Miracle-dz/UniFAR-FTUs huggingface dataset:
Customized Thinning of Silicon-based Neural Probes Down to 2 µm

This paper reports on the customized thinning of neural probes based on silicon (Si) using deep reactive ion etching (DRIE) as a post-processing step. The reduced probe dimensions are expected to minimize local tissue trauma, while guaranteeing probe integrity during implantation. For DRIE, the probes are partially masked by a micromachined Si cover chip comprising tailored cavities enabling any desired thinned length l and probe thickness t by a proper choice of cover chip design and DRIE parameters, respectively. A broad variety of probe designs were realized with shank tip thicknesses ranging from 35 µm down to 2 µm. All probes could successfully be implanted into a brain tissue phantom, demonstrating a pronounced reduction in insertion force from 0.55 mN for unprocessed probes to 0.08 mN for 2-µm-thin shanks. When the dura mater was mimicked by a polyethylene (PE) membrane, forces were reduced from 28.9 mN to 16.6 mN for 15-µm-thin shanks.
The Role of Alignment Between IS Strategy and Social Capital on the IS Capability and Business Performance Relationship: A Cross-Sectional Survey

This study explored the relationship between the information system (IS) capability with the alignment of IS strategy and social capital and the relationship between the alignment of IS strategy and social capital with business performance. Moreover, this study also aimed to investigate the mediating role of the alignment of IS strategy and social capital in the relationship between IS capability and business performance. A cross-sectional field study (i.e. questionnaire survey) was conducted on 179 national scale bank branches across Indonesia. Partial least square (PLS) is utilized to test all of the research hypotheses. The result of the study showed that there are significant relationships between IS capability with the alignment of IS strategy and social capital and significant relationships between the alignment of IS strategy and social capital with business performance. Besides, this study also proved that the alignment of IS strategy with social capital has a significant role in mediating the relationship between IS Capability and Business Performance. This study contributes to the theory development by analyzing the research gap from the previous researchers considering social capital as the strategic factor in the relationship between IS capability and business performance. The findings of this study can provide a reference for companies (i.e. board of directors) in determining investment and information system use policy.
Improving Reactive Power Sharing in Microgrids by Adaptive Virtual Impedance Approach

As a new emerging approach, Virtual Synchronous Generator (VSG) control for interfacing inverters in renewable energy-based microgrids has attracted significant research interest. However, VSG control cannot ensure proportional reactive power sharing among distributed generation (DG) units when unequal transmission line impedances are involved, although active power sharing remains proportional among DGs. In this paper, an adaptive virtual impedance approach is proposed, which can be incorporated in a VSG control to alleviate impedance difference at the inverter output and improve reactive power sharing among DGs. It is applicable to any VSG control, either conventional or modified types. The proposed approach is implemented on a modified VSG control scheme, known as Fuzzy secondary controller (FSC)-based VSG control, in this paper. Its performance is demonstrated through several case and sensitivity studies using MATLAB/Simulink simulation; and is further validated by comparing with an existing method.
Some Performance Guarantees of Global LASSO with Local Assumptions for Convolutional Sparse Design Matrices

We analyze the performance of the LASSO algorithm (basis pursuit, Tibshirani et. al, ’96) for a class of structured matrices known as convolutional sparse matrix. Analyzing such matrices is of paramount interest since in many signal processing applications (including computer vision, image and audio processing), a global analysis of the underlying signal often entails understanding the behavior of convolutional sparse matrix. We show that LASSO (ℓ1 regularized least squares) with such matrices succeeds under a constraint on local sparsity, as opposed to global sparsity. This conversion from global to local constraint has crucial significance in the above mentioned applications. Under sufficiency conditions like Restricted Eigen-value (RE) and Exact Recovery Coefficient (ERC), we obtain the prediction (in ℓ2 norm) error and estimation error rate for LASSO estimator with local sparsity constraints. Furthermore, we obtain an estimation error rate for LASSO estimator in ℓ∞ norm under a gaussian noise model.A full version of this paper is accessible at: https://tinyurl.com/rwy2l3o
A 10-Gbps Continuous-Time Linear Equalizer for mm-Wave Dielectric Waveguide Communication

The data rate-times-distance product in mm-wave dielectric waveguide (DWG) communication links is usually limited by waveguide dispersion. This letter presents the DWG’s baseband impulse response in the context of a homodyne receiver and introduces a continuous-time linear equalizer able to minimize such dispersion. The desired transfer function is implemented with inverter-based transconductance cells. The simulation and measurement results at 10-Gbps data rate show eye-opening improvement at two distances (10 and 15 m) and carrier frequencies (140 and 160 GHz). The equalizer occupies 80 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 32 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}^{2}$ </tex-math></inline-formula> in GLOBALFOUNDRIES 22-nm FD-SOI.
Minimax Prediction in Tree Ising Models

Graphical models are often used to facilitate efficient computation of posteriors in order to make predictions. With this objective in mind, we consider the problem of estimating the parameters of a graphical model with known structure from samples such that posteriors computed using the model are accurate. Focusing on tree-structured binary Markov random fields, our main result is a sharp characterization of the dependence on number of samples needed for all pairwise marginals (and hence posteriors of one variable given another) to be accurate: n = Θ(η−2 logp) samples are necessary and sufficient to estimate model parameters such t√hat all marginals of arbitrary order k are accurate to within kη. The result implies that prediction error is bounded uniformly, with no dependence on the strength of interactions. We will also show that these guarantees are achievable using moment matching techniques.
A Spectral-longitudinal Model for Detection of Heart Attack from 12-lead Electrocardiogram Waveforms

Cardiovascular diseases (CVDs) remain responsible for millions of deaths annually. Myocardial infarction (MI) is the most prevalent condition among CVDs. Although datadriven approaches have been applied to predict CVDs from ECG signals, comparatively little work has been done on the use of multiple-lead ECG traces and their efficient integration to diagnose CVDs. In this paper, we propose an end-to-end trainable and joint spectral-longitudinal model to predict heart attack using data-level fusion of multiple ECG leads. The spectral stage transforms the time-series waveforms to stacked spectrograms and encodes the frequency-time characteristics, whilst the longitudinal model helps to utilise the temporal dependency that exists in these waveforms using recurrent networks. We validate the proposed approach using a public MI dataset. Our results show that the proposed spectrallongitudinal model achieves the highest performance compared to the baseline methods.
Performance Analysis of Ni3GeFe2 / Fe3GeTe2 Composites as Ferromagnetic Layer in MTJ Memory Devices

The studies were performed based on various composites to improve the features of Magnetic Tunnel Junction (MTJ) memory devices. We have studied the electrical and electronic properties of Ni<inf>3</inf>GeFe<inf>2</inf> and Fe<inf>3</inf>GeTe<inf>2</inf>. MgO-hBN-MgO is used as a composite dielectric material. The ferromagnetic material Ni<inf>3</inf>GeFe<inf>2</inf> and Fe<inf>3</inf>GeTe<inf>2</inf> have shown improvement in various electronic and magnetic properties that can be useful for further implementation in MTJ device. The Ni<inf>3</inf>GeFe<inf>2</inf> and Fe<inf>3</inf>GeTe<inf>2</inf> predicted good magneto-transport property that characterizes their ferromagnetic state. In oxide layer the direct bandgap of 1.0437eV at Gamma point is obtained. The validation of Ni<inf>3</inf>GeFe<inf>2</inf> and Fe<inf>3</inf>GeTe<inf>2</inf> as ferromagnetic layer is predicted as it showed 0eV bandgap. Local Density of State (LDOS) of FM and oxide layer is calculated along with it the electron transport is observed in z direction. It is found that these two ferromagnetic (FM) materials showed a high Curie temperature. The DOS and Band Structure of Fe is compared with Ni<inf>3</inf>GeFe<inf>2</inf>, Fe<inf>3</inf>GeTe<inf>2</inf>. In comparison with the DOS of Fe showed peak at the valance band i.e. 0.45eV and 0.69eV which shows instability of memory states, as in case of Ni<inf>3</inf>GeFe<inf>2</inf> and Fe<inf>3</inf>GeTe<inf>2</inf> (stable FM materials) the peaks were observed is conduction band. The study depicts that Ni<inf>3</inf>GeFe<inf>2</inf> and Fe<inf>3</inf>GeTe<inf>2</inf> are promising candidate for its implementation in MTJ memory device. As they show better edge boundaries and good stability of states.
Optimal Scree-CNN for Detecting NS1 Molecular Fingerprint from Salivary SERS Spectra

Dengue fever (DF) is a viral infection with possible fatal consequence. NS1 is a recent antigen based biomarker for dengue fever (DF), as an alternative to current serum and antibody based biomarkers. Convolutional Neural Network (CNN) has demonstrated impressive performance in machine learning problems. Our previous research has captured NS1 molecular fingerprint in saliva using Surface Enhanced Raman Spectroscopy (SERS) with great potential as an early, noninvasive detection method. SERS is an enhanced variant of Raman spectroscopy, with extremely high amplification that enables spectra of low concentration matter, such as NS1 in saliva, readable. The spectrum contains 1801 features per sample, at a total of 284 samples. Principal Component Analysis (PCA) transforms high dimensional correlated signal to a lower dimension uncorrelated principal components (PCs), at no sacrifice of the original signal content. This paper aims to unravel an optimal Scree-CNN model for classification of salivary NS1 SERS spectra. Performances of a total of 490 classifier models were examined and compared in terms of performance indicators [accuracy, sensitivity, specificity, precision, kappa] against a WHO recommended clinical standard test for DF, enzyme-linked immunosorbent assay (ELISA). Effects of CNN parameters on performances of the classifier models were also observed. Results showed that Scree-CNN classifier model with learning rate of 0.01, mini-batch size of 64 and validation frequency of 50, reported an across-the-board 100% for all performance indicators.
Reachable set boundedness and fuzzy sliding mode control of MPPT for nonlinear photovoltaic systems

This paper develops a novel maximum power point tracking (MPPT) control strategy for nonlinear photovoltaic (PV) systems. The MPPT control problem of the considered PV systems is first formulated in the framework of fuzzy descriptor systems. Then, based on the new formulation, a fuzzy sliding mode control (FSMC) law is constructed to drive the state trajectories into a desired sliding surface within the finite-time T* with T* < T. Moreover, sufficient conditions are derived to ensure the reachable set boundings of the closed-loop PV control systems in the finite-time intervals [0,T*] and [T*,T]. A simulation example is given to show the effectiveness of the proposed design method.
The impact of aggregation interval on current harmonic simulation of aggregated electric vehicle loads

Electric vehicles (EVs) poses new challenges for the Distribution System Operator (DSO). For example, EVs uses power electronic-based rectifiers for charging their batteries, an operation that could significantly impact Power Quality (PQ) in terms of harmonic distortion. The DSO responsibilities include ensuring grid code compliance confirmed by PQ metering. In general, 10 min rms values are sufficient. However, the large scale integration of non-linear loads, like EVs, could lead to new dynamic phenomena, possibly lost in the process of time aggregation.This paper presents an analysis of the impact on time aggregation (3 s-, 1min-and 10 min rms), when modelling current harmonics of aggregated EV loads, using power exponential functions. The results indicate that, while3 s rms and 1 min rms marginally affect the outcome, 10 min rms aggregation will lead to a significant deviation (>30%) in terms of maximum current harmonic magnitude.
Deep Multi-instance Networks for Bundle Branch Block Detection from Multi-lead ECG

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat: right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.
Pilot Decontamination in Multi-Cell Massive MIMO Systems via Combining Semi-Blind Channel Estimation With Pilot Assignment

In the multi-cell massive multiple-input multiple-output (MIMO) systems, the mitigation of pilot contamination is an important issue in the semi-blind channel estimations. For the pilot decontamination schemes, the performance of the semi-blind based estimation approaches is constrained by the ratio of the interference signal power to the target signal power. In order to eliminate pilot contamination effectively, the eigenvalues of the target signals should be separated from those of the interference signals. In this paper, we propose a novel uplink channel estimation for mitigating pilot contamination in time division duplex massive MIMO systems, which combines pilot assignments with semi-blind channel estimation methods. In order to reduce the search complexity for the optimal pilot assignment under the condition of a large number of users per cell, we proposed a sector-based pilot assignment method, including inter-sector pilot assignment and intra-sector pilot optimization. Simulation results verify that the joint pilot allocation and semi-blind channel estimation method is capable of improving the system achievable rates and the normalized mean square error performance.
Multimodal Electrophysiological Signal Measurement using a New Flexible and Conductive Polymer Fiber-electrode

A new multi-material polymer fiber electrode has been developed for smart clothing applications. The conductive fiber is optimized for bipotential measurements such as surface electromyogram (sEMG) and electrocardiogram (ECG). The main benefit of this fiber is its flexibility and being a dry and non-obtrusive electrode. It can be directly integrated into a garment to make a smart textile for real time biopoten-tial monitoring. A customized wireless electronic system has been developed to acquire electrophysiological signal from the fiber. The receiver base station is connected to a PC host running Matlab. The multi-material polymer fiber electrode recording setting were first optimized in length and inter-electrode distance by recording different sEMG signals. The typical sEMG signal to noise ratio ranges from 19.1 dB to 33.9 dB depending on the geometry. These value are comparable with those obtained with Ag/AgCl electrodes and dry electrode-base commercial system such as Delsys Trigno. The frequency domain analysis obtained from the power spectral density reveals that the new flexible fiber-electrode enables high sEMG signals recording quality while being suitable for integration in smart clothing fabric. A muscle fatigue analysis and ECG recording are also presented in this study. The multi-material polymer fiber electrodes demonstrate a viable solution for sEMG and ECG data acquisition.
Low Noise IQ Generation Employed in an Active Vector Modulator for 5G Ka-Band Beam Forming Transceivers

This paper investigates a compact, low noise differential in-phase (I) and quadrature signal (Q) generator. The proposed circuit utilizes a pair of mutual inductors to generate 90° phase shift which results in the same voltage gain for both I and Q paths. while it is in contrast with an RC-CR structure where the same gain for IQ paths cannot be achieved with a proper 90° phase shift simultaneously. The simulation results in a 130 nm SiGe-BiCMOS technology, indicate that a 3-dB bandwidth of 1.54 GHz around 28 GHz center frequency is achieved. Employing the proposed structure in a 1ow-IF receiver as part of the phase-shifting block has indicated a voltage gain of 30 dB for the desired signal while 40 dB rejection is provided at 22.275 GHz for the image signal. NF and S11 are less than 3.5 dB and -17dB, respectively, for the entire band of interest while the receiver consumes 8 mA from a 2V power supply. The phase error is less than 5° and the gain variation error is 0.3$\sim$0.5 dB. The input IP3 for the rest of the receiver chain is about -27dBm.
Design and Implementation of a Flash Lamp Pumped Nd-YAG Pulsed Laser for Electro-Optical Sensors Soft Damage

Laser soft damage of electro-optical (EO) sensors can be defined as the destruction of electronic primary devices, or the photo detectors. This damaging effect causes system’s functions to be lost. In this paper dazzling and soft damage of electro-optical sensors, including, complementary metal oxide semiconductors (CMOS) imaging sensors and charge-coupled devices (CCD) imaging sensors were studied both theoretically and experimentally. A MATLAB simulation model was built and used for predicting the design parameters of practical laser system. This laser system is capable of damaging EO sensors from laboratory scale to far field distances; this was achieved by maximizing the laser pulse energy. In addition, we designed and implemented an enhanced high energy flash lamp pumped pulsed Nd:YAG laser system with 2 J energy to achieve soft damaging of numerous types of EO sensors at different distances. Good agreement between the theoretical and experimental results obtained and a CCD image sensor was successfully soft damaged at a distance of 4 Km in free space environments.
Mapping Signal Processing Algorithms Into The Systolic Arrays

In this paper we present some techniques for construction of two dimensional mapping arrays, Appropriate for signal processing, called systolic arrays. These type of array algorithms are implemented on VLSI arrays. Taking into the consideration that matrix operations and applications are crucial for signal processing, we make an overview of the systolic processing for the case of matrix-matrix multiplication, QR decomposition and Singular value decomposition (SVD) of the matrix. Systolic arrays are very appropriate for parallel manipulations with matrices. Application domain of these arrays cover different areas especially the signal and image processing. Generally, all signal processing algorithms can be classified in two groups: Algorithms which perform the matrix operations and other special algorithms for signal processing. Most of the algorithms fall into the algorithms of matrix computations and other matrix transformations. Therefore, we consider that the techniques of construction of such arrays are very important in developing the new ideas and transformations which will enable to optimize and make them more efficient.
A Non-contact Spirometer with Time-of-Flight Sensor for Assessment of Pulmonary Function

Assessment of pulmonary function is vital for early detection of chronic diseases such as chronic obstructive pulmonary disease (COPD) in home healthcare. However, monitoring of pulmonary function is often omitted owing to the heavy burden that the use of specific medical devices places on the patients. In this study, we developed a non-contact spirometer using a time-of-flight sensor that measures very small displacements caused by chest wall motion during breathing. However, this sensor occasionally failed when estimating the values from breathing waveforms because their shape depends on the subject test experience. As a result, further measurements were required to address motion artifacts. To accomplish high accuracy estimation in the face of these factors, we developed methods to estimate parameters from a part of the waveform and remove outliers from multiple-region measurements. According to laboratory experiments, the proposed system achieved an absolute error of 5.26 % and a correlation coefficient of 0.88. This study also addressed the limitations of depth sensor measurements, thereby contributing to the implementation of high-accuracy COPD screening.
Cache-aided Multiuser Private Information Retrieval

This paper formulates the cache-aided multi-user Private Information Retrieval (MuPIR) problem, including Ku cache-equipped users, each of which wishes to retrieve a desired message efficiently from N distributed databases with access to K independent messages. Privacy of the users’ demands requires that any individual database can not learn anything about the demands of the users. The load of this problem is defined as the average number of downloaded bits per desired message bit. The goal is to find the optimal memory-load trade-off while preserving the demand privacy. Besides the formulation of the MuPIR problem, the contribution of this paper is two-fold. First, we characterize the optimal memory-load trade-off for a system with N = 2 databases, K = 2 messages and Ku = 2 users demanding distinct messages; Second, a product design with order optimality guarantee is proposed. In addition, the product design can achieve the optimal load when the cache memory is large enough. The product design embeds the well-known Sun-Jafar PIR scheme into coded caching, in order to benefit from the coded caching gain while preserving the privacy of the users’ demands.