Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Deep Convolutional Neural Network for Identifying and Classification of Silicon Wafer Faults in IC Fabrication Industries

This paper presents the detection and classification of various manufacturing defects on wafer maps using an enhanced deep convolutional neural network (DCNN). Wafers are tiny discs of semiconducting material, often silicon, that form the basis of integrated circuits. Die-separated integrated circuits (ICs) are produced on each wafer. Automated inspection machines evaluate the functionality of ICs on wafers. On a wafer map, the regional pattern of the passing and failing dies might identify the specific production faults. Using techniques of deep learning, the defect patterns on wafers may be efficiently classified, making it possible to rapidly identify production defects, hence enabling early manufacturing process correction and minimising loss. Resampling is performed in order to resolve the data imbalance problem prior to the training modality. Further performance analysis is carried out by incorporating various optimizers to train the model. The simulation results depicted that the proposed DCNN outperforms the conventional CNN.
A survey on SOC complementarity functions and solution methods for SOCPs and SOCCPs

This paper makes a survey on SOC complementarity functions and related solution methods for the second-order cone programming (SOCP) and second-order cone complementarity problem (SOCCP). Specifically, we discuss the properties of four classes of popular merit functions, and study the theoretical results of associated merit function methods and numerical behaviors in the solution of convex SOCPs. Then, we present suitable nonsinguarity conditions for the B-subdifferentials of the natural residual (NR) and Fischer-Burmeister (FB) nonsmooth system reformulations at a (locally) optimal solution, and test the numerical behavior of a globally convergent FB semismooth Newton method. Finally, we survey the properties of smoothing functions of the NR and FB SOC complementarity functions, and provide numerical comparisons of the smoothing Newton methods based on them. The theoretical results and numerical experience of this paper provide a comprehensive view on the development of this field in the past ten years.
Conductive Adhesives at Microwave Frequencies: Silver-Filled vs. Graphite-Filled

Performance and applicability of silver-filled and graphite filled conductive adhesives in the frequency band from 1 to 6 GHz is experimentally examined. To test the possibility of repairing damaged transmission lines and circuits, and replacing missing ones by means of conductive adhesive, a section of microstrip line and a rectangular patch antenna were painted by using both adhesives under test. The sliver filled adhesive performed well while the graphite-filled adhesive did not give good results in this test. Further test showed that graphite-filled adhesive can be used only for gluing conductive parts, not for replacing missing portions of the conductor.
Ethylene Glycol Based Phantoms for High-Loss and Malignant Tissue at Microwave Frequencies

This work presents the dielectric properties of different mixtures of distilled water and ethylene glycol in the frequency range 0.5-8 GHz and at 25 °C. The measured dielectric properties are fitted to Debye and Davidson-Cole relaxation models depending on the percentage of ethylene glycol in the mixtures. The relationship between model parameter values and mixture types are found. Dielectric properties of mixtures at 2.45 GHz are compared with those of high-loss tissues and tumors found in literature suggesting them to be used as liquid phantoms.
Microwave Imaging of Perfectly Conducting Objects

In this paper, a new algorithm for tomographic imaging of perfectly conducting scatterers, with boundary conditions of the Dirichlet or Neumann type, is proposed. The boundary value problem is converted into a volume integral equation with a singular double-layer potential. No approximations are made in the construction of the forward model and derivation of the inversion algorithm. Instead, some elementary facts of the microlocal analysis are used to recover the support of the scattering potential, and therefore, the shape of the obstacle. Mathematically, the problem is reduced to the Radon inversion of a classical computed tomography. It is shown that the algorithm is also capable of classifying the type (Dirichlet or Neumann) of the boundary condition imposed.
A Binary Weight Convolutional Neural Network Hardware Accelerator for Analysis Faults of the CNC Machinery on FPGA

With the development of machine learning technologies and the internet of things, many traditional industrial problems can be solved by combining these two technologies. For example, the CNC machinery fault can be detected by collecting the data from vibration sensors and analysis with machine learning algorithms. However, previous studies only focused on the accuracy of the machine learning model but did not consider the overhead of hardware implementation. This paper proposes a real-time CNC machinery fault detection solution using a binary weight convolutional neural network to identify vibration signals. In addition, the operations in the designed neural network have been converted to fixed-point operations to speed up the calculations and reduce memory usage. Finally, the proposed method has been implemented on FPGA to evaluate the capabilities. According to the experimental result, the proposed method can achieve 95.07% accuracy while implemented on the FPGA with 130MHz clock frequency.
Classification of Brain Tumors Using Ensemble Learning

A brain tumor is an anomalous blowup of cells if not dually detected. A brain excrescence must be found as soon as possible for therapeutic use and survival chances. There are numerous different types of brain tumors with different features, sizes, and treatment choices. Tumor detection manually is difficult, laborious, and prone to mistakes. In this research, deep features are extracted and passed to an ensemble system of AlexNet, EfficientNet, ResNet50, InceptionV3, and VGG16 from which a score vector is acquired from Softmax for demarcation between glioma, meningioma, pituitary, and no tumor. The issues in the published studies have already been estimated on a standard dataset such as Kaggle, 2020-BRATS. The model has detection scores of more than 96%, which shows that the proposed ensemble model outperforms existing works.
A Novel and Efficient CBIR using CNN for Flowers

Image processing is vital to extract the required data from images. Machine learning is an efficient tool used for penetration in most of the classification and identification tasks performed by a computer. This project proposes the identification of a flower after the classification of flower images using a successful artificial intelligence tool named the Convolutional Neural Network (CNN). Models similar to this project have been used in most search engines for a long time, but CBIR (content-based image retrieval) still runs with less accuracy and produces outputs with fewer specifications due to the use of convolutional feed-forward networks for image retrieval. System performance depends a lot on the drawn-out features extracted from images. So, it is required to develop a CBIR system that retrieves similar images without explicit feature extraction and classification by using CNN, which accepts images as input. For experimentation, images from the Oxford-102 flower dataset are used.
Simulation of a Novel Phase Retrieval Technique for Multi-axis Robotic Arm Based Near-Field Antenna Measurements

The simulation of a novel robot-arm based near-field/far-field (NF/FF) antenna measurement system enabling the reconstruction of measured AUT phase without the need for a phase reference cable is described. The mathematical approach follows the principle used within GPS position and time recovery. This work addresses the need for industrial robot arm-based NF/FF antenna measurements at both microwave and importantly millimetrewave frequencies where the transmitting probe RF phase reference cannot be guaranteed to be provided with the necessary stability by cable management or rotary joints. To assess the viability a computer simulation of the measurement system is constructed, and its performance analysed in terms of the accuracy of reconstruction of the AUT phase and true NF probe location, as well as the NF/FF radiation pattern accuracy in terms of Equivalent Multipath Level (EMPL). We demonstrate that a microwave system can offer EMPL below -60dB and that a millimetrewave system operating from 40GHz to 100GHz can offer EMPL below -50dB.
Achieving wide-angle mechanical beam steering in Ka-band with low-profile Transmit-array antennas

Beam scanning and high gain are fundamental requisites for antennas operating at millimeter wave, where many 5G and satellite-on-the-move applications are emerging. Mechanical beam steering using Transmit-arrays (TAs) has been proposed as a viable alternative to the more costly and complex electronic-based solutions. As a spatially fed array, TAs operate with a minimal focal length, distance between the primary source and the radiating aperture, which impacts on the overall size of the antenna. The focal length also limits the maximum scanning range that can be achieved through a linear displacement of the primary source parallel to the aperture (a common mechanical scanning approach using TAs). In this work, an iterative method for optimizing the unit cells distribution in the aperture for low F/D (focal length over aperture ratio) is presented. This approach allows extending the scanning range of the common unifocal TA phase distribution. To validate the proposed method, a TA with F=50 mm and inplane dimensions 195x145 mm (F/D = 0.34) operating at Kaband (30 GHz) was designed. The full-wave results show that this antenna as a 3 dB scanning range of [-59°, 59°], with a maximum gain of 26.3 dBi at 30 GHz and SLL < -11.7 dB.
Multi-User Shadowing Investigation at 28 GHz for 5G Mobile Communication Systems

In this paper, the multi-user measurement has been done at 28 GHz with the handset antenna array for purpose of studying the user blockage. The measurement has been done in the anechoic chamber with one active and 3 passive users. The passive users are oriented in 6 different setups and the base station probe horn antenna has two different angles in order to replicate the most critical real life scenarios. It has been found that presence of multiple users will increase the overall handset antenna gain by adding multiple reflections, scattering and diffractions. In order to increase the power in the shadow region when the user is pointing away from the base station an extra one to three different scan angles on the handset can be added in order to gain 10 dB.
A Two-stage Optimization Method for Storage Coordinated with Thermal Power Unit in AGC

With the large-scale uncertain renewable energy connected to the grid, the frequency regulation demand is growing day by day, and there is a great need to use energy storage to improve the automatic generation control (AGC) performance of the thermal power to meet the grid frequency regulation requirements. Therefore, a two-stage optimization method considering AGC performance and state of charge (SoC) control for energy storage coordinated with thermal power unit in AGC is proposed in this paper. Firstly, the AGC performance assessment index constraints are linearized, which is used to establish the first stage optimization model of the joint system participation in frequency regulation. Further, to reduce the depth charging/discharging situation of energy storage, the second stage optimization model is constructed with the objective of minimizing the SoC deviation. Finally, the simulation result shows that the proposed two-stage optimization method can improve both AGC performance and sustainability of energy storage, and effectively reduce the loss of energy storage working life.
Load oscillation pattern detection for NILM based on scale space decomposition

Load pattern detection is the fundamental step for non-intrusive load monitoring (NILM). Some appliances’ power profiles exhibit long-duration cyclic oscillation patterns when performing certain physical tasks, which contain load signatures with strong discrimination for state recognition, but also tend to interfere with the feature extraction of other appliances. However, detecting load oscillation pattern (LOP) is extremely challenging, and the existing NILM methods cannot effectively cope with the multi-scale characteristics of LOPs in unseen scenarios and the overlapping interference of multiple LOPs. To this end, we propose a LOP detection method based on the scale space decomposition theory. LOPs are mined at different time scales, and then are optimally selected based on the minimum description length (MDL) principle. Finally, experiments are carried out on publicly-accessible Pecan Street dataset and private dataset, showing the proposed method generally outperforms two benchmarks in various evaluation metrics.
Designing Constant-Timed Accelerators using High-Level Synthesis: A Case Study of ECG Biometric Authentication

High-level synthesis (HLS) is an increasingly popular approach for rapidly designing complex, high-performance, and energy-efficient application-specific accelerators, enabling a shorter time to market and increased productivity. This paper explores a workflow for generating constant-timed accelerators using generic HLS tools in order to eliminate the timing-based side-channel vulnerabilities intrinsic to accelerators generated using state-of-the-art HLS. Since security, performance, and energy are often conflicting design objectives, we also explore ways to mitigate the design overhead. We demonstrate the workflow using a case study of an ECG biometric authentication system, which exemplifies a real-world system with significant timing side-channel vulnerabilities. Results show that the workflow successfully generates constant-timed accelerators and enables designers to use generic HLS tools, thereby minimizing any negative impacts on the design process.
Measurement of the V2I Channel in Cell-free Vehicular Networks with the Distributed MaMIMOSA Channel Sounder

In this paper, we present a small, yet realistic, vehicular cell-free massive MIMO (multiple-input multiple-output) architecture deployed at the University of Lille in a typical suburban environment under both Line-of-Sight (LOS) and obstructed LOS (OLOS) shadowing conditions. The radio channels were acquired with a distributed RF-over-Fiber (RoF) upgrade of the real-time channel sounder MaMIMOSA. The system operates at 5.89 GHz with an 80 MHz bandwidth, which corresponds to the ITS frequency band offered by the ITS-G5 and C- V2X technologies. Four omnidirectional receive antennas were placed on the roof of a van moving at a speed of 25 km/h. The propagation channel was measured for various transmit antenna configurations, ranging from co-located antennas to fully distributed antennas. The measurement results show a significant gain in the signal-to-noise ratio (SNR) as well as a more uniform coverage and smaller delay spread values with the distributed scenarios compared to the centralized ones. Finally, the path loss measurement results obtained for the cell-free network provide deployment guidelines for the distributed antennas.
An investigation into Cellular Automata: The Self-Modifying Instruction-Based Approach

Cellular automata is an idealized complex system, where a parallel grid of basic cells communicates locally in order to provide computation. Cellular automata was first proposed as a medium for replicating machines [1]. This thesis aims to return to these root and tries to emerge Stem-cell like behaviour and abilities in Cellular automata. Stem-cells are a vital part of a complex development process in any multicellular organism. They also serve in maintaining the organism once it is fully developed. A cell is classified as a stem-cell if it has the ability to both replicate and develop into a different cell. This is abstracted to mean, the replication and the morphogenesis problem, as one. Self-Modification(SM), from Simon L. Harding and Co [48] involves a method very strong on morphogenesis. M. Bidlo’s Instruction-based approach [10], is a method very strong on replication. This thesis aims to combine the two and show that they work well in combination, and that it can utilize both the strength of IBA on the replication problem, and the strength of SM on the morphogenesis problem. In addition, SM is closer to how genes behave, as SM forms a regulation of how genes are expressed. Moreover, this thesis also aims to show that stemcells or hierarchical-like structures are possible, and often emerge naturally. It was discovered that SM gave great advantage as an extension to IBA. It gave IBA useful tools to solve a number of problems IBA alone could not. Also, through this thesis it was shown that SM with IBA could delay its development and use longer development paths when necessary.
Asymptotic bounds on the time to fatigue failure of bundles of fibers under local load sharing

A fiber bundle is a parallel arrangement of fibers. Under a steady tensile load, fibers fail randomly in time in a manner that depends on how they share the applied load. The bundle fails when all its fibers have failed in a specified region. In this paper we consider the fatigue failure of such a bundle in a fiber load-sharing setting appropriate for composite materials, that is, to bundles impregnated with a flexible matrix. The bundle is actually modelled as a chain of short bundles, and local load sharing is assumed for the fibers within each short bundle. The chain of bundles fails once all the fibers in one of the short bundles have failed. Reasonable assumptions are made on the stochastic failure of individual fibers. A general framework for describing fiber bundles is developed and is used to derive the limiting distribution of the time to the first appearance of a set of k or more adjacent failed fibers as the number of fibers in the bundle grows large. These results provide useful bounds on the distribution of the time to total bundle failure. Some implications and extensions of these results are discussed.
Geolocation Assessment/validation Methods for EUMETSAT Polar System – Second Generation (EPS-SG) ICI conical scanning radiometer

Methodologies have been developed for the validation during the Cal/Val phase of the geolocation error of the ICI radiometer that will be onboard of the Second Generation (SG) of the EUMETSAT Polar System (EPS-SG) satellites. The work investigated how ICI channels can be used to identify optically detectable surface targets, such as landmarks, and atmospheric features, such as water vapour gradients and deep convective clouds. The validation was based on a contour matching technique, using high altitude lakes, ice shelves and mountain chains as landmark targets, and water vapour features and deep convective cloud as atmospheric targets.
Cross-Polarization Discrimination Interference Analysis of Alphasat Satellite Measurements in Ka and Q Bands

The implementation of satellite communication systems with a throughput of terabits per second requires the use of the Ka and Q bands and above, which can accommodate radio channel bandwidths in the gigahertz range. In addition, the spectral efficiency of these systems can be increased by using the dual orthogonal polarization mode. At these high frequencies, among other tropospheric phenomena affecting electromagnetic wave propagation, rain attenuation represents the major obstacle in the design of high-frequency satellite communication systems. Furthermore, the depolarization interference caused by raindrops and ice particles degrades the performance of dual-polarized systems. In this paper, the cross-polarization discrimination (XPD) from the beacon measurements of the Alphasat satellite at 19.7 GHz and 39.4 GHz in Ljubljana, Slovenia, are investigated. The complementary cumulative distribution (CCDF) of the one-year XPD measurements is also presented.
Realization of High Performance Approximate Multipliers for FPGA Application

It is possible to use a variety of computer arithmetic systems to carry out a complex multiplier. The efficiency of an FPGA- VLSI processor relies on how quickly its digital signal processing operations can be carried out. In order to achieve both high accuracy and speed, the authors of this work offer a concept for a 64-bit approximation multiplier technique that utilizes compressors and partial product multipliers. For artificial intelligence (AI) based FPGA-VLSI applications, approximate multipliers are among the fastest multipliers available. The suggested study reveals a 64-bit approximation to a 64-bit multiplicator. To implementation of the proposed 64-bit digital approximate multiplier, we are using the partial product or dada based multiplication technique; in which the 64 bit multiplication process complete in the partial form. 64 bit split into the 32,16,8,4 and 2 bit multiplication. We are considering performance parameters in terms of the area, latency and PDP. The area is reducing up to 55%, latency and PDP is reducing approx. up to 70% then existing work. Proposed multiplier gives 99% accuracy. The FPGA integrated circuit utilized for the simulation is a member of the virtex 7 family.
Proactive Thyristor-Based DC Solid-State Circuit Breaker

This paper introduces a new proactive solid-state circuit breaker (P-SSCB) for direct current (dc) medium voltage applications. The P-SSCB aims to apply high-power rating thyristors while achieving a fast response speed and high efficiency. To manually turn off thyristors, a complimentary commutation technique is used; meanwhile, full-controlled switches are employed in the auxiliary branch to minimize the reaction time interval. To implement the complementary commutation and achieve a reliable turn-off process in the main thyristor, the injection capacitor in the auxiliary branch is charged by the dc system. The charging process of the injection capacitor is controlled by a thyristor, which helps to obtain a fast and reliable re-closing and re-breaking. During fault current interruption, the injection capacitor is prevented from being reversely charged using an anti-parallel diode to facilitate using polar capacitors. The proposed P-SSCB is validated in a medium-voltage dc test setup by experiments of 1.5 kV/265 A. The results show that the peak voltage across the main and auxiliary branches reaches 2895 V and 2852 V, respectively.
Demonstration of 50 ps Per-Position Differential Pulse Position Modulation Data Transmission

To prolong the lifespan of devices with a limited power budget, efficient modulation schemes, such as pulse position modulation (PPM), can be employed. High-order differential pulse position modulation (DPPM), which can be efficiently demodulated using an event timer, presents advantages over other PPM modulation methods because it can provide higher data rates and does not require synchronization. This research aims to demonstrate data transmission using high-order DPPM signal in a laboratory environment and measure bit error ratio (BER) for position widths 50–200 ps. In the theoretical part, an assessment of data transmission rate dependence from the position count and position width of DPPM signal with guard time is presented. Moreover, a theoretical comparison in terms of power efficiency and maximum data rate with other types of PPM, such as transmitted reference pulse-position modulation (TR-PPM), is made. In the experimental part, the performance measurements of DPPM communication system consisting of arbitrary waveform generator (AWG) as DPPM transmitter and Eventech A033-ET/USB event timer as digital-to-time converter (DTC) of DPPM receiver are presented.