Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Reference Circuit Operable at Low Voltages Using Composite MOS Triode Resistor

Invasive biomedical applications involving acquisition and recording of biological signals require current reference generation circuits for low voltage front-end amplifier and filter circuits. However, achieving current reference using Self-Biased Current Source (SBCS) for these circuits at same low supply voltages poses requirement of resistors. In this work, we compare the effect of passive poly resistor and Composite Triode MOS Resistor (CTMR) on the performance of low voltage SBCS circuit using 0.18 µm standard SCL foundry process parameters and models of MOS, capacitor and resistor. For current reference generation of 371 nA, the temperature coefficient (ppm/degree Celsius) values for passive poly resistor and CTMR are 2400 and 2251 respectively. The Figure of Merit, FOM (ppm/degree Celsius) and Coefficient of variance (CV) at different process corners with Monte-carlo simulation (MCS) runs of 100 for CTMR are 0.93 and 0.16 times lower than that of passive poly resistor. The CTMR based SBCS is anticipated to be used in low voltage front-end biomedical applications.
Anonymous Model Pruning for Compressing Deep Neural Networks

Many deep neural network compression algorithms need to fine-tune on source dataset, which makes them unpractical when the source datasets are unavailable. Although data-free methods can overcome this problem, they often suffer from a huge loss of accuracy. In this paper, we propose a novel approach named Anonymous-Model Pruning (AMP), which seeks to compress the network without the source data and the accuracy can be guaranteed without too much loss. AMP compresses deep neural networks via searching pruning rate automatically and fine-tuning the compressed model under the teacher-student diagram. The key innovations are that the pruning rate is automatically determined, and the finetuning process is under the guidance of uncompressed network instead of labels. Even without the source dataset, compared with existing pruning methods, our proposed method can still achieve comparable accuracy with similar pruning rate. For example, for ResNet50, our AMP method only incur 0.76% loss in top-1 accuracy with 32.72% pruning rate.
Statistical Shape Modeling to Determine the Anterior Pelvic Plane for Total Hip Arthroplasty*

The anterior pelvic plane (APP) defined by both iliac spines and the pubic symphysis, is essential in total hip arthroplasty (THA) for the orientation of the prosthetic cup. However, the APP is nowadays still difficult to determine in computer assisted orthopedic surgery (CAOS). We propose to use a statistical shape model (SSM) of the pelvis to estimate the APP from ipsilateral anatomical landmarks, more easily accessible during surgery in computer assisted THA with the patient in lateral decubitus position. A SSM of the pelvis has been built from 40 male pelvises. Various ipsilateral anatomical landmarks have been extracted from these data and used to deform the SSM. Fitting the SSM to several combinations of these landmarks, we were able to reconstruct the pelvis with an accuracy between 2.8mm and 4.4mm, and estimate the APP inclination with an angular error between 1.3° and 2.8°, depending on the landmarks fitted. Results are promising and show that the APP could be acquired during the intervention from ipsilateral landmarks only.
A Novel Reconstruction Method with Time Reversal Algorithm and Evaluations for Photoacoustic Tomography

Photoacoustic tomography (PAT) is a new modality with a high-resolution and a high contrast, the reconstruction process of which can result in improvements of the imaging quality. The aim of this study is to present an attempt to develop a novel reconstruction method with a time reversal algorithm (TR) for the PAT in an inhomogeneous media. By incorporating the finite difference time domain method (FDTD), the imaging reconstruction with the TR algorithm was formulated firstly. Then, two numerical simulations and an ex vivo experiment were both carried out to evaluate the capability of the proposed method in this work. The obtained results showed qualitatively the PAT images could be reconstructed well both in an inhomogeneous numerical model from different scanning ways and in a phantom model embedded with an ex vivo tumor of patients. Although the TR based algorithms may cost a bit more time than another in reconstruction process, it would be more practical approach for photoacoustic tomography with arbitrary scanning ways and better capability of imaging in inhomogeneous media, could also encourage us to further discuss its applicability of tumor detection in patients.
Integration Of Lift-Off Based Lithography Process For Memristor Fabrication

This study introduces a lift-off based process integration scheme for memristor fabrication. Memristors are of promising electronic components for bio-mimicking computers, non-volatile memory devices, etc. The wide-spread use of memristors in electronic systems thoroughly depends on the quality and reproducibility of the fabrication procedure. The lift-off process conventionally combines lithography and sputtering techniques as well as wet processes. Each of these processes requires well-established recipes for a lean fabrication. The structure of our memristor design involves an active memristive layer sandwiched between the top and bottom electrodes. In this study, titanium oxide (TiOx) layer and the electrodes are formed via the lift-off process. Insulator and metal layers are deposited by reactive magnetron sputtering, and lithography is done using a mask aligner. Throughout the process flow, surface structures are characterized using a scanning electron microscope (SEM). Current and voltage measurements have been carried out to characterize memristive properties of the fabricated features. Accordingly, the existence of a successful resistive switching mechanism shows the memristive behavior of the fabricated device.
Contributions of Vision in Human Postural Control: A Virtual Reality-based Study

During human standing, it has been previously observed that information about the position and frequency of visual surround motion improves balance by reducing sway responses to external disturbances. However, experimental limitations only allowed for independent investigation of such parameters while being incapable of providing a fully immersive experience of a real environment. The aim of this study is to investigate the effect of visual information on dynamic body sway in the human upright stance by presenting perturbations through a virtual reality (VR) system. Moreover, we designed a new perturbation signal based on trapezoidal velocity (TrapV) pulses enabling us to simultaneously examine the effects of amplitude and velocity on balance control. The experiments included four different peak-to-peak amplitudes (1–10 degrees), and three velocities (2-10 degree/sec). The body angle, ankle torques and shank angles were measured and analyzed in response to each perturbation. The results reveal that stimuli with higher amplitudes evoked larger responses, while they were initially increased and reached a peak, then decreased by increasing the motion velocity of visual surround.
Covariant Cluster Transfer for Kernel Reinforcement Learning in Brain-Machine Interface*

Brain-Machine Interface (BMI) provides a promising way to help disabled people restore their motor functions. The patients are able to control the external devices directly from their neural signals by the decoder. Due to various reasons such as mental fatigue and distraction, the distribution of the neural signals might change, which might lead to poor performance for the decoder. In this case, we need to calibrate the parameters before each session, which needs the professionals to label the data and is not convenient for the patient’s usage at home. In this paper, we propose a covariant cluster transfer mechanism for the kernel reinforcement learning (RL) algorithm to speed up the adaptation across sessions. The parameters of the decoder will adaptively change according to a reward signal, which could be easily set by the patient. More importantly, we cluster the neural patterns in previous sessions. The cluster represents the conditional distribution from neural patterns to actions. When a distinct neural pattern appears in the new session, the nearest cluster will be transferred. In this way, the knowledge from the old session could be utilized to accelerate the learning in the new session. Our proposed algorithm is tested on the simulated neural data where the neural signal’s distribution differs across sessions. Compared with the training from random initialization and a weight transfer policy, our proposed cluster transfer mechanism maintains a significantly higher success rate and a faster adaptation when the conditional distribution from neural signals to actions remains similar.
Global Prototypical Network for Few-Shot Hyperspectral Image Classification

This article proposes a global prototypical network (GPN) to solve the problem of hyperspectral image classification using limited supervised samples (i.e., few-shot problem). In the proposed method, a strategy of global representation learning is adopted to train a network (fθ) to transfer the samples from the original data space to an embedding-feature space. In the new feature space, a vector called global prototypical representation for each class is learned. In terms of the network (fθ), we designed an architecture of a deep network consisting of a dense convolutional network and the spectral–spatial attention network. For the classification, the similarities between the unclassified samples and the global prototypical representation of each class are evaluated and the classification is finished by nearest neighbor classifier. Several public hyperspectral images were utilized to verify the proposed GPN. The results showed that the proposed GPN obtained the better overall accuracy compared with existing methods. In addition, the time expenditure of the proposed GPN was similar with several existing popular methods. In conclusion, the proposed GPN in this article is state-of-the-art for solving the problem of hyperspectral image classification using limited supervised samples.
A Generic Nano-Watt Power Fully Tunable 1-D Gaussian Kernel Circuit for Artificial Neural Network

This brief presents an ultra-low-power generic fully tunable analog 1-D Gaussian kernel (GK) circuit, which is employed as an activation neuron for the radial basis function artificial neural network. In the proposed GK circuit, the maximum likelihood, center, and width of the Gaussian profile can be independently controlled. Besides, we have developed a mathematical model for the proposed GK circuit and further verified them experimentally. Thereby, the presented modeling can be employed to facilitate the off-chip learning of the neural network hardware. Fabricated in 180 nm CMOS process, the prototype demonstrates the full tunability of the proposed GK circuit and good agreement between the experimental measurements and mathematical model. The relative error of the measured width of the GK’s profile is less than 7 % compared to the value predicted by the presented mathematical model. The total power consumption is 13.5 nW with a supply voltage of 0.9 V, and the core circuit occupies 0.013 mm2.
Repair of RS codes with optimal access and error correction

We address two aspects of the repair problem of Reed-Solomon codes. First, we propose a new repair scheme for the RS codes constructed in [Tamo-Ye-Barg, IEEE Trans. Inf. Theory, May 2019] which in addition to optimal repair bandwidth is also robust to erroneous information provided by the helper nodes. Next, we construct a new family of RS codes with optimal access for the repair of any single failed node. We also prove that any scalar MDS code with optimal repair bandwidth can be furnished with a repair scheme with the optimal access property.
Assessment of Disease Progression in Friedreich Ataxia using an Instrumented Self Feeding Activity

Friedreich ataxia (FRDA), the most common of the inherited ataxias, is a degenerative disease that progressively affects walking and other functions leading to significant impairment associated with a shortened lifespan. It is important to monitor the progression of ataxia over periods of time for clinical and therapeutic interventions. This study was aimed at investigating the use of our instrumented measurement scheme of utilizing a motion detecting spoon in a self-feeding activity to quantify the longitudinal effect of FRDA on upper limb function. Forty individuals diagnosed with FRDA (32.8±14.9 years old) were recruited in a 12-month longitudinal study consisting of equal number of males and females (20). A set of biomarkers was extracted from the temporal and texture analysis of the movement time series data that objectively detected subtle changes during follow-up testing. The results indicated that both analyses generated features that resembled clinical ratings. Although the diagnosis and severity related performances were readily observed by temporal features, the longitudinal progression was better captured by the textural features (p = 0.029). The estimation of severity by mean of random forest regression model and LASSO exhibited a high degree of parity with the standard clinical scale (rho = 0.73, p < 0.001).
Estimating Trunk and Neck Stabilization for Avoiding Head Impact during Real-World Falls in Older Adults

In this work, we quantify the neck’s involvement in stabilizing the head during falls in older adults to avoid head impacts. We tracked kinematics of 12 real-world backward falls in long-term care captured on video, where head impact was avoided. We estimated dynamic spring-dashpot parameters of the neck and hip representing active muscle activity and passive tissue structures. Neck stiffness, damping, and target posture averaged 24.00±6.17Nm/rad, 0.38±0.16Nms/rad, and 76.2±14.7° flexion respectively. The stiffness and target posture suggest that residents actively contracted their neck muscles to maintain the head upright. Our results shed light on the importance of neck strength for avoiding head impact during a fall.Clinical Relevance—Falls account for 80% of traumatic brain injuries in adults 65+ years. While upper limb bracing can reduce the risk of head impacts during a fall in young adults, this protective response is less effective in older adults living in longterm care. Understanding how the neck and torso musculature are used to avoid head impact can guide the design of therapeutic exercise programs and assistive or protective devices.
A Systematic Study of IEEE 802.11 DCF Network Optimization From Theory to Testbed

In IEEE 802.11 wireless local area networks (WLANs), an important technique for medium access control (MAC) is the distributed coordination function (DCF). Two access mechanisms are provided by DCF, the default basic access mechanism and the optional request-to-send/clear-to-send (RTS/CTS) mechanism. The performance of IEEE 802.11 DCF networks has been predicted recently by NS-2 simulator based on a unified analytical model presenting the delay, throughput and stability. NS-3 and OMNeT++ provide an essential platform to model IEEE 802.11 physical (PHY) and MAC layers, nevertheless the accuracy of which is yet not investigated. In this article we present two studies, first is a comparative simulation study of the unified IEEE 802.11 DCF analytical model, by considering distinct network conditions, various topologies, different access modes and discrete system parameters in NS-3 and OMNeT++. A Linux based testbed is setup to validate the mathematical model and the simulation results. The second is the optimization study to adaptively tune the RTS threshold, so that the network operates in an access mode which steers to the maximum network throughput performance. An explicit expression of RTS threshold, verified by the simulations in NS-3 and OMNeT++, is obtained in contrast to previous studies based on channel estimation and numerical calculations. Performance evaluation is done by comparing the simulation, testbed and theoretical results. This study not only proves the credibility of the theoretical model of IEEE 802.11 DCF, but also assures that the results obtained from NS-3 and OMNeT++ are persuasive and provides a foundation for RTS threshold analysis in IEEE 802.11 WLANs for practical network design considerations.
Static Output Feedback Controller Design for Two-Dimensional Roesser T-S Fuzzy Systems

This article proposes a static output feedback (SOF) controller synthesis approach for Roesser type two-dimensional (2-D) Takagi-Sugeno (T-S) fuzzy systems. Based on fuzzy Lyapunov functions together with the proposed descriptor system approach, sufficient controller design conditions are obtained. The proposed controller can ensure the asymptotic stability with $\mathcal{H}_{\infty}$ performance. With simulation studies, the effectiveness of the method is validated.
A Cable-Driven Exosuit for Upper Limb Flexion Based on Fibres Compliance

Flexible soft exoskeletons, so-called exosuits, are robotic devices that interact with their users to assist or enhance muscle performance. Their lightweight design and lack of rigid parts allow assisting the user’s natural motion without any constraints. They are thereby valuable in carrying out daily labour tasks and performing active stances of rehabilitation. Nonetheless, the usage of these devices in long-term applications demands anatomically adaptive designs and mechanisms to tackle textile artefacts and discrepancies in the human constitution. The soft exoskeleton described in this article is a textile-wearable design that assists shoulder and elbow flexion. The cable-driven actuation is embedded in a jacket by using several textiles and deformable parts. The inconveniences of using textile such as slipping, dampening, and pressure sores are tackled by combining textile layers with force-compliant sewing. The design also includes pieces for cable guidance, anchoring and support. These parts employ different tailoring methods so as to ease fabrication, wearing and cleaning. The motors and electronics, whose design is compatible with textiles too, are placed in a backpack. This configuration reduces forces from loads in motion and weight on the arm. Finally, the last part of the document discusses the preliminary results that have been obtained from four subjects who have worn the device.
Effectiveness of GAN-based Synthetic Samples Generation of Minority Patterns in HEp-2 Cell Images*

In this paper, we present a framework to address the augmentation of images for the rare and minor appearance of mitotic type staining patterns, for Human Epithelium Type2 (HEp-2) cell images. The identification of mitotic patterns among non-mitotic/interphase patterns is important in the process of diagnosis of various autoimmune disorders. This task leads to a pattern classification problem between mitotic v/s interphase. However, among the two classes, typically, the number of mitotic cells are relatively very less. Thus, in this work, we propose to generate synthetic mitotic samples, which can be used to augment the number of mitotic samples and balance the samples of mitotic and interphase patterns in classification paradigm. An effective feature representation is used, to validate the usefulness of the synthetic samples in classification task, along with a subjective validation done by a medical expert. The results demonstrate that the approach of generating and mingling synthetic samples with existing training data works well and yields good performance, with 0.98 balanced class accuracy (BcA) in one case, over a public dataset, i.e., UQ-SNP I3A Task-3 mitotic cell identification dataset.
Interleaved Block Coding for Achieving Gaussian Random Access Channel Capacity

This paper studies the detection problem for the Gaussian random access channels, in which only a randomly chosen αK out of K senders wish to convey their messages to a receiver that has the side information of which sender is actively transmitting. A novel coding scheme, referred to as L-interleaved block coding, based on interleaving L single-user codes and successive cancellation decoding is proposed for the symmetric rate tuples in the capacity region of this channel. L-interleaved block coding with L sufficiently large is shown to achieve arbitrarily close to the symmetric capacity. Therefore, it replaces the efforts of finding capacity achieving codes for the Gaussian random access channels with selecting L off-the-shelf codes for Gaussian point-to-point channels for L sufficiently large. An example illustrates that this scheme with a small number of blocks (L = 16) achieves more than 0.95 of the symmetric capacity when two senders are active.
Orthogonalizing the Activity of Two Neural Units for 2D Cursor Movement Control

In the design of brain-machine interface (BMI), as the number of electrodes used to collect neural spike signals declines slowly, it is important to be able to decode with fewer units. We tried to train a monkey to control a cursor to perform a two-dimensional (2D) center-out task smoothly with spiking activities only from two units (direct units). At the same time, we studied how the direct units did change their tuning to the preferred direction during BMI training and tried to explore the underlying mechanism of how the monkey learned to control the cursor with their neural signals. In this study, we observed that both direct units slowly changed their preferred directions during BMI learning. Although the initial angles between the preferred directions of 3 pairs units are different, the angle between their preferred directions approached 90 degrees at the end of the training. Our results imply that BMI learning made the two units independent of each other. To our knowledge, it is the first time to demonstrate that only two units could be used to control a 2D cursor movements. Meanwhile, orthogonalizing the activities of two units driven by BMI learning in this study implies that the plasticity of the motor cortex is capable of providing an efficient strategy for motor control.
Effect of Blood Volume Shift Simulated via Head-up Tilt on Photoplethysmography Morphology

PPG can provide information on cardiovascular responses to fluid shifts from upper to lower part of body under the condition of orthostatic stress. The current study investigated ability of PPG derived LVET and other PPG derived features to identify progressive central hypovolemia induced by head up tilt (HUT) and evaluated potential use of LVET as early noninvasive indicator of blood loss. Continuous finger PPG, blood pressure, and electrocardiography were recorded simultaneously during 5-minutes of baseline and HUT of 20°, 40°, and 60° from 15 participants (age: 26.5 ± 3 years; height: 177 ± 8 cm; weight: 72 ± 10 kg, mean ± SD). Beat-by-beat pulse rate (PR), systolic amplitude (SA), systolic time (ST), diastolic time (DT), and PP Interval (PPI) and Ratio of pulse rate over systolic amplitude (PR/SA) were derived for each stage. LVET was derived from each stage. Friedman test followed by post-hoc analysis using Tukey-HSD was conducted to highlight the significance of changes induced by HUT. Application of 60° HUT (i.e. moderate category simulated hypovolemia) resulted in a significant change in PR (80±3 bpm vs 68±3 bpm, p=0.0008), DT (264±7 ms vs 303±4 ms, p=0.0008), ST (110±6 ms vs 117±7 ms, p=0.02), PP interval (764±39 ms vs 869±25 ms, p=0.0045), PR/SA (112±16 vs 82±21, p=0.012) , SA (0.875± 0.2 vs 1.69±0.6, p=0.012) and LVET(292 vs 351ms,p=0.0008) compared to baseline. LVET has a strong association with the change in central blood volume and may be used as a sensitive early marker of progressive hypovolemia. The findings of the study support the hypothesis of differentiating simulated hypovolemia based on PPG alone. Keywords: Hypovolemia, HUT, LVET.