Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone-based Approach to Enhance Mindfulness Among Undergraduates with Stress

Over the last decade, creating awareness among people has become a common part of the curriculum around the world. To date, researchers have predominately used outcome-based trial designs to understand the efficacy of mindfulness for improving wellness in people. Less research has been directed towards understanding how undergraduates perceive mindfulness experiences. Findings suggest that mindfulness enhances human wellbeing and helps people to develop a greater awareness of their body, mind, and emotions. The main objective of the research was to develop an application, an undergraduate could use to release their stress in tough situations using mindfulness practices. The conducted research project includes VR technologies, Reinforcement learning, Wearable Arduino components together with mindfulness-based stress reduction methods to support the features of the designed application. It may, therefore, provide a key to understanding one of the mechanisms that underlie the observed benefits of mindfulness approaches for mental health and wellbeing. Drawing on a sample of 25 questionnaire-based responses from the users, the generated results indicate the developed algorithm has an accuracy of 96.153%. The conducted research Evidently shows the use of reinforcements learning is a better way of predicting user interests. Implications of these findings are discussed further throughout the study. Moreover, mindfulness stress reduction practices for university undergraduates offer systematic relaxation methods and concrete ways to get rid of recurrent worries, reducing stress and its physical effects.
26.1 A 4.5mm2 Multimodal Biosensing SoC for PPG, ECG, BIOZ and GSR Acquisition in Consumer Wearable Devices

Recently, biosensors have become widely deployed in consumer wearable devices, e.g., smart watches and wrist bands, to track user health conditions during sport, sleep, and daily activity. However, unlike biosignal acquisition in well-controlled medical settings, signal acquisition in consumer devices inevitably suffers from degraded signal quality due to poor sensor interfaces, motion artifacts, and environmental interferences, such as ambient light and power-line coupling, which can be >60dB stronger than the wanted signals. These challenges result in demanding AFE specifications, even under tight constraints on sensor area, chip size, and battery capacity, which are all necessary for wearable devices. This paper presents a multimodal biosensing SoC that addresses these issues to enable reliable Photoplethysmography (PPG), Electrocardiography (ECG), and Bio-impedance (BIOZ) acquisition under highly variable conditions in wearable devices. It contains a 130dB-DR PPG readout, which is 11dB higher than the state of the art [1], to tolerate strong ambient light; an ECG AFE with a pseudo Right-Leg-Drive (RLD) tolerant of >130VPP common-mode interference (CMI) from power-line coupling, which is 3× higher than the technique presented in [2]; and a BIOZ AFE for body-fat measurement using 1cm2 electrodes rather than the typically >10cm2 electrodes in conventional body-fat meters. The area for the entire PPG/ECG/BIOZ AFE is ~3mm2, which is around 50% smaller than prior AFEs with similar features [3], [4].
An OpenCL-Based FPGA Accelerator with the Winograd’s Minimal Filtering Algorithm for Convolution Neuron Networks

Convolutional neural network has been extensively used in image classification, video processing and semantic recognition. Generally speaking, convolutional neural network is accelerated by high power GPU, but this work uses the FPGA to speed up the convolutional neural network because of its configurability and low power consumption advantage over GPU. Especially, OpenCL-based high-level synthesis tools can provide fast verification and implementation flows. This work mainly uses the Winograd’s minimal filtering algorithm to accelerate VGG-16 network on Intel Arria 10 GX FPGA board and has a good effect on 3x3 convolution kernel. The peak performance of 227 GOP/S has been achieved with 544 DSP.
Piezoelectric Thin Film Materials for Acoustic MEMS Devices

Piezoelectric thin film (PTF) materials present a number of unique properties in different applications, such as high sensitivity for micro electro mechanical systems (MEMS) microphones, and a wide dynamic range, wide displacement and low power consumption for MEMS speakers. Generation and detection of acoustic vibration can be made by piezoelectric devices which use the effects of direct piezoelectricity and inverse piezoelectricity. This paper covers popular PTFs and their applications for acoustic MEMS: lead zirconate titanate (PZT) based thin films and lead free PTFs such as aluminum nitride (AIN), zinc oxide (ZnO) and scandium doped aluminum nitride (ScAIN). The design, vibration mode, and fabrication will determine the frequency range of the acoustic MEMS devices. The performance and properties of PTF are the main parameter for certain MEMS applications are introduced. The main ideas of piezoelectric materials are reviewed and introduced for acoustic MEMS transducers.
Calibration-Free Localization Based on Fingerprinting of Channel State Information

Indoor localization is playing a critical role in smart Internet of Things, which require high accuracy with effortless deployment and low cost. Despite decimeter-level accuracy, current localization systems require labor-intensive site survey or the deployment of multiple Access Points and antennas. This paper presents a calibration-free fingerprinting-based localization system implemented on less Commercial Off-the-Shelf (COTS) Wi-Fi devices achieving high accuracy. For this purpose, we first present a comprehensive analysis about how the hardware design (inter-antenna distance) influences the localization performance. Then we promote the localization performance via hybrid inter-antenna distance and context-aware fingerprinting recognition. Finally, we build a prototype on Intel 5300 NIC and conduct experiments in different real-world scenarios with diverse environment. The results show that we can realize the 80% errors within 0.2m, which is more accurate than the existing designs.
Digital Creation of Color Illusion Fabricated by Overlaying Different Colored Translucent Textiles Using Images

Overlaying different colored textiles which are translucent is a straight forward task to complete physically, in contrast this task is difficult to achieve digitally. Amount of information obtained from an image is limited, which is a major difficulty faced when using images to identify the features of a textile such as color, material, texture, thickness and transparency. An algorithmic approach is taken based on three hypotheses; random superimposing, background replacement and color augmentation. These techniques are based on; color identification, background replacement, random selection, pixel superimposing, color blending and image color augmenting. The algorithms are researched, implemented, experimented in-depth and critically compared. Four algorithms are implemented, two based on randomly superimposing and one each based on background replacement and color augmentation. Background replacement algorithm was hardly able to complete the task effectively, thus is the lowest ranked algorithm. In contrast, randomly superimposing and color augmenting algorithms were capable of carrying out the task successfully. Randomly superimposing costed the least time to complete, but the generated images were unnatural whereas color augmenting produced a perfectly natural image though the color of the final output was inaccurate. Further refining the color prediction algorithm is proposed to develop a more effective system.
Design of a THz Microstrip Fast Wave Antenna

A novel terahertz(THz) microstrip fast wave antenna working at 200 GHz is proposed in this paper. Firstly an odd-mode feeding method using CPS is employed to excite fast wave in the THz microstrip structure. Then the THz slot ring antenna is fed by the odd-mode feed network structure based on fast wave theory, which can enlarge the size of antenna for reducing design redundancy of THz antenna, manufacturing difficulty and decreasing fabrication cost. Furthermore, compared with an equally sized ring fed by a common microstrip, simulation results verify that the radiation pattern of our proposed fast wave antenna does not have a deep depression in the zenith direction with a enlarged size. It meets the requirements of many applications.
Smart Protector: A Real-time Theft Prevention System for Transportation Management

Nowadays Intelligent Transportation Systems (ITSs) are widely used for transportation and logistics management. These systems allow transportation providers to track and monitor their goods in real-time and geo-fencing. Most of the ITS frameworks use internet of things (IoT) technology for theft detection and real-time monitoring. This paper presents a new solution named Smart Protector to monitor the status of a sealed package in real-time that can be easily integrated into an existing IoT-based ITS framework. The Smart Protector mechanism is a unique and reliable enhancement for the packages. This system contains three main components, namely, Smart Seal, ESP8266 - 01 microcontroller, and a GPS module. In case of any tampering attempt, the system will notify the relevant parties via email and SMS by obtaining real-time data from the smart seal. This may improve the efficiency of transportation and security. The smart protector is capable of connecting to the nearest Wi-Fi source which can be easily configured. When designing such a device, using a single-board computer is inefficient and impractical due to its high-power consumption. The proposed smart protector uses an ESP8266 - 01 module which has a standby power consumption <1.0mW and an operating voltage of 3.0V ∼3.6V in the standard mode. Deep sleep mode configured in ESP8266 - 01 can be powered up for more than three months using a LiPo battery which has 800 mAh capacity. The device is capable of transferring multi-formatted data through different types of IoT integration protocols such as MQTT, HTTP, Kafka and a few others. Our experimental results show that the proposed system has successfully identified multiple tampering attempts and notified at the same time without having any conflicts or delay. We tested the smart seal with different packaging types and successfully identified the tampering attempts. Moreover, this device will be beneficial to commercial users and the public in general. Finally, this study comes out with recommendations to the researchers to explore low-cost solutions other than expensive satellite communication for message passing during transportation.
Sympathomimetika in der Notfallmedizin: ein Überblick

Pharmakologische Einteilung der Sympathomimetika (▶ Abb. 1) [2] Direkte Sympathomimetika aktivieren biochemisch membranständige Adrenorezeptoren, indirekte Pharmaka wirken über Noradrenalin-Wiederaufnahmehemmung/-Abbauhemmung (MAOund COMT-Abbauenzymhemmung, Noradrenalin [NA, = Norepinephrin, NE]) [3]. Genetische Rezeptorvariabilitäten und Veränderungen der Rezeptordichte/-aktivität bei Erkrankungen führen zu einer hohen Komplexität in der klinischen Notfallpharmakologie.
Kernel Least Mean Square Algorithm with Shrinkage Variable Step Size Approach

A novel kernel least mean square algorithm (KLMS) that uses a shrinkage variable step size is developed. In traditional KLMS, the step size is usually set a constant value for different cases, and this fixed step size methodology leads to undesirable performance in general. To address this problem, a variable step size is obtained by minimizing the energy of the noise-free a posteriori error signal which is obtained by using a known - minimization formulation. According to this approach, a shrinkage KLMS (SKLMS) algorithm is proposed. The SKLMS algorithm outperforms the original KLMS algorithm in terms of the steady-state misalignment. Simulation results show that the proposed algorithm display outstanding performance in chaos time series prediction and nonlinear system identification in terms of testing MSE as compared to the traditional LMS, KLMS, shrinkage LMS, affine projection(AP), and shrinkage AP algorithms.
Cell Image Segmentation Method Based on Convolution Neural Network

Aiming at the problem of the poor cell image segmentation accuracy of traditional segmentation method, this paper introduces a supervised machine learning approach— convolutional neural network. In this paper, we implement training and execution of convolutional neural networks using the Keras, Theano, Numpy, Scipy, and Scikit-image Python packages. And the result shows that we can achieve the image segmentation by using convolutional neural networks.
Gaussian Filtered Gait Energy Template and Centroid Corner Distance Features for Human Gait Recognition

One of the convincing and latest biometric systems is gait recognition because of its ability to unobtrusively identify an individual at a distance and with low-resolution images. This study proposes an efficient method to enhance the performance of the gait detection system. The gait silhouette images are initially processed with two gait portrayal methods as the feature resources: Gait Energy Image (GEI) and Gaussian Filtered-Gait Energy Image (GF-GEI). Further, an effort has been made to present a statistical shape examination method, which is established on GF-GEI, and it is divided into six independent horizontal segments. The centroid corner distance features obtained from these horizontal segments forms the feature vector of the image. The proposed method is assessed on the widely used CASIA A, CASIA B, and OU-ISIR D gait datasets. The empirical results illustrate that the performance of the proposed approach is promising and surpasses some state-of-the-art gait identification methods recorded in literature.
Current Stress Reduction Investigation of Isolated MMC-Based DC-DC Converters

In high voltage direct current (HVDC) and medium-voltage DC (MVDC) grids, isolated modular multilevel converter (MMC)-based DC-DC (IMMDC) converter plays an important role for voltage matching, electric isolation, and power transfer between DC buses. This paper proposes conjoined phase-shift amplitude-ratio (PSAR) index control for IMMDCs. The proposed method uses amplitude ratio index of MMC legs of medium-frequency transformer’s (MFT) secondary side as additional control variables. Compared with conventional single-phase-shift (SPS) control, PSAR control not only provides wider transmission power range and enhances operation flexibility, but also reduces current stress on the MFT and power switches of MMCs. Comprehensive theoretical analysis is provided based on mathematical modeling to show the performance of the converter with SPS and PSAR control. The accuracy of derived formulations and the operation of PSAR control is verified.
29.3 Non-Magnetic 0.18µm SOI Circulator with Multi-Watt Power Handling Based on Switched-Capacitor Clock Boosting

There has been significant recent progress in the implementation of integrated non-reciprocal components based on linear periodically-time-varying (LPTV) circuits [1], [5]. Nevertheless, integrated circulators still require a leap forward in power handling, clock power consumption, insertion loss (IL), and chip area to become compelling when compared with ferrite circulators or integrated reciprocal alternatives, such as the electrical-balance duplexer (EBD) [6]. This paper introduces three innovations - (i) a switched-capacitor clock-boosting scheme, (ii) high-Bragg-frequency quasi-distributed T-lines based on periodically loaded inductors, and (iii) a gyrator based on switched partially reflecting T-lines - that enable such a leap for integrated circulators and for LPTV circuits more broadly. These are showcased in a 1GHz 0. 18µm SOI CMOS circulator that exhibits 2.1/2.6dB TX-ANT/ANT-RX IL (0.3dB better than prior art [4]), +34dBm TX-ANT P1dB (2.5x or 4dB better), and 40% lower chip area, all at 39mW power consumption (4.4x lower).
Dynamic Vehicle Data Gathering via Deep Reinforcement Learning Approach

With the rapid development of vehicular ad hoc networks (VANETs), there have been numerous efforts to big data and analysis of vehicle information for roadside intelligence. However, continuous data gathering is energy consuming and eventually causes data backlog due to the capacity-limited backhaul links, while sparse gathering frequency may miss the timely detection of critical traffic information. Therefore, this paper focus on the dynamic data gathering problem in vehicular networks. In the scenario with environment changes dynamically, we first model the problem as a Markov decision process (MDP), and then propose different deep reinforcement learning(DRL) based maximization frequency matching algorithms, to determine the optimal gathering frequency at each time. The simulation results compare the performance differences of the algorithms, and show the trend of frequency matching in different storage spaces.
A −193.6 dBc/Hz FoMT 28.6-to-36.2 GHz Dual-Core CMOS VCO for 5G Applications

By combining the transformer feedback and VCO multi-core techniques, this paper presents a 0.5 V low voltage 28.6-to-36.2 GHz dual-core VCO for 5G applications. Based on the transformer feedback topology, with the power-ground interconnect inductor and embedded decouple capacitor, the second harmonic real impedance is achieved at the VCO cross-couple pair source terminal. Accordingly, the common-mode current path is reduced and the signal symmetry improved, suppressing the flicker noise up conversion. With a 4 bit switch capacitors and varactors, the proposed VCO achieves a 28.6-to-36.2 GHz (23.5 %) tuning range, and its flicker noise corner is from 250 to 980 kHz. Fabricated in a 65nm CMOS process, the VCO consumes 4.6mW from a 0.5 V supply voltage with a core area of about $0.24\times 0.5$ mm2. At 31 GHz, the proposed VCO achieves a phase noise of −103 dBc/Hz@1MHz offset, resulting in a figure-of-merit (FoM) of −186.2 dBc/Hz and tuning range figure-of-merit (FoMT) of −193.6 dBc/Hz, respectively.
In Situ Caching using Combined TTL-FIFO Algorithm

This research proposes an algorithmic cache arrangement scheme to efficiently utilize existing hardware that are currently plagued with memory wall problem. The proposed scheme exploits straightforwardness of First-in, First-out (FIFO) scheduling algorithm and in situ placement technique. FIFO allows the proposed scheme a fair caching of processes. In situ replacement economically utilizes spaces by replacing the expired process with a new process in the same memory space without flushing. This combination helps reduce operating overheads, which in turn lower power consumption. The benefits of their simplicity and hardware implementable will accelerate operational speed that eventually closes the gap between processing speed and memory access/retrieval speed, thereby lessens the memory wall problem.
A Secure-by-Design Framework for Automotive On-board Network Risk Analysis

Vehicles have evolved from isolated and mechanical systems into complex ecosystems of on-board networks, composed of Electronic Control Units (ECUs), sensors and actuators, which govern their functionalities. These networks have been traditionally designed as trusted, closed systems, but modern needs have opened them to remote and local connections. Researchers have shown that modern vehicles are vulnerable to multiple types of attacks leveraging remote and physical access, which allow attackers to gain control and affect safety-critical systems. Therefore, the interest of manufacturers for embedding security into the design phase of new vehicles is rising. In this paper, we propose a semi-automated and topology-based risk analysis framework that helps in designing and assessing the security of automotive on-board networks. The tool receives the network topology as input and evaluates its security using state-of-the-art risk metrics. Then, it provides the analyst with security-hardened network topologies, as a countermeasure against the most dangerous attacks. We evaluate our approach on known topologies and demonstrate its effectiveness.
Wireless Localization of Mapping Node Identifiers to Known Positions

Recently, wireless localization has attracted great interest. However, a type of localization problem has not received much attention and has not been well studied, in which all locations of nodes are predetermined, but when nodes are deployed to these positions, the relationship between identifiers and locations of nodes is unknown. Applications with such localization problems include smart lighting, structural health, smart roads and industrial monitoring, etc. In this paper, we investigate how to map node identifiers to known locations. Although the locations of nodes are given, it is still challenging to map the node identifier to known location because the combination of mapping possibilities is huge, and incorrect mapping may occur when several known locations are close to each other. To address this problem, we first propose a localization algorithm, called Q-Hop, in which the distance of two nodes is measured by a fine-grained hop count. Then, we propose a mapping method that maps the node positions estimated by Q-Hop to known locations. The simulation results demonstrate that our solution achieves satisfactory mapping accuracy, which will provide helpful insights for applications with such localization problems.
Energy Efficiency of Downlink Cell-Free Massive MIMO System with Full-Pilot Zero-Forcing

This paper investigates the downlink energy efficiency (EE) of cell-free massive multiple-input multiple-output (MIMO) system, where all the geographically distributed access points (APs) with large numbers of antennas coherently serve a number of independent users. In contrast to conventional zero-forcing (ZF) precoding scheme, this paper aims at suppressing the inter-cell interference by leveraging on the full-pilot ZF(fpZF) precoding strategy. In particular, the fpZF precoding can be implemented at the APs–in a distributed manner, there is no channel state information (CSI) exchange between the APs and the central processing unit (CPU). In this work, based on the obtained EE, a downlink EE maximization problem is explored under each user’s quality-of-service (QoS) requirement and each AP’s power constraint. Considering the proposed EE maximization problem is non-convex, a sub-optimal sequential convex approximation (SCA) algorithm is presented. Simulation results demonstrate that the proposed SCA algorithm can converge to a stable value after a few iterations and outperform the full power transmission counterpart.