Miracle-dz/UniFAR-FTUs huggingface dataset:
Improve Image Classification Tasks Using Simple Convolutional Architectures with Processed Metadata Injection

Image Analysis tasks have increased with the advent of the Deep Learning-based approach. Especially techniques based on Convolutional Neural Networks have been applied to reach significant improvements to solve some important problems such as, among other things, image classification, and their segmentation. This led developers and researchers to study more complex architectures in order to deal with increasing complexities deriving from those challenges. In some cases, however, those architectures may prove overkilling in relation to the resolution of some specific task. In a recent project we had to completely automate the defect image identification in an industrial context: images are accompanied by a set of more than 200 metadata and both metadata and images are used to classify the samples distinguishing severe defects from non-severe ones. In this case using a Convolutional Neural Network with simple architecture does not completely resolve the task, while the usage of metadata only is not sufficient to distinguish well between the severe and not severe defects. On the other hand, using CNN with more sophisticated architectures, e.g. Inception or DenseNet, it is possible to reach significant improvements at the cost of longer training time of the models and of larger size in terms of memory occupation. In this paper we propose an approach that combines different networks and techniques: by means of a PCA on available metadata, Hotelling's T2 and Q residuals have been calculated and used to remove anomalous samples. One dataset was composed of those clean samples, while another one has been defined using a PLS-DA to identify which metadata were useful for classification. Then, we structured a combination of two networks: we designed a simple convolutional architecture to extract significant features from images, while another neural network has been designed to elaborate the preprocessed metadata corresponding to the image in input on the convolutional side: a feed-forward neural network was used to combine features extracted from image and features calculated from metadata to get the final classification. The results of combining and applying techniques of dimensionality reduction and feature selection to support deep learning in this way allowed us to reach the same, often better, results of more complex and larger models.
AFM Surface Roughness and Depth Measurement of Trenches with High Aspect Ratio

Roughness measurements on epitaxial layers before and after etching were done with an atomic force microscope (AFM) with sub-Angstrom repeatability. Furthermore, surface roughness was monitored with AFM after chemical mechanical polishing, before a wafer was bonded to another wafer. It was observed that measuring in non-contact mode reduces the tip wear and extends the life time of AFM tips. We also show that a resist recess in narrow trenches which cannot be measured with scatterometry is easily measured with a high-aspect-ratio tip on AFM. The offset between the AFMs in two different fabs is currently less than 5 nm, when a trench depth recipe is transferred from one tool to another.
DFT Scheme for Hard-to-Detect Faults in FinFET SRAMs

Hard-to-detect faults such as weak and random faults in FinFET SRAMs represent an important challenge for manufacturing testing in scaled technologies, as they may lead to test escapes. This paper proposes a Design-for-Testability (DFT) scheme able to detect such faults by monitoring the bitline swing of FinFET memories. Using only five operations per cell, we are able to detect defects that cause deterministic, random, and weak faults. Compared to the state of the art, this leads to an improved detection capability at reduced area overhead
Optimization of Traffic Sign Detection and Classification Based on Faster R-CNN

Traffic sign detection and recognition is a key area of research on intelligent transportation, which has significant theoretical value and an expansive market application prospect. As a crucial part, the algorithm of traffic sign detection and classification has great impact on subsequent procedures. In this way, implementing a faster and robust algorithm is what most researchers are pursuing in this area. However, sometimes, such a great variety of signs are hard to be detected or classified especially if they are spoiled or the driving environment is complicated. Traditional methods are mostly based on extracting features like color or shape, which need higher quality of images and may sometimes lead to a poor precision and robustness. This paper provides an optimization based on Faster R-CNN combining with ZF and VGG network. This algorithm improves validation accuracy and robustness, which also reduces the requirements of quality of images and related computation.
Hardware-Based Aging Mitigation Scheme for Memory Address Decoder

Designers typically add design margins to memories to compensate for their aging. As the aging impact increases with technology scaling, bigger margins become necessary. However, this negatively impacts area, yield, performance, and power consumption. Alternatively, mitigation schemes can be used to reduce the impact of aging. This paper proposes a hardware-based mitigation scheme for the memory's address decoder logic. The scheme is based on adapting the decoder's workload during idle cycles by stressing the short paths and putting long paths into relaxation. Thanks to the adapted workload, the impact of aging on the address decoder is reduced, resulting in a more reliable memory. To validate the benefit of the mitigation scheme, the decoder's degradation of the L1 data and instruction caches for an ARM v8-a processor is analyzed. The experimental results show that the proposed mitigation scheme reduces the degradation of the decoder's timing margin with up to 4.1x at negligible area and no more than 3% power overhead.
An Edge-Guided Exact Histogram Specification Method for Enhancing Extremely Dark Images

Histogram equalization (HE), and especially exact histogram specification (EHS), is a commonly used method for enhancing images. Although the EHS makes it possible to transform an image to follow exactly any specified target histogram through an image intensity mapping function, how to practically choose an appropriate target histogram remains an open problem. This paper first shows that, although the goal of image contrast enhancement focuses mainly on the image edges, the EHS performance is also affected by the image non-edge content. Then it proposes a new edge-guided EHS method that solves such drawbacks. In this method, it first carries out the exact histogram equalization (EHE) on only the edge points, and then it applies the resulting intensity mapping function to the whole image. This generally results in a non-uniform histogram for the transformed image. Experimental results show that the proposed method is very efficient and achieves state-of-the-art performance for enhancing extremely dark images.
Ion identification with the Plastic Scintillator Detector for the High Energy cosmic-Radiation Detection (HERD) experiment

The High Energy Cosmic Radiation Detection (HERD) facility onboard the future China’s Space Station (CSS) will provide high quality data on charged cosmic rays and gamma rays reaching the measured range from few GeV to PeV energies. Because of this capability, HERD experiment would give a valuable contribution in several scientific topics as dark matter searches, study of cosmic ray chemical composition and high energy gamma-ray observations. HERD will be equipped with a deep 3D cubic calorimeter surrounded by a microstrip Si tracker detector. The entire instrument will be surrounded by a plastic scintillator detector (PSD). The PSD will be used to discriminate charged from neutral particles in order to correctly identify gamma-rays and nuclei. One configuration proposed and studied for the HERD PSD detector the geometry of scintillator segmented in tiles coupled to Silicon Photomultipliers (SiPMs). SiPMs provide similar or even better performances to the standard photomultiplier tubes (PMTs) with lower power consumption and cost benefits. In 2018, beam test campaigns were performed at CERN SPS with ion beam to study the scintillator response with a SiPM-based readout and to test the capability of the PSD to discriminate the ion charges. The results will be presented.
Efficient Generation of Multiple Sketch Styles Using a Single Network

In the real world, different artists draw sketches of the same person with different artistic styles both in texture and shape. Our goal is to synthesize realistic face sketches of different styles while retaining the input face identity, only using a single network. To achieve this, we employ a modified conditional GAN with a target style label as input. Our method is capable of synthesizing multiple sketch styles even though it is based on a single network. Sketches created by our method show sketch quality comparable to the state-of-the-art sketch synthesis methods that use multiple networks.
Microchannel based Flexible Dynamic Strain Sensor

The dynamic measurement of strain is needed in several applications where frequent bending is experienced. For example, in the case of robotics and prosthesis, the strain sensors could indicate the bending of fingers or hand joints [1] , [2] . Likewise, it is needed to detect the damages to interconnects due to frequent bending in the flexible and wearable electrouics [3] - [5] . To this end, microchannel based teclmology can provide an efficient solution. This paper presents a flexible microfluidic channel-based sensor for the detection of dynamic strain. The sensor has been developed using Polydimethylsiloxane (PDMS). The micro-channel (dia~175 pm), fabricated using replica molding technique, was made conductive by filling with poly (3.4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) polymer. The developed strain sensor was subjected to various strains, which led to changes in the channel diameter and hence the resistance. We observed about 3 order (Δ R / R ~2800) increase in the resistance ( R ) value for 10% applied strain (Δ L / L. L = length of sensor) This lead to a gauge factor (GF= (Δ R / R )/(Δ L / L )) of ~280 for 10% applied strain, which is better (~70 tunes) than reported polymer based strain sensors [6] - [9] .
An Analysis of the Performance of Named Entity Recognition over OCRed Documents

The use of digital libraries requires an easy accessibility to documents which is strongly impacted by the quality of document indexing. Named entities are among the most important information to index digital documents. According to a recent study, 80% of the top 500 queries sent to a digital library portal contained at least one named entity. However most digitized documents are indexed through their OCRed version which includes numerous errors that may hinder the access to them. Named Entity Recognition (NER) is the task that aims to locate important names in a given text and to categorize them into a set of predefined classes (person, location, organization). This paper aims to estimate the performance of NER systems through OCRed data. It exhaustively iscusses NER errors arising from OCR errors; we studied the correlation between NER accuracy and OCR error rates and estimated the cost of character insertion, deletion and substitution in named entities. Results show that even if he OCR engine does contaminate named entities with errors, NER systems can overcome this issue and correctly recognize some of them.
Adaptive Power Optimization for Mobile Traffic Based on Machine Learning

As 5G high-speed mobile communications developing rapidly, services and contents that people get from the Internet have been enriched significantly, which necessitates the user equipment (UE) to have least power consumption, reduced latency, enhanced lifetime and better QoS. However, the tail energy of LTE interface on UE leads to low energy efficiency which is caused by applying fixed Radio Resource Control (RRC) inactivity timer. In this paper, we propose a novel approach to eliminate the tail whenever possible and improve the user equipment power efficiency. We design a self-adaptive tool to optimize the LTE RRC inactivity timer for individual users based on user model. Firstly, The tool collects runtime network information from cellular networks and uses machine learning method to predict the session length. Then it adjusts inactivity timer dynamically based on the predicted session length. In addition, we propose an enhanced RRC protocol to support our proposed tool. To demonstrate the effectiveness of our energy-saving tool, we applied it on commercial off-the-shelf phones. Simulation results show the proposed tool can reduce the energy consumption of smart-phone by 27-33.5%.
Genetic Algorithm for Topology Optimization of an Artificial Neural Network Applied to Aircraft Turbojet Engine Identification

Artificial neural networks (ANN) has attracted attention of the academic community by the current progress that this technique has provided in speech recognition and digital media such as as image, video, audio, and signal processing. Some fields, as industrial process control and product development can be highly benefited by the development of techniques based on the proven potentialities of ANN models, allowing more accurate simulation, better adaptation to changing environments, and greater robustness in model-based fault diagnosis. Along with the advance of ANNs, there is a trend of open-source softwares use for soft computing which facilitates the access of the interested readers to implement their own codes and to explore other applications. Historically evolutionary algorithms such as the Genetic Algorithm (GA) have been implemented to evolve the architectures to search for solutions, in order to solve this fundamental issue that is still an open problem in the general case. Therefore, the present paper investigates the application of ANN to model the nonlinear aircraft turbojet engine through black-box approach. For that purpose it was used real-world measurements of aircraft engine’s fuel and rotation as input and output, respectively. In order to facilitate the design, the ANN was optimized aiming to determine the best topology according to the one-step-ahead and free-run simulation. The results obtained encourage the use of automatically generated ANN architectures for dynamic system modeling.
Radiation effects of floating-gate (FG) and charge-trapping (CT) Flash memory technologies

The 60Co-γ ray total ionizing dose (TID) radiation responses of 55 nm SONOS (Silicon-Oxide-Nitride-Oxide- Silicon) memory cells and 65 nm FG memory cells are investigated. The threshold voltage (Vth) and off-state leakage current (Ioff) of memory cells are measured before and after radiation, respectively. The physical mechanisms of charge loss process are analyzed to explain experiment results.
Design and Evaluation of GaN-based Over-Temperature Protection Circuit

Over-temperature protection is becoming essential for the power converter design with the increase of switching frequency and power density demand. Especially for Gallium Nitride (GaN) based power integrated circuits (ICs), the issue of over-heating may result in the system destructive breakdown. Originated from the GaN based power integration technique, this paper presents a GaN-based on-chip temperature sensing and protection (OTSP) circuit that can deliver a desirable signal at the critical temperature. Specifically, the OTSP circuit is constructed using Advanced Designed System (ADS). The single D-mode and E-mode components are simulated with the different VGS to show its characteristic. The drain current of lateral fieldeffect rectifier is examined to imply the temperature effect on this component. Finally, the voltage transfer characteristic is stated.
Generation of a Corporate Sound Logo Based on Symbiotic Evolution

A corporate sound logo is a sound that represents a brand or product name through a melody, voice, or sound effect. Sound logos are usually created by a professional sound creator according to the intentions of a corporation’s leadership and are widely used in advertisements, including TV commercials. Given that one role of a sound logo is appealing employees’ attitudes toward working to achieve the company’s future goals, sound logos should ideally reflect input from many employees. However, obtaining and incorporating this input can be difficult for a creator. This study aims to reflect the various corporate images held by many employees into a corporate sound logo based on the company name and proposes a method for generating such a corporate sound logo. The creation of a sound logo for the 100th anniversary project of Fukoku Mutual Life Insurance Company is used as a case study for the proposed method.
Decision Making from Multiple Crowd Opinions

Crowdsourcing has already been established as an effective mechanism with the proliferation of World Wide Web (WWW) to address diverse real-life problems. Majority of the state-of-the-art opinion aggregation problems deal with the independent opinions of the crowd workers. In these cases, quality enhancement of the aggregation of their opinions is a major issue. In our preliminary work, we propose different opinion aggregation models that consider independent crowd opinions having binary or multiple options. Recently, different emerging real-life problems demand to aggregate crowd opinions where there is a provision of observing others' opinions before providing someone's own opinions. This unfolds a new opinion aggregation problem, namely dependent opinion aggregation. My research focus is on finding better crowd opinion aggregation models to deal with those independent and dependent crowd opinions. Moreover, we introduce another new paradigm of opinion aggregation that deals with the constrained opinions of the crowd workers. Most of the traditional crowd opinion aggregation models fail to derive final decision from constrained crowd opinions. In the current research, we propose different novel variants of opinion aggregation models while the opinions are collected from the crowd.
Susceptibility Evaluation of 3D Integrated Static Random Access Memory with Through-Silicon Vias (TSVs)

Three-dimensional integrated circuit (3DIC) technology has a greater advantage over conventional planar technology in terms of higher performance and yield. Circuits with different functions are placed on each tier. In this paper, the susceptibility of static random access memory (SRAM) which integrated with three-dimensional integrated circuit technology, are evaluated using a Monte Carlo simulation method based on the Geant4 simulation toolkit. Cross sections of single event upset (SEU) induced by different energy ions are presented. The results show that the sensitivity of each die of 3D SRAM is similar. The cross section of 3D SRAM is one order of magnitude smaller than that of the planar process due to the reduction of cell density. Additionally, the impact of TSVs on single event upset of 3D SRAM are explored using heavy ions with different energies. The high-energy ions can increase cross section of 3D SRAM due to the increase of nuclear reaction between ions and tungsten. However, the TSV filled with tungsten can shield the incident low-energy ions and decrease the cross sections.
Coordinated Control Scheme for Electric Vehicles Connected to Droop-Controlled MicroGrids

This paper proposes a coordinated control scheme for electric vehicles (EVs)connected to droop-controlled AC microgrids that consist of energy storage systems (ESSs)and EVs. The control scheme consists of two control levels: primary control and secondary control. The primary control is implemented on the local controllers of the microgrid (MG)units and is based only on local measurements. The EVs connected to the MG are used as controlled units, they contribute to the frequency and voltage control, their charging power is variable, and they can also support the MG with reactive power. The secondary controller is implemented in a microgrid central controller (MGCC)and the communication between the MGCC and the MG units is based on a low bandwidth communication link. The secondary control restores the amplitude and the frequency of the load bus voltage to their nominal values and consists of frequency and a voltage controller. The restoration signals are sent to all units of the MG, they are used to coordinate between all the MG units and control their active and reactive power. The control scheme is implemented in Matlab/Simulink environment and is validated through dynamic simulations.
A Weak Supervision Technique with a Generative Model for Improved Gene Clustering

In the field of computational bioinformatics, for tasks such as medical diagnosis or disease gene classification, acquiring class labels is very much essential and costly. Now a days, labelling biomedical data became one of the crucial bottlenecks for developing supervised machine learning systems, as well as deep learning systems. On the contrary, weak supervision sources that generate diverse, semi-accurate or programmatically-generated labels are comparatively cheaper and less time consuming. In this paper, we have proposed a framework for integrating weak supervision sources with a generative model to estimate probabilistic class labels of the gene expression data. Here as the weak supervision source, a multi-objective optimization (MOO) based clustering technique is utilized. The non-dominated solutions of Pareto optimal front are viewed as the weak supervised labels. These weak supervised labels are fed to a generative model, which finally assigns probabilistic class labels to different genes. In this work, we have shown that our proposed generative model based clustering technique attains better Silhouette score (some quality measure of clustering) for three real-life NCBI gene expression data sets than other stateof-the-art methods, without using any labeled data. Finally, the superiority of the proposed method is validated by using a statistical and a biological significance test.
Determination of D and Q Axes Inductances for Steady State Model Analysis of PM Motor with Non-Ideal Machine Parameters

The permanent magnet (PM) motors are widely used in electric vehicle (EV) application due to their high power density. Since EVs have wide operating speed range, it is necessary that the motors used in EVs satisfy this condition. One way to achieve the wide speed range operation in PM motors is by using field weakening. The field weakening capacity of the PM motors depends on the ratio of direct axis and quadrature axis inductances $(L_{d}, L_{q})$. The analytical models of PM motor assumes constant values of $L_{d}$ and $L_{q}$. Since the B vs. H curve of the lamination material is non-linear, the $L_{d}$ and $L_{q}$ inductances vary as the stator current increases. This results in erroneous prediction of motor's behavior, especially in field weakening region. To overcome this, an improved analytical model is proposed in this work. Here, the inductances are made a function of current and the results obtained by the proposed model are compared with results obtained from FEA.