Miracle-dz/UniFAR-FTUs huggingface dataset:
Exhaustive Data- and Problem-Driven use Case Identification and Implementation for Electric Drive Production

Within the context of Industry 4.0, data is being recorded and collected at an increasing rate in the production environment of electric drives. On the one hand, the data serves the producer as a backup against potential failure induced returns. On the other hand, some of the recorded data is used specifically for data analytics methods with the expectation to generate additional information and thus knowledge through intelligent data aggregation and processing. In the latter case, i.e. the use of data analytics methods, there are several possibilities to identify use cases and to implement them in a later step. Use cases can be discovered and identified on the basis of actual problems, so they can be considered as problem-driven. Problems and challenges in current electric drive production are taken here as a possible starting point for the identification of use cases. It is also conceivable that future problems can be anticipated by, for example, expert knowledge. Therefore, actual problems can be prevented prematurely with data analytics. A completely different approach is to analyze the currently available data bases and develop possible use cases based on the existing data. Currently, there is no systematical approach to cover the use case implementation for electric drives holistically. Therefore, in this paper, the possible fields of tension of the use case identification, evaluation and implementation will be presented. In the second step, based on the findings, a systematical approach to identify, evaluate and eventually implement use cases will be derived. The approach will then be applied to the generic production process chain of electric motors. Within the application of the approach, the whole drive production process chain was systematically analyzed. Out of the variety of process steps, a total of three use cases were selected due to the availability of data or due to identified process instabilities. For each of these three process steps, a use case was identified by conducting interviews with experts and the process-related operators. Out of the three developed use cases, one is being implemented, following the systematic approach presented within this paper
Enhanced Bandwidth and Directivity of a Dual-Mode Compressed High-Order Mode Stub-Loaded Dipole Using Characteristic Mode Analysis

A dual-mode compressed dipole resonating at high-order modes is proposed to enhance bandwidth and directivity by loading the two dipole arms with two stubs. Characteristic mode analysis shows that with by introducing the loading stubs, the fifth-order mode of the proposed dipole shifts close to its third-order mode for achieving a wideband operation with enhanced gain. A prototype is designed to validate the principle and design approach experimentally. These measurements show that the gain of the proposed compressed dipole is 4 dBi, higher than that of a conventional dipole over the desired operating band of 3.3–3.6 GHz for the coming 5G applications.
Morphology of Testing Technology and Selection Advice for Automotive Process Qualification of Electric Motor Production

Electric mobility poses new challenges for electric motors in the traction sector. Until now, electric motors have been used mainly for applications such as actuators, or for fixed operating points such as for industrial or household applications. Driving cycles of vehicles characterized by pronounced dynamics require a robust and high-quality powertrain. This results in an increase in the complexity of the production of the electric motor and thus, adequate quality assurance and testing processes are required in order to be able to manufacture and assemble the products with minimum scrap rate and in a quality that meets automotive requirements in corresponding production level scenarios. The aim of this paper is to present a holistic morphology of essential testing processes along the electric motor production process as well as to develop a planning guide for the selection of necessary testing processes for the design of electric motor production to reach economic production and quality. For this purpose, a morphological box including suitable features and characteristics is defined. Summarizing, the procedure outlined should sharpen the understanding of necessary testing processes for effective quality assurance within electric motor production and subsequently, make the selection of suitable testing processes more efficient in the course of designing an electric motor production system by deriving evaluated selection recommendations from numerous technological alternatives of testing processes.
Comparative Analysis of DC-DC Converter Topologies and MPPT Techniques Used in SPV Based Water Pumping System Using BLDC Motor

This paper deals with comparative analysis of DC-DC converters and MPPT Techniques BLDC motor driven water pump powered by Solar Photovoltaic Array (SPV). The better selection of DC-DC converter and suitable MPPT techniques is important for maximum utilization of Solar Photovoltaic (SPV)array power and soft starting of BLDC motor. The two prevalent MPPT Techniques, Incremental conductance (INC)and Perturb and Observe (P&O)are uses and their result is compared under changing climate condition. This work also compares the two DC-DC converters i.e. Buck-Boost and Cuk for 3–0 VSI connected BLDC motor and their result demonstrate that Incremental Conductance (INC)with the Cuk converter is a compatible combination for Solar Photovoltaic (SPV)based water pumping system.
Simultaneous Stabilization and Tracking of Nonholonomic WMRs With Input Constraints: Controller Design and Experimental Validation

Considering input constraints and parameter uncertainty of the nonholonomic wheeled mobile robots, a new control law has been designed in this paper to solve the tracking and stabilization control problems simultaneously. The asymptotic convergence of the stabilization or tracking errors is achieved by a smooth controller with time-varying feedback parameters subject to set conditions. A geometric-based parameter design strategy is employed to overcome the difficulties resulting from input constraints. With the parameter design strategy, the designed parameters satisfy the set conditions and the control inputs stay in the constrained domain. Experimental studies have been conducted to verify the effectiveness of the proposed control law.
Multi-View and Multi-Modal Action Recognition with Learned Fusion

In this paper, we study multi-modal and multi-view action recognition system based on the deep-learning techniques. We extended the Temporal Segment Network with additional data fusion stage to combine information from different sources. In this research, we use multiple types of information from different modality such as RGB, depth, infrared data to detect predefined human actions. We tested various combinations of these data sources to examine their impact on the final detection accuracy. We designed 3 information fusion methods to generate the final decision. The most interested one is the Learned Fusion Net designed by us. It turns out the Learned Fusion structure has the best results but requires more training.
Rrp1, a cyclic‐di‐GMP‐producing response regulator, is an important regulator of Borrelia burgdorferi core cellular functions

Two‐component systems (TCS) are universal among bacteria and play critical roles in gene regulation. Our understanding of the contributions of TCS in the biology of the Borrelia is just now beginning to develop. Borrelia burgdorferi, a causative agent of Lyme disease, harbours a TCS comprised of open reading frames (ORFs) BB0419 and BB0420. BB0419 encodes a response regulator designated Rrp1, and BB0420 encodes a hybrid histidine kinase–response regulator designated Hpk1. Rrp1, which contains a conserved GGDEF domain, undergoes phosphorylation and produces the secondary messenger, cyclic diguanylate (c‐di‐GMP), a critical signaling molecule in numerous organisms. However, the regulatory role of the Rrp1–Hpk1 TCS and c‐di‐GMP signaling in Borrelia biology are unexplored. In this study, the distribution, conservation, expression and potential global regulatory capability of Rrp1 were assessed. rrp1 was found to be universal and highly conserved among isolates, co‐transcribed with hpk1, constitutively expressed during in vitro cultivation, and significantly upregulated upon tick feeding. Allelic exchange replacement and microarray analyses revealed that the Rrp1 regulon consists of a large number of genes encoded by the core Borrelia genome (linear chromosome, linear plasmid 54 and circular plasmid 26) that encode for proteins involved in central metabolic processes and virulence mechanisms including immune evasion.
Radar Echoes Classification Based on Local Descriptor

The use of radar in meteorology is in a permanent development, where the data collected are subject to many undesirable echoes. Nonetheless, we propose to process these lasts using Local Binary Pattern (LBP) descriptors and others variants that we combined to Support Vector Machine (SVM) classifier. The approaches were validated on images recorded in Bordeaux (France) by incoherent radar. Different neighborhood’s sizes are explored, the results reveal that the variance (VAR) gives the best preservation of precipitations using radius 1 where 82.99% of ground echoes are suppressed when LBPriu2 are combined to VAR. The best identification is then obtained with simply the VAR.
Sphingosine‐1‐phosphate produced by sphingosine kinase 2 in mitochondria interacts with prohibitin 2 to regulate complex IV assembly and respiration

The potent lipid mediator sphingosine‐1‐phosphate (S1P) regulates diverse physiological processes by binding to 5 specific GPCRs, although it also has intracellular targets. Here, we demonstrate that S1P, produced in the mitochondria mainly by sphin‐gosine kinase 2 (SphK2), binds with high affinity and specificity to prohibitin 2 (PHB2), a highly conserved protein that regulates mitochondrial assembly and function. In contrast, S1P did not bind to the closely related protein PHB1, which forms large, multimeric complexes with PHB2. In mitochondria from SphK2‐null mice, a new aberrant band of cytochrome‐c oxidase was detected by blue native PAGE, and interaction between subunit IV of cytochrome‐c oxidase and PHB2 was greatly reduced. Moreover, depletion of SphK2 or PHB2 led to a dysfunction in mitochondrial respiration through cytochrome‐c oxidase. Our data point to a new action of S1P in mitochondria and suggest that interaction of S1P with homomeric PHB2 is important for cytochrome‐c oxidase assembly and mitochondrial respiration. —Strub, G. M. , Paillard, M. , Liang, J., Gomez, L. , Allegood, J. C. , Hait, N. C. , Maceyka, M. , Price, M. M. , Chen, Q. , Simpson, D. C. , Kordula, T. , Milstien, S. , Lesnefsky, E. J., Spiegel, S. Sphingosine‐1‐phos‐phate produced by sphingosine kinase 2 in mitochondria interacts with prohibitin 2 to regulate complex IV assembly and respiration. FASEB J. 25, 600–612 (2011). www.fasebj. org
Multi-target Compiler for the Deployment of Machine Learning Models

The deployment of machine learning models into production environments is a crucial task. Its seamless integration with the operational system can be quite challenging as it must adhere to the same software requirements such as memory management, latency, and scalability as the rest of the system. Unfortunately, none of these requirements are taken into consideration when inferring new models from data. A straightforward approach for deployment consists of building a pipeline connecting the modeling tools to the operational system. This approach follows a client-server architecture and it may not address the design requirements of the software in production, especially the ones related to efficiency. An alternative is to manually generate the source code implementing the model in the programming language that was originally used to develop the software in production. However, this approach is usually avoided because it is a time-consuming and error-prone task. To circumvent the aforementioned problems, we propose to automate the process of machine learning model deployment. For this, we have developed a special-purpose compiler. Machine learning models can be formally defined using a standard language. We use this formal description as an input for our compiler, which translates it into the source code that implements the model. Our proposed compiler generates code for different programming languages. Furthermore, with this compiler we can generate source code that exploits specific characteristics of the systems hardware architecture such as multi-core CPUs and graphic processing cards. We have conducted experiments that indicate that automated code generation for deploying machine learning models is, not only feasible but also efficient.
A ZVS APWM Half-Bridge Parallel Resonant DC–DC Converter With Capacitive Output

In this paper, a modified asymmetric half-bridge (AHB) isolated converter is proposed, which does not have dc current offset in the transformer. The topology operates in the same way as the conventional half-bridge (HB), using asymmetric pulsewidth modulation with constant frequency. A voltage doubler rectifier is used in the output stage, which avoids the flow of the dc component current in the primary side of the transformer. In order to investigate the application of the proposed topology, a parallel-resonant (PR) version of the modified AHB was tested, which uses the parasitic elements of the transformer, such as the leakage inductance and the winding capacitance, to achieve zero voltage switching in the primary side switches over a wide load range. Theoretical and commutation analysis results are reported and design guidelines are presented herein. Experimental data were obtained from a parallel resonant 1-kW prototype, operating with a 50 kHz switching frequency, 400 V input voltage, and 400 V output voltage, to verify the theoretical study, and 97% efficiency was achieved. The proposed solution was also tested at high voltage, operating with a rated power of 1.8 kW, switching frequency of 50 kHz, input voltage of 400 V and series-connected output voltages, the maximum voltage being 8620 V.
Cloud Point Matching for Text-Independent Speaker Identification

In Text-Independent speaker identification, the individual that produced some captured speech signal has to be identified without his collaboration, he might not even know that he is being the subject of an identification process. The system could not ask the individual to utter some specific word or phrase, which is precisely what is done in Text-Dependent speaker recognition. Text-Independent speaker identification is far more complicated since we cannot simply measure the similarity of an utterance of a word or phrase to another utterance made by the same speaker of the same word or phrase in which case we could use the dynamics of the speech signal. In this paper we search in the speech signal looking for voiced speech segments and estimate its first three formants, so we end up with a three-dimensional point cloud for each speaker of the collection of known speakers. To identify a speaker we have to measure the similarity of a point-cloud from an unknown speaker to the point-clouds that belong to known speakers, we do that by searching for local structures in the cloud in a way that is highly scalable and robust. We performed tests with both a collection of our own in Spanish and with the English Language Speech Database for Speaker Recognition (ELSDSR) from the Technical University of Denmark achieving results that improve recent published work with ELSDSR.
22.7 A Programmable Wireless EEG Monitoring SoC with Open/Closed-Loop Optogenetic and Electrical Stimulation for Epilepsy Control

The number of studies on closed-loop detection and electrical stimulation systems [1]–[2] for efficient control of neurological disorders is increasing, because recent clinical studies have shown their efficiency and usefulness in symptom suppression. Electrical stimulation can produce enough stimulation to affect a large range of nerves. However, all nerves near the stimulus are excited and hurt, and over time, currents start to exceed acceptable limits. Therefore, optogenetic stimulation [3]–[4] has become compelling in recent years due to several advantages: (1) no artificial noise on the EEG; (2) ability to stimulate specific nerves; and (3) no injurious effects on nerves. In this study, a wireless programmable stimulating system-on-chip (WPSSoC) is reported that provides wireless open/closed-loop optogenetic and electrical stimulation to improve treatment for epilepsy suppression. The system is demonstrated on programmable stimulation parameters wirelessly controlled by a software Graphical User Interface (GUI) on a computer. Moreover, an animal experiment conducted on optogenetic tissue was successful, thereby demonstrating that the nerve injury on optogenetic stimulation is lower than that of electrical stimulation.
Hybrid Text-to-Speech for Articulation Disorders with a Small Amount of Non-Parallel Data

Conventional approaches to statistical parametric speech synthesis usually require a large amount of speech data. But it is very difficult for persons with articulation disorders, in particular, to utter a large amount of speech data, and their utterances are often unstable or unclear so that we cannot understand what they say. In this paper, we propose a hybrid approach for a person with an articulation disorder, using two models of a physically unimpaired person and a person with an articulation disorder to generate an intelligible voice while preserving the speaker's individuality (with an articulation disorder). Our method has two processes - the speech synthesis part and voice conversion part. Speech synthesis is employed for obtaining a speech signal (of a physically unimpaired person), where a large amount of training data of a physically unimpaired person is used. Then, voice conversion (VC) is employed for converting the voice of the physically unimpaired person to that of a person with an articulation disorder, where a small amount of speech data of a person with an articulation disorder is only used for training VC. Also, a cycle-consistent adversarial network (CycleGAN) that does not require parallel data is employed for VC. An objective evaluation showed that the mel-cepstrum obtained using our method are close to the target in terms of global variance (GV) and modulation spectrum (MS).
A Multi-Radio Gateway Architecture and Implementation for Consumer Electronics

The main purpose of Internet of Things (IoT) is to embed connectivity into everyday devices, making them smarter and adding more value. Due to constraints such as cost, bandwidth, power consumption, and communication range, not all IoT devices have direct connection to the Internet. This requires a gateway to deal with the communication protocol used by each device and connect it with the Internet towards providing external access for users. In this paper, we introduce an open-source multi-radio gateway to consumer electronics applications.
Trustworthy Electronic Voting Using Adjusted Blockchain Technology

The electronic voting has emerged over time as a replacement to the paper-based voting to reduce the redundancies and inconsistencies. The historical perspective presented in the last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time. This paper suggests a framework by using effective hashing techniques to ensure the security of the data. The concept of block creation and block sealing is introduced in this paper. The introduction of a block sealing concept helps in making the blockchain adjustable to meet the need of the polling process. The use of consortium blockchain is suggested, which ensures that the blockchain is owned by a governing body (e.g., election commission), and no unauthorized access can be made from outside. The framework proposed in this paper discusses the effectiveness of the polling process, hashing algorithms’ utility, block creation and sealing, data accumulation, and result declaration by using the adjustable blockchain method. This paper claims to apprehend the security and data management challenges in blockchain and provides an improved manifestation of the electronic voting process.
Model of GaSb-InAs p-i-n Gate All Around BioTunnel FET

This paper investigates the role of a hetero-junction p-i-n gate all around tunnel FET architecture for biosensing applications. The device offers a better sensitivity and has been modeled in terms of various parameters such as surface potential, threshold voltage, and drain current. Analytical modeling scheme relates to the exact resultant solution of the two-dimensional Poisson equation. The shift in the threshold voltage has been considered as the sensing parameter to detect the sensitivity when the biomolecules are immobilized in the cavity region.
Crystal structure of the Vibrio cholerae ferric uptake regulator (Fur) reveals insights into metal co‐ordination

The ferric uptake regulator (Fur) is a metal‐dependent DNA‐binding protein that acts as both a repressor and an activator of numerous genes involved in maintaining iron homeostasis in bacteria. It has also been demonstrated in Vibrio cholerae that Fur plays an additional role in pathogenesis, opening up the potential of Fur as a drug target for cholera. Here we present the crystal structure of V. cholerae Fur that reveals a very different orientation of the DNA‐binding domains compared with that observed in Pseudomonas aeruginosa Fur. Each monomer of the dimeric Fur protein contains two metal binding sites occupied by zinc in the crystal structure. In the P. aeruginosa study these were designated as the regulatory site (Zn1) and structural site (Zn2). This V. cholerae Fur study, together with studies on Fur homologues and paralogues, suggests that in fact the Zn2 site is the regulatory iron binding site and the Zn1 site plays an auxiliary role. There is no evidence of metal binding to the cysteines that are conserved in many Fur homologues, including Escherichia coli Fur. An analysis of the metal binding properties shows that V. cholerae Fur can be activated by a range of divalent metals.
Efficient Transient Stability Analysis of Electrical Power System Based on a Spatially Paralleled Hybrid Approach

With continually increasing complexities of power systems, transient stability analysis as an important task for system security operation becomes very time consuming and thus an efficient analysis tool is urgently needed. In this paper, a spatially paralleled hybrid approach combining the high-order Taylor series algorithm and the block bordered diagonal form (BBDF) was proposed to improve the computational efficiency of power system transient stability analysis. The proposed approach only exchanged high-order derivatives of generator voltages and currents between partitioned power network subsystems and the coordinated-bus cluster based on BBDF, and enhanced the triangular factorization recursive utilization rate of admittance matrix using the Taylor series algorithm with a large integration step. Finally, the proposed spatially paralleled hybrid approach was tested in the New England 39-bus, IEEE 145-bus and an expanded 580-bus systems, and simulation results have validated the proposed approach is very effective and computationally efficient for power system transient stability analysis.