Miracle-dz/UniFAR-FTUs huggingface dataset:
Monolithic Integration of Pressure/Humidity/Temperature Sensors for CMOS-Mems Environmental Sensing Hub with Structure Designs for Performances Enhancement

This study presents a monolithic integration of pressure, humidity and temperature sensing units to achieve an environmental sensing hub (Fig. 1). The proposed environmental sensing hub is realized using the TSMC $0.18\mu \mathrm{m}$ 1P6M CMOS platform, and follow-up in-house post-CMOS processes. The presented environmental sensing hub has three merits: (1) single-side microfabrication processes: for monolithic integration of pressure, humidity, and temperature sensors, (2) vertical integration design: for the temperature sensor and the high sensitive pressure sensor with corrugated diaphragm, and (3) novel capacitive humidity sensor design: pillar sensing electrodes array and surrounded polyimide filler for response time enhancement. Measurements show performances of proposed environmental sensing hub are: sensitivity of pressure sensor with corrugated structure is 0.969fF/kPa (0.197fF/kPa for reference type w/o corrugated structure), humidity sensor with sensitivity of 2.897fF/%RH and response time of 12.1 seconds, and resistance temperature detector with sensitivity of 0.26%/°C.
Least Square Saliency Transformation of Capsule Endoscopy Images for PDF Model Based Multiple Gastrointestinal Disease Classification

Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) diseases, such as bleeding, ulcer, and tumor. In order to avoid a tedious manual review process of long duration WCE video recordings, automatic disease detection schemes have received significant attention from the researchers. In particular, instead of the conventional approach of dealing with a single disease, developing a unified scheme, which is capable of detecting multiple GI diseases, is getting more importance while being challenging. In this paper, a unified computer-aided scheme is developed for detecting multiple GI diseases from WCE videos based on a proposed least-square saliency transformation (LSST) followed by a probabilistic model-fitting approach. Commonly in the training phase, image-level labeling of images is used, as pixel-level annotations are available only for a small number of images. In view of utilizing the knowledge from pixel-level annotated diseased images, an LSST scheme is proposed to extract a set of optimum prior coefficient-vectors which is later used to capture the salient pixels of interest (POI) in a larger WCE image dataset that do not have pixel-annotations. The intensity distributions of salient POI are modeled by a suitable probability density function (PDF) and the fitted PDF parameters are utilized as features in the proposed supervised hierarchical classification scheme. A large number of WCE images obtained from publicly available WCE videos are used for performance evaluation and it is found that the result obtained by the proposed method outperforms the results obtained in case of some state-of-the-art methods.
Searching for Truth in the Post-Truth Age

Despite the increasing use of social media platforms for information and data collection, its immoderate nature often leads to the spread of rumors - unverified information. At the same time, the opening of social media platforms offers the opportunity to explore how users share and discuss input, as well as to automatically evaluate the assessment of their verification using techniques and different methodologies. To overcome these problems, this study aims to contribute effectively to the area of rumor verification by scraping the web and verifying the information provided. To this end, a Rumor Tracking System is proposed in which a web scraping technique is used to evaluate the news verification based on various sources and features for news checking. The proposed system automatically detects these sources and features on the websites and verifies the content by consulting an information matching algorithm and completing a truth table of the sources and features provided. A combination of information on all the sources and features gathered through the websites is maintained by the system, which is used to determine whether an article is based on rumor or not.
Predicting Depression Symptoms in an Arabic Psychological Forum

Recently, social media platforms have been widely used as a communication tool on social networks. Many users have utilized these platforms to reflect their personal lives. These users differ in terms of background, language, age, and educational level. The close relationship between these platforms and their users has created rich information that is related to these users and can be exploited by researchers. Their posts can be analysed using natural language processing (NLP) to predict psychological traits such as depression. However, to the best of our knowledge, no study has utilized social media to predict mental health disorders in Arabic posts, especially depression. Therefore, in this study, we investigate the application of natural language processing and machine learning on Arabic text for the prediction of depression, and we evaluate and compare the performance. Our research method is based on the collection of Arabic text from online forums and the application of either a lexicon-based approach or a machine-learning-based approach. In the former approach, the ArabDep lexicon is created, and a rule-based algorithm is used to predict depression symptoms using the created lexicon; however, in the latter approach, the data are annotated with the help of a psychologist, text features are extracted from Arabic posts, and machine learning algorithms are ultimately applied to predict depression symptoms. We demonstrate that our applied approaches exhibit promising performance in predicting whether a post corresponds to depression symptoms, with an accuracy of more than 80%, a recall of 82% and a precision of 79%.
Shape Manipulation of Diffusion Curves Images

Diffusion curves image (DCI) is a kind of vector graphics with its geometric primitives defined by a set of Bézier curves. As one of the vector graphics, editability is an important property for diffusion curves. However, most of the current DCI shape manipulation methods are typically limited to single curve. It is tedious for users to edit the shape of DCI by deforming each curve individually, especially with the increasing number of curves. In addition, the image contents cannot well preserved because there is no constraints among curves. We present an efficient and flexible DCI manipulation method by combining global and local deformations. The overall shape is easily changed by our global manipulation tool with a few user interactions, while the local manipulation tool is used to modify details. These two tools can be combined to obtain desired effects. In both cases, the constraints during deformation preserve the quality of the editing results. Our experimental results demonstrate that the proposed method provides more convenient control for DCI shape editing than existing techniques based on diffusion curves.
High-Density Microneedle Array (HIDMA): An in vivo Electroporation Method for Low-Voltage Gene Delivery

In this work, we report a novel in vivo electroporation method for gene delivery utilizing high-density microneedle array (HIDMA). Owing to the minimized needle spacing, hexagonal distribution, and tri-polarity electric connection of the device, the low voltage and uniform electric field during electroporation can dramatically reduce cell damage and enhance delivery efficiency. The HIDMA is constructed with modified acupuncture needles, and can be customized for both superficial and deep tissues with different electroporation areas. According to the in vivo experiments on DNA plasmid delivery, our 0.6 and 1.2-mm-long microneedles with $\sim 5\ \mu \mathrm{m}$ tips and $750\ \mu\mathrm{m}$ pitch, can penetrate the outer skin to reduce the contact impedance that achieves effective electroporation within tens of volts and without obvious injury.
Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field.
A 110.3-bits/min 8-Ch SSVEP-based Brain-Computer Interface SoC with 87.9% Accuracy

A wearable brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied to enable paralyzed patients to communicate with others. However, target identification accuracy and information transfer rate (ITR), which are general performance indicators of SSVEP-based BCI system, still need to be further improved in wearable devices. This paper proposes 8-channel SSVEP-based visual target identification system-on-chip (SoC) to improve the ITR of low-cost wearable BCI device while dramatically reducing the computational complexity without accuracy degradation. The proposed target identification algorithm, CCA-CR, includes algorithmic optimizations and candidate reduction (CR) method that reduce signal processing load by at least 75% without degrading target identification accuracy and ITR. This paper also proposes a matrix decomposition processor (MDP) that calculates complex matrix arithmetic operations through systolic array based CCA-CR engines. Compared to the state-of-the-art CCA-based algorithm, the proposed SoC implemented in FPGA exhibits 63% better ITR with 33% reduction of recording time without accuracy degradation.
BSBL-based Block-Sparse Channel Estimation for Affine Precoded OSTBC MIMO-OFDM Systems

This work presents affine precoded superimposed pilot-based sparse channel estimation in orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. A pilot-based block sparse Bayesian learning (P-BSBL) technique is developed initially, which leverages the sparsity as well as the spatial correlation of the MIMO channel for improved estimation. Subsequently, a data aided-BSBL (D-BSBL) technique is presented for joint maximum likelihood (ML) decoding of the symbols and sparse channel estimation, which is shown to lead to a further improvement in the accuracy of the estimated channel. In addition to a significant decrease in the mean squared error (MSE) of estimation, the proposed schemes are also shown to lead to a substantial increase in spectral efficiency over the existing schemes. Moreover, they are also applicable in ill-posed CSI estimation scenarios, where conventional approaches fail due to a large delay spread. The Bayesian Cramér-Rao bounds are derived to analytically benchmark the estimation performance followed by simulation results that show the improved performance of the proposed techniques.
An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the grinding wheel is worn or not, this study applies artificial intelligence technology to attempt to learn the experiences of auditory recognition of experienced operators. Therefore, we propose an intelligent system based on machining sound and deep learning to recognize the grinding wheel condition. This study uses a microphone embedded in the grinding machine to collect audio signals during the grinding process, and extracts the most discriminated feature from spectrum analysis. The features will be input the designed CNNs architecture to create a training model based on deep learning for distinguishing different conditions of the grinding wheel. Experimental results show that the proposed system can achieve an accuracy of 97.44%, a precision of 98.26% and a recall of 96.59% from 820 testing samples.
Network-Coded SD-WAN in Multi-Access Systems for Delay Control

An industrial IoT network may involve hundreds of IoT end-devices deployed over large geographical wide areas networks (WAN). These devices are expected to operate over several years and their individual communication interface may be gradually upgraded with multiple communication links in order to fit the changing application and data rate requirement. As industrial networks are expected to perform with high reliability and availability, a continuous monitoring and maintenance of these communication links is essential. A software defined WAN (SD-WAN) based solution allows to adaptively monitor and manage the connectivity links as per the application requirement. In this paper we design and implement a working SD-WAN based multi-access system for upstream communication between an IoT device and the control center. Each IoT device may have multiple cellular interfaces, and we control the end-to-end delay jitter variation across the different interfaces via an intelligent traffic splitting scheme.We provide an analysis of end-to-end delay jitter under LTEs uplink connection and use the structural results so obtained to design a traffic split mechanism. We validate our analysis, and provide traffic split performance results from our working multi-access system.
Sim-To-Real Transfer Learning Approach for Tracking Multi-DOF Ankle Motions Using Soft Strain Sensors

A data-driven approach has recently been investigated for identifying human joint angles by means of soft strain sensors because of the corresponding modeling difficulty. However, this approach commonly incurs a high computational burden due to the voluminous amount of data required and the time-series-oriented network architecture. Moreover, the nature of soft sensors makes the problem worse due to the inherent nonlinearity and hysteresis of the material. In this study, we developed a novel wearable sensing brace design for measuring multiple degrees of freedom (DOF) ankle motions to minimize hysteresis and to improve the measurement repeatability and developed a computationally efficient calibration method based on sim-to-real transfer learning. By attaching the soft sensors to shin links rather than directly to the ankle joint, the effects of external disturbances during joint motions were minimized. To calibrate the sensors to body motions, transfer learning was used based on the results from musculoskeletal simulation(OpenSim) and sensor data. The average tracking error for ankle motions using the proposed method was found to be 12.0° for five healthy subjects, while the direct deep neural network approach showed an error of 17.9°. The proposed method could be used to calibrate the soft sensors with 1000 times faster training speed while maintaining comparable tracking accuracy with a smaller amount of data.
A Review of Security Machanism in internet of Things(IoT)

The web of the IoT has been started but also had some serious issues related to its security. It doesn't matter to what level its vulnerability affects. It can be domestic or any enterprise level. Billions of devices are linked with the internet. It is the network of objects and sensors by which these devices exchange data with each other without human involvement. On a large scale, there is a struggle to protect data from leaking. The main purpose of this is to review the survey of the work done in security in the specific area. As a whole, this study is measuring the flaws in the security related to the Internet of Things. Design and Protocols are studied in this survey for securing the communications between so-called ‘things'. Open inspection problems and security application challenges in IoT security are still present. This study is aimed to be a helpful guidebook to learn the vulnerabilities and threats related to security in IoT and increases the security design.
Designing and Characterizing a Multi-core PCF SPR Biosensor

In this paper, a multi-core double D like PCF based plasmonic biosensor is proposed and analyzed. The sensor performance is investigated by applying a mode solver based numerical method, FEM. Both sensible and gold coatings are deposited on the external surface for comfortable and more practical realization. This biosensor exhibits the maximum wavelength sensitivity of 8000 nm per RIU and amplitude sensitivity of 522 per RIU with resolution 1.25×10−5 RIU and 1.92×10−5 RIU respectively in the detection range between 1.45 and 1.48. This can also show good linearity with 170 FOM. Owing to the simple design and highly sensitive nature, the proposed biosensor can be precisely applicable for biomolecular analytes detection.
Lens Antenna Arrays Aided Co-Existing Radar and Communication Systems With Energy Harvesting

Coexisting radar and communication (CRC) systems on the same spectrum band have been recently recognized as a promising approach to solve the spectrum scarcity problem. Additionally, with the new requirements of achieving higher energy efficiency and restoring the consumed power, an initiative has been exploited to embed both information and energy within the same signal. Motivated by that, this paper presents a unified model to implement the concept of simultaneous wireless information and power transfer (SWIPT) in the coexistence environment between radar and communication systems. Specifically, we propose a novel energy harvesting based CRC model (CRCEH), in which the signals transmitted from both the communication and radar transmitters are precoded towards their conventional targets, communication receiver and radar targets respectively, as well as to an energy harvesting receiver. This system is capable of solving the spectrum scarcity problem as well as supporting low power devices with their energy requirements from the energy harvested at the energy harvesting receiver. The design problem is formulated as maximizing the radar detection probability subject to communication capacity and energy harvesting constraints. However, directly processing the data from the radar receiver requires high computational complexity and hardware costs. In order to circumvent this issue, this paper also proposes to deploy lens antenna array (LNA) to CRCEH system to sharply direct the signals to the energy harvesting receiver together with their respective destinations. Since LNA is able to focus the signal power on a subset of antennas, we may only deal with the data from the selective antenna subset associated with the maximal power. Numerical results show that the proposed method is able to achieve improved performance in terms of radar detection under communication capacity constraints compared to the conventional approaches besides the great benefit of harvesting the energy to feed some limited energy devices.
Performance of Spatially Coupled LDPC Codes over Underwater Acoustic Communication Channel

Underwater acoustic (UWA) channel is complex because of its multipath environment, Doppler shift and rapidly changing characteristics. Many of the UWA communication- based applications demand high data rates and reliable communication. The orthogonal frequency division multiplexing (OFDM) system is very effective in UWA channels and provides high data rate with low equalization complexity. It is a challenging task to achieve reliability over these channels. The low-density parity-check (LDPC) codes give a better error performance than turbo codes, for UWA channels. The spatially-coupled low-density parity-check (SC-LDPC) codes have been shown to have the capacity-achieving performance over terrestrial communication. In this paper, we have studied by simulation, the performance of protograph based SC-LDPC codes over shallow water acoustic environment with a communication range of 1000 m and channel bandwidth of 10 KHz. Our results show that SC-LDPC codes give 1 dB performance improvement over LDPC codes at a Bit Error Rate (BER) of 10−3 for the same latency constraints.
Mitigating Jamming Attacks in a MIMO System with Bursty Traffic

In this paper, we study the role of multiple antennas in mitigating jamming attack under Rayleigh fading environment with random arrival of data at the transmitter. The jammer is assumed to have energy harvesting capability with infinite battery size. The outage probabilities under jamming attack are derived for Rayleigh fading scenario with different assumptions on the number of antennas at the transmitter and receiver. The outage probability is also derived for the Alamouti space-time code under the jamming attack. The average service rate and delay performance of the system are characterized with random arrival of data and energy at the transmitter and jammer, respectively. The derived results help to explore the benefits of using multiple antennas in improving average service rate and delay of the system under jamming attack. It is also found that exploitation of space and time diversity with the use of space-time code can improve the performance of the system significantly even under the jamming attack.