Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Search and Rescue using Remotely Piloted Aircraft System with Frame Dropping

Usage of Artificial Intelligence (AI) technology to aid the Remotely Piloted Aircraft System (RPAS) helps to get accurate imagery along with vital ground details, which as a result boosts the Search and Rescue operations. Since the search must be done quickly, real-time video processing is essential for survival. Our solution attempts to integrate image processing, more specifically, the You Only Look Once (YOLO) algorithm to detect humans in all environmental conditions. Moreover, traditional methods of AI use Graphics Processing Units (GPU) instead of Central Processing Units (CPU). We solved the issue of low frame-per-second processing on the CPU with a newly designed frame-skipping algorithm. This improved method results in accurate and quick detection of humans and allows real-time detection.
A 2.98pJ/conversion 0.0023mm2 Dynamic Temperature Sensor with Fully On-Chip Corrections

Nowadays, many battery-operated SoCs for loT and environmental monitoring applications are equipped with temperature sensors. In these miniaturized systems, power and area are two critical concerns. One challenge for temperature sensors is that they are sensitive to process corners and random mismatch. Generally, a 2-point trim and systematic non-linear error removal are required, especially for resistor-based sensing front-ends with two types of resistors, whose spread is partially uncorrelated [1], [2]. These corrections are done off-chip and digitally in most publications. In particular for low power sensors, they may consume more power and area than the sensor itself when integrated on-chip [3]. This work presents a resistive temperature sensor that integrates on-chip analog offset, gain and non-linearity correction techniques, while keeping state-of-the-art power and size performance. The prototype consumes 2.98pJ/conversion with an area of 0. $0023\text{mm}^{2}$ including all the correction techniques and achieves $+0.7/-0.6^{\circ}\mathrm{C}$ inaccuracy.
ComMit: Blind Community-based Early Mitigation Strategy against Viral Spread

In the early stages of a pandemic, epidemiological knowledge of the disease is limited and no vaccination is available. This poses the problem of determining an Early Mitigation Strategy. Previous studies have tackled this problem through finding globally influential nodes that contribute the most to the spread. These methods are often not practical due to their assumptions that (1) accessing the full contact social network is possible; (2) there is an unlimited budget for the mitigation strategy; (3) healthy individuals can be isolated for indefinite amount of time, which in practice can have serious mental health and economic consequences. In this work, we study the problem of developing an early mitigation strategy from a community perspective and propose a dynamic Community-based Mitigation strategy, ComMit. The distinguishing features of ComMit are: (1) It is agnostic to the dynamics of the spread; (2) does not require prior knowledge of contact network; (3) it works within a limited budget; and (4) it enforces bursts of short-term restriction on small communities instead of long-term isolation of healthy individuals. ComMit relies on updated data from test-trace reports and its strategy evolves over time. We have tested ComMit on several real-world social networks. The results of our experiments show that, within a small budget, ComMit can reduce the peak of infection by 73% and shorten the duration of infection by 90%, even for spreads that would reach a steady state of non-zero infections otherwise (e.g., SIS contagion model).
MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density SSDs

QLC (4-bit-per-cell) and more-bit-per-cell 3D NAND flash memories are increasingly adopted in large storage systems. While achieving significant cost reduction, these memories face degraded performance and reliability issues. The industry has adopted two-step programming (TSP), rather than one-step programming, to perform fine-granularity program control and choose gray-code encoding, as well as LDPC (Low-Density Parity-Check Code) for error correction. Different flash manufacturers often integrate different gray-codes in their products, which exhibit different performance and reliability characteristics. Unfortunately, a fixed gray-code encoding design lacks the ability to meet the dynamic read and program performance requirements at both application and device levels.In this paper, we propose MGC, a multiple-gray-code encoding strategy, that adaptively chooses the best gray-code to meet the optimization goals at runtime. In particular, MGC first extracts the performance and reliability requirements based on application-level access patterns and detects the reliability degree of SSD. It then determines the appropriate gray-code to encode the data, either from host/user application or due to garbage collection, before writing the pages to the flash memory. MGC is integrated in FTL (flash translation layer) and enhances the flash controller to enable runtime gray-code arbitration. We evaluate the proposed MGC scheme. The results show that MGC achieves better performance and lifetime guarantee compared with state-of-the-arts and introduces little overhead.
Low-Profile Broadband Metasurface Antenna with Stable Gain

This paper demonstrates a low-profile broadband metasurface antenna with stable gain. A novel broadband metasurface is optimized by analyzing the characteristic modes. Broadside radiation pattern is achieved by splitting edge patches and etching Minkowski fractal structure in the corner patches. A slot antenna is loaded below to excite the broadband metasurface. The experiment results show that the operating bandwidth of the proposed antenna is 4.8-6.6 GHz (31.6%), with a low profile of 0.057 λ0 and the gain ripple of 2.4 dB.
Intelligent Classification of Brain Cancers by Deep Learning with Inception Network

Brain cancer is a complex disease, and it is increasing rapidly. Although the incidence rate of brain tumours is lower than other cancers, it is still the most serious disease threatening human lives. For effective treatment, detecting and diagnosing brain tumours are essential by an accurate and quick method. Though there has been an interest in using pattern recognition techniques to classify and grade tumours from Magnetic Resonance Imaging (MRI) images, effective and accurate grading remains difficult and subjective. This paper employs deep learning with the inception concept to classify the MRI brain images. A simple architecture is designed with convolution layers, max-pooling layers for feature extraction, and a fully connected layer for classification. 200 MRI images from the Repository of Molecular Brain Neoplasia Data (REMBRANDT) are used. Experimental results show that the proposed architecture obtains the best accuracy of 98% with a 0.01 learning rate and 20 epochs.
Registration and Localization of Unknown Moving Objects in Monocular SLAM

Augmented reality (AR) applications require constant device localization, which is often fulfilled by visual simultaneous localization and mapping (SLAM). SLAM provides realtime camera localization by also dynamically building a 3D map of the environment, but the functionality of SLAM systems generally stops here. Useful applications of AR could make great use of additional information about the environment, such as the structure and location of moving objects in the scene (including objects that were not previously known to be separate from the static points of the map). We present an approach for solving the visual SLAM problem while also registering and localizing moving objects without prior knowledge of the objects’ structure, appearance, or existence. This is accomplished via analysis of reprojection errors and iterative use of the ePnP algorithm in a RANSAC scheme. The approach is demonstrated with the accompanying prototype system, LUMO-SLAM. The initial results achieved by this system indicate that the approach is both sound and potentially viable for some practical applications of AR and visual SLAM.
1.2 Shape the World with Mixed-Signal Integrated Circuits - Past, Present, and Future

The last 50 years has been an era where analog equipment has been replaced by digital devices. LP records have been replaced by Compact Discs, NTSC and PAL TV systems have been replaced by digital TVs, VHS video recorders have been replaced by DVD recorders, film cameras have been replaced by digital cameras, and analog wireless communications have been replaced by digital wireless communications. Magnetic recording, optical recording, and signal processing circuitry for wired and wireless networks were replaced from analog to digital. The digitization of these devices and systems was due to the technological shift from bipolar to CMOS and the development of logic and memory circuits supported by scaling laws, but analog-digital mixed integrated circuit technology such as ADCs and DACs was indispensable. Therefore, this talk looks back on the digitization of equipment and the mixed signal integrated circuit technology that contributed to it and discuss future developments.
Remaining Useful Life Prediction Using an Ensemble Learning-Based Network for a Belt Conveyor System

The belt conveyor system is widely used in production and distribution industries because it is more cost-effective than manpower and can be used in a variety of ways. Prognostics of the belt conveyor system is the main activity to maintain efficiency. Lack of performance of the system is most often an error in which the system is no longer available to meet the desired performance which arises the entire system can be damaged and fatal industrial accidents may occur. In this paper, we present a model that predicts the remaining useful life of the head pulley, a key part of the belt conveyor system. The ensemble learning-based model to predict is composed of a deep learning-based representation model and boosting model. The model is trained using a combination of classification and regression rather than simple regression to predict the remaining useful life. The data used to train the model was collected by directly building a test bed with an environment similar to a belt conveyor system.
Combining Natural Language and Machine Learning for Predicting Survey Responses of Social Constructs in a Dyad

Measuring social constructs such as engagement, rapport, and trust often rely heavily on surveys and behavioral observations. This paper describes a method to use features identified by psychology-based language analysis, combined with machine learning, to predict participant survey responses in a training context based on 120 dyad transcripts. The method analyzed data collected from subjects performing a circuit board training task within the project called SCOTTIE, Systematic Communication Objectives and Telecommunications Technology Investigations and Evaluations. In this study, the collected data showed low utterance count and a lack of correlation between features and survey responses, suggesting that the context in which the interactions occurred may limit opportunities for interlocutors to manifest social behaviors verbally, which in turn affected the ability to use language analysis to predict subject perceptions of the interaction. However, the methodology appears sound.
5.6 A 400 $\times$ 200 600fps 117.7dB-DR SPAD X-Ray Detector with Seamless Global Shutter and Time-Encoded Extrapolation Counter

X-ray detectors using hard X-rays $(\lambda < 0.1\text{nm})$ are widely used in industrial and medical equipment, and their market size has increased in recent years [1]–[3]. There is a great demand for real-time X-ray detectors to deliver high-frame-rate, and high-dynamic-range (HDR) images. However, conventional approaches such as switching modes between frames or combining images from different exposures, make X-ray images susceptible to motion artifacts, and require multiple exposures with X-ray sources. Therefore, to overcome these limitations, a global-shutter X-ray detector is required [4], which also should operate seamlessly due to the slow response of X-ray sources. To meet these requirements of X-ray detectors while providing high-quality images with low radiation dose, a photon-counting detector using single-photon avalanche diodes (SPADs) is now seen as a promising candidate [5], [6]. Generally, the X-ray detectors do not use lenses, and hence the size of the detector must be as large as their targets, which poses additional challenges. Therefore, no clinical-grade SPAD X-ray detector has been reported yet. This paper presents a photon-counting SPAD X-ray detector with seamless global shutter. To minimize the power consumption of the SPAD pixels, a time-encoded extrapolation counter is proposed, which minimizes the number of control signals and recycles the counter cells when they overflow, achieving 18b outputs only using 10b hardware. Two compact counters are implemented in a pixel to provide a seamless global shutter. The detector, implemented in 65nm CMOS, has a pixel pitch of $49.5\mu \mathrm{m}$ and a pixel resolution of $400\times 200$, resulting in a chip size of $24\times 14\text{mm}^{2}$. It achieves a frame rate of 600fps and a dynamic range of 117.7dB, while consuming 127.2mW.
High-Precision Phase-Based Ranging Algorithm in Multipath Environments

With the development of the internet of things (IoT), localizing IoT gadgets has become very important, which can benefit various applications. Using the ranging-based method for target localization only needs a single antenna, which is very suitable for size-constrained IoT devices. However, phase errors due to the lack of time synchronization between the transmitter and receiver and multipath effects usually seriously affect the accuracy of ranging. This paper uses the two-way channel response to eliminate the phase errors. Then, the generation of additional unknown multipath signals is suppressed by taking the square root of the two-way channel response. Finally, the orthogonal matching pursuit (OMP) algorithm is used to estimate multipath signals. Experiment results show that the proposed method can achieve high-precision ranging in indoor multipath environments.
Entangled Light Quantum Imaging for Target Recognition Based on Denoising RestoreGAN

To address the poor recognition performance problem that the target after entangled light quantum imaging loses local appearance structure, this paper proposes an entangled light quantum imaging method for target recognition based on denoising RestoreGAN. Specifically, we first prepare the entangled photon pairs via spontaneous parametric down-conversion to achieve quantum imaging. Then, we design a conditional generative adversarial network, named RestoreGAN, to restore the missing edge contour structure of the target. Finally, the denoised image is input into the designed feature extraction network for target recognition and classification. Tested on the MNIST, the experimental results demonstrate that our denoising RestoreGAN can improve target recognition accuracy by 3.92 percentages.
Load-Specific Performance Fatigability, Coactivation, and Neuromuscular Responses to Fatiguing Forearm Flexion Muscle Actions in Women

Abstract Benitez, B, Dinyer-McNeeley, TK, McCallum, L, Kwak, M, Succi, PJ, and Bergstrom, HC. Load-specific performance fatigability, coactivation, and neuromuscular responses to fatiguing forearm flexion muscle actions in women. J Strength Cond Res 37(4): 769–779, 2023—This study examined the effects of fatiguing, bilateral, dynamic constant external resistance (DCER) forearm flexion on performance fatigability, coactivation, and neuromuscular responses of the biceps brachii (BB) and triceps brachii (TB) at high (80% 1 repetition maximum [1RM]) and low (30% 1RM) relative loads in women. Ten women completed 1RM testing and repetitions to failure (RTF) at 30 and 80% 1RM. Maximal voluntary isometric force was measured before and after RTF. Electromyographic (EMG) and mechanomyographic (MMG) amplitude (AMP) and mean power frequency (MPF) signals were measured from the BB and TB. Performance fatigability was greater (p < 0.05) after RTF at 30% (%∆ = 41.56 ± 18.61%) than 80% (%∆ = 19.65 ± 8.47%) 1RM. There was an increase in the coactivation ratio (less coactivation) between the initial and final repetitions at 30%, which may reflect greater increases in agonist muscle excitation (EMG AMP) relative to the antagonist for RTF at 30% than 80% 1RM. The initial repetitions EMG AMP was greater for 80% than 30% 1RM, but there was no difference between loads for the final repetitions. For both loads, there were increases in EMG MPF and MMG AMP and decreases in MMG MPF that may suggest fatigue-dependent recruitment of higher-threshold motor units. Thus, RTF at 30 and 80% 1RM during DCER forearm flexion may not necessitate additional muscle excitation to the antagonist muscle despite greater fatigability after RTF at 30% 1RM. These specific acute performance and neuromuscular responses may provide insight into the unique mechanism underlying adaptations to training performed at varying relative loads.
A 28nm 11.2TOPS/W Hardware-Utilization-Aware Neural-Network Accelerator with Dynamic Dataflow

With the rapid evolution of AI technology, various neural network structures have been developed for diverse applications. As a typical ease, Fig. 22.4. 1 shows that the convolution (Conv) layer used in the convolutional neural networks (CNNs) features distinct shapes and types. Neural network accelerators with high peak energy efficiency have been demonstrated [1–4]. However, they usually suffer decreased hardware (mainly multiply-accumulate (MAC) units) utilization for various network structures, which reduces the attainable energy efficiency accordingly. To improve the MAC utilization, the Nvidia deep learning accelerator (NVDLA) [5] applies hardware parallelism along the channel direction, but the MAC utilization is still low for the shallow layers. According to our experiments, NVDLA achieves 23% MAC utilization in the worst case. A Scatter-Gather scheme [4] is utilized to mitigate the utilization drop for shallow layers by rearranging the input features (IF), but the improvement is limited. As depthwise convolution (Dwcv) has been widely used, the accompanying low MAC utilization also needs to be considered. Taking MobileNetV2 as an example, NVDLA only achieves 0.4% utilization for Dwcv. To address these critical issues, this work presents a utilization-aware neural network accelerator, which can dynamically change the level of parallelism along multiple dimensions to maximize the MAC utilization. The chip achieves $> 97.3{\%}$ MAC utilization on benchmark networks while delivering $4.7\times$ higher attainable energy efficiency than state-of-the-art designs [1–4].
Spatial-Query-by-Sketch

Today’s methods for interacting with geographic information systems (GISs) and geographic databases are primarily aspatial, as they require users to deal with geographic data primarily through alphanumeric command languages. Spatial querying by typing a command in some spatial query language or by selecting the same syntax from pull-down menus is a tedious process, because it often requires extensive training in the use of the particular query language, and forces users to translate a spatial image they may have in their mind into a non-spatial language. To overcome this conceptual gap, we propose Spatial-Query-by-Sketch, a sketch-based GIS user interface that focuses on specifying spatial relations by drawing them. This query style supports more directly human spatial thinking, which is critical, because users frequently have an image-like representation in their minds when they query about spatial configurations. This paper introduces the fundamental concepts of Spatial-Query-bySketch, provides examples of typical interactions, and discusses query-processing strategies by relaxing the constraints drawn in terms of a qualitative model.
Factors impacting the Czech employees' job satisfaction

The article aimed to explore the drivers of employees' job satisfaction. Empirically, the study is based on the Czech data from the 2017 edition of the European Union Labour Force Survey (EU LFS), accounting for 12,699 employed individuals. The implemented methods relied on the employment of the multivariate ordered logistic regression analysis. The estimated model found empirical support for the significance of job-related determinants and individual characteristics. The main findings are that job satisfaction is significantly related to the organization's size, skill level classification of the profession, supervisory responsibilities, working hours, level of education and years of experience. The highest levels of job satisfaction were, on average, observed among highly skilled professions, especially in managerial occupations. From the perspective of the organization's size, employees working in organizations with less than ten persons were, on average, mostly satisfied with their jobs. The study contributes to a better understanding of the drivers of job satisfaction from the context of the Central-European country, and it offers several suggestions for future research.
Three-domain image representation for personal photo album management

In this paper we present a novel approach for personal photo album management. Pictures are analyzed and described in three representation spaces, namely, faces, background and time of capture. Faces are automatically detected and rectified using a probabilistic feature extraction technique. Face representation is then produced by computing PCA (Principal Component Analysis). Backgrounds are represented with low-level visual features based on RGB histogram and Gabor filter bank. Temporal data is obtained through the extraction of EXIF (Exchangeable image file format) data. Each image in the collection is then automatically organized using a mean-shift clustering technique. While many systems manage faces and typically allow queries about them we use a common approach to manage multiple aspects, that is, queries regarding people, time and background are dealt with in a homogenous way. We report experimental results on a realistic set, i.e., a personal photo album, of about 2000 images where automatic detection and rectification of faces lead to approximately 800 faces. Significance of clustering has been evaluated and results are very interesting.
Half-Metallicity in CuCr 2 S 4 Film: A Density Functional Study

Copper chromium chalcospinel (CuCr2S4) is a metallic ferromagnet with high Curie temperature and large magnetization. Due to these characteristics and highly spin-polarized electronic structure near the Fermi level, it has been considered as an attractive candidate for spintronic application. By using the first-principle calculations based on density functional theory, we discovered that a complete spin polarization, or the so-called half-metallicity, can be realized in (100) CuCr2S4 film. The results of electronic structure show that by decreasing the film thickness, the delocalized hybridization between Cu-d and S-p states is weakened, which shifts the valence band maximum of the minority spin to lower energy, and thus leads to the half-metallicity. Furthermore, the half-metallicity of the CuCr2S4 film is found to be robust with respect to different terminations and surface vacancies.