Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Measurement of Complex-valued Delta Radar Cross-section

While the magnitude of the delta radar cross-section (DRCS) is sufficient to characterize narrowband UHF radio frequency identification systems, new broadband localization approaches require knowledge of the frequency dependent complex-valued DRCS. In this paper, a calibrated measurement method of the complex-valued DRCS in an anechoic chamber is introduced. Furthermore, a practical implementation based on a vector signal analyzer and standard lab components is shown. This comprehensive setup is capable of determining the complex-valued DRCS in different frequency bands, for different power levels, and for different incident angles of the electromagnetic waves. To demonstrate this new method, measurement results of an off-the-shelf UHF EPC Class-1 Gen2 tag are shown and discussed.
A Novel Population Initialization Method Based on Support Vector Machine

The majority of evolutionary algorithms (EAs) adopt Pseudo-Random Numbers Generator (PRNG) to initialize their population. This can affect the behavior of an EA for high dimensional problems due to the curse of dimensionality and has been known as a potentially serious challenge. Therefore, intelligent initialization of individual candidates has been more explored recently. As a different approach, this study proposes a machine-learning based algorithm to address the aforementioned problem. The introduced SVM based Smart Sampling, we call as SVM-SS, employs Support Vector Machine (SVM) to discover promising regions faster. The proposed method and Differential Evolution (DE) are then combined to evaluate our approach. Numerical results on a set of classic benchmark functions show that the proposed algorithm performs better in comparison with several state-of-the-art population initialization methods. To examine the scalability of the SVM-SS, it is also applied on large scale optimization problems and such results were also in consonance with the previous experiments.
Predictive Current Control of Three Level Neutral Point Clamped Grid Connected Inverter in Photovoltaic Generation Systems

This paper presents predictive current control technique for three-level NPC inverter connected to a photovoltaic panel through a DC/DC inverter, the application of MPCC strategy to 3L-NPC inverter based on the cost function is defined to minimize the error between the reference currents and predicted values, and balance the DC-link capacitor voltage, the optimal switching state that minimizes the cost function is selected and applied to the inverter. For the optimum use of the PV module, a maximum power point tracking (MPPT) controller is used which enables the maximum power extraction under varying irradiation and temperature conditions. Finally, the simulation results show that the suggested control achieves high performance and high degree of robustness while balancing the DC-link capacitor voltages.
Decoding Visual Stimulus in Semantic Space from Electrocorticography Signals

Recent studies using functional magnetic resonance imaging (fMRI) have enabled quantitative evaluation of the semantic space during processing of visual stimuli. In the semantic space of the natural language processing model, called a skip-gram, decoders were shown to generalize to natural scenes of a movie that was not included in the training data of the decoders. Combined with electrocorticography (ECoG), which has a higher sampling rate than fMRI, this approach is expected to aid the development of a practical brain-machine interface. Here, we decoded vector representations of scenes within the semantic space of a skip-gram model to assess whether a decoder trained using ECoG features still generalizes to scenes new to the decoder.
Secure Control of Wide-Area Power Systems: Confidentiality and Integrity Threats

A cyber-physical model for wide-area control of power systems is considered, where the state variables of each generator are measured and sent to the cyber-network and the corresponding control inputs are computed distributively. The secure control of such wide-area power systems is considered in the presence of cyber attacks that introduce threats that compromise their integrity and confidentiality. Detection, prevention, and resilience for these attacks and algorithms for accomplishing these goals are proposed. In particular, an algorithm to overcome confidentiality attacks of the underlying control gains is presented. Also proposed is an algorithm for defense against integrity attacks that might take place on the cyber-network. For this purpose, a resilient information retrieval approach is leveraged which recovers the true state variables despite the malicious attacks on both virtual machines and communication links. The retrieved states are then used to detect possible attacks on phasor measurement units (PMU) in the next time-step. Simulation studies are included to validate our proposed approaches.
Computationally Efficient Model Predictive Control for Multi-agent Surveillance Systems

In this paper, a surveillance system by multiple agents, which is called a multi-agent surveillance system, is studied. A surveillance area is given by an undirected connected graph. Then, the optimal control problem for a multi-agent surveillance system is to find trajectories of multiple agents that travel each node as evenly as possible. In our previous work, this problem is reduced to a mixed integer linear programming problem. However, the computation time for solving it exponentially grows with the number of agents. To overcome this technical issue, a model predictive control method, which is efficient from the computational viewpoint, is proposed. First, a procedure of individual optimization, which is a kind of approximate solution methods, is proposed. Next, a method to improve the control performance is proposed. In addition, an event-triggering condition is also proposed. The effectiveness of the proposed method is presented by a numerical example.
$1.5\mu \mathrm{m}$ Dual Conversion Gain, Backside Illuminated Image Sensor Using Stacked Pixel Level Connections with 13ke-Full-Well Capacitance and 0.8e-Noise

A $1.5\mu \mathrm{m}$ pixel size, 8 mega pixel density, dual conversion gain (DCG), back side illuminated CMOS image sensor (CIS) is described having a linear full-well capacity (FWC) of 13ke- and total noise of 0.8e-RMS at 8x gain. The sensor adopts a world smallest $1.5\mu \mathrm{m}$ pitch, stacked pixel-level connection (SPLC) technology with greater than 8M connections, maximizing fill-factor of the photodiode and dimensions of the associated transistors to achieve a large FWC and low noise performance at the same time. In addition, by allocating transistors into two different layers, the DCG function can be realized with $1.5\mu \mathrm{m}$ pixel size.
Joint Scheduling of Electric Vehicle Charging and Energy Storage Operation

Motivated by the potential of utilizing used electric vehicle (EV) batteries as the battery energy storage system (BESS) in EV charging stations, we study the joint scheduling of EV charging and BESS operation in the presence of random renewable generation, EV arrivals, and electricity prices. We formulate cost-minimizing scheduling problem faced by an EV charging station operator as a dynamic program. We characterize an important priority rule for an optimal scheduling policy, and apply the established optimal policy characterization to improve the performance of existing heuristic policies. Numerical results demonstrate that optimal policy characterization established in this paper can significantly improve the performance of an least laxity first (LLF) policy. We also show by counter-intuitive examples that an optimal policy may charge the BESS and discharge an EV simultaneously, even when the EV's per-unit non-completion penalty is higher than the per-unit salvage value of the BESS and the highest possible electricity price.
Scene Conversion for Physically-Based Renderers

Physically-based rendering systems use proprietary scene description formats. Thus, by selecting a given renderer for the development of a new technique, one is often constrained to test and demonstrate it on the limited set of test scenes available for that particular renderer. This makes it difficult to compare techniques implemented on different systems. We present a solution for automatic conversion among scene description formats used by physically-based rendering systems. It enables algorithms implemented on different renderers to be tested on the same scene, providing better means of assessing their strengths and limitations. Our system can be integrated with development and benchmarking APIs, lending to full orthogonality among algorithms, rendering systems, and scene files.
Planar Simplification of Indoor Point-Cloud Environments

The reconstruction and visualization of threedimensional point-cloud models, obtained by terrestrial laser scanners, is interesting to many research areas. This paper presents an algorithm to decimate redundant information in realworld indoor point-cloud scenes. The key idea is to recognize planar segments from the point-cloud and to decimate their inlier points by the triangulation of the boundary, describing the shape. To achieve this RANSAC, normal vector filtering, statistical clustering, alpha shape boundary recognition and the constrained Delaunay triangulation are used. The algorithm is tested on various large dense point-clouds and is capable of reduction rates from approximately 75-95%.
Deep Learning Based Fault Correction in 3D Measurements of Printed Circuit Boards

The inspection of printed circuit boards shifted from regular inspections based on two-dimensional images to sophisticated inspections using three-dimensional images. Light-based three-dimensional measurement systems tend to produce defects in the measured surface of the electrical components. A new method is proposed and tested to use deep neural network models to detect and repair such defects reliable. The neural network uses masked convolution to predict valid three-dimensional data based on damaged three-dimensional data and a set of two-dimensional images of the scene. The neural model is trained using an advanced online learning system, taking data from and feeding results to multiple inspection systems at the same time. The proposed method is tested with real data and different defects recorded on inline inspection systems. The benefits and drawbacks of this method are discussed and an outline for future work is given.
An Always-On 3.8  $\mu$ J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS

The trend of pushing inference from cloud to edge due to concerns of latency, bandwidth, and privacy has created demand for energy-efficient neural network hardware. This paper presents a mixed-signal binary convolutional neural network (CNN) processor for always-on inference applications that achieves 3.8 <inline-formula> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula>/classification at 86% accuracy on the CIFAR-10 image classification data set. The goal of this paper is to establish the minimum-energy point for the representative CIFAR-10 inference task, using the available design tradeoffs. The BinaryNet algorithm for training neural networks with weights and activations constrained to +1 and −1 drastically simplifies multiplications to XNOR and allows integrating all memory on-chip. A weight-stationary, data-parallel architecture with input reuse amortizes memory access across many computations, leaving wide vector summation as the remaining energy bottleneck. This design features an energy-efficient switched-capacitor (SC) neuron that addresses this challenge, employing a 1024-bit thermometer-coded capacitive digital-to-analog converter (CDAC) section for summing pointwise products of CNN filter weights and activations and a 9-bit binary-weighted section for adding the filter bias. The design occupies 6 mm<sup>2</sup> in 28-nm CMOS, contains 328 kB of on-chip SRAM, operates at 237 frames/s (FPS), and consumes 0.9 mW from 0.6 V/0.8 V supplies. The corresponding energy per classification (3.8 <inline-formula> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula>) amounts to a 40<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvement over the previous low-energy benchmark on CIFAR-10, achieved in part by sacrificing some programmability. The SC neuron array is 12.9<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> more energy efficient than a synthesized digital implementation, which amounts to a 4<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> advantage in system-level energy per classification.
Adaptive $\mathcal{H}_{\infty}$ Synthesis for Linear Systems with Uncertain Parameters

A performance-based approach is developed for adaptive robust control of linear systems with uncertain parameters. The dual control objective involves the disturbance attenuation and worst case identification of the unknown parameters. The proposed synthesis procedure relies on sufficient conditions, given in terms of suitable solutions of perturbed differential Riccati equations to exist. Although being reminiscent from the standard $\mathcal{H}_{\infty}$ synthesis, the resulting Riccati equations are to be updated on-line with estimated values of the unknown plant parameters. Capabilities of the proposed synthesis are illustrated by simulations made for a scalar linear system with unknown parameters.
The implementation of a smart microchips system to applications on the modern medical applications

An individual and patient-centric healthcare smart microchips system was designed for a wide variety of otolaryngology applications. The dynamical acquisition of physiological data in this system can identify the early warning signals of each specific conditions. This smart microchip system mainly composed of Arduino, Zigbee, and specific sensors with accessories. In addition, the construction price of such system is less expensive, and the maintenance of the microchip system is rather simple. The system can promptly provide the corresponding treatment solutions to the emergency medical incidents by providing reliable diagnostic orientation as the sensors constantly receive signal feedbacks. In this paper, we deployed this wireless system to demonstrate the functions and performances of this smart microchips system in otolaryngology ward.
Five-Level Dynamic Hybrid DC-DC Converter

In this paper, a topology for a hybrid 5-level buck converter is studied and implemented. The circuit architecture and operating principles are shown. The topology uses two flying capacitors to support up to five voltage levels at the inductor input, enabling several operation regions. Additionally, modulation of the duty cycle is possible. This allows wide coverage of output voltage values and a minimization of the output voltage ripples while using a small, low quality factor inductor, a suitable feature for on-chip and portable DC-DC converters. The test circuit is simulated and implemented off-chip using commercially available components to verify the functionality of the working principle. Under the same experimental test conditions, the 5-level hybrid converter achieves up to 15 % efficiency improvement over a traditional and a 3-level hybrid buck converter at selected conversion ratios and load currents.
Real Time Fixed Point Adaptive Chaotic System Generator for Deep Brain Stimulation Using FPGA

Parkinson disease (PD) is a neurodegenerative disorder which is caused by the untimely death of dopamine generating neurons situated in substantia nigra pars compacta and striatum. The depletion of striatal dopamine induces the synchronization in brain activity in motor regions which results in tremors. Deep brain stimulation (DBS) is used as an obvious choice in therapy when the medication stops working. Traditional DBS systems are open loop systems in which stimulation parameters need to be adjusted manually from among a large pool of available combinations. This makes the whole process tedious for the doctors and traumatic for the patients. We have developed an FPGA based device which can monitor, learn and mimic the behavior of chaotic systems on the go. The purpose of this device is to learn the chaotic behavior of brain, when in normal state, and generate the chaotic stimulation signal during PD based tremors.
Layer Skip Learning using LARS variables for 39% Faster Conversion Time and Lower Bandwidth

In this paper, a method for the improvement of the relationship between calculation time and recognition accuracy in deep learning is proposed. A major problem with respect to deep learning is that a large calculation time is required for higher recognition accuracy. Because of this problem, the implementation of deep learning in hardware and its application to real problems are limited. In this study, layer-wise adaptive rate scaling (LARS) variables are adopted to evaluate the necessity of the learning of each layer. When the variable of a certain convolution layer exceeds the threshold value, the learning for that layer is considered unnecessary; thus, the layer is skipped. When a layer recognized as the layer that does not require learning, only the lower layers below than that layer are learned in the next epoch. By adaptively skipping the layer, the calculation time is reduced. Furthermore, the recognition accuracy is improved. Consequently, the proposed methods accelerate the calculation time in VGG-F to achieve the highest accuracy for the top1 and top5 test accuracy by a speed up factor of 2.14, and 2.25, respectively. Moreover, the respective top1 and top5 test accuracy was improved by 3.0%, and 2.8% which obtained as the final accuracy. In addition, the operation process was reduced by approximately 39.0% and required bandwidth was reduced by 38.9%, when compared with the case of conventional full layer learning.
Efficient Antenna Scan Response Models for Large Phased Arrays

Global surrogate models are presented for antenna elements in large phased arrays, to cheaply and accurately model scan-dependent impedance and radiation responses for systems with wide-angle scanning requirements. Adaptive sampling combined with rational and Kriging interpolation is found to produce accurate, convergent models within 50 full-wave EM simulations of a patch element within a planar PBC unit cell.
Stacked Microstrip Patch Antenna Array with High-Gain and Improved Thermal-Stability for Microwave Power Transmission Applications

This paper presents a stacked patch antenna array that operates at a 5.8 GHz of ISM band for microwave power transmission (MPT) applications. The stacked patch antenna consists of three layers that include a circular patch antenna with an air-gap, a stacked parasitic patch, and a feed-network. Each patch has a crossed-slot to form the right-handed circular polarization. A $2\times 2$ element array has been made up of the stacked patch antennas connected with a sequentially rotated feed-network. The measured axial ratio and gain of a $2 \times 2$ array are 1.08 dB and 11.6 dBic at 5.8 GHz. The feed network has been printed on a substrate with a relatively high thermal-conductivity to withstand high-power. The thermal analysis of the feed network is compared with cases of substrates.
Symmetrical to Asymmetrical Model Transformation for I/Q Imbalance in Zero-IF Transceivers

Two main types of modeling are considered in the $I/Q$ imbalance problem literature: symmetric models and asymmetrical models. Specifically, the symmetric models are applied in the $I/Q$ imbalance testing area, while the asymmetrical models have applications in the $I/Q$ imbalance calibration area; each model offers advantages in their respective areas, with applications in real systems. In this paper, a study of both types of modeling was carried out, covering main characteristics, advantages and differences between these models. Symmetrical and asymmetrical models in matrix form are developed for transmitter and receiver bearing on a zero-IF architecture, taking into account the main sources of imbalance in the analog front end (AFE). Based on these models, a matrix model transformation methodology is developed, which is capable of transferring the $I/Q$ imbalance characteristics of a symmetric model into an asymmetric model. Simulation results confirm the proposed model transformation through a performance comparison of four $I/Q$ imbalance testing algorithms in the sense of the AFE imbalances estimation, allowing a fair comparison under the same conditions of imbalance.
Optimization and Realization of Quadruple-Ridge Flared Horn With New Spline-Defined Profiles as a High-Efficiency Feed From 4.6 GHz to 24 GHz

In this communication, we present a new optimization and realization of a quadruple-ridge flared horn (QRFH) as a feed for reflector antennas of the square kilometer array (SKA) project. The QRFH has been numerically optimized by spline-defined profiles for both the ridges and the horn sidewall, with a conical cavity in the back short for maximum aperture efficiency. The final aperture efficiency better than (for horizontal polarization) or around (for vertical polarization) 78% over 4.6–8 GHz, 70% over 8–15 GHz, 65% over 15–20 GHz, and 60% over 20–24 GHz in the SKA offset-Gregorian dual-reflector antenna has been achieved. The realization of the horn has been carried out carefully by applying several new mechanical design solutions in order to guarantee accurate positioning of the ridges, the feed pins, and a good electrical contact. The measured reflection coefficients are mostly better than −10 dB and the predicted aperture efficiency based on the measured far-field patterns agrees well with the simulated result. System performance, such as the sensitivity and system noise temperature, is also estimated and presented.
Human Interaction Through an Optimal Sequencer to Control Robotic Swarms

The interaction between swarm robots and human operators is significantly different from the traditional humanrobot interaction due to unique characteristics of the system, such as high cognitive complexity and difficulties in state estimation. In this paper, we concentrated on the method of conveying input from the operator to the swarm. Previous research has shown that control through switching between behaviors offers the greatest flexibility but is particularly difficult for human operators. A recently developed method for finding optimal sequences for composing behaviors offered a potential tool for aiding human operators controlling swarms through behavior switching. This paper compared participants performing a navigation task with and without the availability of the optimal sequencing aid. Results showed that the task of preplanning a sequence of behaviors and durations appeared more difficult for participants than switching between executing behaviors to navigate. Users who used the aid frequently was found to create shorter paths than infrequent users and the control group. In the trails that the aid was used, participants tended to generate more complicated sequences and achieve the first attempt more rapidly, compared to the trails that the aid was not used.
Sub-30 ppm/°C High-frequency Temperature-compensated CMOS Relaxation Oscillator

This paper describes the design and realization of a high-frequency temperature-compensated CMOS relaxation oscillator in a $0.35-\mu \mathrm {m}$ CMOS technology. The proposed oscillator employs the current-controlled topology with a resistive source-degeneration transconductor and a current comparator in order to achieve high oscillation frequency and low power dissipation. Temperature compensation of the oscillation frequency is achieved by simulation results show that, at a nominal oscillation frequency of 32.48 MHz, the proposed oscillator exhibited a temperature coefficient of less than 28.41 ppm/°C over a temperature range of -40°C to +120°C, while consuming $241.5 \mu \mathrm {W}$ from a 2.5-V single power supply voltage.