Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate Cell Segmentation Based on Biological Morphology Features

Microscopic imaging has many applications in biological experiments. It is important to determine the cell locations in an image before other analysis tasks take place. In this paper, we propose a novel method to obtain cell and membrane segmentation based on the combination of k nearest neighbor clustering and biological morphology constraints. First, we produce preliminary segmentation with the watershed transformation. Then the segmentation results are optimized based on the morphological characteristics of the membrane. This method provides us with well-segmented cells and membranes, which significantly reduces errors in cell image analysis.
Voicer: A Crowd Sourcing Tool for Speech Data Collection

Speech corpora do not exist for most low-resource languages. Thus, creating speech corpora for a language of such a nature is challenging and involves a significant amount of time and effort. This paper provides an overview of related data collection strategies, highlighting a few issues prevalent in the existing approaches. The objectives of this paper encompass firstly the introduction of an open-source tool called “Voicer” that is accessible via both handheld devices and computers that can be used to conduct a speech data collection for a specific domain in a short span of time irrespective of the language. Secondly, it demonstrates the power of the tool, utilizing the same to build a Sinhala speech corpus that consists of 10 hours of speech data for 39 different sentences in the banking domain. Finally, this paper provides a framework to evaluate a speech data corpus along with the lessons learned during the process of data collection with a view to contributing towards future researches.
Generation of Orbital Angular Momentum Vortex Waves on Both Sides of the Cascaded Metasurfaces

In this paper, the cascaded metasurface is proposed for simultaneous control of the reflected and transmitted wavefronts to generate vortex beams carrying orbital angular momentum (OAM). The metasurface is composed of four metallic layers and the amplitude and phase of reflection and transmission can be controlled independently. The simulation results show that the x-polarized incident waves can be reflected into the OAM vortex beam with $l=3$, while the y- polarized incident waves can be transmitted into the OAM vortex beam with $l=2$.
Multiantenna GNSS and Inertial Sensors/Odometer Coupling for Robust Vehicular Navigation

Location information is one of the most vital information required to achieve intelligence and context-awareness for Internet of Things applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on the use global navigation satellite system (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions. Radio frequency interference, either intentional or unintentional, has a direct impact on GNSS navigation performance related to observability and accuracy. In terms of security, spoofing is the major issue of concern. This paper focuses on multiantenna GNSS and inertial navigation system (INS)-odometer integration to improve robustness, security, and privacy of navigation solutions. Multiantenna GNSS provides robustness against different interference sources and integration with INS provides continuous navigation solutions during short-term signal outages. Performance of the proposed architecture is evaluated using different user scenarios in the presence of spoofing and interference signals in real vehicular environments.
Backstepping for Partially Unknown Nonlinear Systems Using Gaussian Processes

In this letter, a backstepping control approach is developed and analyzed for a setting where the system model is partially unknown and is modeled using Gaussian processes. The proposed approach encompasses the classical backstepping and command filtered approaches as special cases. The tracking error is globally uniformly ultimately bounded, and the performance is shown to be improved by adding new training data. The stability analysis is carried out by employing a quadratic Lyapunov function and Tikhonov’s theorem. The proposed method outperforms an established adaptive backstepping approach given sufficient training data.
Parameters Tuning of TFM and Defect Detection of CFRP Sheet Based on Ultrasonic Array Probe

Carbon fiber-reinforced plastics (CFRP) becomes wildly used while easily delaminated during manufacture and use, thus it is important to detect those delamination defects. This paper aims to provide an effective method to detect small delamination defects in CFRP sheets, and Total Focusing Method (TFM) holds the trend of development in the testing field. In order to improve the imaging effect, several parameters of TFM including excitation signal length, time-delay matrix, characteristic value and weight function of superimposed echo signals are analyzed and tuned, and then simulations and experiments are performed to verify the validity of the parameters tuning.
Seeking RSVP Task Features Correlated with P300 Speller Performance

Brain-computer interface (BCI) has been developed so that people can control computers or machines using their brain activity. Thus, BCI provides people who cannot move their bodies with new communication tools. The P300 speller is one of the applications used most commonly among the various types of BCI. Compared to other BCIs, the P300 speller achieves relatively high accuracy, but there remains room for improvement, such as reduction in calibration time and speeding up the speller by modulating the number of repetitions needed to enter a character. Hence, seeking correlates of the P300 speller performance may help improve the application. In this experiment, we investigated the correlation between a rapid serial visual presentation (RSVP) task and P300 speller performance to determine whether such a task is useful in predicting P300 speller performance. We found statistically significant correlations between the RSVP task ERP (P300) features and the P300 speller offline accuracy. Based on this observation, we expect that P300 speller performance can be predicted using a relatively simple cognitive task.
A Novel Krylov Method for Model Order Reduction of Quadratic Bilinear Systems

A novel Krylov subspace method is proposed to substantially reduce the computational complexity of the special class of quadratic bilinear dynamical systems. Based on the first two generalized transfer functions of the system, a Petrov-Galerkin projection scheme is applied. It is shown that such a projection amounts to interpolating the transfer functions at specific points which, in fact, is equivalent to constructing the corresponding Krylov subspace. For single-input single-output systems, the relevant Krylov subspace can be readily constructed for the interpolation points. For multi-input multi-output systems, also user-specified directional information is required so that a tangential interpolation can be determined. The method is demonstrated by numerical examples.
Thermal-Aware 3D Symmetrical Buffered Clock Tree Synthesis

The semiconductor industry has accepted three dimensional integrated circuits (3D ICs) as a possible solution to address speed and power management problems. In addition, 3D ICs have recently demonstrated a huge potential in reducing wire length and increasing the density of a chip. However, the growing density in chips such as TSV-based 3D ICs has brought increased temperature on chip and temperature gradients depending on location. Thus, through silicon via (TSV)-based 3D clock tree synthesis (CTS) causes thermal problem leading to large clock skew. We propose a novel 3D symmetrical buffered clock tree synthesis considering thermal variation. First, 3D abstract tree topology based on nearest neighbor selection with median cost (3D-NNM) is constructed by pairing sinks that have similar power consumption. Second, the layer assignment of internal nodes is determined for uniform TSV distribution. Third, in thermal-aware 3D deferred merging embedding (DME), the exact location of TSV is determined and wire routing/buffer insertion are performed after thermal profiles based on grid are obtained. The proposed method is verified using a 45nm process technology and utilized a predictive technology model (PTM) with HSPICE. Also, our CTS is evaluated for IBM and ISPD'09 benchmarks with no blockages. In experimental results, we can achieve average 18% of clock skew reduction compared to existing thermal-aware 3D CTS. Therefore, thermal-aware 3D symmetrical buffered clock tree synthesis presented in this work is very efficient for circuit reliability.
A Divide-and-Conquer Clustering Approach Based on Optimum-Path Forest

Data clustering is one of the main challenges when solving Data Science problems. Despite its progress over almost one century of research, clustering algorithms still fail in identifying groups naturally related to the semantics of the problem. Moreover, the technological advances add crucial challenges with a considerable data increase, which are not handled by most techniques. We address these issues by proposing a divide-and-conquer approach to a clustering technique, which is unique in finding one group per dome of the probability density function of the data — the Optimum-Path Forest (OPF) clustering algorithm. Our approach can use all samples, or at least many samples, in the unsupervised learning process without affecting the grouping performance and, therefore, being less likely to lose relevant grouping information. We show that it can obtain satisfactory results when segmenting natural images into superpixels.
A Multiple-Model MRAC Scheme for Discrete-Time Multivariable Systems with Uncertain Actuation Delays

In this paper, a novel multiple-model based model reference adaptive control (MRAC) scheme is designed for discrete-time multivariable systems with uncertain actuation delays. Such an adaptive control scheme is capable of handling the structure uncertainties caused by uncertain actuation delays. A set of system structure uncertainty patterns is formed, and corresponding to each pattern, a reference model system is chosen. Multiple direct adaptive controllers are designed for the system under different structure uncertainties, based on different reference model systems. A control switching mechanism is designed with multiple modified performance indexes based on normalized estimation errors, which is desirable for selecting the most appropriate controller. The results shows an important step forward in employing the minimal amount of necessary prior knowledge to design a stable control scheme for a linear multivariable system.
Neural Networks on Chip: From CMOS Accelerators to In-Memory-Computing

Artificial neural networks, which dominate artificial intelligence applications such as object recognition and speech recognition, are in evolution. To apply neural networks to wider applications, customized hardware are necessary since CPU and GPU are not efficient enough. Numerous architectures are proposed in the past 4 years to boost the energy efficiency of deep learning inference processing, including Tsinghua and Deephi's effort. In this talk, we will talk about different architectures based on CMOS technologies, including 200GOPS/W FPGA accelerators, about 1-5TOPS/W chips with DDR subsystems, and over 50TOPs/W chips with everything on chip. The possibilities and trends of adopting emerging NVM technology for efficient learning systems, i.e., inmemory-computing, will also be discussed as one of the most promising ways to improve the energy efficiency.
A Summary of Traffic Identification Method Depended on Machine Learning

Traffic classification is an invaluable tool which can category network traffics into a number of traffic classes according to various parameter settings. It can not only provide better QoS, but also protect the security of networks. In this paper, the methods of traffic classification are analyzed and summarized based on machine learning through three aspects: supervised machine learning, unsupervised machine learning, and semi-supervised machine learning. Finally, we suggest several strategies to improve the accuracy and scalability of traffic classification.
Best Arm Identification for Both Stochastic and Adversarial Multi-armed Bandits

We study the problem of best arm identification in multi-armed bandits, where the underlying setting can be either stochastic or adversarial and is not revealed to the forecaster a priori. We propose an algorithm, called S3-BA, whose key idea is to simultaneously explore the arms and learn the problem model, and switch when adversarial is declared. We analyze the performance of the proposed algorithm for both stochastic and adversarial bandits. For stochastic bandits we prove an upper bound on the error rate of S3-BA that is on the same order as UCB-E and SH. For adversarial bandits we show that the best arm can be guaranteed as long as the budget is above a threshold. More importantly, we prove that the “learning loss” due to the formulation uncertainty is negligible compared to the arm selection policies, for both stochastic and adversarial bandits. The performance of S3-BA is evaluated using the real-world US stock market dataset, and it is observed that it outperforms existing algorithms designed for either stochastic or adversarial best arm identification problems.
A Metric Learning Method for Improving Neural Network Based Kernel Learning for SVM

A gated linear network is able to mimic the functionality of a pre-trained neural network with a compound activation function R(x) = x * S(x). An SVM can then be formulated to further implicitly optimize the gated linear network, in which a quasi-linear kernel is composed by using the gate signal S(x) generated from the pre-trained neural network. In this way, we realize a neural network based kernel learning. In this paper, a distance metric learning is applied to improving the kernel learning. In the pre-training of neural network, the loss function of distance metric learning is used as a regularization term. With the loss function of distance metric learning, the samples from within-class become closer and that from between-class become farther, which can improve the quasi-linear kernel. Accordingly, the classifier optimized by SVM with quasi-linear kernel will have better performance. The proposed classification method is applied to different real-world datasets and simulation results confirm the effectiveness of the proposed method.
An Improvement of BM3D Image Denoising and Deblurring Algorithm by Generalized Total Variation

In this work we propose a post-processing method for BM3D algorithm that has become a state-of-the-art image denoising and deblurring algorithm. Although BM3D algorithm produces results with high objective metrics values, it also adds noticeable high-frequency artifacts. We suppress these artifacts using second order Total Generalized Variation (TG V) algorithm. TGV algorithm is an extension of Total Variation denoising method but it does not tend to make images piecewise constant. We also suggest an efficient numerical scheme for TGV minimization. In order to validate the proposed idea, tests were performed on noisy real images and synthetic images with different levels of noise.
Velocity Estimation of Wheeled Vehicles with Micro-Doppler Phenomenon for Automotive Radar

over the last years, radar based driver assistance and active safety systems have been widely used all over the world. Multiple targets can be simultaneously detected and tracked with the range, velocity and azimuth information of targets measured by millimeter-wave automotive radar sensors. However, the micro-Doppler phenomenon resulting from rolling wheels makes it difficult to get the correct estimate of the wheeled vehicle's radial velocity. Without the accurate velocity estimation of wheeled vehicles, Lane Change Assistant (LCA) may wrongly issue an alarm and Blind Spot Detection (BSD) may terminate an alarm too late or too early in 24GHz BSD & LCA radar system. In this paper a novel method is proposed to get the accurate velocity estimation of wheeled vehicles with micro-Doppler phenomenon. Based on the proposed algorithm that takes the symmetry of micro-Doppler spectrum, spatial locations of the reflectors and the relative amplitudes of various Doppler components into account together, the reflector corresponding to the vehicle body is extracted from those reflectors which belong to the same target. The validity of the proposed method has been verified by the Experimental results.
The Improved CESSC Algorithm Based on Meanshift Sparse Subspace Clustering for Hyperspectral Images

Hyperspectral images are rich in spatial information and spectral information. And hyperspectral image (HSI) clustering is very important for the study of HSI. However, just like all pixel-based clustering methods, traditional sparse subspace clustering(SSC) algorithm also faces these problems: (1) the effect of salt-and-pepper noise on the experimental results is more prominent; (2) it can not make full use of the different neighborhood relationships; (3) it result in high computational complexity. In this paper, we have improved the Cosine-Euclidean similarity matrix(abbreviated as CE) Construction Based on Sparse Subspace Clustering(CESSC) algorithm for Hyperspectral images to solve these three problems. First of all, we use the Meanshift algorithm to segment the original hyperspectral data, so, we get a series of regions and use the mass center of each region to represent all points in this region, then, the algorithm of Cosine-Euclidean Similarity Matrix Construction Based on Sparse Subspace Clustering is conducted on all this center of mass to get clustering labels; finally the else points in each region obtain their final clustering result by giving the same labels with their center of mass. Experimental results show that the proposed algorithm can improve the recognition accuracy to some extent.
MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis

Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space. We then extend our sampling method to define a better initial training set, without the need for a trained model, by using Oriented FAST and Rotated BRIEF (ORB) feature descriptors. We validate MedAL on 3 medical image datasets and show that our method is robust to different dataset properties. MedAL is also efficient, achieving 80% accuracy on the task of Diabetic Retinopathy detection using only 425 labeled images, corresponding to a 32% reduction in the number of required labeled examples compared to the standard uncertainty sampling technique, and a 40% reduction compared to random sampling.
Truly Innovative 28nm FDSOI Technology for Automotive Micro-Controller Applications embedding 16MB Phase Change Memory

For the first time we propose a 28nm FDSOI e-NVM solution for automotive micro-controller applications using a Phase Change Memory (PCM) based on chalcogenide ternary material. A complete array organization is described exploiting body biasing capability of Fully Depleted Silicon On Insulator (FDSOI) transistors. Leveraging triple gate oxide integration with high-k metal gate (HKMG) stack, a true 5V transistor with high analog performance has been demonstrated. Reliable PCM 0,036um2 analytical cell with 2 decades programming window after 1 Million of cycles has been demonstrated. Finally, current distributions based on a fully integrated 16MB macro-cell is presented achieving Bit Error Rate (BER) < 10−8 after multiple bakes at 150°C and 10k cycling of code storage memory.
A Therapist-Taught Robotic System for Assistance During Gait Therapy Targeting Foot Drop

The adoption of robots in rehabilitation medicine settings has become increasingly attractive in recent years. Robots are capable of providing repetitive, high-intensity physiotherapy. In this paper, we apply kinesthetic teaching principles to a robotic system in order to allow it to first learn and then imitate a therapist's behavior while assisting a patient in a lower-limb therapy task. A therapist's assistance in lifting a patient during treadmill-based gait therapy is statistically encoded by the system using Learning-from-Demonstration (LfD) techniques. Later, the therapist's assistance is imitated by the robot, allowing the patient to continue practicing in the absence of the therapist. Preliminary experiments are performed with inexperienced users playing the role of the assisting therapist, and with healthy participants (wearing an elastic cord to simulate foot drop) playing the role of a patient. Toe clearance values are recorded, which show that the system is able to provide the full clearance needed by the patient to practice in the absence of the therapist.
A Rule Based Reasoning System for Initiating Passive ADAS Warnings Without Driving Distraction Through an Ontological Approach

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and comfort. Unlike active ADAS which provide direct intervention to avoid accidents, passive ADAS increase driver's awareness of hazardous situations by giving warnings in advance. It has been noted that these systems can cause distraction when the relevant HMIs (Human-Machine Interfaces) are poorly designed. Current research is limited to address this problem in specific settings which may not be applicable in wider context. This papers aims to provide a universal rule-based solution to allow passive ADAS to initiate warnings without triggering driver distraction through an ontological approach.
3nm GAA Technology featuring Multi-Bridge-Channel FET for Low Power and High Performance Applications

As the most feasible solution beyond FinFET technology, a gate-all-around Multi-Bridge-Channel MOSFET (MBCFET) technology is successfully demonstrated including a fully working high density SRAM. MBCFETs are fabricated using 90% or more of FinFET processes with only a few revised masks, allowing easy migration from FinFET process. Not only on-target but also multiple Vt is achieved in challengingly limited vertical spacing between channels. Also, reliability of MBCFETs is shown to be comparable to that of FinFETs. Three representative superior characteristics of MBCFET compared to FinFET have been demonstrated — better gate control with 65 mV/dec sub-threshold swing (SS) at short gate length, higher DC performance with a larger effective channel width (Weff) at reference footprint, and design flexibility with variable nanosheet (NS) widths. The optimization of the standard cell design by using variable NS width is evaluated. The usefulness of MBCFET as a multi-purpose performance provider is proven by the modulation of effective capacitance (Ceff), effective resistance (Reff) and frequency by Weff control. Finally, mass production feasibility with MBCFET is proven through a fully working high density SRAM circuit.
LCP Ultra-Wideband Self-Packaged Microstrip to Stripline Transition with Multilayer Balun Demonstration

An Ultra-wideband (UWB) microstrip to stripline transition using liquid crystal polymer (LCP) technology for multilayer circuits with self-packaging feature is presented in this paper. The proposed design is light in weight, cost effective and remarkably miniaturized. In order to validate this approach, a prototype is designed, simulated and fabricated. In addition, this packaged transition is tested on a new multilayer balun to account for any losses introduced to the hosted balun further ensuring the significance of this work.