Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient ISAR Autofocus Imaging Algorithm Utilizing the Block Sparse Structure

In this paper, an efficient phase error correction algorithm for inverse synthetic aperture radar (ISAR) imaging is proposed based on the structured sparse Bayesian learning. Considering the block-sparse structure of the targets, a modified pattern-coupled prior model is developed to encourage the continuity of the nonzero scatterers in the autofocus imaging problem. Meanwhile, a new phase error estimation method utilizing the weighted least-squares (WLS) criteria is presented in our algorithm. As a result, the estimation accuracy of the scattering coefficients and the phase errors has been improved significantly. Additionally, the generalized approximate message passing (GAMP) technique is used to reduce the computational complexity of the Bayesian inference. Experimental results, based on both the simulated and the measured data, demonstrate the effectiveness of the proposed algorithm.
Real-Time Video Server Implementation for a Mobile Robot

Most of robots are using vision for various applications. In some cases, mobile robots are provided with an insufficient onboard processing hardware, and therefore video from cameras needs to be transmitted in an efficient and reliable way to a more powerful system for further off-board processing. Multiple difficulties could be faced during video streaming software development, including high latencies, network congestion, packet losses, distortions and others, which makes trade-offs between video quality, bitrate, frame rate, and packet loss inevitable. Thus, the key problem is to find such parameters, which will satisfy the specified needs. In our work we implement a video streaming server on mobile robot Servosila Engineer. A set of experiments demonstrated that high bitrates and frame rates increase load on CPU. Packet losses could be mitigated by decreasing bitrate to 100-200 kbps.
Classification Model for Credit Data

Data mining algorithms have become over the time more related to business and artificial intelligence. Data mining techniques are used for decision-making and prediction purpose. In this research paper, a model of three data mining algorithms is proposed. The model is tested on a credit dataset for different customers to determine if the credit tested is good (approved) or bad (denied). The three different algorithms used in this model are: Decision Tree (DT), K-Nearest Neighbors Algorithm (KNN), and Logistic Regression (LG). The accuracy, sensitivity and other performance measures of the models are also calculated.
Zero-shot Action Recognition via Empirical Maximum Mean Discrepancy

In the recent years, with a rapid development of video capture devices (Kinect, etc) and online video providers (Youtube, Youku, etc), the number of video data, categories of actions and complexities of videos’ content are explosively increasing. Thus, for the traditional supervised human action recognition, it’s expensive and intractable to annotate the huge number of videos manually. In the consideration of this situation, researchers dedicate to predict the label of an unseen video using disjoint training videos, which is referred to as zero-shot learning for human action recognition. In this paper, we propose a novel zero-shot action recognition method on the basis of linear regression model, which aims at learning an appropriate visual-to-semantic mapping to project unseen video into a proper semantic representation. On the one hand, we take advantage of both training videos and testing videos and preserve their structural information simultaneously. On the other hand, we introduce an empirical maximum mean discrepancy regularized term into our objective function, which will shorten the differences between learned semantic representations and known action prototypes, and the domain shift problem would be relieved accordingly. Experiments on HMDB51 datasets demonstrate the effectiveness of our novel method.
An Agile Supply Modulator With Improved Transient Performance for Power Efficient Linear Amplifier Employing Envelope Tracking Techniques

This article presents an agile supply modulator with optimal transient performance that includes improvement in rise time, overshoot and settling time for the envelope tracking supply in linear power amplifiers. For this purpose, we propose an on-demand current source module: the bang–bang transient performance enhancer (BBTPE). Its objective is to follow fast variations in input signals with reduced overshoot and settling time without deteriorating the steady-state performance of the buck regulator. The proposed approach enables fast system response through the BBTPE and an accurate steady-state output response through a low switching ripple and power efficient dynamic buck regulator. Fast output response with the help of the added module induces a slower rise of inductor current in the buck converter that further helps the proposed system to reduce both overshoot and settling time. This article also introduces an efficient selective tracking of envelope signal for linear PAs. To demonstrate the feasibility of the proposed solution, extensive simulations and experimental results from a discrete system are reported. The proposed supply modulator shows 80% improvement in rise time along with 60% reduction in both overshoot and settling time compared to the conventional dynamic buck regulator-based solution. Experimental results using the LTE 16-QAM 5 MHz standard shows improvement of 7.68 dB and 65.1% in adjacent channel power ratio (ACPR) and error vector magnitude (EVM), respectively.
When It Rains It Pours: The Long-Run Economic Impacts of Salt Iodization in the United States

Abstract In 1924, the Morton Salt Company began nationwide distribution of iodine-fortified salt. Access to iodine, a key determinant of cognitive ability, rose sharply. We compare outcomes for cohorts exposed in utero with those of slightly older, unexposed cohorts, across states with high versus low baseline iodine deficiency. Income increased by 11%, labor force participation rose 0.68 percentage points, and full-time work went up 0.9 percentage points due to increased iodine availability. These impacts were largely driven by changes in the economic outcomes of young women. In later adulthood, both men and women had higher family incomes due to iodization.
Ceramic Interposers for Ultra-High Density Packaging and 3D Circuit Integration

Higher data speeds spurred on by the arrival of 5G technology and the Internet of Things (IoT) have accelerated the need for increased circuit integration with shorter interconnects. 3D integration and packaging techniques that employ silicon interposers with Through Silicon Vias (TSVs) have emerged as one of the key technologies in enabling this trend. In this paper, a cost-effective alternative to silicon interposers based on Low Temperature Co-fired Ceramic (LTCC) technology is proposed and demonstrated. Using ultra-thin ceramic layers and laser ablation, ceramic interposers with micro-via holes as small as $20 \mu \mathrm{m}$ in diameter and $40 \mu \mathrm{m}$ pitch have been successfully realized. In addition to the standalone interposers, the developed fabrication process has been used to design a high-density package for the integration of Silicon Photonic (SiP) and electronic chips with operational bandwidth up to 48 GHz. This novel cost-effective packaging technology offers a viable alternative to silicon interposers for the integration of multi-chip high-speed electronic systems in a single package with high reliability and very good performance to maintain signal integrity.
Free Space Detection Using Stereo Confidence Metrics and Obstacle Position Probability Maps

This paper proposes a novel free space detection method in complex traffic scenes. First, stereo confidence cues are adopted to construct a pixel-wise outlier detection model, which can reduce the false detecting rate caused by inaccurate disparity values. Second, in order to detect slender and farther obstructions, our approach gives a global optimization model which estimates free space by calculating the obstacle position probability map. An obstacle position probability map is a 2D map which shows the probability that each pixel belongs to an obstacle position. The proposed method can stably detect free space in real time in a variety of complex traffic situations, such as urban scenes, highways and secondary structured roads. An in-depth assessment of the actual traffic database validates the effectiveness of our approach. We show that the new confidence-based detection model is robust to the environment and it can greatly improve the free space detection effect.
Energy-Efficient MISO Wireless Powered Communications

This paper considers a multiple-input single-output (MISO) wireless powered communication network (WPCN) and aims to maximize the energy efficiencies (EEs) of the individual users. Specifically, we study user-wise EE maximization problems for TDMA-based WPCN (T-WPCN) and SDMA-based WPCN (S-WPCN), which are both non-convex due to their sum-of-ratios objective functions. We first solve the T-WPCN problem by transforming into a convex parametric problem and obtain its optimal solution by iteratively solving convex problems. Next, we solve the S-WPCN problem by convexifying it using feasible set reduction scheme. We show that T-WPCN outperforms S-WPCN when there is no minimum throughput requirement, while we can observe that S-WPCN outperforms T-WPCN when minimum throughput requirement is high. Simulation results verify our theoretical findings and show the effectiveness of our proposed schemes.
A CMUT-based gas density sensor with high sensitivity

In this paper, a gas density sensor based on capacitive microfabricated ultrasonic transducers (CMUTs) is developed. The working principle is based on the resonant frequency shift of the membrane by the change of gas density under the fluid-solid interaction. Due to the advantages of high frequency, the frequency shift will be larger, which also results in higher detection measuring sensitivity (DMS). Combined with the fluid added mass model and the electromechanical coupling reduced order model of a CMUT in vacuum, the relationship between gas density and resonant frequency is established. Furthermore, the influences of the structural parameters and the bias voltage on DMS are further analyzed. As a result, a good linearity is shown between gas density and resonant frequency of CMUTs in a certain density range. Simultaneously, the CMUT-based density sensor shows a high measuring sensitivity. After fabricating a sensor with the low-temperature direct wafer-bonding technique and building the gas density detection platform, the resonant frequency and phase-frequency curves of the CMUTs-based density sensor under different bias voltages are achieved by the experimental analysis in the different mixed gas. The results demonstrate the excellent capability of fabricated CMUTs for gas density measurement with a good linear relationship between gas density and resonant frequency and a higher DMS of 9760 Hz · kg−1 · m3, which make it a promising sensor.
A Hybrid Prediction Model Integrating a Modified Genetic Algorithm to K-means Segmentation and C4.5

Improving accuracy of a prediction model is an ongoing subject of research in the field of information technology. Improved efficiency of prediction model helped an organization in their decision-making activities. The study proposed a prediction model that integrates genetic algorithm (GA) with cross average crossover (CAX) and rank-based selection function to the existing model with k-means segmentation and C4.5 algorithm. The study determined the accuracy of the proposed model and compared its accuracy with the model integrating the generic GA with spliced crossover and roulette wheel selection mechanism. A total of 986 student leavers of a university which includes gender, course, reasons for leaving and a possibility of enrolling back to the institution in the future used as training datasets. There were 423 records of the same data served as the test data for evaluation purposes. Results showed that the proposed prediction model having GA with CAX and rank-based selection outperformed the model with generic GA with spliced crossover and roulette wheel selection method. Hence, the model is recommended to be used by educational institutions as decision support mechanism.
ECG Quality Assessment Using 1D-Convolutional Neural Network

Recently many wearable devices have been developed for casual health management. ECG is an important tool for monitoring of cardiac arrhythmias. Wearable devices are able to collect electrocardiogram (ECG) data in a convenient and low-cost way. ECG should have sufficient quality for ECG experts to analyze and make a clinical decision. Identifying low quality signals in advance will significantly facilitate clinical diagnosis. In this paper, we use 1D convolutional neural network (CNN) to classify single-lead ECG signals with a duration of 10 s as "acceptable" or "unacceptable". Through validating with the database of PhysioNet/Computing in Cardiology Challenge 2011 and the PhysioNet/Computing in Cardiology Challenge 2017, the accuracy of the proposed method is 94.3%.
Convolutional neural network-based optical performance monitoring for optical transport networks

To address the open and diverse situation of future optical networks, it is necessary to find a methodology to accurately estimate the value of a target quantity in an optical performance monitor (OPM) depending on the high-level monitoring objectives declared by the network operator. Using machine learning techniques partially enables a trainable OPM; however, it still requires the feature selection before the learning process. Here, we show the OPM that uses a convolutional neural network (CNN) with a digital coherent receiver to deal with the abundance of training data required for convergence and pre-processing of input data by human engineers needed for feature (representation) extraction. To proof a concept of the OPM based on CNN, we experimentally demonstrate that a CNN can learn an accurate optical signal-to-noise-ratio (OSNR) estimation functionality from asynchronously sampled data right after intradyne coherent detection. We evaluate bias errors and standard deviations of a CNN-based OSNR estimator for six combinations of modulation formats and symbol rates and confirm that the proposed OSNR estimator can provide accurate estimation results (<0.4 dB bias errors and standard deviations). Additionally, we investigate filters in the trained CNN to reveal what the CNN learned in the training phase. This is a valuable step toward designing autonomous "self-driving" optical networks.
The Effectiveness of Credit Policy: Evidence from the Republic of Korea

In response to the global financial crisis and subsequent Great Recession, central banks embarked on a variety of unconventional measures. Among others, credit policy has been widely employed in many advanced economies. However, credit policy is far less understood than unconventional monetary policy by both policy makers and academic scholars. This paper sheds new light on what credit policy is, how it differs from other central bank policies, and what its risks and limitations might be. In particular, I examine whether credit policy has been effective in stimulating the real economy in the Republic of Korea.
Via Interconnections for Half-Inch Sized Package Fabricated by Minimal Fab

A Ball Grid Array (BGA) packaging tool-set and packaging process using a half-inch sized package have been developed. The packaging tool-set is integrated with device manufacturing process line of Minimal Fab that uses half-inch wafers. The packaging line for the half-inch sized package with packaging tools of a die-bonding, a compression molding, a laser ablation, a sputtering and an electroplating of Cu, a redistribution layer (RDL) patterning, a Cu wet-etching, an inkjet, a ball mounting, and a reflow were developed. The packaging process was evaluated by cross-bridge Kelvin resistor (CBKR) test-structures with 3-vias using the BGA package fabricated by the line. Moreover, we have improved electrical contacts between devices and the package by changing Al-Si electrode pads on the device to Cu/Ti pads. As a result, via contact-resistances of the vias with the Cu/Ti electrode pads have reduced by three orders of magnitude than those of the Al-Si electrode pads.
Study on Electrical Performance and Mechanical Reliability of Antenna in Package (AIP) with Fan-Out Wafer Level Packaging Technology

In this study, antenna in package (AIP) is fabricated based on fan-out wafer level packaging (FO-WLP) technology. Co-design modelling and simulation have been conducted considering electrical performance, wafer process, and solder joint reliability to optimize package structure design and material selection. Electrical simulation results shows that package with 45 ° rotated chip has better electrical performance than package without chip rotation. However, package with 45 ° rotated chip has lower solder joint reliability. Parametric study has been carried out to enhance board level solder joint life under TCOB condition. Process dependent wafer warpage simulation is carried out to identify critical process and reduce wafer warpage through optimizing structure and materials. Through Co-design modelling and simulation, final test vehicle design is finalized and wafer process is successfully conducted.
Secure Data sharing in Distributed Cloud Environment

In this paper we proposed a novel technique to store the data authentically in the cloud. Since because of elastic property from the point of view of storage space cloud is most popular to store data. Cloud is inquiring about stores data on it. Therefore, security is must for store data for preserving it from malicious attackers as well as there may be leakage in stored data security. Many times there is necessary condition for hiding the user or data owner identity from cloud. A technique use in this paper shows anonymous authentication for stored data on cloud. This system preserves the identity of the users before storing it on cloud. The main aim of this paper is only valid user have ability to encrypt stored data with help of accessing its control features. The basic idea behind this scenario is to prevent stored data of cloud as well as its creation, modification etc. In this approach, cloud is familiar with access policies of every stored data also several KDCs are used for key management. Expensive operations are carried out easily in decentralized cloud system than centralized cloud system approach. The proposed system is implemented and tested in java with mysql database.
Decimator systolic arrays design space exploration for multirate signal processing applications

This study presents a new systolic array structure for a decimator that merges the antialiasing finite impulse response (FIR) filter with the downsampler. The development of the structure is based on a systematic methodology. Using this methodology, a dependence graph for the decimator was obtained that combined the antialiasing filter and the downsampler. Different data scheduling and projection operations were developed to obtain different proposed designs. Six systolic array design options were obtained and evaluated. The fastest design was selected for hardware implementation and compared with the other two well known decimator designs; namely, conventional design, in which the antialiasing filter is followed by a downsampling and the polyphase design, in which a commutator is followed by the polyphase antialiasing filter. Field-programmable gate array implementations for the proposed and the other two designs confirm that the proposed decimator implementation outperforms in terms of area, speed, and power as the decimation factor increases regardless of the number of FIR filter coefficients.
A Single Point Sensing Approach for Residential Power Monitoring with Appliance Recognition Using Random Forest

This work utilized machine learning, specifically Random Forest, as a classifier to recognize appliance signal from an aggregate energy consumption signal obtained using a single point, nonintrusive load monitoring approach. Appliance level feedback allows energy consumers to make informed decisions and employ energy management strategies to reduce the use of electricity. A mixture-of-experts approach was applied and the appliance models were trained to recognize appliance signals both from pure and aggregate signals of up to three appliances at the same time. Consumption signals of appliances with highly differentiated and slightly differentiated wattages were considered in this study. The Random Forest algorithm resulted in high scores averaging between 97 % to 100 % for both precision and recall for the desired appliance signal.
Lifetime Improvement Method using Threshold-based Partial Data Compression in NoC

Network-on-chip (NoC) is widely used in the multiprocessor system-on-chip (MP-SoC) because it has advantages in parallel processing and scalability. However, as the transistor density and the volume of communication increase, maintaining the reliability is becoming a major challenge. Especially, heat generation due to heavy traffic is one of the main causes of reliability degradation, that accelerates aging of chips and results in permanent faults. In previous studies, mapping and routing methods had been used to distribute unbalanced traffic for extending the lifetime. However, these methods have a limitation because the overall amount of traffic is not changed. In this paper, we propose a method to extend the lifetime by threshold-based partially applied Huffman coding in order to reduce the total amount of traffic considering implementation overhead. Experimental results show that the traffic volume is reduced by 17.24 % on average, mean time to failure (MTTF) is increased by 13.55 % compared to that of the aging aware fully adaptive routing without compression.
Limits of Covert Communication over AWGN Channels in the Presence of Multiple Wardens

Covert communication has recently been recognized as a highly promising security technology that enables a transmitter Alice to reliably and covertly communicate with a receiver Bob without being detected by a malicious warden. Available works on covert communication mainly focus on scenarios with only one warden, while the fundamental limits of covert communication in more practical multi-warden scenarios remain largely unknown. This paper therefore investigates the fundamental limits of covert communication over an additive white Gaussian noise (AWGN) channel in the presence of multiple wardens. We first consider a scenario with two wardens and derive the maximum number of bits Alice can transmit to Bob over $n$ channel uses while ensuring a negligible detection probability at wardens. We then generalize this result to the multi-warden case and find that the minimal power of wardens has impacts on the scaling law of the performance limits of covert communication over AWGN channels.