magnetic target, a modified version called SRIOD is implemented here, where the orientation of the magnetic target can also be detected. This is essential information if the unmanned aerial vehicle is to grab the measuring unit with a robotic arm. In this regard, two identical permanent magnets are integrated into the measuring unit, whose gradient tensor contraction of the magnetic flux density distribution has a directed asymmetric surface when considering the contours of constant values. This creates a magnetic invisible reference plane that can be detected with a magnetic gradiometer. The method is verified both in simulation and experimentally in a laboratory setup.

A voltage-controlled delay cell is proposed. Two voltage-controlled oscillator architectures are fabricated based on the proposed delay cell. Measurement results emonstrate a linearity error of 0.1 %, considerably lower power-delay-product, and a smaller circuit area compared to the state-of-the-art VCO implementations in Organic thin-film-transistor technologies. A current-controlled ramp-generator circuit is proposed, which is the first of its kind implemented in flexible thin-film-technologies. The measurement results show a linearity error of 0.02 % for the ramp-generator circuit. All circuits are fabricated in Organic thin-film-transistor technology on plastic foil.

This paper details an experiment utilizing ESP8266 modules as servers to wirelessly control diverse electrical appliances in home automation. The experiment showcased the modules' capability to respond to commands via a web interface on both mobile and desktop platforms or even tablets. While most of the experiment ran smoothly, occasional freezing and connectivity disruptions were observed. The abstract encapsulates the experiment's successes, discusses encountered challenges, and outlines a forward-looking perspective, including the integration of a custom PCB for enhanced system stability.

This paper presents the design and implementation of a multilayered microservices-centric in-network computation framework tailored for the Internet of Vehicular Networks. The proposed framework integrates a vehicular network layer, Edge computing layer, and Cloud to enhance network efficiency and minimize latency for time-critical vehicular applications. In the proposed work, modifications were made to the NDNsim codebase, integrating the C++ Boost Asio library to facilitate NDNsim-Edge communication for designated microservice computation. in additions, a microservice-centric driver assistance application is also emulated in Dotnet core with microservices deployed on various Edge and cloud servers. Moreover, Restful APIs have been developed, allowing the NDNsim layer, physical Edge layer, and Cloud stations to offload a microservice-centric computation request and retrieve the corresponding compute outcomes. Furthermore, the Entity framework is utilized to ensure proper tracking and management of the requests while an HTML-based user interface is developed for a visual representation of the request pattern. Extensive testbed experimentation revealed that the proposed framework highly optimized bandwidth consumption, reduces latency and amplifies the computed results delivery ratio compared with the conventional monolithic system.

Edge clouds (ECs) have recently been shown with outstanding advantages in enhancing customized user service experiences, benefiting from user proximity and location-aware characteristics. However, operating a large-scale EC network would inevitably result in a significant energy cost for EC providers, potentially offsetting their service revenue without proper energy cost management. In this paper, we focus on conserving energy cost for EC providers by leveraging both electricity price-aware geographical load balancing and dynamic central processing unit (CPU) provisioning, considering the spatio-temporal diversities of electricity prices and user task demands. Due to the significant “switching cost” associated with turning CPUs and services on/off, we formulate a multi-timescale energy cost minimization problem that integrates large-timescale CPU provisioning and service placement, as well as small-timescale geographical task dispatching and CPU resource allocation. The Lagrange dual decomposition theory is exploited to handle the spatio-temporal variable couplings. A fully distributed mini-batch learning (MBL) algorithm that relies on parameter approximation for large-timescale decision makings is proposed to learn the optimal dual variables, i.e., the Lagrange multipliers. We present rigorous algorithm performance analysis, and conduct extensive simulations based on real data of electricity prices of Canada to demonstrate the superior performance of the MBL algorithm compared to several baseline schemes.

Young’s double slit experiment produces a single interference pattern, which has been explained as the consequence of the Superposition of waves. The cross double slit consists of at least two components. A component is either a single slit or a double slit or a triple slit, etc. A cross double slit experiment produces a combination of at least two interference patterns. In this article, we propose the Superposition of patterns of cross double slit experiment: at certain distance from the diaphragm, the complete pattern is the Superposition of the patterns produced by each component, respectively. L P1 P2 P3 Superposition of pattern is valid: yes or no 10 mm blob:https://www.techrxiv.org/0225329d-c29e-4a7f-8264-62f3bc5f1904 blob:https://www.techrxiv.org/dac479cd-4906-4256-b348-39b19724dcb6 blob:https://www.techrxiv.org/d4272632-b638-4971-b40f-6c8c2c30e138 No. Since there is fine structure in P3, so blob:https://www.techrxiv.org/48ecc511-852c-4690-9743-1312883b14b3 P1 and P2 are Particle patterns. P3 is Pre-Particle pattern 50 mm blob:https://www.techrxiv.org/95016f94-eebc-4597-8458-bba92c89dac6 blob:https://www.techrxiv.org/961ef964-c70f-4281-899e-7b6cafbff899 blob:https://www.techrxiv.org/79cef8a0-0745-45f2-a944-53b1ca2eba5c Yes. Since there is no fine structure in P3, so blob:https://www.techrxiv.org/a4abea3f-b78f-4ebf-9ab8-d2c232d47794 P1, P2 and P3 are Particle patterns. 1100 mm blob:https://www.techrxiv.org/ec3ee46a-e57b-41c5-b144-b871c544a8fd blob:https://www.techrxiv.org/a6a6e289-43c6-43cc-9fec-1cd68fcfbef8 blob:https://www.techrxiv.org/4ebe7c79-43c2-4657-8bfc-ae8d35aa1805 Yes. blob:https://www.techrxiv.org/a4abea3f-b78f-4ebf-9ab8-d2c232d47794 P1, P2 and P3 are interference patterns. The Superposition of patterns is valid for the Particle patterns and the Interference patterns at certain distances from the diaphragm, while is not valid for the Pre-particle patterns and Transition patterns. The mystery is that when crossing two or more components at an angle, the fine structures emerge, which destroyed the Superposition of patterns. We show the Superposition of patterns in detail to provide phenomena for further theoretical study.

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It is well known that CMOS Cross-coupled Differential Drive (CCDD) is the commonly used approach in the design of RF-DC converters. However, multistage designs are usually used to produce higher DC output voltage. This in turn will lead to using the twin-well CMOS process for fabrication since the NMOS source will be at higher potential in the next and consequent stages. The twin-well process technology is expensive compared to the n-well process. On the other hand, the DC output of these designs will saturate when higher voltage is required using multistage. This brief presents a new CMOS n-well process RF-DC converter. The design is hybrid in which the first stage is designed using CCDD since the NMOS source is grounded and the second and consequent stages are designed using only PMOS transistors. The functionality of the proposed design is confirmed using CADENCE in 0.18µm TSMC CMOS technology. Simulation results show that the design is working properly and achieves linear DC output voltage with the number of stages and an input power range of 33dB for PCE >20%.

Automated vehicles use light detection and ranging (LiDAR) sensors for environmental scanning. However, the relative motion between the scanning LiDAR sensor and objects leads to a distortion of the point cloud. This phenomenon is known as the motion distortion effect, significantly degrading the sensor’s object detection capabilities and generating false negative or false positive errors. In this work, we have introduced ray tracing-based deterministic and analytical approaches to model the motion distortion effect on the scanning LiDAR sensor’s performance for simulation-based testing. In addition, we have performed dynamic test drives at a proving ground to compare real LiDAR data with the motion distortion effect simulation data. The real-world scenarios, the environmental conditions, the digital twin of the scenery, and the object of interest (OOI) are replicated in the virtual environment of commercial software to obtain the synthetic LiDAR data. The real and the virtual test drives are compared frame by frame to validate the motion distortion effect modeling. The mean absolute percentage error (MAPE), the occupied cell ratio (OCR), and the Barons cross-correlation coefficient (BCC) are used to quantify the correlation between the virtual and the real LiDAR point cloud data. The results show that the deterministic approach matches the real measurements better than the analytical approach for the scenarios in which the yaw rate of the ego vehicle changes rapidly.

This paper focuses on the modeling of two mechanical translation systems and the design of a linear quadratic controller. The state-space model for both systems is obtained and implemented, with the purpose of simulating the operation. Related to the design of the linear quadratic controller, the K matrix is obtained, which allows for minimizing the cost function related to the energy costs, produced in the states and inputs present in the state-space models of both mechanical systems. Then the optimized system model is obtained and studied.

This paper presents an analytical approach to explore the damping effect of inner loops on grid-forming converters. First, an impedance model is proposed to characterize the behaviors of inner loops, thereby illustrating their influence on output impedance shaping. Then, based on the impedance representation, the complex torque coefficient method is employed to assess the contribution of inner loops to system damping. The interactions among inner loops, outer loops, and the ac grid are analyzed. It reveals that inner loops shape the electrical damping torque coefficient and consequently influence both synchronous and sub-synchronous oscillation modes. The virtual admittance and current control-based inner-loop scheme is employed to illustrate the proposed analytical approach. The case study comprises the analysis of impedance profiles, the analysis of damping torque contributed by inner loops under various grid strengths, and the comparison between dq-frame and αβ-frame realizations of inner loops. Finally, simulation and experimental tests collaborate with theoretical approaches and findings.

With the rapid development of large-scale Low Earth Orbit (LEO) satellite internet, Very Low Earth Orbit (VLEO) Earth observation constellations are increasingly using InterSatellite Links (LISLs) for cross-layer access to Internet satellites.It is an effective means to enhance data return throughput. When both observation and communication satellite constellations use lasers for networking, cross-layer access between the VLEO and LEO satellite networks requires reallocating lasers. This reallocation disrupts the original topology and impacts network performance. To maximize the collaborative operational efficiency of the double-layer network, we fundamentally analyze the relationship between topology links, throughput, and delay using graph theory. Innovatively, we discover the superiority of two axioms in addressing the cross-layer topology optimization problem, including the “Minimum Hop Count” and “Minimum Overlap Path”. Based on these axioms, a many-objective crosslayer topology optimization model is established that considers the hop count and the average link utilization frequency of both VLEO and LEO satellite networks. To reduce the reliance on centralized algorithms on global transmission demand, a Local Distributed Interaction Mechanism (LDIM) is proposed for cross-layer LISL establishment. An onboard Novel Distributed Many-Objective Topology Optimization (NDMTO) algorithm is also introduced for VLEO satellites to manage access strategies. Finally, we use real data from Typhoon LEKIMA to create a multi-task scenario and conduct packet-level simulations based on the Starlink and Dove constellations. The results indicate that, compared to existing benchmarks, the NDMTO algorithm improves data throughput by 26.73% and reduces the average transmission delay of emergency task data by 20.7%.

A tuning element with controllable air gap integrated and implemented in a rectangular Ku-band WR62 waveguide is presented. The proposed tuning element concept, consists of two, top and bottom, thin conductive parallelepiped arms, which are placed in the middle of the rectangular waveguide structure. The bottom arm is bending to control the gap width between the two arms, while the second arm is fixed. The curvature of the bending arm is controlled by a piezoelectric actuator, which affects the phase shift of the re-scattered E-field at the output port. The compact size, low-cost and the easy-to-manufacture proposed design offers considerable phase shift with very low insertion loss, given its electrical size and operational waveband. A prototype, for the distinct frequency band, has been manufactured and measured. The same prototype has been simulated in ANSYS HFSS. The numerical results will be later used to validate the actual electromechanical prototype.

Microwave transversal filters, which are implemented based on the transversal filter structure in digital signal processing, offer a high reconfigurability for achieving a variety of signal processing functions without changing hardware. When implemented using microwave photonic (MWP) technologies, also known as MWP transversal filters, they provide competitive advantages over their electrical counterparts, such as low loss, large operation bandwidth, and strong immunity to electromagnetic interference. Recent advances in high-performance optical microcombs provide compact and powerful multi-wavelength sources for MWP transversal filters that require a larger number of wavelength channels to achieve high performance, allowing for the demonstration of a diverse range of filter functions with improved performance and new features. Here, we present a comprehensive performance analysis for microcomb-based MWP spectral filters based on the transversal filter approach. First, we investigate the theoretical limitations in the filter spectral response induced by finite tap numbers. Next, we analyze the distortions in the filter spectral response resulting from experimental error sources. Finally, we assess the influence of input signal’s bandwidth on the filtering errors. These results provide a valuable guide for the design and optimization of microcomb-based MWP transversal filters for a variety of applications.

Kuramoto model based analysis of Josephson junction array in series and comparison with small numbers of junctions as compared to higher number as proposed in the Kuramoto model.