Miracle-dz/UniFAR-FTUs huggingface dataset:
A Miniaturized Low Pass Filter with Extended Stopband and High Passband Selectivity

In this work, an ultra-wide stopband low pass filter (LPF) with high selectivity is proposed using coupled stepped impedance resonators (SIRs), open shunt stubs and circular slots in the ground plane. The proposed LPF has been modeled using a lumped equivalent circuit which is extracted from the EM model. The design has been validated through the simulation and experimental results. The fabricated prototype has a 3-dB cutoff frequency (f c ) of 2.44 GHz and an ultra-wide stopband extended up to 20.5 GHz (8.4 f c ) with an attenuation level > 20 dB. The transition bandwidth (from 3 dB to 20 dB) is 0.09 GHz and the roll-off rate is 225 dB / GHz (reference to 30 dB). The passband insertion loss is 0.35 dB at 1.22 GHz and the normalized circuit size of the filter is 0.045.
Energy efficient RANSAC algorithm for flat surface detection in point clouds

Mobile robots control systems achieve greater efficiency through the use of robust environmental analysis algorithms based on data collected from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs). These data sources provide information about control object environment in point cloud. The work of such algorithms, as a rule, is aimed at detecting the objects of interest and searching for the specified objects, as well as relocating its own position on the scene. There are many different approaches for solving object detection problem in point clouds, but most of them require high computational resources. In this work, many variations of the random sample consensus (RANSAC) method are analyzed for objects defined by a mathematical model of an analytical form. Statistical characteristics of data analysis were used to compare the methods. The results demonstrate the most energy efficient flat surface detection method that processes 60 RGB-D camera frames per second.
Electromechanical Modeling of Softening Behavior for Dielectric Elastomers

Dielectric elastomer (DE) is a promising electroactive polymer. As DE material, rubbers are often filled with functional particles to improve their electromechanical performance. However, the filled particles also bring stress softening, which is known as Mullins effect. In this paper, we prepared the carbon nanotube filled silicone elastomer (SE) as DE composite and modeled its Mullins effect using the pseudo-elastic theory. Then, the thermodynamics of DE was combined to predict the idealized electromechanical softening behavior. Two cases are considered: linear dielectric and saturated dielectric. For linear dielectric with an initial force, “residual strain” will appear after every voltage-controlled cycle, and instability may be eliminated in reloading. For saturated dielectric, the material response changes a lot after saturation, which also affects the subsequent softening behavior. At last, viscoelasticity was further incorporated to account for rate-dependent softening deformation, and we also carried out some simple electromechanical experiments on VHB 4910 to explore its softening behavior. This work may lead to a better understanding of the softening behavior in DEs undergoing electromechanical coupling situations.
Quantum theory of plasmon-mediated light emission by non-Gaussian noise of a tunnel junction

In this thesis, motivated by the recent experiments that detect the light emission from the STM junctions and observe the unusual but interesting photon emission related to electron-electron interactions, we propose and establish a model circuit and theoretically investigate these enhanced light emission from a tunnel junction coupled to a surface plasmon polariton (SPP). It is known that the current noise can be characterized as the photon emission or absorption probability due to the coupling between the environment circuit and the current. To model and specify this coupling between the electrical current and the electromagnetic environment, by introducing the phase operator φ(t) = e ~ ∫ t −∞ dt ′U(t′) to describe the voltage fluctuation across the junction, we can transfer the calculation of the tunnelling rate Γ into the evaluation of the correlation function 〈eiφ(t)e−iφ(0)〉. For a simple case, such as a harmonic environment where the equilibrium density matrix in the φ− representation is a Gaussian, it just follows the P (E) theory, offering the maximum energy for tunneling at the bias voltage eV , as we all understand and expect. Next, we go further, beyond the limit of the Gaussian action, and turn into checking the non-Gaussian contributions to the light emission. By means of the Keldysh path integral method, we expand the action of the tunnel conductor up to the fourth order of the phase φ. Under the assumption gcz 2 ω 1 motivated by the experiment, in the lowest order of tunnel conductance gc and the weak coupling to the detector α, we derive the expression for the total transition rate of the tunneling electrons. Through the numerical plotting, we analyse the properties and find out that the non-Gaussian terms is responsible for the overbias light emission shown in the experiments and our work well reproduces the experimental observation, clearly exhibiting the emitted photons with energy larger than the bias voltage hν > eV . Moreover, we continue the studying and check how the temperature affects the contribution. At zero temperature, the energy threshold at eV is evidently displayed while it is sensitively smearing out by the increase of the temperature, due to the softening of the Fermi edge. In addition, we also find a possibility to distinguish the Gaussian and non-Gaussian contribution by setting the plasmon resonance far from the bias voltage. vii With the study of the intensity properties, we find that the thermally induced overbias light emission can overmask the contribution from the quantum noise, but can be distinguished by characteristic spectral differences. Finally, we expand furthermore to the six-order fluctuation, giving rise to the three-electron tunneling processes, which is considered by the experiments as well. And from the numerical fitting to the experiments, we show a fairly nice agreement and propose a good way to explain the unusual experimental phenomena closing to the conductance quantum G0 as well as extending our calculations beyond the tunneling limit F 6= 1.
Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging with Convergence Analysis

Plug-and-play priors are popular for solving ill-posed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-and-play priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual, which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering (BM3D) denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging (CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.
Rituximab in steroid-refractory immune-related pancreatitis: a case report

The use of immune checkpoint inhibitors (ICIs) for treating several types of cancer is increasing, but they may be associated with immune-related adverse events (irAEs). Pancreatitis is a rare irAE, mostly responsive to steroid treatment. There are no published data on the management of steroid-refractory ICI-induced pancreatitis. Rituximab has shown efficacy in the setting of relapsing non-ICI-induced autoimmune pancreatitis. However, its use has not been tested for treating immunotherapy-related pancreatitis. Here, we present the case of a patient with steroid-refractory immune-related pancreatitis successfully treated with rituximab as a potential strategy for irAE management.
Fabrication-Tolerant O-Band WDM Filter With Phase-Balanced Tapered Arms and Wavelength-Independent Couplers

We design, simulate, and experimentally demonstrate a coarse wavelength-division de-multiplexing system based on cascaded Mach-Zehnder interferometers (MZIs) operating in the O-band. The fabricated devices are built on a monolithic silicon photonics platform in a state-of-the-art CMOS foundry. The fabrication tolerance of the device was obtained by a combination of analytical and 3D Finite-difference time-domain (FDTD) methods. Fabrication-tolerant wavelength-independent couplers were used in order to achieve broadband splitting ratios (SRs) and ensure device stability along the entire wavelength range. In addition, phase-balanced linear tapers were used to allow for arbitrary waveguide width values for the MZIs. Experimental results show very high-performance stability across different wafer test sites with a mean channel spectral shift of only 1.03 nm and a total device footprint of 0.582 mm$^{2}$.
SSVEP unsupervised adaptive feature recognition method based on self-similarity of same-frequency signals

Introduction As an important human-computer interaction technology, steady-state visual evoked potential (SSVEP) plays a key role in the application of brain computer interface (BCI) systems by accurately decoding SSVEP signals. Currently, the majority SSVEP feature recognition methods use a static classifier. However, electroencephalogram (EEG) signals are non-stationary and time-varying. Hence, an adaptive classification method would be an alternative option to a static classifier for tracking the changes in EEG feature distribution, as its parameters can be re-estimated and updated with the input of new EEG data. Methods In this study, an unsupervised adaptive classification algorithm is designed based on the self-similarity of same-frequency signals. The proposed classification algorithm saves the EEG data that has undergone feature recognition as a template signal in accordance with its estimated label, and the new testing signal is superimposed with the template signals at each stimulus frequency as the new test signals to be analyzed. With the continuous input of EEG data, the template signals are continuously updated. Results By comparing the classification accuracy of the original testing signal and the testing signal superimposed with the template signals, this study demonstrates the effectiveness of using the self-similarity of same-frequency signals in the adaptive classification algorithm. The experimental results also show that the longer the SSVEP-BCI system is used, the better the responses of users on SSVEP are, and the more significantly the adaptive classification algorithm performs in terms of feature recognition. The testing results of two public datasets show that the adaptive classification algorithm outperforms the static classification method in terms of feature recognition. Discussion The proposed adaptive classification algorithm can update the parameters with the input of new EEG data, which is of favorable impact for the accurate analysis of EEG data with time-varying characteristics.
Dense pedestrian detection based on multiple link feature pyramid networks

Pedestrian detection is widely employed in a variety of industries, including security, autonomous driving, and pedestrian flow statistics. However, because most pedestrians in real-world scenes are densely distributed with occlusion issues, scale variation and pose variability, majority pedestrian detection algorithms have low detection accuracy and a high miss detection rate. In this paper, we propose a new FPN neck module, Multiple Link Feature Pyramid Networks (MLFPN) for dense pedestrian detection, which innovative information transfer pathways and incorporation of Poly-Scale Convolution (PSconv) capable of long-range interaction into feature extraction tasks to reduce feature information loss and enhanced representation of effective pedestrian features, achieving 0.6MR-2, 0.35AP, and 0.21JI improvements on CrowdHuman dataset, improving detection accuracy and reducing missed detection rates.
Molecular characterization of ginger genotypes using RAPD and SSR markers

Genetic diversity among ginger genotypes collected from different parts of the country was studied using molecular markers (30 RAPD and 55 SSR). Compared to RAPD primers SSR primers were efficient in distinguishing the genotypes. A total of 86 and 23 polymorphic bands were observed with RAPD and SSR primers, respectively. Percentage polymorphism observed between RAPD and SSR primers was 97.40 % and 56.54 %. Grouping of genotypes by using combined data of RAPD and SSR primers indicated that irrespective of their place of collection or geographical origin, 30 genotypes were clustered into different groups which showed that, each individual genotype is having wider variability or it might be due to the genetic similarity existing among them.
Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

In the modern era, artificial intelligence (AI) and deep learning (DL) seamlessly integrate into various spheres of our daily lives. These cutting-edge disciplines have given rise to numerous safety-critical applications such as autonomous driving with a paramount concern on ensuring a high promise of dependability because of the high risk of human injury in the case of malfunction. Even the dependability becomes more crucial as shrinking CMOS technology feature size enhances resilience concerns due to factors like aging. In the context of DL accelerators, which heavily rely on the efficiency and speed of computations, addressing the effects of aging is of utmost significance to ensure their optimal design and performance. This paper addresses the overarching dependability issue of advanced deep neural networks (DNN) accelerators from the aging perspective. Especially, a comprehensive survey and taxonomy of techniques used to evaluate and mitigate aging effects are introduced. We cover different aging effects like permanent faults, timing errors, and lifetime issues. We review research by the layer-wise approach and categorize several resilience classes to bring out major features. The concluding part of this review highlights the questions answered and several future research directions. This study is expected to benefit researchers in different areas of DNN deployment, especially the dependability of this emergent paradigm.
Enabling Fine-Grained Spatial Multitasking on Systolic-Array NPUs Using Dataflow Mirroring

Neural Processing Units (NPUs) frequently suffer from low hardware utilization as the efficiency of their systolic arrays heavily depends on the characteristics of a deep neural network (DNN). Spatial multitasking is a promising solution to overcome the low NPU hardware utilization; however, the state-of-the-art spatial-multitasking NPU architecture achieves sub-optimal performance due to its coarse-grained systolic-array distribution and incurs significant implementation costs. In this paper, we propose dataflow-mirroring NPU (DM-NPU), a novel spatial-multitasking NPU architecture supporting fine-grained systolic-array distribution. The key idea of DM-NPU is to reverse the dataflows of co-located DNNs in horizontal and/or vertical directions. DM-NPU can place allocation boundaries between any adjacent processing elements of a systolic array, both horizontally and vertically. We then propose DM-Perf, an accurate systolic-array NPU performance model, to maximize the spatial-multitasking performance of DM-NPU. Utilizing the existing performance models achieves sub-optimal performance as they cannot accurately capture the resource contention caused by spatial multitasking. DM-Perf, on the other hand, exploits the per-layer performance profiles of a DNN to accurately capture the resource contention. Our evaluation using MLPerf DNNs shows that DM-NPU and DM-Perf can greatly improve the performance by up to 35.1% over the state-of-the-art NPU architecture and performance model.
THE EFFECT OF QUALITY SERVICE AND EMOTIONAL PROXIMITY ON CUSTOMER LOYALTY AND SATISFACTION AS AN INTERVENING VARIABLE AT PT PNM CIREBON BRANCH

Banks are one of the financial institutions engaged in services that can provide the 'excellent service' to compete with increasing competition. Therefore, to maintain and increase its customers, banks need to maintain a positive image within the community. Maintaining this image can be achieved through product quality, quality service, and security order. This research aims to determine the effect of quality service and emotional proximity on customer loyalty and customer satisfaction as an intervening variable at PT PNM Cirebon Branch. This research used descriptive quantitative approach with Structural Equation Modeling (SEM) using AMOS 26 program. Sobel test was conducted to determine the effect of mediation or intervening. The unit of analysis used is all customers of PT PNM Cirebon Branch who have topped up at least twice. The sample consisted of 150 respondents who filled out the questionnaire online. This research is expected to prove that quality service and emotional proximity have a positive and significant effect on customer satisfaction and loyalty.
Bioinformatics-based screening of key genes for transformation of tyrosine kinase inhibitor-resistant lung adenocarcinoma to small cell lung cancer

Purpose Lung adenocarcinoma (LUAD) is a common type of lung cancer. Cancer in a small number of patients with EGFR mutations will transform from LUAD to small cell lung cancer (SCLC) during epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) therapiesr. The purpose of the present study was to identify the core genes related to the transformation of LUAD into SCLC and to explore the associated molecular mechanisms. Methods GSE29016, GSE1037, GSE6044 and GSE40275 mRNA microarray datasets from Gene Expression Omnibus (GEO) were analyzed to obtain differentially expressed genes (DEGs) between LUAD and SCLC tissues, and the results were used for network analysis of protein–protein interactions (PPIs). After identifying the hub gene by STRING and Cytoscape platform, we explored the relationship between hub genes and the occurrence and development of SCLC. Finally, the obtained hub genes were validated in treated LUAD cells. Results A total of 41 DEGs were obtained, four hub genes (EZH2, NUSAP1, TTK and UBE2C) were identified, and related prognostic information was obtained. The coexpressed genes of the hub gene set were further screened, and the analysis identified many genes related to the cell cycle. Subsequently, LUAD cell models with TP53 and RB1 inactivation and overexpression of ASCL1 were constructed, and then the expression of hub genes was detected, the results showed that the four hub genes were all elevated in the established cell model. Conclusion EZH2, NUSAP1, TTK and UBE2C may affect the transformation of LUAD to SCLC and represent new candidate molecular markers for the occurrence and development of SCLC.
Quantum mechanism of condensation and high Tc superconductivity

The main objective of this paper is to introduce a new quantum mechanism of condensates and superconductivity based on a new interpretation of quantum mechanical wavefunctions, and on recent developments in quantum physics and statistical physics. First, we postulate that the wavefunction [Formula: see text]e[Formula: see text] is the common wavefunction for all particles in the same class determined by the external potential V(x), [Formula: see text](x)[Formula: see text] represents the distribution density of the particles, and [Formula: see text] is the velocity field of the particles. Although the new interpretation does not alter the basic theories of quantum mechanics, it is an entirely different interpretation from the classical Bohr interpretation, removes all absurdities and offers new insights for quantum physics and for condensed matter physics. Second, we show that the key for condensation of bosonic particles is that their interaction is sufficiently weak to ensure that a large collection of boson particles are in a state governed by the same condensation wavefunction field [Formula: see text] under the same bounding potential V. For superconductivity, the formation of superconductivity comes down to conditions for the formation of electron pairs, and for the electron pairs to share a common wavefunction. Thanks to the recently developed principle of interaction dynamics (PID) interaction potential of electrons and the average-energy level formula of temperature, these conditions for superconductivity are explicitly derived. Furthermore, we obtain both microscopic and macroscopic formulas for the critical temperature. The field and topological phase transition equations for condensates are also derived.
Experimental Study of Simplified UTW-OFDM Receiver Technology for Application to 5G Using Software-Defined Radio Platform

The simplified universal time-domain windowed-orthogonal frequency-division multiplexing (Simplified UTW-OFDM) has been proposed to improve spectral efficiency. This study proposed a novel receiving method using an optimal inter-carrier interference (ICI) cancellation technique to improve communication quality during the application of the simplified UTW-OFDM to 5G. The simplified UTW-OFDM suppresses out-of-band emission (OOBE) through the application of very long time-domain windowing to the conventional OFDM with a cyclic prefix (CP-OFDM) symbol. However, in exchange for the OOBE suppression performance, a large ICI is generated due to the symbol distortion caused by the application of the time-domain window, resulting in the degradation of the reception quality. Therefore, in this study, we proposed a new method for calculating the log-likelihood ratio that takes into account the effect of the time-domain window. Further, an ICI canceller that included a process for suppressing the noise enhancement effect of the time-domain window was proposed. The effectiveness of the proposed method was evaluated through computer simulations and experiments using software-defined radio. The experimental evaluation showed that the proposed ICI canceller could suppress the OOBE by 23.5 dB compared to CP-OFDM under the condition that BLER of 0.1 is achieved even when 64QAM is applied.