Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.
Security Evaluation of Android Mobile Healthcare and Fitness Applications

The biggest risk to the privacy of data and personal information of Android users that have mobile fitness and healthcare applications is the unencrypted connection between the application and an internet server. Another risk is the potential security threats, brought about because users often skip the alert message at install time. In this paper, both the applications and network communication processes are evaluated. The security part of the application takes place during downloading and using the applications, then determining what personal information the applications save in the local storage inside these devices, comes after. The security part of network communication includes analyzing the WIFI communication between the instead application and the Internet. We try to help Android health and fitness applications users to easily understand how trustworthy an application is by proposing a new security evaluation method based on both multi-criteria evaluations and privacy risks of an application. Out of the 110 apps found in different markets which were tested, 51 apps were trusted, 31 apps were untrusted, and 18 gave a short report to help the user to make a decision about installing the application or not
A Prosthetic Gripper with Three Opposing Fingers Driven by a Hydraulic Actuator

Electric prosthetic hands have problems of heavyweight, unsuitability for working near water, and driving noise. Although body-powered prosthetic hands solve these problems, they have operability and wearability problems related to a cable control system. In this paper, we report a prosthetic gripper with three opposing fingers driven by a hydraulic actuator without electrical components. The three fingers are controlled simultaneously by pushing the lever of an operating interface mounted on the user’s upper arm on the affected side. The hydraulic actuator enables users to open the fingers with a force of 16.6 N. The operating interface can be worn without a harness. The total weight is 316 g. Grasping tests of abstract and daily objects in which two amputees participated demonstrated that two amputees were able to grasp various objects. Quietness was also confirmed by them.
A Zero-Power Ubiquitous Wireless Liquid-Level Sensor Based on Microfluidic-Integrated Microstrip Antenna

Harmonic transponder sensors, receiving radio-frequency (RF) signal and converting it to a modulated second harmonic, have been demonstrated to be effective for signal interrogation in environments with strong clutters. Here, we propose a compact dual-resonance microstrip antenna consisting of an elliptical patch loaded with shorting pins. Such an antenna utilizes the TMe110 resonant mode to intercept the perpendicularly-polarized fundamental tone (2.86 GHz) and uses the TMo110 resonant mode to retransmits the parallelly-polarized second harmonic (5.72 GHz). Moreover, we have developed a harmonic-based wireless liquid sensor comprising the proposed antenna and a micromachined liquid channel. Our measurement results show that even in the noisy environment, the second harmonic strength can precisely indicate the dielectric property of liquid filled in the microfluidic tank (e.g., acetone-water mixtures with various concentrations). However, a similar setup without frequency and polarization modulations (i.e., linear and passive backscatter tag) fails to provide a sensitive and quantitative measurement of liquid property. The proposed low-profile dual-resonance antenna with sensing capability will pave the way for the development of harmonic sensors and nonlinear radio-frequency identification (RFID) tags.
Predicting Age with Deep Neural Networks from Polysomnograms

The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines. We validated the framework by predicting the age of subjects. We designed a hierarchical attention network architecture, which can be pre-trained to predict labels based on 5-minute epochs of data and fine-tuned to predict based on whole-night polysomnography recordings. The model was trained on 511 recordings from the Cleveland Family study and tested on 146 test subjects aged between 6 to 88 years. The proposed network achieved a mean absolute error of 7.36 years and a correlation to true age of 0.857. Sleep can be analyzed using our end-to-end deep learning framework, which we expect can generalize to learning other subject-specific labels such as sleep disorders. The difference in the predicted and chronological age is further proposed as an estimate of biological age.
Distributed Consensus Control Protocols for Heterogeneous Multi-Agent Systems With Time-Varying Topologies

Distributed group consensus control problems of heterogeneous multi-agent systems with time-varying topologies and communication delay are investigated in this paper. Two different types of control protocols without relative velocity information are designed. By reformulating the original problem and utilizing the property of Metzler matrix, sufficient group consensus criteria under some assumptions for the two proposed protocols are developed, respectively. Simulation examples are exploited to illustrate the results.
The effect of transcranial alternating current stimulation (tACS) on cognitive function in older adults with dementia

Recent studies have shown a positive effect of transcranial alternating current stimulation (tACS), a noninvasive brain stimulation technique, on cognitive function of healthy individuals [1]. However, investigation into the effects of tACS on individuals with dementia or mild cognitive impairment (MCI) is lacking. In this pilot study, we investigated the hypothesis that following a regular schedule of challenging brain exercises combined with simultaneous tACS application would improve the working memory and cognitive function of older adults with memory impairments. Further, we explored whether pairing brain exercises with tACS would result in longer-lasting positive effects on cognitive function than brain exercises alone. A total of 17 older adults (12 males, 5 females, 70 ± 7 years), each with a diagnosis of mild to moderate dementia were enrolled in the study. All participants completed brain exercises in the lab on the following schedule: two 30-minute sessions per day, 5 days/week for 4 weeks consecutively. Eleven of the participants received brain exercises paired with tACS application at 40Hz. We evaluated cognitive function of the participants at baseline, post-intervention and 1-month followup using the Wechsler Memory Scale (WMS-IV) as an independent assessment of our brain exercises. Both the non-tACS and tACS groups significantly improved their WMS scores from baseline to post-intervention assessments. Comparison of the post-intervention and 1-month follow-up assessments indicated that the tACS group maintained their improvement significantly better than the non-tACS group.
Design and Simulation of Neuro-Fuzzy Based MPPT Controller for PV Power System

This paper introduces a full simulation and design of a photovoltaic power generation system to supply Al-Mahmoudia hospital in Baghdad/Iraq. The design steps include; daily average load estimation, annual solar irradiance and temperature monitoring and logging. PV panel selection and datasheet study to design the PV array and decide the number and connection method for these panels. (DC-to-DC) boost converter is designed. Then design and simulate a new type of (MPPT) controller based on mixed technique of artificial neural networks (ANN) with fuzzy Logic Controller (FLC) or (neuro-fuzzy) technique. The power generated from photovoltaic (PV) panels rely on the sun radiation and the ambient temperature. For this reason, the use of (MPPT) controller is a necessity to get as much as possible of the photovoltaic power from the system despite of variation in climatic conditions. The most famous method used for (MPPT) control is the Perturb and Observe (P&O) algorithm. Despite of the simplicity of this method, it is suffering from many problems including the oscillation around the (MPP), delay in system response and the maximum overshoot especially when the perturbation step size is large. The proposed (neuro-fuzzy) controller has been designed to get rid of the drawbacks of the classical (MPPT) methods including the P&O method. MATLAB/Simulink software has been used to design a photovoltaic module and array to supply the load of the hospital with 459kW. In addition the (DC-to-DC) boost converter and (MPPT) neuro-fuzzy controller are modeled. The performance has been compared between the proposed controller with the classical P&O and fuzzy logic controllers. The simulation results illustrated an enhanced performance in terms of oscillation, the response time, power harvesting and highest power overshoot
Implicit Land Use Mapping Using Social Media Imagery

Land use classification is a central remote sensing task with a broad range of applications. Typically this is represented as a supervised learning problem, the first step of which is to develop a taxonomy of discrete labels. However, such categories are restricted in the range of uses they can convey and arbitrary decisions are often required when defining the categories. Instead, we argue that the abstract notion of land use can be indirectly characterized by the types and quantities of common objects found in an area. To capture the presence of such objects, we propose an implicit approach to defining and estimating land use that relies on sparsely distributed social media imagery but retains the benefits of dense coverage provided by satellite imagery. Our method is formulated as a convolutional neural network that operates on satellite imagery and outputs a probability distribution over quantities of objects common in social media imagery at that location. We show that the learned feature representation is discriminative for existing land use categories.
Reliable and Low-Complexity Chirp Spread Spectrum-Based Aerial Acoustic Communication

Aerial acoustic communication (AAC) has been developed for a variety of Internet-of-things (IoT) applications, thanks to the merit of embedding small amounts of data directly into audio contents and transmitting them through commercial off-the-shelf (COTS) devices without additional infrastructure. However, AAC has clear limitations due to ambient noise, severely frequency-selective acoustic channels, and high power consumption of COTS devices. We adopt chirp spread spectrum (CSS) modulation and design new quaternary symbols to overcome frequency selectivity of audio interfaces of COTS devices. We also develop a computation-efficient method to demodulate the proposed symbols, aiming to reduce the power consumption of COTS devices. We employ a frame combining technique without increasing computational complexity, to mitigate the effects of multipath fading and ambient noise in acoustic channels. To evaluate the proposed methods, we conduct extensive experiments with several smartphones in various environments. The experimental results and evaluation demonstrate that the proposed symbols and frame combining method contribute to improving the frame reception ratio by up to 59.8%p (267.9%) and 14%p (84.3%), respectively. The frame combining increases the possibility of receiving a frame within two attempts at extremely low SNR, by up to 107.9%, resulting in reduced excessive delay. The method to demodulate the proposed symbols lowers power consumption by tens to hundreds of milliwatts, depending on the device. Our proposal significantly helps to achieve high reliability and low power consumption of COTS devices in AAC.
Deep learning with long short-term memory networks for classification of dementia related travel patterns

Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PWD). In this paper, we leverage the orientation data available on mobile devices to recognize dementia-related wandering patterns. In particular, we propose to use deep learning (DL) with long short-term memory networks (LSTM) as classifiers for detecting travel patterns including direct, pacing, lapping and random. Experimental results on a real dataset collected from 14 subjects show that deep LSTM classifiers perform better than traditional machine learning (ML) classifiers. Our proposed method can thus be potentially used in healthcare applications for dementia related wandering monitoring and management.Clinical Relevance— This demonstrates the potential of using readily available yet non-privacy information to detect dementia-related wandering patterns with high accuracy.
Glioma Growth Prediction via Generative Adversarial Learning from Multi-Time Points Magnetic Resonance Images

Gliomas are the most dominant and lethal type of brain tumors. Growth prediction is significant to quantify tumor aggressiveness, improve therapy planning, and estimate patients' survival time. This is commonly addressed in literature using mathematical models guided by multi-time point scans of multi/single-modal data for the same subject. However, these models are mechanism-based and heavily rely on complicated mathematical formulations of partial differential equations with few parameters that are insufficient to capture different patterns and other characteristics of gliomas. In this paper, we propose a 3D generative adversarial networks (GANs) for glioma growth prediction. Specifically, we stack 2 GANs with conditional initialization of segmented feature maps. Furthermore, we employ Dice loss in our objective function and devised 3D U-Net architecture for better image generation. The proposed method is trained and validated using 3D patch-based strategy on real magnetic resonance images of 9 subjects with 3 time points. Experimental results show that the proposed method can be successfully used for glioma growth prediction with satisfactory performance.
Weak–Noise Modulation–Estimation of Vector Parameters

We address the problem of modulating a parameter onto a power–limited signal, transmitted over a discrete–time Gaussian channel and estimating this parameter at the receiver. Continuing an earlier work, where the optimal trade–off between the weak–noise estimation performance and the outage probability (threshold–effect breakdown) was studied for a scalar parameter, here we extend the derivation of the weak–noise estimation performance to the case of a multi–dimensional vector parameter. This turns out to be a non–trivial extension, that provides a few insights, and has some interesting implications. Several modifications and extensions of the basic setup are also studied and discussed.
Glitch-Optimized Circuit Blocks for Low-Power High-Performance Booth Multipliers

This article presents a novel implementation scheme of the essential circuit blocks for high-performance, full-precision Booth multipliers leveraging a hybrid logic style. By exploiting the behavior of parasitic capacitance of MOSFETs, a carefully engineered design style is employed to reduce dynamic power dissipation while improving the glitch immunity of the circuit blocks. The circuit-level techniques along with the proposed signal-flow optimization scheme prevent the generation and propagation of spurious activities in both partial-product and adder-tree stages. Two full-precision Booth multipliers built from proposed strategies were compared to the state-of-the-art versions known from literature by means of extensive post-layout simulations in 65-nm CMOS technology. The proposed versions on average demonstrated up to 10% and 30% power savings in general.
Stepped Gate Profiles over DRE TFET: A Proposal to Improve Off-State Breakdown Voltage

The prime focus of this work is towards improving the device off-state breakdown voltage without hampering the on-state resistance value. For this, Drain Region Extended (DRE) TFET architecture has been introduced with stepped gate profiles. The stepped gate profile implementation has been done in two ways. Firstly, the impact of one such previously reported three stepped gate profile has been tested on TFET architecture. Thereafter, a more feasible and cost effective two stepped gate profile has been proposed which helps in improving the device breakdown voltage without hampering other characteristics such as device on-state resistance, on-state current and threshold voltage value. The modified configuration successfully delivers improved breakdown voltage and reduced on-state resistance value and can be used for better functioning in System on Chip (SoC) applications.
Stability of Hybrid SET-CMOS Based NOT Gate

This work demonstrates the stability analysis of SET-CMOS based hybrid NOT gate. Under different temperature condition the stability of the circuit is checked using one of the popular software SIMON, dedicated for the Single electron Device simulation. For the straightforward stability simulation of the circuit in SIMON simulator the circuit has been modified by replacing the MOS with an equivalent resistance as SIMON does not support the co-simulation of SET and MOSFET. Simulation results of the logic operation and stability analysis are furnished in support.
An Incremental Algorithm for Granular Counting with Possibility Theory

Data counting is non-trivial when data are uncertain. In the case of uncertainty due to incompleteness, possibility theory can be used to define a granular counting model. Two algorithms were proposed in literature to compute granular counting: exact granular counting, with quadratic time complexity, and approximate granular counting, with linear time complexity. However, both algorithms require that all data are available before counting. This paper presents an incremental granular counting algorithm which provides an efficient and exact computation of the granular count without the need of having all data available, thus opening the door to applications involving data streams.
W-Band Plumb Shaped Patch Antenna for Automotive Radar and 5G Applications

Millimeter-wave technology is getting very popular nowadays for fifth-generation (5G) communications due to increased data rate and bandwidth. The world radio conference 2015 by International Telecommunication Union (ITU) has allocated the band 81 GHz to 86 GHz for mobile applications on a primary basis. This work presents a very compact tapered plumb shaped patch antenna for the applications in a higher band (which covers 80.99 GHz to 84.95 GHz for $\text{VSWR} < 2$ and 74.9 GHz to 86.2 GHz for $\text{VSWR} < 3$) of 5G with a gain (maximum 7.14 dBi at the resonance frequency of 83.2 GHz and it is more significant than 1.72 dBi in the overall band for $\text{VSWR} < 3$), and the total efficiency at resonance frequency is 93.64%. To realize a high gain and to cover the assigned bandwidth of 5 GHz, the partial ground technique is applied, instead of using a fully grounded patch. The proposed compact antenna will be the key element of automotive radar (vehicular communication) and future generation applications.
DRAN: Densely Reversed Attention based Convolutional Network for Diabetic Retinopathy Detection

Diabetic Retinopathy (DR), the complication leading to vision loss, is generally graded according to the amalgamation of various structural factors in fundus photography such as number of microaneurysms, hemorrhages, vascular abnormalities, etc. To this end, Convolution Neural Network (CNN) with impressively representational power has been exhaustively utilized to address this problem. However, while existing multi-stream networks are costly, the conventional CNNs do not consider multiple levels of semantic context, which suffers from the loss of spatial correlations between the aforementioned DR-related signs. Therefore, this paper proposes a Densely Reversed Attention based CNN (DRAN) to leverage the learnable integration of channel-wise attention at multi-level features in a pretrained network for unambiguously involving spatial representations of important DR-oriented factors. Consequently, the proposed approach gains a quadratic weighted kappa of 85.6% on Kaggle DR detection dataset, which is competitive with the state-of-the-arts.
A Reconfigurable Random Number Generator Based on the Transient Effects of Ring Oscillators

This brief presents a reconfigurable Random Number Generator (RNG) based on transient effect of ring oscillators. Users can select a method based on the irregular sampling of a regular waveform or on the regular sampling of an irregular waveform to obtain a random bit sequence to be used in different applications, such as lightweight cryptography or high-security communication. The entropy is acquired by exploiting Transient Effect Ring Oscillators (TEROs). The proposed fully-digital RNG structure is firstly implemented on a Zynq-7000 FPGA (Field Programmable Gate Array) without any post-processing method such as the Von Neumann. In addition to the RNG structure, an on-the-line test module based on FIPS 140–2 is also implemented to check the randomness of the produced data statistically in real time. Users can change the statistical test parameters according to their desired security levels. Finally, an ASIC (Application Specific Integrated Circuits) implementation of the proposed RNG is done following the Cadence digital design flow for the TSMC 180 nm CMOS process. The implemented ASIC design occupies an area of 0.85 mm x 0.85 mm and the estimated power required is 11.827 mW.
Rate-Diversity Optimal Multiblock Space-Time Codes via Sum-Rank Codes

Just as rank-metric or Gabidulin codes may be used to construct rate-diversity tradeoff optimal space-time codes, a recently introduced generalization for the sum-rank metric, linearized Reed-Solomon codes, accomplishes the same in the case of multiple fading blocks. We provide the first explicit construction of minimal-delay rate-diversity optimal multiblock space-time codes as an application of linearized Reed-Solomon codes. We then demonstrate in simulation an example of a 2-block 2-by-2 code which, with a small performance penalty—less than 1 dB at a codeword error rate of 1e-4—matches the bit rate of a full diversity alternative while using a much smaller transmitted constellation. A stack decoder for this code is then suggested.