Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Principles for a Novel Lightweight Configurable PUF Using a Reconfigurable FET

This article presents a mixed signal-based novel configurable ring oscillator (CRO) physical unclonable function (PUF) architecture for resource constrained Internet of Things (IoT) devices. PUF can protect the IoT devices through their unique inherited fingerprints obtained from the manufacture variations. However, the conventional ring oscillator (RO) PUF need multiple replicas of ROs to produce a large set of challenge response pairs (CRPs) which invalidates its usage in an IoT environment. Using the concept of reconfigurable hetero material hetero dielectric FET (HM-HD-RFET) proposed in this manuscript, we have designed a novel CRO which allows 512 challenges and a sigma delta analog to digital converter (ADC) at the end generates a stream of response bits. HM-HD-RFET can be configured into four different combinations of transistors through two polarity gates (PGs) and their switching can be controlled using a control gate (CG). Next, we compare HM-HD-RFET with configurable silicon FET (Si-RFET) and homogenous dielectric FET (HM-RFET) to show that the proposed device performs well in all of the four configurations with negligible ambipolarity. The device characteristics is realized in Silvaco Atlas tool, circuit implementation is done in Cadence using Verilog-A based look table approach and properties of CRO PUF are computed in MATLAB.
Physical Layer Security Proposal for Wireless Body Area Networks

Over the last few decades, and thanks to the advancement of embedded systems and wireless technologies, the wireless sensors network (WSN) are increasingly used in many fields. Many researches are being done on the use of WSN in Wireless body Area Network (WBAN) systems to facilitate and improve the quality of care and remote patient monitoring.The broadcast nature of wireless communications makes it difficult to hide transmitted signals from unauthorized users. To this end, Physical layer security is emerging as a promising paradigm to protect wireless communications against eavesdropping attacks. The primary contribution of this paper is achieving a minimum secrecy outage probability by using the jamming technique which can be used by the legitimate communication partner to increase the noise level of the eavesdropper and ensure higher secure communication rate. We also evaluate the effect of additional jammers on the security of the WBAN system.
Performance Analysis of Multi-Cell Full-Duplex Cellular Networks

We analyze the performance of a cellular network, where Poisson point process distributed half-duplex (HD) downlink (DL) and uplink (UL) users are served by multiple full-duplex (FD) base stations (BSs). To address the surge in interference in the network due to the simultaneous operation in time and frequency of the FD BSs, we (a) adopt a self-interference cancellation scheme at each BS, and (b) apply linear interference alignment in each cell to cancel the intra-cell interference. Further, to better capture the distribution of the FD BSs, we model the BSs as a Matérn hard-core point process, in which a minimum distance is imposed between points. The performance of both UL and DL users is analyzed by deriving general expressions and closed-form approximations for the outage probability and throughput. Next, simulations are carried out for both macro and micro cell environments under both FD and HD operations with respect to various network parameters. Our results reveal several fundamental characteristics and the necessary conditions required for the successful deployment of such networks.
Ultra-Compact Multimode Waveguide Bends Based on the Inverse Design

A method of designing silicon multimode waveguide bends using inverse design algorithm is presented in this paper. High–performance and ultra-compact multimode waveguide bending structures are realized by using a special curve consisting of multiple arcs with different radii of curvature. The bent waveguide supporting three modes with an effective bending radius of only 9.35um is designed and tested. The theoretical excess losses of TE0, TE1 and TE2 modes are less than 0.04 dB in a wide spectral range from 1500 to 1600 nm, and the crosstalk’s between all guided modes are all lower than –26 dB. Besides the fabrication of the present structure is simple without additional etching step.
Accelerating 3D Vertical Resistive Memories with Opportunistic Write Latency Reduction

The 3D Vertical Resistive Memory (3D-VRAM) is emerging as a promising non-volatile memory (NVM) technology to construct large capacity main memories in future computing systems. In addition to low energy-efficiency and non-volatility, ReRAM (Resistive Memory) achieves excellent density and scalability by vertically stacking multiple layers of cross-point arrays. However, 3D-VRAM arrays have enormous amount of sneaky paths, which degrade write performance and reliability dramatically. In this paper, we propose a collection of techniques to reduce write latency in 3D-VRAM arrays. We propose an in-memory data encoding scheme to reduce the worst-case number of low resistance state (LRS) cells, a data pattern estimator for assessing cell resistance distributions, and a write time reduction scheme that opportunistically reduces RESET latency with runtime data patterns. On average, our proposed design reduces write latency by 25.98×, improves overall performance by 6.92× and reduces dynamic energy consumption by 52.4% compared to a baseline design.
Automated Maxillofacial Segmentation in Panoramic Dental X-Ray Images Using an Efficient Encoder-Decoder Network

The panoramic dental X-ray images are an essential diagnostic tool used by dentists to detect the symptoms in an early stage and develop appropriate treatment plans. In recent years, deep learning methods have been applied to achieve tooth segmentation of dental X-rays, which aims to assist dentists in making clinical decisions. Because the original images contain plenty of useless information, it is necessary to extract the region-of-interest (ROI) to obtain more accurate results by focusing on the maxillofacial region. However, a fast and accurate maxillofacial segmentation without hand-crafted features is challenging due to the poor image quality. In this study, we create a large maxillofacial dataset and propose an efficient encoder-decoder network model named EED-Net to solve this problem. This dataset consists of 2602 panoramic dental X-ray images and corresponding segmentation masks annotated by the trained experts. Based on the original structure of U-Net, our model structure contains three major modules: a feature encoder, a corresponding decoder, and a multipath feature extractor that connects the encoding path and the decoding path. In order to obtain more semantic features from the depth and breadth, we replace the convolution layer with the residual block in the encoder and adopt Inception-ResNet block in the multipath feature extractor. Inspired by the skip connection in FCN-8s, the lightweight decoder has the same channel dimension as the number of segmented objects. Besides, a weighted loss function is used to enhance segmentation accuracy. The comprehensive experimental results on the new dataset demonstrate that our model achieves better accuracy and speed trade-offs for maxillofacial segmentation than the latest methods.
Exponentiated magnitude spectrogram-based relative-to-maximum masking for speech enhancement in adverse environments

In this study, we extend the capability of the method of relative-to-maximum masking (RMM) in speech enhancement by further leveraging the importance of each time-frequency unit in the spectrogram of the noise-corrupted signal. Our recently developed RMM is shown to enhance the speech quality in adverse noise situations by pursuing a multiplicative mask for the speech spectrogram in a test data-driven manner. Regardless of its simplicity and no requirement of any learning data and procedure, RMM can behave quite well for speech enhancement especially for low signal-to-noise ratio (SNR) scenarios. This study focuses on refining RMM in order to enhance its speech enhancement ability particularly for speech signals at moderate and high SNRs while maintaining its performance for low-SNR signals. The resulting new algorithm, termed exponential magnitude spectrogram-based RMM (with a short-hand notation “EMS-RMM”) differs from the original RMM whereby EMS-RMM uses the exponentiated version of the magnitude spectrogram to determine the used mask, while RMM uses the original magnitude spectrogram. This novel presented method, EMS-RMM, is evaluated on a subset of the TIMIT database, corrupted by three types of noise at six SNRs. The speech quality evaluation results in terms of Perceptual Evaluation of Speech Quality (PESQ) scores reveal that by setting a proper exponent coefficient (power factor), EMS-RMM can outperform the baseline results significantly, and it behaves fairly better than the original RMM.
Dual-Output LUT Merging during FPGA Technology Mapping

Modern commercial Field-Programmable Gate Array (FPGA) architectures support dual-output look-up tables (LUTs). If the number of total inputs in two small LUTs do not exceed the constraint, e.g., 5 in Xilinx UltraScale+ series, we can pack them into one dual-output LUT to reduce area, i.e., the number of LUTs. However, previous works have not fully utilized this feature. They usually generate single-output LUTs in the technology mapping phase and merge LUTs in a later packing phase. In this situation, they cannot get LUT merging information during technology mapping and will generate some single-output LUTs that are not suitable for merging. In this work, we directly generate dual-output LUTs in the technology mapping phase and propose a novel cut-based mapping flow. The mapping flow consists of several mapping passes with different cut selection metrics. In each pass, we first compute the priority single-output cuts of each node. Then, we merge dual-output cuts from the priority cuts to generate a mapped LUT netlist. Finally, we do some local refinement to further improve the merging rate and reduce area. Experimental evaluation shows that our mapping flow can merge up to 14.89% more LUTs and save up to 13.98% area on average, compared to the state-of-the-art technology mapping tool ABC, without worsening the total delay.
Remedial Action Scheme based on Automatic Load Shedding for Power Oscillation Damping

This paper presents an algorithm for automatic load shedding as a Remedial Action Scheme (RAS) for power oscillation damping. The algorithm computes the exact amount of power for the load shedding in all buses and then detects the critical areas in the system. Therefore, the trip actions are only in the areas where are necessary. In addition to this, the RAS is equipped with an oscillation monitoring stage since the system presents oscillatory transitions. The proposed RAS methodology is based on the online computation of the PV curves and the voltage stability margin for calculating the amount of power to trip and used a load shedding strategy based on modal participation factors to identify the most critical areas. On the other hand, the algorithm identifies ringdown data for the detection of oscillations, and Prony's analysis monitors the damping of oscillations to determine online if the load disconnection has the desired effect. Performance evaluation of the proposed scheme has been made on a 12-bus network and, the results show excellent damping of oscillations for critical contingencies and an increase in the stability margin of the whole power system.
A Weak Fault Diagnosis Method Based on Sparsity Overlapping Group Lasso for Rolling Bearing

The rolling bearing is a vital part of modern equipment. It has great significance to effectively extract fault periodic pulses from bearing signals. Due to the noise mixed in bearing signal, the extraction of weak fault feature is facing difficult. For this, a new method for diagnosis of rolling bearing is developed in this paper, based on weighted sparsity norm and overlapping group sparse/shrinkage (OGS), which called sparsity overlapping group lasso (SOGL). The signal obtained from working rolling bearing is prone to be interfered with noise and occur coupling in time domain, so the algorithm processes signal in the Fourier domain, and combines with iterative shrinkage threshold (ISTA) and majorization–minimization (MM) to solve the model proposed in this study. Because signal mixed with noise will reduce the processing accuracy, based on the variational mode decomposition (VMD), this paper derives a simple and fixed center frequency filtering algorithm to reduce the useless components in the signal and improve the effect of SOGL. The SOGL is applied to simulated signals and measured signals. From the experimental results, the weak fault periodic pulse obtained by SOGL is obviously prominent, and this method has better performance in feature extraction compareng with the method of fast Fourier transform and OGS (FFT-OGS).
Lip Reading modeling with Temporal Convolutional Networks for medical support applications

Automated Lip Reading (LR) task is the process of predicting a spoken word using only visual information of a sequence of frames. This sequence modeling task has been approached with Convolutional Neural Networks (CNNs) combined with Long Short-Term Memory networks (LSTM). In this work, a novel scheme for modeling LR sequences with a combination of Temporal Convolutional Networks (TCN) driven by the feature vectors produced by CNN is presented. More specifically, the contribution of this work is two-fold. Firstly, a novel approach that utilize the TCN topology as an alternative way to deal with the sequential data of the LR task is presented. Secondly, this approach is evaluated on a new real-world challenging dataset particularly designed for the problem of LR in Greek words related to biomedical and clinical conditions. More specifically, the Greek words of the dataset are selected to be words that a patient would like to communicate when receiving medical treatment using the frontal camera of a mobile phone. Experimental results indicate that the proposed CNN-TCN architecture can surpass recurrent oriented approaches based on CNN-LSTM while also providing major benefits for deployment in model hardware architectures and more stability during training.
IoT-CAD: Context-Aware Adaptive Anomaly Detection in IoT Systems Through Sensor Association

The deployment of Internet of Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. The new security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, the majority of works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is often overlooked. In this paper, we utilize IoT sensors that capture the physical properties of the system to ensure the integrity of IoT sensors data and identify anomalous incidents in the environment. We propose an adaptive context-aware anomaly detection method that is optimized to run on a fog computing platform. In this approach, we devise a novel sensor association algorithm that generates fingerprints of sensors, clusters them, and extracts the context of the system. Based on the contextual information, our predictor model, which comprises an Long-Short Term Memory (LSTM) neural network and Gaussian estimator, detects anomalies, and a consensus algorithm identifies the source of the anomaly. Furthermore, our model updates itself to adapt to the variation in the environment and system. The results demonstrate that our model detects the anomaly with 92.0% precision in 532ms, which meets the real-time constraint of the system under test.
Utilizing the eSIM for Public Key Cryptography: a Network Security Solution for 6G

The future brings a whole suite of technical challenges that are no match for the current standard, 5G. Therefore, those challenges must be resolved by the next generation, 6G. Although features such as extremely high data rate is important, it is even more important to plan the security at this phase. In this paper, we propose to use traditional public key cryptography in 6G instead of experimental technologies such as quantum communications, artificial intelligence, or blockchain. We do so by utilizing the subscriber identity module (SIM) to store the cryptographic keys needed for authentication. While the capabilities of current SIMs are limited by their physical attributes, future SIM technologies such as eSIM show great promise to enable sufficient resources for our scheme as they are virtual. The proposed scheme is simpler, easy to implement, requires no third party, cost effective, and utilizes algorithms that have proven their security for decades.
PRISME: Toward a model linking tangible interaction and domain-specific tasks in mixed reality environments

The PRISME model introduced in this article is part of ongoing research in ergonomics on the design and manufacture of industrial operator platforms. The underlying research objective is based on new advances in Mixed Reality when used as an intermediate stage within the simulation process. While using Virtual Environments as such a way holds great potential in cutting down production costs, it also requires user interactions to be fit-to-use to each specific line of work. PRISME is introducing an innovative solution to this issue by proving an automatic link (removing the need for domain-specific adaptations) between operator tasks and interactions with their platform. This research will be presented in two stages: a generic typology of interactors allowing to represent the majority of tangible and MR devices will be followed by an interaction model based on MASCARET’s activity description meta-model syntax. Finally, an aeronautical use case will validate the model by simulating the standard operations performed by an airplane controller.
A DBN Hardware Accelerator for Auditory Scene Classification

As the population ages and enters the aging society, related medical problems are gradually being taken seriously, and the deterioration of hearing due to aging is also a common problem. However, hearing aids are easily affected by different auditory scene, and different compensation methods are needed in the different auditory scene. In this paper, the deep belief network (DBN) is used to identify the scene of the auditory scenes. After the deep neural network operation, the current auditory scene is judged. In this paper, the memory requirement for the DBN is reduced in post-training quantization using the K-mean clustering algorithm, and 93.743% memory space of the DBN is reduced with only 1.65% accuracy loss.
An Implementation of Area Skyline Query to Select Facilities Location Based on User's Preferred Surrounding Facilities

Selecting a good location to build facilities such as stockiest to support e-business becoming important parts of strategic decision as a profound effect on supply chain excellence in the market competition and operation. Previous location selection methods have their predefined candidate points. However, in a real-world situation candidate points for selection is not always available. This research presents an implementation of Grid Based Area Skyline Query (GASKY), a big data-based approach to select a group of interesting areas by using preferred surrounding facilities without considering any candidate point. In order to provide the efficacy of this approach, we equipped a dataset based on a real case study at a sub-district around industrial estate. For initial steps, four different numbers of grids were tested in each dataset, with size of 144 (derived from 12 x 12 grid), 256 (16 x16), 400 (20 x 20), and 625 (25 x 25) grids, with restaurant and housing complex acts as the preferred desirable surrounding facilities. In parallel, similar competitor’s food supplier, meat poultry shop, garbage dump, and chemical plants act as the undesirable surrounding facilities. This approach worked well to provide solution in the target area in real-world problems. The result shows that the recommended interesting areas reflect priority location without any pre-defined list of candidates.
Implantable Electrodes Based on Poly-aniline (PANI) and Silicone for Neural Sensing/Stimulations

Implantable bioelectrodes have the potential to advance neural sensing and muscle stimulation in peripheral nerve injuries. The current emerging prostheses rely on the use of metals to assist the nerve recovery process, which is often slow. The implantable electrode works as a bridge between the proximal end of the injured peripheral nerve and the respective muscle. Current electrodes suffer from several drawbacks including the high cost, high interfacial impedance, and stiffness, hence, damaging the body soft tissue. This paper discusses the fabrication and characterization of a novel, low-cost, flexible bioelectrodes based on silicone, and polyaniline (PANI). This work synthesized and characterized three types of implantable electrodes based on their electrochemical and mechanical properties; where PANI and silicone were varied. The electrode samples were fabricated and characterized for their conductivity, capacitive behavior and their mechanical properties. The PANI based samples displayed a bulk impedance of 10.7 kΩ with an impedance of 1.3 MΩ at the frequency of 1 kHz; furthermore, it had a modulus of elasticity of 0.1468 MPa. The results for the silicone with PANI showed promising electrochemical and mechanical characteristics with flexible and ductile properties when compared with the values reported in the literature.
Thermal Image based Remote Heart Rate Measurement on Dynamic Subjects Using Deep Learning

In this work, we propose a method of heart rate estimation by using the infrared thermal images as input. When the heart beats that causes the blood vessels to periodically vasoconstriction and vasodilation, there will be a tiny periodic temperature change on face area. Therefore, the temporal signal from thermal images is considered as the input to our designed DEWNet (DEnseWave Net) CNN model for heart rate estimation. In the experiment, we collected a dataset containing 24 subjects who were running on the treadmill with five different running speeds. Consequently, using this dataset the average error is 13.592 bpm, which shows its feasibility for real-world applications.
A Novel Three-Dimensional Coordinate Positioning Algorithm Based on Factor Graph

In this paper, a novel three-dimensional coordinate positioning algorithm based on factor graph is proposed to improve the measurement accuracy of the indoor global positioning system (iGPS) under large scale conditions. Different from the traditional iGPS positioning algorithm based on the least squares estimation, which has the problems of fixed solution model, low confidence results and poor stability, this proposed algorithm utilized Bayesian filtering to solve the coordinate positioning problem. Aiming at the character of plug and play for iGPS, a factor graph model based on Bayesian network is built, and then a sum product algorithm is used to convert the fixed model into the form of the product of every node, which reduces the independence of measurement information, and improves the confidence of the results. Furthermore, to further improve the positioning accuracy of the algorithm, an idea of maximum posterior estimation is integrated into the proposed algorithm, which enhances the stability of the algorithm at the same time. In order to verify the effectiveness of the proposed algorithm, a series of simulation and prototype tests have been carried out. The results show that compared with the traditional positioning algorithm based on least squares estimation, the accuracy of the proposed algorithm is improved by about 50%, and the positioning accuracy can achieve 0.3mm within a range of 10 meters, which realizes a high-precision measurement under large scale conditions.