Miracle-dz/UniFAR-FTUs huggingface dataset:
A Virtual Smart Home Based on EEG Control

Virtual reality (VR) is a new technology which can create a virtual world based on computer system, providing users with a 3D immersive simulation environment. And Brain Computer Interface (BCI) technology makes it possible for users to communicate and interact with devices without any handle, keyboard or mouse. Combining VR and BCI, this paper proposes and designs a novel platform which is called virtual smart home based on electroencephalograph (EEG) control (VSH-EEG). P300 is an event-related potential (ERP), which can be evoked when random stimulus happens. In VSH-EEG system, the paradigm display of P300 is embedded in the virtual smart home. Users' P300 is extracted, processed and classified by self-adaptive Bayes linear discriminant analysis (SA-BLDA) algorithm. Besides, asynchronous BCI paradigm based on P300 can offer an appropriate number of control instructions and enables users to control the appliances in virtual home freely. In order to test the accuracy and effectiveness of the system, 8 subjects are asked to control the relative objects. Finally, the experiment results show that the VSH-EEG system is acceptable and users can enjoy a new way of entertainment.
Model counting for #2SAT problem in outerplanar graphs

The satisﬁability problem for formulas in two conjunctive Normal Form (2SAT) is solved in polynomial time, and (cid:93) 2SAT which is the count version of 2SAT is (cid:93) P-complete. It has been shown that for certain types of formulas, (cid:93) 2SAT can be computed in polynomial time. In this paper we deﬁne a new method, based on embedded cycles, to compute (cid:93) 2SAT on the so-called outerplanar formulas. Our algorithm’s time complexity is given by O ( n + m ) where n is the number of variables and m the number of clauses of the formula. Although the time complexity is similar to other methods, experimental results show that the new method is faster.
Intelligent Device System of Urban Transportation Service

Intelligent bus and public urban transportation service provide quality requirements for passengers, cities and development and economy of society. We designed the intelligent device terminal of urban transportation service wireless connecting the vehicle terminal integrated by various sensors with interface and modular technologies, which satisfied the public services of bus and private for public car with legal permission. It renders the transparent services for passenger and connects to urban bus system for private for public cars, and highlights the services of whole router demonstration, exchange information, surrounding information and tourist information that give the convenient, effective, humanizing, informational and intelligent services. Additionally, the propaganda, language, tourism, fair and random selections achieved by the intelligent platform, which was favored by all the parts in public transports decreasing the disputes.
Reducing Reconfiguration Time in FPGAs

Despite the growing popularity of field-programmable gate arrays (FPGA), this technology still suffers from drawbacks such as larger area, higher power consumption and longer runtime than application-specific integrated circuits (ASIC). Reconfiguration capability gives FPGAs an advantage over ASIC and can make it a more appropriate option for implementing some applications. One important issue regarding the reconfiguration process is the time it takes to carry out this procedure. This paper seeks to accelerate the reconfiguration process by using a bitstream compression method with a primary focus on FPGA architecture. Since multiplexers are among the main components of FPGAs, the proposed method is focused on encoding and compression of configuration bitstream for such resources. In addition, the bitstream format and the decompression operation are designed to obtain desirable results in terms of area overhead and time improvement. Results of experiments show that in comparison to the basic scheme, the proposed design improves the reconfiguration time by 33%.
Discussion on Control Methods with Finite-Control-Set Concept for PMSM Drives

This work closely investigates the generalized relationship among the four popular control methods using Finite-Control-Set Model Predictive Control (FCS-MPC)concept for voltage source power converter fed PMSM drivers. The as-investigated control methods include: direct model predictive torque control (DMPTC)in $dq$ frame, direct model predictive current control (DMPCC)in $dq$ frame, direct model predictive current control (DMPCC)in $\alpha\beta$ frame and direct model predictive flux control (DMPFC)in $\alpha\beta$ frame. Our conclusion is that, for nonsaliency based PMSM, DMPTC, DMPCC (both in $\alpha\beta$ and $dq$ frames), DMPFC are theoretically equivalent in achieving the control targets. However, realization effort and computational demands, i.e., the amount calculation required, are different.
On the Research of Frequency offset estimation algorithm of FTN signal based on improved steepest descent method

Faster-than-Nyquist signaling is a kind of information transmission technology. It can compress the transmission symbol interval to improve the spectrum utilization, which has good application prospects. In this paper, an improved steepest descent method based low complexity frequency offset estimation algorithm for FTN signals is proposed. The idea is to replace the derivation process of the original method with the comparison of amplitude values under the idea of differential, so as to reduce the complexity of frequency offset estimation at the receiving end of FTN, reduce the estimation threshold, improve the estimation accuracy and save the cost, which provides an important reference for the practical engineering application of FTN transmission.
Fault Diagnosis of Carrier Rocket Actuator Based on Multiple-Model Method

Fault diagnosis problems of the carrier rocket actuator are investigated based on the multiple-model method. Firstly, the model of the carrier rocket with the actuator failure is established. Different from the traditional models where only additive faults are considered, this paper considers the multiplicative faults. Then a set of multiple-model unknown input observers are designed to generate a certain residual value, where an adaptive method is applied to estimate the value of the unknown input. Based on the residual value and multiple-model, the faults are determined. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.
Geographic Routing With Energy Constraint in Wireless Multimedia Sensor Networks

Energy distribution to get more lifetime and noninterfering multipath are two major challenges in Wireless Multimedia Sensor Networks (WMSNs) routing. The Greedy Perimeter Stateless Routing (GPSR) is a simple routing protocol which uses minimum geographic distance to forward packets toward sink node. However it does not consider energy level and uses single path. Geographic energy-aware non-interfering multipath Routing (GEAM) overcomes this problem by multipath routing and energy consideration, but keeping state of the multiple non-interfering areas has an overhead. In our proposed routing protocol which is called Energy Aware Greedy Stateless Geographic Routing (EAGSGR) we will introduce two path routing without interference only by using two strategic areas. We meanwhile introduce a simple way to deal with the dead end nodes and clusters problem. EAGSGR showed a better performance compared to GPSR and GEAM in terms of QoS parameters such as end-to-end delay, average hop count, energy distribution and packet delivery ratio.
Trade-offs and Other Challenges in CMOS Implementation of Parallel FIR Filters

The paper presents methods of implementing finite impulse response (FIR) filters in hardware. Considering their functioning, FIR filters require only simple arithmetic operations such as multiplication, addition and shifting of signal samples in the delay line. In the case of their implementation as a device in which parallel processing of signals is assumed, one of the main challenges is an efficient implementation of the block of filter coefficients. This applies in particular to high order, N filters and a relatively high resolution (in bits) of the processed signals and filter coefficients. When the objective is to reach a high filter selectivity, understood as the attenuation in the stop band and the steepness in the transient band of the frequency response, the filter coefficients usually also require high resolutions. In the presented work we perform an analysis of trade-offs between such parameters as data rate, occupied chip area (number of transistors), simplicity of the control block as well as energy consumption. We investigate possible approaches to the implementation of such filters, particularly in terms of the complexity of the block of coefficients. One of the key possibilities is the use of at least partially asynchronous signal processing, which has a significant impact on the complexity of the circuit structure and data rates.
Design and Analysis of Modular Axial Flux Switched Reluctance Motor

This paper presents a new modular structure of the axial flux Switched Reluctance Motor (SRM). The design consists of four stator disks with each adjacent disk rotated 30 degrees apart and four rotor disks connected to a common shaft. The proposed design aims to reduce the unwanted radial force, mitigate the torque ripple, and improve the efficiency. The modular structure distributes the radial force and torque strokes along the axial length of the motor, potentially damping the torque pulsation. In addition, the modular structure would deliver the rating power at a lower current level, reducing the overall ohmic loss. Moreover, if a fault occurs on a motor disk or its control unit, the motor would still operate through other disks, increasing the reliability of the system. To verify the effectiveness of the proposed design, the magnetostatic and transient performance of the motor are compared with the conventional single layer structure using 3-D Finite-Element (FE) software tool.
Traffic Flow Prediction Based on BRNN

Accurate and real-time traffic flow prediction plays an important role in building intelligent transportation systems and traffic control and induction. As traffic flow data is mostly time series data, selecting a bidirectional recurrent neural network (BRNN) model in a recurrent neural network (RNN) that is good at processing time series data for speed prediction, comparing with long short term memory (LSTM) model and gated recurrent unit (GRU) model. The results show that BRNN has better prediction performance than the other two models.
Modeling and Analysis of Novel Star-Delta Winding Configuration with Odd Slot Numbers for Reduced Space Harmonics Using Winding Function

Existing literature on 3-phase combined star-delta winding topologies only focuses on even slot numbers or conventional multiples of 6, since they emulate a 6-phase configuration with a phase difference of 30° between the star and delta connected sets. Contrarily, if a turns ratio of √3 can be achieved with proper coil and turns distribution, unconventional odd slot numbers and non-multiples of 6 can be implemented resulting in various possible design solutions with minimum spatial harmonic contents. Therefore, this paper focuses on modeling and analysis of a novel star-delta winding configuration using unconventional odd slot numbers for fractional-slot wound machines towards maximum torque density and reduced space harmonic content. Initially, a generalized analytical model using winding function theory for any slot-pole combination is presented. Furthermore, a comprehensive comparative analysis of a novel odd slot-pole combination and a conventional topology is presented in terms of spatial harmonic contents, saliency, torque density, torque ripple, rated machine efficiency and overall operating speed range.
Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey

Unmanned aerial vehicles (UAVs) have gained popularity for diverse applications and services in both the military and civilian domains. For cooperation and collaboration among UAVs, they can be wirelessly interconnected in an ad hoc manner, resulting in a UAV network. UAV networks have unique features and characteristics that are different from mobile ad hoc networks and vehicular ad hoc networks. The dynamic behavior of rapid mobility and topology changes in UAV networks makes the design of a routing protocol quite challenging. In this paper, we review the routing protocols for UAV networks, in which the topology-based, position-based, hierarchical, deterministic, stochastic, and social-network-based routing protocols are extensively surveyed. The routing protocols are then compared qualitatively in terms of their major features, characteristics, and performance. Open issues and research challenges are also discussed in the perspective of design and implementation.
Reducing Consumed Data Volume in Bandwidth Measurements via a Machine Learning Approach

Measurements that determine the available download and upload bandwidth of an end-user Internet connection (so-called speed tests) are typically performed by maximizing the utilization of the connection for a fixed time interval. Especially in broadband connections, such tests consume a huge amount of data volume during their execution. As a result, only a few tests can be performed per month on mobile connections with limited data volumes, since otherwise a significant portion of the volume is used for tests or additional costs are incurred. To reduce the required average data volume of these tests, we present a novel approach with a dynamic test duration based on a machine learning model. We train this model via a supervised learning process, using the recorded data of real speed tests executed by end-users in cellular 4G networks. The evaluation of the resulting method suggests that the amount of saved data volume is significant, while the deviation of the determined bandwidth (compared to a usual test with fixed duration) is negligible.
Second Harmonic Torque Suppression Methods for Single-phase Open-circuit Fault-tolerant Operation of PMSM Considering Third Harmonic BEMF

This paper proposes a novel fault-tolerant control strategy to reduce torque and speed ripples when considering the 3rd harmonic back electromotive force (BEMF) of permanent magnet synchronous motor (PMSM). The structure of PMSM is no longer symmetrical during single-phase open-circuit fault, and hence in the conventional fault tolerant operation strategy, the 3rd harmonic BEMF interacting with fundamental current can result in additional 2nd and 4th harmonic torque components. This paper proposes two different methods of 3rd harmonic currents injection into the two remaining healthy phases to produce additional 2nd harmonic torque ripple and cancel out the original one. The performance of two different injecting methods are investigated and compared against the traditional method. A four-leg inverter is utilized to implement such control strategies to a three-phase star-connected surface PMSM. Simulation of vector control and the experimental results based on a 10-pole/9-slot non-salient PMSM prototype demonstrate that Method A can effectively reduce the speed and torque ripples, which confirms the theoretical analysis.
SSEEP: State-Switchable Energy-Conserving Routing Protocol for Heterogeneous Wireless Sensor Networks

An energy-conserving protocol of routing is critical to a wireless sensor network (WSN) because of limited energy supply and communication capabilities of wireless sensor nodes. LEACH protocol has been widely used, which is based on a homogeneous network without considering most heterogeneous sensor networks in practical application. In addition, instantly sending data perceived by sensor nodes to the cluster head is not suitable for specific applications such as anti-submarine underwater, in this kind of application environment, most of the sensing data is redundant. Therefore, this paper proposes an improved clustering protocol with data transmission status switchable, which can be used in heterogeneous sensor networks. Cluster heads filter the perceived information and start data transmission link, and then send data to the sink when the information intensity perceived exceeds the preset threshold. Otherwise, cluster heads record the received data and continue receiving data sent by the next round of cluster nodes. We fortunately found in the simulation results that the network lifetime is prolonged several times than the protocol of LEACH.
LDLCT An Instance-Based Framework for Lesion Detection on Lung CT Scans

Medical images have played a crucial role in transforming diagnostic medicine by providing the medical staff with several insights into the health status of every patient. Diagnosis of medical images is a very subjective process which is solely based on the physicians' expertise. In particular, lesion detection is a challenging task due to the various types, shapes and sizes of the lesions in the different organs. Thus, there is a crucial need to build computer-aided frameworks for automated analysis of medical images. In this paper, we present LDLCT, an instance-based framework for automated Lesion Detection on Lung CT Scans. The framework employs Restricted Botlzman Machines (RBM) network for feature extraction stage as an unsupervised feature mapping. The Random Forest (RF) classifier is used to distinguish between pixels from lesion and normal regions. Finally, a post-processing stage is implemented to filter out the false positive candidate lesions. In this study, we select 909 slices with 917 lesions from DeepLesion data set. LDLCT achieves lesion detection sensitivity of 89% with 5 false positives per image, the majority of them can be easily detected by the medical staff.
Experimental Assessment of Motor Core Loss, Inverter Loss and Ringing Phenomenon Under SiC-MOSFET Inverter Excitation

This study investigates the characteristics of core loss of the interior permanent magnet synchronous motor (IPMSM)and inverter loss under the silicon carbide (SiC) MOSFET inverter excitation in various experiments; the IPMSM system under the SiC-MOSFET inverter excitation is tested with two different control methods. First, the SiC-MOSFET inverter is controlled by the pulse-width modulation (PWM)method with a fixed DC-link voltage, very high voltage modulation index of 1.1 and carrier frequency of 10 kHz and 20 kHz; the ringing phenomenon happens in motor voltage in this case, which causes high-frequency harmonics significantly. Then, as an alternative way to reduce the harmonics caused by the ringing phenomenon, the pulse-amplitude modulation (PAM)control technique with an automatically-adjusted DC-link voltage and 120° excitation angle is applied to drive the SiC-MOSFET inverter; this test has never been considered before. In addition, experimental results of motor core loss and inverter loss obtained with the SiC-MOSFET inverter are compared to the results obtained with another silicon (Si)IGBT inverter at same operational conditions for evaluation purpose. The motor rotational speed is also varied in experiments. Furthermore, measured motor voltage and current waveforms and total harmonic distortion (THD) values under the two inverter excitations are analyzed.
Modified U-Net block network for lung nodule detection

Since early detection of lung cancer is the most effective way to improve the survival rate of lung cancer patients, accurate detection of lung nodules is a key step in the diagnosis of lung cancer from CT images. In this paper, a deep learning-based approach is develop to detect lung nodules. First, a segmentation network is used to extract the suspected lung nodules, and then a classification network is used to perform the precise detection of the suspected lung nodules. On the one hand, according to the residual network and shortcut connection in ResNet, the gradient disappearance phenomenon and the more accurate acquisition of features can be effectively solved. We proposes a modified U-Net Block network, by applying the ResNet network to the U-Net network, improves the accuracy of segmentation of suspected lung nodules. On the other hand, multi-classification networks are combined to detect the suspected lung nodule areas, which effectively reduces the problem of false detection in single-class network, especially improving the accuracy of small nodule detection. Finally, superior performance was obtained in the evaluation of the lung nodule detection system using the scoring function, and the FROC score was 0.73.
Massive Amniotic Fluid Aspiration in a Case of Sudden Neonatal Death With Severe Hypoplasia of the Retrotrapezoid/Parafacial Respiratory Group

We report a case of a baby, who, after pregnancy complicated by maternal Addison's disease and Hashimoto's thyroiditis and natural delivery, unexpectedly presented a cardiorespiratory collapse and died 1 hour after birth without responding to prolonged neonatal resuscitation maneuvers. The cause of death was reliably established by carrying out a forensic postmortem examination. More specifically, the histological examination of the lungs showed the presence of abundant endoalveolar and endobronchial cornea scales caused by absorption of amniotic fluid. The neuropathological examination of the brainstem highlighted severe hypodevelopment of the retrotrapezoid/parafacial respiratory group, which is a complex of neurons located in the caudal pons that is involved in respiratory rhythm coordination, especially expiration, in conditions of enhanced respiratory drive, as well as in chemoreception. This neuropathological finding shed new light on the mechanisms underlying the massive amniotic fluid aspiration which led to this early death.
Design Rationale Reuse based on Design Cognitive Script

In the engineering design, the establishment of the design rationale model can not only show the designer's ideas, but also support the reuse of the design rationale. Because of the importance and universality of design reuse in engineering design, the research related to design reuse has always been a hot topic in the field of engineering design. However, there are few general examples of design rationale at present, so the previous knowledge model cannot be correctly expressed. This paper proposes a schema-based design cognitive script model to reuse the design rationale, and focuses on the expression, processing and important processes of design cognitive script. In this paper, two model segmentation techniques, structure-based clustering of design rationale and logic-based extraction of design rationale, are proposed respectively, and a design cognitive script search method using the design cognitive script header as index and based on the inheritance relation of design intention is proposed. Design cognitive scripts cannot only provide the process of design thinking, but also explain the reasons behind them and predict possible problems, so that designers can understand the root causes when reusing the existing design rationale.