Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumer Technology Perspective of a Trinomial Random Walk Model

Recently, the consumer technology association has widened the horizons of the consumer electronic society to include a variety of technologies such as the Internet of Things, Artificial Intelligence, vertical farming and etc. Moreover, among many others, the consumer technology highlights the importance of organic dairy cows and they are well beings so that timely based energy balance and body condition scores of individual cows are needed to be thoroughly studied. In this paper, we shall introduce a Trinomial Random Walk Model as an illustration of consumer technology for analyzing changes in dairy cow body conditions from time to time. In doing so, we will use the 5-point scale system with increment 0.25. Some experimental simulation results are presented by varying the parameters involved.
Underdetermined Wideband DOA Estimation Based on Proximity Searching DCS-SOMP

Underdetermined wideband direction of arrival (DOA) estimation using sparse array is studied in this paper. The wideband array signal processing model based on difference coarray is deduced firstly and transformed into a distributed compressive sensing (DCS) problem through sparse representation. The DCS Simultaneous Orthogonal Matching Pursuit (DCS-SOMP) algorithm is modified from the perspective of proximity searching to improve estimation accuracy. Simulation results verify the effectiveness of the modification and show that the proposed algorithm has significant advantage in computational complexity.
UAV for Energy-Efficient IoT Communications: Matching Game Approach

Unmanned aerial vehicles (UAVs) are a promising technology to provide an energy-efficient and cost-effective solution for data collection from the ground Internet of Things (IoT) devices. In this paper, the optimal associations that provide reliable connections between UAVs and IoT devices are investigated. We aim at maximizing the IoT devices' benefits, by assigning them to most suitable UAVs. We formulate the problem as a many-to-one matching game where UAVs and IoT devices are the players. In this game, the players rank one another based on individual utility functions that capture their needs. Each IoT device aims to minimize its transmitting energy while meeting its SNR requirements and each UAV aims to maximize the number of served IoT devices while respecting its energy constraints. Simulation results show that the proposed approach provides a low average total transmit power, ensures fast data transmission and optimal utilization of the UAVs' bandwidth.
Discrimination of Brain Activity using Near-Infrared Spectroscopy Signal Change in Prefrontal Cortex During Mental Arithmetic Task

BCI is a technology that enables brain communication using brain signals, and has gained much attention in the field of occupational therapy that supports communication for patients with intractable neurological diseases. In this study, with the aim of applying BCI, we investigated whether it is possible to distinguish the brain activity state using near-infrared spectroscopy (NIRS) signal between mental arithmetic task and a resting state measured in healthy subjects. As a result, it was found that the accuracy rate of two class classification at cognitive task and at rest was 62–73%. From the results of this study, it is suggested that by constructing a BCI system using a brain activity state estimation method using NIRS signal, it may be possible to establish a new occupational therapy method for communication support for patients with neurological intractable diseases.
Optimized Process Conditions for Plethysmography Measurement using Multispectral Video

For monitoring vital data in telecare systems, performing noncontact plethysmography measurement is important. We herein propose a precise plethysmography measurement method using multispectral video and investigate optimized process conditions for that measurement. Using independent component analysis of combination signals of RGB videos and 900-nm nIR video yields a better reproduction of plethysmography than other combinations. Concerning region-of-interest selection, using a region of high nIR signal area is favorable for plethysmography estimation. With these optimizing conditions, we attempt obtaining more precise measurements of vital data using multispectral video.
High-Mobility Polymer Monolayer Field-Effect Transistors with Graphene Electrodes

Polymer monolayer field-effect transistors provide an excellent platform to investigate the charge transport mechanism, however, most studies only reported the mobility of ≤ 10<sup>−2</sup>cm<sup>−2</sup> V<sup>−1</sup> S<sup>−1</sup>. This study select the graphene as electrode materials, significantly improving the morphology of polymer monolayer at the edge of the electrodes. As a result, a maximum saturated mobility of 0.73 cm<sup>−2</sup> V<sup>−1</sup>S<sup>−1</sup>is obtained. Additionally, the channel length is found to have minor effect on the charge transport ranging from 20 to 100 µm.
An improved scan matching algorithm in SLAM

Simultaneous localization and mapping (SLAM) technology has always been the research focus of robot navigation in unknown environment. Aiming at the problem of cumulative errors of robot pose in the localization process of SLAM algorithm based on particle filter, a loop detection algorithm based on graph-SLAM was proposed. The algorithm uses constraints to adjust the robot attitude at different moments. In this paper, the constraint refers to the scanning matching of lidar. In the process of drawing, when the robot returns to the known area, if the current laser scanning is successfully matched with the previous laser scanning, the robot's posture can be adjusted to eliminate the accumulated errors caused by the odometer. In the process of laser scanning matching, the method of grouping step threshold value judgment is proposed to match the laser point cloud, which can effectively reduce the computation. Experimental results show that the proposed algorithm can effectively eliminate the cumulative errors of positioning and achieve a better mapping effect.
A Machine Learning Approach for Grading Autism Severity Levels Using Task-based Functional MRI

Autism is a developmental disorder associated with difficulties in communication and social interaction. Autism diagnostic observation schedule (ADOS) is considered the gold standard in autism diagnosis, which estimates a score explaining the severity level for each individual. Currently, brain image modalities are being investigated for the development of objective technologies to diagnose Autism spectrum disorder (ASD). Alterations in functional activity is believed to be important in explaining autism causative factors. This paper presents a machine learning approach for grading severity level of the autistic subjects using task-based functional MRI data. The local features related to the functional activity of the brain is obtained from a speech experiment. According to ADOS reports, the adopted dataset is classified to three groups: Mild, moderate and severe. Our analysis is divided into two parts: (i) individual subject analysis and (ii) higher level group analysis. We use the individual analysis to extract the features used in classification, while the higher level analysis is used to infer the statistical differences between groups. The obtained classification accuracy is 78% using the random forest classifier.
Adaptive Output Feedback Tracking of Nonlinear Time-Delay Systems Subject to Nonsymmetric Dead-Zone Input

In this paper, an output feedback control scheme was proposed to achieve global adaptive output tracking by prescribing arbitrarily small error for a class of dead-zone nonlinear systems including time-delay and uncertain nonlinear functions with unknown output-polynomial growth rate. Two dynamic gains based respectively on identification and non-identification mechanism were introduced in the observer and controller. Picking the appropriate Lyapunov-Krasovskii functional, the constructive proof was given by combining high-gain scaling technique and one-step backstepping method. Finally, two simulation examples showed the effectiveness of the control scheme.
An mm-Wave Synthesizer With Robust Locking Reference-Sampling PLL and Wide-Range Injection-Locked VCO

In this article, a two-stage millimeter (mm)-wave frequency synthesizer with low in-band noise and robust locking reference-sampling techniques is presented. Using a two-stage scheme allows separately dealing with the low phase noise (PN) frequency synthesis in the first stage and the mm-wave frequency multiplication in the second stage, achieving the best overall power efficiency. In the first stage, a voltage domain reference-sampling phase detector (RSPD)-locked loop (RSPLL) is adopted to achieve both low PN and robust locking without additional frequency locking loop. A reference reshaping buffer is implemented to improve the phase detector gain and in-band PN. The reference rising/falling time is programmable to achieve optimal RSPLL performance even under external disturbances. The second stage employs an injection-locked voltage-controlled oscillator (ILVCO) for 4 $\times $ frequency multiplication. A low-power digital frequency tracking loop (FTL) detecting actual frequency errors is implemented in order to achieve wide operation range for the ILVCO while using a high ${Q}$ tank with low power. The prototype synthesizer was fabricated in a 45-nm partially depleted silicon on insulator (PDSOI) CMOS technology. The first stage 9-GHz RSPLL achieves 144-fs integrated jitter with 7.2-mW power consumption, achieving a figure of merit (FoM) of −248 dB and the overall mm-wave synthesizer achieves 251-fs integrated jitter with 20.6-mW power consumption at 35.84 GHz, achieving an FoM of −238.9 dB.
Industrial Shop-Floor Integration Based on AMQP protocol in an IoT Environment

Decision making and data management are critical features for enterprises to improve their productivity and revenues. With the advancement of Industry 4.0, smart factories, the Internet of Things (IoT) and Cyber-Physical Systems (CPPS) currently are using as the controller during the manufacturing products process. This new approach results in new insights that can enhance decision-making processes and provide a competitive business advantage. This study deals with the implementation of CPPS using low-cost devices in a simulated factory to control the industrial process and integrate shop-floor communications using the AMQP (Advanced Message Queuing Protocol) protocol. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks in a real process.
A Blockchain-Based Approach for the Creation of Digital Twins

The rapid advancements in computing, storage, communications, and networking technologies have enabled the creation of Digital Twins (DTs). A DT is a digital representation of a real-world physical component, product, or equipment. A DT can be used for 3-D design, testing, simulation, and prototyping prior to the manufacturing of the physical component. Once a physical component is in operation, a DT can be used for configuration, monitoring, diagnostics, and prognostics. It is expected that DTs will gain significant attention in the foreseeable future, and will play a key role in Industry 4.0. However, today’s approaches, systems, and technologies leveraged for the creation of DTs are mostly centralized and fall short of providing trusted data provenance, audit, and traceability. Also, data related to transactions, logs, and history are not secure or tamper-proof. In this paper, we propose a blockchain-based creation process of DTs to guarantee secure and trusted traceability, accessibility, and immutability of transactions, logs, and data provenance. Our proposed approach uses smart contracts to govern and track transactions initiated by participants involved in the creation of DTs. Our approach also employs decentralized storage of interplanetary file systems to store and share DTs data. Moreover, we present details on our system design and architecture, implementation, and algorithms. Furthermore, we provide security and cost analysis, and show how our approach fulfills the requirements of DTs process creation. We make the smart contract code for creating DTs publicly available on Github.
Age of Information and Delay Tradeoff with Freshness-Aware Mobile Edge Cache Update

Mobile edge caching is an effective way to reduce the service delay of content delivery, where the popular contents can be pro-actively stored in proximity to users. In practice, the cached contents should be updated timely to avoid information staleness, in case that the information of a content changes with time and environment. However, cache update consumes additional transmission resources, which can degrade the delay performance. This work studies the fundamental tradeoff relationship between the content freshness (depicted by the age of information (AoI)) and service delay in mobile edge caching networks, and proposes a freshness-aware cache update scheme to achieve the AoI-delay balance. In specific, the base station will fetch the latest version of a content before delivery, if the AoI is larger than a certain threshold (i.e., update window size). The average AoI and service delay are derived in closed forms through approximated analysis of queueing systems, revealing a tradeoff relationship with respect to the update window size. Extensive simulations are conducted on the OMNeT++ platform, which validates the analytical results. Both the analytical and simulation results show that the proposed scheme can flexibly balance the average AoI and delay on demand, by tuning the update window size. Furthermore, the proposed scheme can also avoid frequent update in case of heavy content requests, whereby the AoI and delay are regulated by setting the appropriate update window size.
Low-Profile High-Gain and Wide-Angle Beam Scanning Phased Transmitarray Antennas

Combining the inherent multiple beam capabilities of transmitarray antennas (TAAs) and the flexible fields launched by phased array antennas (PAAs), PAA-fed TAAs are proposed in this paper for high-gain and wide-angle beam scanning in millimeter-wave (MMW) band. After thoroughly analyzing the operating mechanisms of the PAA-fed TAAs, a divisionally multi-focal method is proposed to deploy the TAA elements, which can be better adapted to the PAA feed whereby the scanning performances are improved. Subsequently, two PAA-fed TAAs with feeding distance-to-diameter ratios ( $H/D\text{s}$ ) of mere 0.28 are numerically demonstrated at 39 GHz, wherein both TAAs are composed of the square subwavelength low-pass elements and the cavity-backed bowtie patch antenna is identified as the PAA feed element. Simulated results show that the first design example achieves a conical beam steering over ±30° with a 30.9 dBi peak gain and gain drop below 2.1 dB. The second design can scan across ±50°/±10° along the E/H planes, respectively, together with the gain losses of 3.3 and 1.6 dB from the peak value of 31.1 dBi. Finally, the first design case is fabricated and measured for experimental verification purpose, whose measured results agree reasonably well with the simulated ones.
Fading-Averaged Spectral Efficiency Analysis of Millimeter Wave Communication Links over Nakagami Fading Channels

Wedged between microwave and infrared waves, the millimeter Wave (mmWave) spectrum can be used for high-speed fifth generation (5G) wireless communications. In this paper, we consider a mmWave wireless link via small scale and large scale fading channels. We investigate the average spectral efficiency performance of the mmWave wireless channel and also evaluate average interference by deriving a tractable approximation for it. Specifically, we derive analytical expressions for the fading-averaged spectral efficiency (FSE) and study its behavior in various scenarios, and for different system parameters. Furthermore, we also obtain upper bound for the average spectral efficiency. To quantify the spectral efficiency performance, we perform numerical simulations and interpret the results qualitatively. The analysis that we develop is useful for mmWave communication systems and networks.
Modeling and Analysis of Micro-ring Resonator for Bio-sensing Applications

In this paper, a micro-ring resonator for bio-sensing application is designed. The micro-ring resonator is designed with a single ring waveguide and two bus waveguides. The light traverse through the drop port of bus waveguide and couples to the ring waveguide is observed for various refractive indices. The analysis of the design with various refractive indices is performed by varying the covering medium on the top surface of the waveguide structure. The resonance parameters like free spectral range, transmission spectrum and resonance shifts are observed in the modeling of the micro-ring resonator.
Multi-Channel AlGaN/GaN In-Plane-Gate Field-Effect Transistors

In this letter, we present a multi-channel in-plane-gate field effect transistor (MC-IPGFET). In the proposed device, multiple vertically stacked two-dimensional electron gases (2DEGs) are simultaneously controlled by lateral in-plane gates formed with the same multi-2DEG stack. The multi-channel heterostructure allows to increase carrier density in the channel while keeping high electron mobility. Besides, the in-plane gate geometry provides an effective control of multiple channels with a smaller intrinsic gate capacitance. As compared to single-channel IPGFETs, multi-channel structure resulted in a three-time enhancement in current density and transconductance, offering opportunities for efficient scaling up of in-plane gate devices. High current density of 4.35 A/mm along with 2.05 S/mm transconductance are achieved in an optimized device. The effective control of the multiple high-mobility channels along with the reduced intrinsic capacitance of the in-plane gate open a pathway for new device concepts.
Design of High Gain Low Noise Amplifier with Current-Reuse Technology for UWB Systems

A high power gain low noise amplifier (LNA) is usually required for ultra-wideband systems (UWB) based on TSMC 0. 18μm CMOS technology is reported. In this paper, current-reuse technology is applied with LC low-pass filter (LPF) for reduce power consumption. Cascode amplifier provides power gain to solve low power gain at middle band frequency. The LNA circuit achieves high power gain (|S21|) of 20.54dB with input return loss (|S11|) better than −12.15dB, output return loss (|S22|) better than −11.99dB and noise figure (NF) of 4.3dB ± 0.69dB for frequencies 3.1-10.6 GHz. The fabricated LNA occupies a die area of 1.198 × 0.952 mm2including pads and draw 10.87mW from 1.3-V dc supply. The figure of merit (FoM) is 11.26.
Sparse Coding with Outliers

Sparse coding is invalid to learn parts-based representations when data is corrupted by outliers. In this paper, matrix completion is considered into sparse coding to handle outliers and a novel sparse coding method is proposed to learn a robust subspace. Experiments on the ORL dataset with salt and pepper noise and contiguous occlusion demonstrate that our proposed sparse method is more effective and robust in achieving a robust subspace.
Time Transfer in Wireless Media for Distant Device Synchronization through UHF Link

Time synchronization is most vital in event management and co-relating time critical real time data. InterRange Instrumentation Group (IRIG) time formats are well established time synchronization format used worldwide. This paper describes two customized techniques which utilizes the IRIG-B time code format. These techniques provide time synchronization to remote devices without any wired connection. In one of the techniques IRIG-B AC signal is Frequency modulated (FM) in particular Ultra High Frequency (UHF) with proper modulation settings and transmitted by a low power amplifier. In other technique, IRIG-B DC signal modulates two sub-carriers to generate a Continuous Wave Frequency Shift Keying (CWFSK) signal, which is further up-converted to the UHF band. A remote receiver down converts and demodulates the signal to baseband domain. The baseband signal is further demodulated and decoded to synchronize the remote devices or sensors
Performance Analysis of Blood Vessel Segmentation in Sclera Images

Sclera is used for the authentication of biometric threats. Sclera is the opaque and white outer layer of the eyeball. This and only those part of eye has noticeable blood veins which are unsystematically disseminated and differs from one person to another, hence it is used for the recognition systems. The main focus of this paper is to develop algorithm of Sclera identification and segmentation with high accuracy and less time complexity. The system comprises of segmentation using active contours without edges method then enhancement, extraction and matching process. UBIRIS.v1 and UTIRIS databases are used so has to obtain high accuracy and less complexity.
Dynamic Tuple Scheduling with Prediction for Data Stream Processing Systems

For data stream processing systems such as Apache Heron, workload imbalance across processing instances often causes significant system performance degradation. To mitigate such issues, Apache Heron leverages a naive throttling-based back-pressure scheme, which may lead to unexpected system disruption. This calls for a finer-grained control to distribute data stream units (tuples) between successive instances, a.k. a. tuple scheduling, which well adapts to data stream variations and workload discrepancy. Besides, the benefits of predictive scheduling to data stream processing systems still remain unexplored. In this paper, we formulate tuple scheduling problem as a stochastic network optimization problem, with careful choices in the granularity of system modeling and decision making. With non-trivial transformation, we decouple the problem into a series of online subproblems. By exploiting unique subproblem structure, we propose POTUS, an efficient, online, and distributed scheduling scheme that employs the power of predictive scheduling but requires only limited system dynamics to achieve a tunable trade-off between communication cost reduction and system queue stability. Theoretical analysis and simulations show that POTUS effectively shortens response time with mild-value of future information, even in the face of misprediction. Our solution is also applicable to other data stream processing systems.
Parasitic Inductance Reduction Design Method of Vertical Lattice Loop Structure for Stable Driving of GaN HEMT

GaN HEMT is one of the most promising switching devices to be used. GaN HEMT has very low input capacitance enabling fast switching and very low conduction losses due to the 2DEG structure. However, the effect of parasitic inductance is prominent during fast switching, and the device's stability is degraded due to the small driving voltage range. The low threshold voltage can also cause the switch to turn on in unwanted situations. Therefore, the parasitic inductance should be reduced to stably drive sensitive GaN HEMT. Effects caused by parasitic inductance can be reduced through the circuit and physical value of parasitic inductance can also be reduced by design such as PCB. In this paper, a new method is used to reduce parasitic inductance, rather than reduce the influence of parasitic inductance through the circuit. Through this method, gate loop inductance and power loop inductance are reduced by more than 50% compared to the existing parasitic inductance reduction method. Using this method reduces ringing and overshoot caused by parasitic inductance, and reduces the rise and fall time of voltage and current. It also reduces switching on and off losses.