Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Device-to-Device Continuous Authentication Protocol for the Internet of Things

The Internet of Things (IoT) is a heterogeneous environment that connects billions of devices. Thus, it is a significantly high-value target for attackers and suffers from several threats, especially impersonation attacks during the session. Moreover, the denial of service attack (DoS) threatens IoT environments, as it affects the availability and energy of communicating devices. Continuous authentication solves session hijacking since it checks user legitimacy during the session. Several continuous authentication schemes were proposed to authenticate users to IoT devices, while few works addressed device-to-device authentication. Therefore, it is essential to authenticate devices because if one device gets compromised, then the whole system is at risk. Continuous authentication between devices differs from user-to-device authentication since it cannot rely on biometrics and passwords. This research proposes a fast and secure device-to-device continuous authentication protocol that relies on devices’ features (token, battery, and location), and mitigates DoS attacks using shadow IDs and emergency keys. Moreover, it takes the sensor movement into account while preserving privacy. To evaluate the robustness and validate the security of the proposed protocol, we conducted informal and formal analyses using Scyther. In addition, we tested its performance to establish computation costs relative to the system counterparts. The results show the protocol is robust against security threats, incurring reasonable computational costs.
Passive Hand Rehabilitation Training Through Robots: an Iterative Learning Control Approach

Robots are widely used in the field of medical rehabilitation to assist patients to conduct rehabilitation training. Due to the repetition nature of the rehabilitation training, this paper proposes an iterative learning controller equipped with feedback mechanism for the passive hand rehabilitation training, which is based on a cable-driven hand exoskeleton robot. Thanks to the use of the information from previous iterations, the desired trajectory can be perfectly tracked over a finite duration. Moreover, the monotonic convergence of the proposed iterative learning controller can be achieved under a sufficient condition. In addition, our iterative learning controller is applied to the hand exoskeleton robot in both the simulation tests and the physical trajectory tracking experiments, which demonstrates its effectiveness and desired control performance.
A Tree-based Mortality Prediction Model of COVID-19 from Routine Blood Samples

COVID-19 has been declared by The World Health Organization (WHO) a global pandemic in January, 2020. Researchers have been working on formulating the best approach and solutions to cure the disease and help to prevent such pandemics in the future. A lot of efforts have been made to develop a fast and accurate early clinical assessment of the disease. Machine Learning (ML) has proven helpful for research and applications in the health domain as a way to understand real-world phenomena through data analysis. In our experiment, we collected the retrospective blood samples data set from 1,000 COVID-19 patients in Jakarta, Indonesia for the period of March to December 2020. We report our preliminary findings on the use of common blood test biomarkers in predicting COVID-19 patient mortality. This study took advantage of explainable machine learning to examine the data set. The contribution of this paper is to explain our findings on predicting COVID-19 mortality, including the role of the top 11 biomarkers found in our dataset. These findings can be generalized, especially in Indonesia, which is now at its highest peak of the epidemic. We show that tree-based AI models performed well on predicting COVID-19 mortality, while also making it easy to interpret the findings, as they lend themselves to human scrutiny and allow clinicians to interpret them and comment on their viability.
Analysis of the Effect of Benefit and Trust to the Intention Using Mobile Payment and Its Impact on Positive WOM

The objective of this study was to determine the effect of benefits and trust on the intention to use mobile payment and its impact on positive WOM in the DKI Jakarta area. Primary data were obtained from questionnaires that 400 respondents answered in the Jakarta area. Data analysis techniques using Structure Equation Modeling with software LISREL 8.7. This study indicates the benefit of using mobile payments, continuance intention, intention to recommend, and trust in using mobile payments.
Acquisition and Enhancement of Remote Human Vocal Signals Based on Doppler Radar

In order to achieve remote voice acquisition, a 9.75-GHz microwave Doppler radar with a high-sensitivity coherent homodyne demodulator is used to detect voice signals. However, the combined noise sources, including background noise and shock noise, are included in the Doppler audio radar-captured voice, which seriously damages the quality of speech. In the current work, a multi-stage enhancement method is used to enhance Doppler audio radar-captured voice. The first stage is dictionary learning, which uses the K-singular value decomposition algorithm to train speech and noise dictionaries. The noise training data come from the Doppler audio radar-captured shock noise signals under the influence of different relative motion modulations. The second stage generates a position mask of the shock noise. The third stage is the removal of background noise and shock noise. The removal of background noise adopts our proposed constrained low-rank sparse matrix decomposition algorithm based on power spectral density. The final speech spectrum with shock noise removed is obtained through sparse restoration using the LARC algorithm. The experimental results show that the 9.75-GHz Doppler audio radar can effectively acquire human voice signals, and the proposed multi-stage enhancement method can effectively remove the background noise and shock noise in the radar speech, thereby improving the quality of the audio radar voice. The obtained results imply the potential to implement practical Doppler audio radar systems for human voice signal acquisition.
A Two-Stage Low-Complexity Human Sleep Motion Classification Method Using IR-UWB

With increasing concern about sleep quality, human sleep motion recognition has come into sight. Recent years have witnessed that almost all researches use deep neural networks for human motion recognition based on radar sensors. However, it is difficult to deploy these methods in edge computing in the case of limited hardware resources. This paper proposes a two-stage low-complexity method for real-time human sleep motion classification using an ultra-wideband (UWB) radar, which can be applied in edge computing. Firstly, we implement power burst curve (PBC) detection to decide whether sleep motions occur. Secondly, a method named zero-forcing processing for radar spectrum is proposed in order to reduce the number of parameters of classifiers. Later, in stage one, we propose a three-feature extraction approach based on micro-Doppler signatures and utilize support vector machine (SVM) to roughly classify micro-motion and body-movement motions. In stage two, a long short-term memory (LSTM) neural network with fewer parameters is applied to further classify body movements. Experimental results show that the proposed method has a low computational complexity, and can reach the average accuracy of the five typical sleep motions over 97% using train and test split samples, and 91.50% using the leave-two-subject-out (LTSO) samples.
CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels

Contactless palmprint recognition has recently made significant progress in palm-scanning payment and social security. However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural network (CompNet) with constrained learnable Gabor filters is proposed for contactless palmprint recognition. The proposed CompNet is built on multisize competitive blocks, which are applied to effectively exploit the rich direction ordering information of the palmprint patterns by means of the ad-hoc softmax and channel-wise convolution operations. Compared to the current deep neural networks, the backbone of the proposed network contains only very few parameters, making it quite easy to train, especially on small-scale datasets. Experimental results obtained on four popular contactless palmprint datasets demonstrate that the proposed CompNet achieves the lowest equal error rate compared to the most commonly used methods.
Earbud-Embedded Micro-Power mm-Sized Optical Sensor for Accurate Heart Beat Monitoring

This work demonstrates that accurate hear beat monitoring, via photoplethysmography (PPG), can be performed at micro-power consumption level with an ear worn device. This is achieved thanks to the combination of a highly sensitive low power PPG chip, an optimal optical module, and an ergonomic design of the wearable device. The monolithic optical sensor is fabricated in a 180nm CMOS image sensor (CIS) process and features an area of 1.5mm by 1.5mm only. It implements an array of pixels, newly designed for PPG, and reaching a quantum efficiency of 85% up to 800nm. The presented sensor consumes 60<inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> at 122Hz sampling frequency, including digital communication. The optical design optimization enables high signal-to-noise ratio (SNR) PPG signals with less than 10<inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> average LED current. Heart beat monitoring, using the presented wearable device, is performed on 7 subjects and compared with a medical electrocardiogram (ECG). The earbud device correctly detects 98.47% of the beats on a total of 72.21 hours of recordings. The inter-beat interval (IBI) estimation from these recordings features a mean absolute error (MAE) as low as 7.52ms.
Hybrid Optimization Toward Proactive Resilient Microgrid Scheduling

Microgrids are one important lever to increase power system resilience and to tightly integrate renewable energies at the same time. Commonly, an optimization-based proactive scheduling controls assets in advance in a cost-effective way and ensures that contingencies may be successfully mitigated. However, often strong simplifications are introduced to manage the high computational complexity of scheduling, which can adversely impact fault mitigation. To consider essential phenomena such as power flow limitations and low-level control capabilities in detail, a novel hybrid scheduling approach is presented that integrates mathematical programming and arbitrary nonlinear constraint models via decision trees. A detailed case study compares the new method to an extended hybrid scheduling approach from literature. It is demonstrated that hybrid optimization can efficiently solve proactive resilient scheduling problems and that the tree-based algorithm provides a feasible solution, even in case the reference algorithm fails. Details on the convergence of both algorithms give further insights into the working principles and show that the novel method quickly finds a feasible solution that is successively improved afterwards. By the novel combination of highly-developed solvers for both mathematical programming and detailed asset models it is expected that this study further supports the operation of power systems and reduces costly reserve requirements.
Low-Latency Detection of Epileptic Seizures from iEEG with Temporal Convolutional Networks on a Low-Power Parallel MCU

Epilepsy is a severe neurological disorder that affects about 1 % of the world population, and one-third of cases are drug-resistant. Apart from surgery, drug-resistant patients can benefit from closed-loop brain stimulation, eliminating or mitigating the epileptic symptoms. For the closed-loop to be accurate and safe, it is paramount to couple stimulation with a detection system able to recognize seizure onset with high sensitivity and specificity and short latency, while meeting the strict computation and energy constraints of always-on realtime monitoring platforms. We propose a novel setup for iEEG-based epilepsy detection, exploiting a Temporal Convolutional Network (TCN) optimized for deployability on low-power edge devices for real-time monitoring. We test our approach on the Short- Term SWEC-ETHZ iEEG Database, containing a total of 100 epileptic seizures from 16 patients (from 2 to 14 per patient) comparing it with the state-of-the-art (SoA) approach, represented by Hyperdimensional Computing (HD). Our TCN attains a detection delay which is 10s better than SoA, without performance drop in sensitivity and specificity. Contrary to previous literature, we also enforce a time-consistent setup, where training seizures always precede testing seizures chronologically. When deployed on a commercial low-power parallel microcontroller unit (MCU), each inference with our model has a latency of only 5.68 ms and an energy cost of only 124.5 μJ if executed on 1 core, and latency 1.46 ms and an energy cost 51.2 μJ if parallelized on 8 cores. These latency and energy consumption, lower than the current SoA, demonstrates the suitability of our solution for real-time long-term embedded epilepsy monitoring.
Heart Rate Monitoring System Using Feature Extraction in Electrocardiogram Signal by Convolutional Neural Network

A new deep learning architecture, which is heart rate monitoring system using feature extraction in electrocardiogram signal by Convolutional Neural Network (CNN). Electrocardiogram based healthcare applications is presented in a federated context. The proposed system correctly diagnoses arrhythmias using an auto encoder and a classifier, both based on CNN. The module is provided to explain the classification findings in which the proposed classifier via employing an auto encoder and a classifier could check whether the rhythms of heart are normal, paced up or the heartbeat rate is irregular depending on the patient's situations. The module could offer the explanations of the classification findings in order to allow medical practitioners to quickly make the trustworthy judgments in preliminary diagnoses. Finally, the result shows that the proposed classifier could explain the classification for finding the two arrhythmias conditions which allow healthcare practitioners to rapidly make the correct conclusions.
Process Analysis and Experimental Research of Robot Abrasive Belt Grinding for Blisk*

As a key component of an aero-engine, the blisk has the characteristics of complex structure and difficult processing of materials. The quality of its processed surface has a vital impact on the overall performance of the aero-engine. However, at present, most blisks are polished manually, which has low polishing efficiency and difficulty in ensuring the consistency of polishing quality. Therefore, this paper analyzes the process of the robot abrasive belt grinding of the blisk and carries out relevant experiments to verify it. The experimental results show that: compared with manual polishing, the polishing efficiency is increased by 1.5 times; the blisk blades after polishing have good consistency, the milling cutter lines are effectively removed; the surface roughness can reach Ra0.24 μm, the smooth transition of the polishing area does not hurt and the blisk body; the robot belt grinding technology can meet the grinding requirements of the blisk.
Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

Novel intelligent systems to assist energy transition and improve sustainability can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which has led to the emergence of the concept of “smart campus” over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart regions.
Convolutional Neural Network-based Model for Lung Sounds Classification

Deep learning approaches are gaining popularity in the medical field for diagnostics and predictive analytics. This study aims to improve the classification of respiratory sounds recorded by electronic stethoscopes using a deep approach based on a convolutional neural network (CNN). The proposed scheme’s performance is studied using the largest publicly available lung sound dataset (ICBHI 2017). We implement variant frequency representations, including CQT, STFT, and Mel-STFT, combined with Empirical mode decomposition (EMD). We propose the use of data normalization on top of data augmentation techniques. Furthermore, to increases the model performance, we used hyperparameters optimization and regulators. Experimental results show that our method outperforms competing methods on anomaly prediction tasks.
Applying Discriminant Analysis to Improve Telemedicine Diagnostics Quality

In the issue of improving the quality of telemedicine visual diagnostics, the current task is to increase the reliability of differential diagnostics and assess the severity of the disease. This is primarily due to the widespread introduction of virtual methods of telemedicine visual diagnostics according to the criteria of evidence-based medicine. An important task, in this case, is the choice of informative parameters of diagnostics and control, as well as criteria by which the discriminant capabilities of the methods will be compared. The authors have carried out research into the possibility of improving the quality of telemedicine diagnostics, using an integrated approach to studying the significance of the measured parameters and justifying the feasibility of their use in the diagnosis of specific pathologies. In this paper, the authors considered the effectiveness of this technique on the example of atopic dermatitis diagnosis.
Temporal dynamics of bacterial communities and predicted nitrogen metabolism genes in a full-scale wastewater treatment plant

The dynamics of bacterial communities in a full-scale wastewater treatment plant (WWTP) were investigated using Illumina MiSeq sequencing and PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) over a one-year period. Results showed that the temporal dynamics of structure, diversity and abundance of bacterial community were not obvious. The persistent species had low diversity but high relative abundance. Dechloromonas, Thauera, Nitrosomonas and Nitrospira were the four dominant persistent genera. The intermittent and transient genera showed different dynamics with persistent genera. Variation partitioning analysis suggested that water quality indexes (13.40%) had the greatest contribution to bacterial community variation, followed by temperature (9.20%). Quantitative polymerase chain reaction analysis revealed the stable abundances of nitrifying and denitrifying bacteria, and the occurrence of complete ammonia oxidizers (Comammox) in the full-scale WWTP. Functional prediction showed that the bacterial functional compositions were also relatively stable, and metabolism was the most dominant functional category. Many genes involved in the nitrogen metabolism pathway were predicted to be present with high gene counts, e.g., nirB, nirD, nasA, norB, nirK, nosZ and nifH. Most of these genes were predicted to be mainly contributed by Rhizobiales, Burkholderiales, Rhodocyclales and Actinomycetales. Overall, the composition and function of bacterial community in a long-term monitoring were relatively stable, which is important for maintaining the high wastewater treatment capability in full-scale WWTPs.
Fused RRAM-Based Shift-Add Architecture for Efficient Hyperdimensional Computing Paradigm

Memristor-based implementations promises efficient in-memory computing architectures. Hence, it has been extensively utilized as multiply-and-add accelerator engines in signal processing and artificial intelligence applications. Hyperdimensional computing (HDC) paradigm is an encouraging brain-inspired computational framework that performs computations on hyperdimensional vectors. The encoding operation in HDC takes about 80% of the execution time and consists of multi-plication, addition and shifting. In this paper, a reconfigurable memristor array is used to implement in-memory shifting and addition to the seeds of the hyperdimensional vectors. The presented scheme fuses the circular shifting with summation operations. This is the first work to introduce such scheme and it provides savings in time, power and area compared to traditional computations and other crossbar approaches that performs separate operations on the crossbar. Spice simulation of the proposed scheme using 65nm foundry has been used to verify the functionality.
Threshold Detection ADC For Continuous Monitoring Applications

The proposed threshold detection based ADC targets continuous monitoring systems, where full reconstruction of the input signal isn’t required. The ADC observes whether the input signal stays within safety margins keeping the microcontroller in the standby mode until alarm occurs. This approach generates significantly less output data that needs to be wirelessly transmitted, thereby resulting in significant power savings in the system. This proposed ADC takes full advantage of a ∆Σ DAC allowing for high flexibility and requiring only one decision clock cycle independent of the target resolution. This compact design occupies only 0.012 mm2 in 65 nm CMOS with 10 bits resolution.