Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Atrial Fibrillation Detection

Atrial Fibrillation (AFib) is one of the most common cardiovascular diseases that can cause serious consequences if it is not treated on time. Although AFib is defined as an irregular rhythm with the absence of P waves in the electrocardiogram signal, still the algorithms for AFib detection are not perfect. When analyzing the state-of-the-art literature, we found several problems in identification consistent methodology that will allow comparison of different methods. The inconsistency problems are found in defining the set of benchmark tests and in interpretation of the key performance indicators.In this paper, we present three different methodologies and argue which key performance indicators should reveal the best performance evaluation. Choosing the F1 score allows conveying the balance between the positive predictivity value (precision) and sensitivity (recall). Also, we found that the way the true and false positives, and true and false negatives are determined varies from one approach to another and argue on which performance evaluation approach is the most relevant.
Automatic Segmentation of The Renal Collecting System in 3D Pediatric Ultrasound to Assess the Severity of Hydronephrosis

Hydronephrosis is a common kidney abnormality in the pediatric population. It is generally defined as the dilation of the renal collecting system due to a build-up of urine. Ultrasound is a safe imaging modality to detect hydronephrosis. Hydronephrosis index (HI) is one of the metrics used for hydronephrosis severity assessment. In this paper, we first develop a novel automated collecting system segmentation method using a 3D U-net deep neural network. The initial segmentation is refined using anatomical location prior of the renal fat spots around the collecting system. Then, we measure HI to assess the severity of hydronephrosis. The performance of the method was evaluated using a dataset of 3D ultrasound images from 64 hydronephrotic cases, showing an average Dice similarity coefficient of 0.76 ± 0.12, an average symmetric surface distance of 1.29 ± 0.95 mm, and an average HI error value of 2.1 ± 2.8 %.
Explore the Hierarchical Auditory Information Processing Via Deep Convolutional Autoencoder

Combined with neural encoding models, hierarchical feature representation of sensory information via deep neural network (DNN) has been used to explore the hierarchical organization of sensory cortices. With those advancements, previous studies have revealed a representational gradient in the superior temporal gyrus (STG) in auditory information processing, where hierarchical feature representation of auditory stimuli used in fMRI experiments is derived in a supervised manner, that is, the DNN models are trained to classify auditory stimuli. However, feature representation is biased towards discriminative ones in such a supervised DNN and consequently may contaminate brain encoding models. In this study, we propose to derive hierarchical features of auditory stimuli via unsupervised DNN, namely, deep convolutional auto-encoder (DCAE), and develop an encoding model based on LASSO algorithm to explore the relationship between features in multilayers and fMRI brain responses. The results show that auditory cortex is more sensitive to low-level features represented in shallower layers whereas the visual cortex and insula are more sensitive to high-level features represented in deeper layers. These results may provide novel evidence to understand the hierarchical auditory information processing in the human brain.
On the Maximal Code Length of Optimal Linear LRC Codes with Availability

A code over finite alphabet is said to be locally recoverable (LRC) if each code symbol is function of small number of other symbols forming the recovering set [1], [2], [3], [4], [5]. These codes were first proposed in [1] and immediate become popular due to obvious applications in distributed and cloud storage systems. Natural generalization of LRC codes is LRC codes with availability in which each code symbol has more than one disjoint recovering set. A LRC codes with availability is said to be optimal if its minimum distance achieves the Singleton- like bound developed by Kruglik et. al in this paper we study the maximum code length of q-ary optimal LRC with availability and then derive some structural properties.
A Very Deep Densely Connected Network for Compressed Sensing MRI

Convolutional neural network (CNN) has achieved great success in the compressed sensing-based magnetic resonance imaging (CS-MRI). Latest deep networks for CS-MRI usually consist of a stack of sub-networks, each of which refines the former image prediction to a more accurate one. However, as the sub-network number increases, the information in prior sub-networks has a little influence on subsequent ones, which increases the training difficulties and limits the reconstruction performance of the deep model. In this paper, we propose a novel network, named very deep densely connected network (VDDCN), for CS-MRI. Dense connections are introduced to connect any two sub-networks of VDDCN, so each sub-network can make full use of all former predictions, boosting the reconstruction performance of the whole network. The sub-network of VDDCN is composed of feature extraction and fusion block (FEFB) processing data in the image domain and data consistency (DC) layer enforcing the data fidelity in k-space. Specifically, in FEFB, multi-level features are extracted by the recursive feature extraction and fusion sub-blocks (RFEFSBs) and fused locally to obtain the compact features. The VDDCN is much deeper than the prior deep learning models and able to discover more MR image details. The experimental results demonstrate that our proposed VDDCN outperforms other state-of-the-art CS-MRI methods.
Generating Waveforms for EMI Filters Testing

In recent decades, the demand for energy produced from renewable sources has increased due to environmental problems and declining fuel classical sources: oil, coal and natural gas. One of the most commonly renewable energy used to connect to the grid are photovoltaic systems. This paper addresses specific problems regarding the design and simulation of EMI filters. Throughout this paper we have presented a method of simulating harmonics waveforms using CIGUI software.
Analysis of Patent Databases Using VxInsight

We present the application of a new knowledge visualization tool, VxInsight, to the mapping and analysis of patent databases. Patent data are mined and placed in a database, relationships between the patents are identified, primarily using the citation and classification structures, then the patents are clustered using a proprietary force-directed placement algorithm. Related patents cluster together to produce a 3-D landscape view of the tens of thousands of patents. The user can navigate the landscape by zooming into or out of regions of interest. Querying the underlying database places a colored marker on each patent matching the query. Automatically generated labels, showing landscape content, update continually upon zooming. Optionally, citation links between patents may be shown on the landscape. The combination of these features enables powerful analyses of patent databases.
Design of a Current-Mode Linear-Sum-Based Bitcounting Circuit with an MTJ-Based Compensator for Binarized Neural Networks

A bitcounting operation, which counts l's/0's in input values and decides the output depending on their majority, is one of the important operations in binarized neural networks (BNNs). In this paper, we propose a bitcounting circuit for compact and low-power BNN hardware. This circuit performs the bitcounting operation by utilizing current-mode linear summation, which achieves a high-speed and low-power operation as well as a compact circuit. In order to guarantee a reliable analog operation of the circuit under possible process variation conditions, magnetic tunnel junction (MTJ) devices integrated with the bitcounting circuit can adjust its operating point and keep this adjustment information in a nonvolatile manner. Through an experimental-data-based evaluation of a 25-input bitcounting circuit under a 40-nm MOS/MTJ-hybrid process, we show that this circuit reduces the power-delay product and circuit area by 92% and 93%, respectively, compared to a conventional accumulation-tree-based one, while guaranteeing the correct operation under the process variation condition assumed on the target fabrication process technology.
A Common Service Middleware for Intelligent Complex Software System

With the rapid development of the Internet of Things (IoT) and artificial intelligence (AI) technology, various intelligent complex software systems (i-CSS) are increasingly popular, becoming one of the most important software system development paradigms. Its inherent growth construction and adaptive evolution properties pose new challenges to existing software design and development methods. Especially, how to achieve growth construction by quickly reusing existing excellent software resources, and how to establish data flow across system boundaries around the business flow to achieve adaptive evolution based on data intelligence. Facing the above challenges, this paper proposes novel data-oriented analysis and design method (DOAD), microservice and container-based mashup development method (SCMD). On this basis, the paper implements i-CSS common service middleware to support the above methods in engineering. In a real cloud-based PHM system and the other three industry projects, the proposed methods and middleware are used for application verification, the results show that they can greatly reduce the complexity of i-CSS design and development, reduce the ability threshold of the i-CSS development team, improve the development efficiency of the development team, reduce the team development workload by 31.5% on average, and help the i-CSS team effectively cope with the challenges of growth construction and adaptive evolution.
A Ternary Fuzzy Extractor for Efficient Cryptographic Key Generation

This paper presents an efficient cryptographic key generation from ternary physically unclonable functions (PUFs). The basic concept of our cryptographic key generation is to introduce ternary fuzzy extractors (FEs) to take advantage of ternary PUFs. First, we show the insecurity present when combining ternary PUF responses with conventional binary FEs. We then present the ternary FE that is compatible with ternary PUFs. Through experimental implementation, we confirm that the proposed FE can realize PUF-based cryptographic key generation with a 61% lower hardware cost than conventional binary FEs.
Static and Dynamic Figures of Merits (FOM) for Superjunction MOSFETs

The introduction of the superjunction has challenged the well-known limit of silicon. In spite of its widespread use, and despite its correct application in classical power MOSFETs, Baliga's figure of merit no longer can be used to give an accurate picture of the material potential in power electronics. Fujihira has updated both the static and dynamic figures of merit, but, as the cell pitch of the SJ scale down, these figure of merits need to include the parasitic JFET effect and an analytical formulation of the gate-drain capacitance and its intricate dependence on the drain voltage. This work presents for the first-time new static and dynamic figure of merits which take into account the spectacular advances in the SJ technology, where the cell dimensions are approaching the sub-micrometer level.
Investigating modern cars

Car forensic is one of the branches of digital forensics that is recently becoming more and more popular and important. Because of the accelerated growth of technology and its implementation in various industries, modern cars have additional features to raise the level of security, improve the driving experience, and now add the advanced option to connect the car to the internet. The possibilities today are much more advanced, which means that today’s cars can drive autonomously, read the email, respond to messages, automatically receive software updates over the internet that provide new features. Today “smart” cars can even be controlled over the key fob or mobile app. This paper will show common information that can be found in the car; who drove it, how he drove, where and in what state did he drive, information that is related to the car and its mechanics, all the synchronized information etc. It will also explain the extraction process, software used for the extraction, and which difficulties the forensic examiner can encounter during the extraction. Furthermore, the information extracted may prove to be useful in resolving different types of crime, and it can show the importance of digital forensics of cars.
Sensors for Prognostic and Health Management of Armored Vehicles

Recently, the field of Prognostics and Health Management (PHM) has received more and more attention. Applying PHM to armored vehicles can improve the security and availability, reduce the life-cycle cost, and minimize unscheduled maintenance through timely repair. However, PHM for armored vehicles not only relies on various algorithms and intelligent models, but also highly on various sensors to monitor environmental, operational, and performance-related parameters. This paper analyzes the sensors in armored vehicles, discusses the selection principles of sensors in the armored vehicle PHM system, gives an example of applying the selection principles of sensors to PHM system for an armored vehicle gear box, and presents the future development trends of sensors used in the armored vehicle PHM system.
CapsuleNet for Micro-Expression Recognition

Facial micro-expression recognition has attracted researchers in terms of its objectiveness to reveal the true emotion of a person. However, the limited number of publicly available datasets on micro-expression and its low intensity of facial movements have posed a great challenge to training robust data-driven models for recognition task. In 2019, Facial Micro-Expression Grand Challenge combines three popular datasets, i.e. SMIC, CASME II, and SAMM into a single cross-database which requires the generalization of proposed method on a wider range of subject characteristics. In this paper, we propose a simple yet effective CapsuleNet for micro-expression recognition. The effectiveness of our proposed methods was evaluated on the cross-database micro-expression benchmark using the Leave-One-Object-Out cross-validation. The experiments show that our method achieved superiorly higher results than the baseline method (LBP-TOP) provided and other state-of-the-art CNN models.
HPSG 2005 - The 12th International Conference on Head-Driven Phrase Structure Grammar: Conference Notes

obj nom-obj message soa property eventuality phys-obj info-obj . sem-obj Fore the constraints on the infinitive-headcomplements-cx, see (15) and (16). While an infveP with no nominal dependent is free to have either an abstract-obj or a nom-obj content, the others must have a content of type nom-obj. The relation which is the content of the verbal lexeme is a building block for two different content types at the level of the construction. ‘eventuality’ is the primary nom-obj compatible with this relation (this implies that, if an eventuality is associated with the verb-word in an eventbased semantics for the S, it may not denote exactly the same object, see Asher 1993). However, the interpretation of the nominal infveP seems to be able to shift towards ‘manner of doing sth’, as do IE nominalizations. Only nom-objects seem to be anaphorized, or represented by an agreement index on the verb. Predicates select the so-called verbal or nominal InfveP on a semantic basis. Control and raising verbs select an InfveP with an abstract-obj content, and let one of their argument NP control or be identified with the unrealized subject of the InfveP. Psychological verbs (in our ex., ‘to like’, ‘to marvel at’), which are known not to constrain their source argument, can precisely accept both types. Locative prepositions select a nom-obj, etc. The subject of nominal InfveP is not controlled, since it does not combine with a control predicate. It remains to be seen whether the subject of a (verbal) goal InfveP (1) is syntactically controlled or pragmatically interpreted. References Bouma, G. , R. Malouf and I.A. Sag. 2001. Satisfying constraints on extraction and adjunction. Natural Language and Linguistic Theory, 19: 1-65. Cole, D.T. 1955. An Introduction to Tswana grammar. Longman, Cape Town. Creissels, D. 2003. Présentation du tswana. Lalies, 23: 5-128. du Plessis, J.A. 1982. Sentential infinitives and nominal infinitives. South African J.of African Languages, 2/1. du Plessis, J.A. and M. Visser. 1992. Xhosa Syntax. Via Africa, Pretoria. Ginzburg, J. and I.A. Sag. 2000. Interrogative Investigations. CSLI Publ. , Stanford. Malouf, R. 2000. Mixed categories in the hierarchical lexicon. CSLI Publ. , Stanford. Mugane, J. 2003. Hybrid Constructions in Gikuyu: Agentive Nominalizations and infinitive-Gerund constructions. Butt, M. and T. Holloway King (eds), Nominals inside and out, CSLI Publ. , Stanford, 235265. Sag, I.A. 2003. Coordination and underspecification. HPSG Conference (CSLI Publ. web site). Visser, M. 1989. The Syntax of infinitive in Xhosa. South African J. of African Languages 9/4.
The effects of time-delay spread on QAM with nonlinearity switched filters in a portable radio communications channel

A spectrally efficient quadrature amplitude modulation (QAM) scheme with a simple nonlinearly switched filter is shown to provide a signaling waveform that is free from both intersymbol interference and zero-crossing timing jitter in a flat-fading channel. This modulation with coherent detection is evaluated by a computer simulation in a frequency-selective fading channel. Timing offset between in-phase and quadrature rails is varied. The effects of receiver filter bandwidths are also studied. It is found that this modulation is resistant to delay spread with moderate spectral efficiency and low complexity in a portable radio system, as compared to other spectrally efficient modulations. >
Particle swarm inspired evolutionary algorithm (PS-EA) for multiobjective optimization problems

We describe particle swarm inspired evolutionary algorithm (PS-EA), which is a hybridized evolutionary algorithm (EA) combining the concepts of EA and particle swarm theory. PS-EA is developed in aim to extend PSO algorithm to effectively search in multiconstrained solution spaces, due to the constraints rigidly imposed by the PSO equations. To overcome the constraints, PS-EA replaces the PSO equations completely with a self-updating mechanism (SUM), which emulates the workings of the equations. A comparison is performed between PS-EA with genetic algorithm (GA) and PSO and it is found that PS-EA provides an advantage over typical GA and PSO for complex multimodal functions like Rosenbrock, Schwefel and Rastrigrin functions. An application of PS-EA to minimize the classic Fonseca 2-objective functions is also described to illustrate the feasibility of PS-EA as a multiobjective search algorithm.
Conceptual Framework for entity integration from multiple data sources

Entity matching and entity resolution are becoming more important disciplines in data management over time, based on increasing number of data sources that should be addressed in economy that is undergoing digital transformation process, with growing data volumes and increasing requirements related to data privacy. It is necessary to automate processes as much as possible by using predefined scenarios supported by frameworks.
Determination of Psychogenic Markers in Speech Signals Using the HHT Theory

The article discusses the issues of increasing the efficiency of diagnostics of psychogenic states by analyzing speech signals (SS). To increase the efficiency and effectiveness of SS processing, a technological process of recording-processing-analysis of signals in conditions of an increased level of interference and free motor activity of a person is proposed. It is proposed to use the Hilbert-Huang theory (HHT) as the basis of signal processing technology. The article analyzes the known theory and reveals its advantages and disadvantages. Based on the results of the technological process using the HHT theory, a method for determining psychogenic markers in SS is developed, studies are carried out, and conclusions of prospective studies are drawn.
Intelligent Glaucoma Diagnosis Via Active Learning And Adversarial Data Augmentation

Glaucoma is the leading causes of blindness in the world. We develop a convolutional neural network for glaucoma diagnosis based on visual fields (VF), which is the gold standard to show functional damages of optic nerve. However, we have to deal with two major problems common in medical imaging domains. 1) It is difficult and expensive to label a large amount of data, while most modern deep learning methods require it. 2) Severe data imbalance makes the classifier easily over-fitting. In this work, for the first problem, we train an AutoEncoder with all the data (labeled and unlabeled) to obtain good features and introduce an active learning (AL) scheme to select and annotate a few most valuable samples from the unlabeled date set for model training. Then, we address the second problem by augmenting negative samples generated by a deep convolutional generative adversarial network (DCGAN). Experiments on our dataset (738 Samples) suggest the effectiveness of the proposed approach.
Distributed Control and Power Management Strategy for an Autonomous Hybrid Microgrid with Multiple Sub-Microgrids

This paper proposes a novel approach of distributed coordination control for multiple sub-microgrids (SMGs) within a hybrid AC/DC microgrid. The conventional control approach for managing power flow among AC and DC SMGs is based on the proportional power sharing principle. This is mainly implemented by equalising the normalized voltage at the DC side and the frequency at the AC side for any interfaced SMGs. The proposed method suggests a distributed control system that ensures a total controllability for the interlinking converters. It overcomes the total dependency on a specific variable for power exchange. The proposed method not only enables control of the power flow between SMGs but also ensures the continuity of power transfer if any single SMG fails. Three case studies are presented to demonstrate the validity and capability of the proposed approach using the MATLAB/Simulink software. From the obtained results, it is found that the proposed control system provides a high level of flexibility in managing the power flow among SMGs.
A chip-scale packaged amplifier MMIC using broadband hot-via transitions

The performance of RF hot-via transitions for use in chip-scale package (CSP) MMICs are presented. This is illustrated with the realization of a low noise amplifier MMIC using optimized hot-vias. Based on our standard 0.25-/spl mu/m GaAs low-noise PHEMT process, with BCB coating and backside metallization, this 2-stage low noise microstrip amplifier mounted with bumps on a carrier substrate achieved a linear gain of 15 dB over the 15- to 32 GHz frequency range. To the author's knowledge, this is the first demonstration of chip-scale packaged active MMICs using hot-via transitions.