Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of Bronchial Breath Sounds for Person Identification

Bronchial breath sound is the sound of turmoil flow produced by the inspiratory air through glottis, trachea or major bronchi. It cannot be only used to diagnose the respiratory tract and lung-related diseases but also used to distinguish one person from the other and thereby identifying patients since it contains personal physiological characteristics. This study captures the bronchial breath sound by using a stethoscope attached on a subject's neck. For each person, the Mel Frequency Cepstral Coefficients (MFCCs) are computed for his/her bronchial breath sounds, and then represented by a stochastic model. Given an unknown breath sound recording, the proposed person identification system determines who among a set of candidate people produced the breath sound by matching the MFCCs of the sound to each of the stochastic models. Furthermore, we apply the i-vector approach in the system to boost the identification accuracy. To evaluate the generality of our experimental results, we additionally utilize other general identification schemes including support vector machine, random forest, and naive Bayes. Our experiments conducted on a dataset composed of 8 persons show that the accuracy of identifying people from their breath sounds can attain 92%.
Design of Inlet Conditions for High Pressure NOx Measurements in Lean Premixed Combustors

Semi–empirical characteristic time models (CTMs) for NOx emissions index (EI) and lean blowoff are used in the design of an inlet condition matrix for measurement of NOxEI from a lean premixed combustor. Such models relate either NOxEI or the weak extinction limit to times representing relevant physical and chemical processes in the combustor. Lean premixed (LP) natural gas/air combustion is considered for the following conditions: inlet temperature, 300–800 K; combustor pressure, 1–30 atm; and equivalence ratio, 0.5–0.7. The NOx model is used to determine combinations of inlet conditions corresponding to greatest NOx sensitivity. A dependence of NOx emissions on pressure is included in the model. Emissions of oxides of nitrogen are found to he most sensitive to variations in inlet temperature and combustor pressure, in the 560–800 K and 20–30 atm ranges, respectively, while sensitivity to variations in equivalence ratio is substantial over the entire range considered. Thus it is found that operating conditions for high thermal efficiency in LP turbine combustors conflict with the goal of lowering NOx emissions, a result consistent with thermal NOx from conventional, diffusion flame combustors.A lean blowoff model is used to estimate the lowest equivalence ratio at which a flame can he held, as well as to determine whether a flame can be stabilised at the operating conditions suggested by the NOx sensitivity analysis. The results suggest a nominal lower limit on equivalence ratio of 0.4, and that a flame can be held for most of the combinations of inlet conditions suggested by the NOx sensitivity analysis. Autoignition of the fuel/air mixture is also considered in relation to the location and/or design of the premixing system.The current NOx CTM is applied to LP natural gas fired data from the literature. A model modification, thought to better represent the fluid mechanics relevant to LP NOx formation, is applied, and its implications discussed.Copyright © 1995 by ASME
Determination and Removal Methods for Cyanogenic Glucoside in Flaxseed

A study was conducted to determine the effectiveness of reducing the hydrogen cyanide (HCN) content of flaxseed by various processing. Flaxseed was processed by oven heating, solvent extracting, autoclaving, microwave roasting, and water boiling. The optimal parameters for each processing methods were gained. Microwave roasting of flaxseed reduced the HCN content by 82%; autoclaving flaxseed reduced its HCN content by 27%; solvent extracting once reduced HCN content by 52%, twice by 80%, and thrice by 89%; and the water boiling reduced HCN by 100%. The determination of cyanogenic glucosides in flaxseed was performed by “picrate method” presented by Dr Howard Bradbury and colorimetric method promulgated by AOAC (Association of Official Agricultural Chemists).
Carbohydrate and lignin partitioning in sorghum stems and blades.

(.) This study was conducted to determine quantities of hemicellulose (HC), cellulose (CL), lignin (LG), and nonstructural carbohydrates (NSC) partitioned between stems and blades of a spectrum of sorghums. Six entries, E35-1, AT×623×«Rio» (primarily forage types), Giza-114 (biomass type), RT×432, AT×623×RT×432, and AT×623×RT×430 (grain types), were produced on a Ships clay (very-fine, mixed, thermic Udic Chromustert) (.)
WHIM Descriptors of Shape

WHIM descriptors are 3D structural descriptors obtained from the (x,y,z)-atomic coordinates of a molecular conformation of a chemical, and are used successfully in QSAR modelling. They are built in so as to capture relevant 3D information regarding different features of molecular structure: size, shape, symmetry and atom distribution. Different weights are used to obtain particular information for each set of descriptors. Recently, some doubts have been raised in the literature regarding the ability of Weighted Holistic Invariant Molecular (WHIM) descriptors of shape (the K non-directional WHIM descriptors) to describe the difference between linear and non-linear molecules. A data set of seventy aliphatic and aromatic chemicals of different shape (linear and branched congeners) has been studied here, and is represented by K descriptors (global non-directional WHIM descriptors of shape) based on different weights. The K descriptors range from 0 for a spherical molecule to 1 for a perfectly linear molecule. The present study confirms the findings, already reported by the author, that the WHIM descriptors of shape (K) are unequivocally able to differentiate linear and non-linear molecules.
Title Development of the paternal brain in expectant fathers during early pregnancy

The human parenting brain network mediates caregiving behaviors. When exposed to the stimuli of their infants, compared with non-parents, both fathers and mothers exhibit distinct patterns of neural activation. As human males, relative to females, do not undergo robust physiological changes during pregnancy, when and how the paternal brain networks begin to form remains unclear. Thus, using functional MRI, we examined brain activation in response to infant-interaction videos in two groups, childless males and first-time expectant fathers during their partners’ early pregnancy before remarkable changes in their partners’ appearances commenced. Multivoxel pattern analysis revealed that expectant fathers’ left anterior insula and inferior frontal gyrus showed incipient changes in response to parenthood during early pregnancy. Furthermore, these changes were associated with several paternal traits, such as a negative image toward parenting. Such external factors might influence the paternal brain’s development during early pregnancy.
Compact microstrip balanced phase shifter with common-mode suppression

Balanced phase shifter (BPS) plays an important role in differential beamforming networks and phased array antenna systems due to the capabilities of differential-mode (DM) phase shift and common-mode (CM) suppression. The compactness of BPS also becomes highly demand for its benefit to miniaturize the system. The reported BPSs can be classified into symmetric type and asymmetric type according to the structure features and analysis method. In this paper, the two types of the state-of-the-art BPSs are presented and discussed. Two symmetric and asymmetric type cases designed by our research group are introduced for demonstration.
A 16b 1.62MS/s Calibration-free SAR ADC with 86.6dB SNDR utilizing DAC Mismatch Cancellation Based on Symmetry

This paper presents a THD improved SAR ADC without any calibration or DEM for DAC mismatch. The proposed SAR ADC has a new method of cancellation of all even order distortions caused by a DAC mismatch. In order to improve THD performance, we focused on symmetric property in the DAC mismatch. This method just needs very simple circuits with a few extra clocks.
Harnessing Ambient RF Waves for Novel Applications

The wireless revolution has created a mobile information society. One key element in this revolution has been the use of RF waves for information transmission. RF waves can be used directly for transmission but it is also possible to harness RF waves that are already in use for other applications. These existing RF waves can be thought of as ambient RF waves or signals and can be harnessed for a wide variety of applications. Using sunlight as an analogy, for ambient RF waves, we can deduce that applications such as cameras, navigation, solar cells, heliostats and heliographs can be readily carried over to ambient RF waves in the form of imaging, device free location estimation, ambient RF energy harvesting, reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC). In this paper we explore technologies and techniques that can harness ambient RF waves for our benefit, without adding further RF transmissions to the environment. In essence we treat the harnessing of ambient RF waves as a single research area rather than a collection of unrelated applications. This allows us to explore and develop new and novel applications. It also allows us to focus on the fundamental phenomena and applications of ambient RF waves.
A 10MHz-1GHz Mitigated Shunt Capacitance Hybrid LNA for Use in a Ka-Band Envelope Detector Baseband Output

A simulated hybrid LNA exhibiting over 45 dB of power gain and <2 dB noise figure from 10 MHz to 1 GHz is designed as a baseband amplification stage for a zero-bias diode envelope detector in on-off-keying systems. The high source impedance of the diode requires mitigation and compensation of parasitic shunt capacitance between the detector and first gain stage. Here, we remove the ground plane between the first transistor stage and the diode output, as well as increasing pad-to-pad and pad-to-via spacing to reduce on-PCB parasitic shunt capacitance. The transistors are low-Cgs HEMTs to further ensure low shunt-capacitance to ground. We also employ inductive peaking to compensate for the dominant pole caused by the source shunt capacitance. The amplifier is expected to dissipate 8 mW from a 0.5 V source.
Eccentric Split Ring Resonator (ESRR) - an Approach Towards Realizing Dynamic Range Rotational Structures

A new SRR structure inspired by the geometry of eccentric circles is proposed. A broadside coupled eccentric split ring resonator (BC-ESRR) is used as a Stator-Rotor assembly where the Rotor moves angularly around the common axis with respect to the Stator. The trajectory of the first eigenfrequency of the proposed structure is skewed to either right or left by 75° with respect to the uniform width broadside coupled SRR’s (BC-SRR) symmetric curve. The possible applications of the structure can be as a rotation sensor, as a chipless RFID tag with a frequency signature or in stereometamaterials.
Security and Privacy of Medical Internet of Things Devices for Smart Homes

The Internet of Things, IoT, provides various applications in the health and care sector, enhancing assisted living, and the ability to remotely monitor patients in hospitals, at homes, and in care facilities [1]. Additionally, many patients who require constant health monitoring prefer the comfort of home monitoring to hospital environments [2]. Therefore, with the increased interest in smart home configurations and the need for Medical IoT, MIoT, device development, we may witness a rise in in-home smart healthcare set-ups. In this paper, we present a Ph.D. research proposing an improve architecture for ensuring security and privacy of smart medical devices in smart home environments.
Millimeter-wave Chipless RFID Tag for Authentication Applications

In this paper, a millimeter-wave chipless RFID tag for authentication applications is presented. The concept is based on the idea that it is extremely difficult to identically reproduce materials that inherently have a random aspect, due to manufacture process variations. This paper introduces the paradigm of the millimeter-wave authentication based on labels without any chip, including an identifier capable to communicating with waves in the millimeter-wave range. For this purpose, millimeter-wave chipless tags without a ground plane are designed using the RF Encoding Particle (REP) technique. This approach establishes a relation between the geometrical parameters of the isolated scatter and its electromagnetic signature. To increase the RCS of the tag an elementary particle multiplication strategy is envisaged. The evaluated probability of error around 17% is reached with a fabricated tag set from the same manufacturer at the same time. This probability is two times lower than the one obtained with a similar approach implemented in the X-band.
A Novel Gated Recurrent Unit Network Based on SVM and Moth-Flame Optimization Algorithm for Behavior Decision-Making of Autonomous Vehicles

The behavior decision-making algorithm plays an important role in ensuring the safe driving of autonomous vehicles. However, existing behavior decision-making methods lack the capability to cope with future motion uncertainty in traffic, because the historical state of vehicles are not considered. This article proposes a novel driving behavior decision-making method EnMFO-ImGRU based on Gated Recurrent Unit (GRU) and Moth-Flame Optimization algorithm (MFO). Four improvements are proposed in EnMFO-ImGRU. First, to consider the driving information of the vehicles on the road, ImGRU is designed based on a double-layer GRU. Second, to promote decisions accuracy, Support Vector Machine (SVM), which has good performance in classification problems, replaces the softmax classifier to train the output of the ImGRU. Third, to promote the classification capability of SVM, MFO is introduced to optimize the key parameters that affect the performance of SVM. Finally, to promote the optimization capability of MFO, we propose the Enhanced Moth-Flame Optimization algorithm (EnMFO). A new position updating method is proposed in EnMFO. The experimental results on the NGSIM dataset show that EnMFO-ImGRU brings higher accuracy than existing methods for the behavior decision-making results of autonomous vehicles.
A Power Efficient ECG Front-End with Input-Adaptive Gain Reaching 67.6-dB Dynamic Range

For Electrocardiograph (ECG) detection, the analog front-end (AFE) needs to distinguish the signals with high amplitude fluctuation, in which normally an ADC with 10 to 12 bit resolution with high dynamic range (DR) is required. In this paper, an input-adaptive control logic is proposed and implemented, which enables AFE to regulate the gain of amplifier according to the amplitude of input signal, which enables an 8-bit SAR ADC reaching 67.6 dB DR near the common-mode level. This design relies on the powerful backend calibration process that the design difficulty of the analog end is converted to the digital back end, which is a good and practical way in nowadays. The proposed structure is implemented with a 0.18\ \mu\mathrm{m}$ standard CMOS process. Measurement results show that the SNDR remains basically unchanged when the input amplitude decreases by a range from 6 dB to 35 dB, and the DR is boosted by 20.3 dB compared with the conventional 8-bit SAR ADC. The ADC and extra control logic consume 8.4 nW under 1 V supply at a sampling rate of 500 S/s.
High-Precision Multicamera-Assisted Camera-IMU Calibration: Theory and Method

This article presents a high-precision single-camera inertial measurement unit (IMU) extrinsic calibration method by tightly fusing the visual information from other cameras. Specifically, multiple additional cameras are added to the monocular camera-IMU system for assisting calibration as we theoretically prove that more cameras used in calibration can lead to smaller lower bound on the covariance of the estimated extrinsic parameters, which then results in better calibration accuracy. Moreover, we provide two degenerative motion conditions in the resulting multicamera visual-inertial system, which impair the calibration accuracy and should be avoided in real application whenever possible. More importantly, we present the requirement of minimum motion for a reliable extrinsic calibration to provide the practical guideline. Finally, the full validation on both simulation and real-world data is demonstrated. By evaluating the Cramér–Rao lower bound on the covariance, the proposed camera-IMU calibration method is shown to be statistically efficient for accurate calibration with errors less than 0.01 m in translation and 0.5° in rotation, which is consistent with the theoretical analysis in this article.
Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks

This paper presents a solution to the current challenges of the imaging radar to respond the demands of autonomy for detection and classification of targets in radar imagery, which traditionally has been considered as clutter. The proposed object detection method is defined in a new way, as opposed to the traditional object detection methods in the radar related contexts. The current paper presents the first application of this novel approach, based on deep neural networks for object detection, on outdoor radar images, as well as indoor images taken in controlled environment. Object detection was performed using two detectors, Faster R-CNN and SSD and the evaluation proved that this method can be successfully used on radar imagery for autonomous applications.
A Novel Active Gate Driver with Auxiliary Gate Current Control Circuit for Improving Switching Performance of High-Power SiC MOSFET Modules

Silicon carbide (SiC) MOSFETs are gaining increasing usage in industrial applications demanding for higher power density and lower power dissipation. Despite the high switching rate nature of this device, some problems, including intensified oscillations, overshoots, electromagnetic interference (EMI) and possible additional losses, do arise. In this paper, a new active gate driver (AGD) with Auxiliary Gate Current Control (AGCC) unit is proposed. To optimize the switching transients, the gate current is regulated throughout the whole switching process by adjustment of the AGCC reference voltage. Advantageously, this regulation is effective both for normal operation and short-circuit fault conditions. Extensive empirical results on a 1.2kV, 300A SiC MOSFET confirmed performance improvement achieved by the proposed AGD, especially on the suppression of induced overshoots and oscillations.
Link Scheduling in Rechargeable Wireless Sensor Networks With Imperfect Battery and Memory Effects

This paper considers the novel problem of deriving a Time Division Multiple Access (TDMA) link schedule for rechargeable wireless sensor networks (rWSNs). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that is used to overcome so called memory effects, and (iii) battery imperfections, i.e., leakage. This paper shows analytically that the battery cycle constraint and leaking batteries lead to unscheduled links. Further, it presents a greedy heuristic that schedules links according to when their corresponding nodes have sufficient energy. Our simulations show that enforcing the battery cycle constraint increases the link schedule by up to 1.71 (0.31) times for nodes equipped with a leaking (leak-free) battery. When nodes have a leaking battery, the derived schedules are on average 1.05 times longer than the case where nodes have a leak-free battery. Finally, the battery cycle constraint reduces the number of charge/discharge cycles by up to 47.41% (45.67)% when nodes have a leak (leak-free) battery. Between leak-free and leak battery scenarios, using the former produces up to 51.46% fewer cycles than the latter.
Landmark Attribute Analysis for a High-Precision Landmark-Based Local Positioning System

GPS signals are not reliable in urban canyons or inside tunnels. In those situations, accurate local positioning is possible through local landmarks. This paper presents the potential accuracy of such a landmark-based local positioning system and the desired attributes of landmarks influencing positioning accuracy. The analysis of the achievable accuracy and the sensitivity of the factors for precise vehicle positioning is performed using LiDAR sensor measurements in a controlled environment and a generic positioning method. The landmark-based positioning can achieve better than 0.2 m accuracy. The diameter and geometric configuration of the landmarks are the most important factors for higher accuracy. The results presented can guide the design and construction of a local positioning system in urban areas.
Deep Learning Models for Heterogeneous Big Data Analytics

in recent times, Big data is modifying the style life of workplaces and thinking by improved performance in knowledge discovering and decision making ever-greater volumes of data are being produced data due to the network of sensors and communication technologies Heterogeneous data is a category of unstructured data with an unknown pace in several ways. Current data analysis techniques are inadequate to handle the huge volumes of data produced, this data difficult to manage, store, handle, interpret, analyze using traditional techniques. Deep learning (DL) is extremely popular among many data scientists and experts thanks to the high precision in speech recognition, image handling, and data analytics. DL has become much more important because it can be used for largescale heterogeneous data. DL has been applied efficiently in several fields and has exceeded most of the traditional techniques, DL algorithmic can study large unclassified data with the ability to select features. This study concentrates on the discussion of a variety of new algorithms that handle this data and DL models that provide greater accuracy for heterogeneous data.
A Wideband LNA with Gm-Boosted and Noise Cancel Technique

This paper presents the design of a 0.1 to 5GHz wide-band common-gate (CG) low-noise amplifier (LNA) in 130nm CMOS technology. The capacitive cross-coupling topology boosts the equivalent transconductance of input CG transistor. The active differential pairs for bias path contribute to not only noise cancel of input CG and bias transistors, but also flexible input matching. Moreover, the circuit is inductorless, only occupying an area of 0.015mm2. The voltage gain ranges from 17.7 to 20.0dB. The minimum noise Figure is 3. 04dB, and IIP3 is -1.7dBm. The circuit consumes 4.4mW under a 1.2V single voltage supply.
Radar Imaging Using Electrically Large Arrays With High Range Resolution at 160 GHz

In order to improve the resolution of imaging radars, electrically large arrays and high absolute bandwidths are required. At a large off-boresight angle of the incident wave, the difference in path length at the antennas becomes a multiple of the range resolution of the radar. Consequently, the radar responses of the targets at the different receiving channels are distributed over a large number of range bins, depending on the direction-of-arrival (DoA) of the target echo. Applying conventional signal processing, the resolution of the radar is considerably reduced. In this paper, the key aspects of DOA estimation are discussed when radars with both a large aperture size and a high absolute bandwidth are employed. A signal processing method is proposed avoiding errors that occur in conventional DOA estimation techniques. As shown by measurements, an angular resolution of 0.4°can be achieved with an array size of 200 An and a bandwidth of 10 GHz.