Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Compressive Sensing of Biomedical Signals Using A Permuted Kronecker-based Sparse Measurement Matrix

Compressive sensing (CS) is an innovative approach to simultaneously measure and compress signals such as biomedical signals that are sparse or compressible. A major effort in CS is to design a measurement matrix that can be used to encode and compress such signals. The measurement matrix structure has a direct impact on the computational and storage costs as well as the recovered signal quality. Sparse measurement matrices (i.e. with few non-zero elements) may drastically reduce these costs. We propose a permuted Kronecker-based sparse measurement matrix for sensing and data recovery in CS applications. In our study, we use three classes of sub-matrices (normalized Gaussian, Bernoulli, and BCH-based matrices) to create the proposed measurement matrix. Using ECG signals from the MIT-BIH Arrhythmia database, we show that the reconstructed signal quality is comparable to the ones achieved using well known CS methods. Our methodology results in an overall reduction in storage and computations, both during the sensing and recovery process. This approach can be generalized to other classes of eligible measurement matrices in CS.
Palmvein Recognition Using Block-Based WLD Histogram of Gabor Feature Maps and Deep Neural Network With Bayesian Optimization

The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber’s local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber’s law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method attains higher identification rate and lower EER than state-of-the-art methods.
Design, Analysis, and Experimental Evaluation of a New Secure Rejoin Mechanism for LoRaWAN Using Elliptic-Curve Cryptography

LoRaWAN (Long Range Wide Area Network) is a Low-Power Wide Area Networks (LPWAN) technology with very rapid uptake during the previous years, developed by the LoRa (Long Range) Alliance as an open standard operating over the unlicensed band. Current LoRaWAN architecture foresees specific techniques for bootstrapping end-to-end encryption during network initialization. In particular, this work focuses on the Over-The-Air Activation (OTAA) method, which uses two keys (Network key (NwkKey) and Application key (AppKey)) that are hard-coded into the device and do not change throughout the entire lifetime of the deployment. The inability to refresh these two keys is as a weak point in terms of the overall security of the network especially when considering deployments that are expected to operate for at least 10–15 years. In this paper, the security issues of OTAA are presented in detail highlighting the vulnerabilities against the specific type of attacks. A new scheme for network activation is proposed that builds upon the current LoRaWAN architecture in a way that maintains backwards compatibility while resolving certain vulnerabilities. Under the new mechanism, the devices periodically negotiate new keys securely based on elliptic-curve cryptography. The security properties of the proposed mechanism are analyzed against a specific type of attacks. The analysis indicates that the new secure rejoin mechanism guarantees (i) computational key secrecy, (ii) decisional key secrecy, and (iii) key independence, forward and backward, for both root keys thus properly addressing the considered security vulnerabilities of LoRaWAN. Moreover, the method is implemented in software using the RIOT-OS, a hardware-independent operating system that supports many different architectures for 8 bit, 16 bit, 32 bit and 64 bit processors. The resulting software is evaluated on the FIT IoT-Lab real-world experimentation facility under a diverse set of ARM Cortex-M* devices targeting a broad range of IoT applications, ranging from advanced wearable devices to interactive entertainment devices, home automation and industrial cyber-physical systems. The experiments indicate that the overall overhead incurred in terms of energy and time by the proposed rejoin mechanism is acceptable given the low frequency of execution and the improvements to the overall security of the LoRaWAN1.1 OTAA method.
An Extraction Method for Glacial Lakes Based on Landsat-8 Imagery Using an Improved U-Net Network

Remote sensing monitoring of glacial lakes is an indispensable tool for identifying and preventing glacial lake disasters. At present, the existing extraction methods of glacial lakes based on Landsat remote sensing image have achieved remarkable results, but the algorithms used lack the ability to analyze glacial lake spectral and shape and texture features, and require manual design parameters to fine tune the automation of the algorithm. As a result, it cannot mine the depth features of glacier lakes in remote sensing images accurately enough. To address these challenges, this study designed a self-attention mechanism module U-net network that enhances the propagation of features, reduces information loss, strengthens the weight of glacial lake areas, restrains the weight of irrelevant features, reduces the influence of low image contrast on the model, and deals with the variety of pixel categories in glacial lakes. These features improve the performance of the model. Based on Landsat-8 images, we first extracted glacial lakes in large-scale alpine areas using a U-net network model. To make it a self-attention U-net network, we introduced the attention mechanism into the step connection part of the U-net network to adjust feature weight, focus on learning glacial lake features, and strengthen the network to extract the glacial lake features. Finally, we selected the combination of bands 3, 5, and 6 and all bands of Landsat-8 images sing the self-attention U-net network to extract glacial lakes in the study area and compared and analyzed the extraction results. The experimental results and analyses revealed that the proposed method can effectively segment glacial lakes from Landsat-8 remote sensing images. Its effectiveness was proven by different evaluation indices. Compared with a standard U-net network, the true positive for the combination of 3, 5, and 6 bands increased by 15.95% and for all bands by 5.79%. The area under curve for the whole study area reached 85.03% for all bands. The improved U-net network can, thus, meet the real time needs of glacial lake disaster information acquisition.
Two-Stage In-Storage Processing and Scheduling for Pattern Matching Applications

In-storage processing technology allows applications to run on embedded processors and accelerators inside solid-state drives (SSDs) for efficient computing distribution. Especially, in pattern matching applications, in-storage computing can be processed quickly due to low data access latency, and the number of I/Os can be reduced by returning only a small amount of results to the host system after processing. Previously proposed in-storage processing is separated into three phases: command decoding, data access, and data processing. In this case, data processing is strictly isolated from data access, and the isolation constraints the utilization of storage. Merging data access and data processing among the phases can enhance the utilization of storage. To efficiently merge them, we propose two-stage in-storage processing and scheduling, especially for the pattern matching application. The first stage processing during data access reduces the second stage processing latency. Also, leveraging the pattern matching results of the first stage processing, our scheduler prioritizes key requests that should return the results to the host system so that they are completed earlier than non-key requests. The proposed scheduling reduces the response time of in-storage processing requests by 52.6 % on average.
Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions

This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into a two-stage unit commitment optimization. The planning objective consists in minimizing operating costs and/or equivalent carbon dioxide (CO2) emissions. Based on distributions of forecasting errors of the net demand, a LOLP-based risk assessment method is proposed to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction and the prescribed OR requirement. In a second stage, possible future forecasting uncertainties are considered. Hence, a stochastic operational planning is optimized in order to commit enough flexible generators to handle unexpected deviations from predic-tions. The proposed methodology is implemented for a local energy community. Results regarding the available operating reserve, operating costs and CO2 emissions are established and compared. About 15% of economic operating costs and environmental costs are saved, compared to a deterministic generation planning while ensuring the targeted security level.
An Elastic Task Scheduling Scheme on Coarse-Grained Reconfigurable Architectures

Coarse-grained reconfigurable architectures (CGRAs) are increasingly employed as domain-specific accelerators due to their efficiency and flexibility. A CGRA typically relies on compilers to perform task scheduling. The longstanding problem of static scheduling is that it suffers from insufficient parallelism in handling irregularities due to over-serialization and workload imbalance, which leads to severe resource underutilization and performance loss. To counteract the limitations of static scheduling in CGRAs, it is essential to exploit dynamic parallelism automatically and manage hardware resources adaptively. However, existing dynamic scheduling mechanisms, e.g., work stealing, often reschedule aggressively for instant performance but sacrifice efficiency, which is unfavorable to CGRAs that emphasize efficiency and fewer reconfigurations. This article proposes an elastic task scheduling scheme that enables lightweight dynamic scheduling in CGRAs. Tasks are rescheduled at runtime according to the classic tagged-token dataflow paradigm to enable dynamic task-level parallelism. Meanwhile, tasks are dynamically resized according to run-time throughputs via duplication, combination, and substitution operators for balanced multitask execution. We implement the elastic task scheduling scheme on a well-known reconfigurable architecture - triggered instruction architecture (TIA). Evaluation on the MachSuite benchmarks shows that the proposed scheme is effective in improving performance and energy efficiency. The average speedup is <inline-formula><tex-math notation="LaTeX">$2.2\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>. </mml:mo><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq1-3084804.gif"/></alternatives></inline-formula> over the baseline. Also, our design attains a 57 percent improvement in the area-normalized performance and a 49 percent better energy efficiency. Compared with a state-of-the-art dynamic scheduling method, our scheme achieves <inline-formula><tex-math notation="LaTeX">$1.6\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq2-3084804.gif"/></alternatives></inline-formula> speedup and <inline-formula><tex-math notation="LaTeX">$1.6\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq3-3084804.gif"/></alternatives></inline-formula> energy efficiency than work-stealing mechanism on the same substrate.
Toward Continuous Estimation of Cardiorespiratory Parameters in Oscillometry: A Simulation Study

Blood pressure’s oscillometric waveform (OMW) comprises several cardiovascular components such as cardiac activity, respiration-related changes, and Mayer wave that contribute to its total variability over time. Accurate modeling of the OMW as a function of these components and continuous tracking of their underlying parameters can provide insights into the cardiovascular system dynamics and help determine the role played by each component in blood pressure variability. This paper presents a new state-space model for the OMW consisting of different parameters such as cardiac and respiration frequencies, amplitudes, and phases. Since the dynamic state-space model of the OMW is highly nonlinear and dependent on a large number of parameters, we utilized the extended Kalman filter (EKF). Since the EKF accuracy is highly dependent on the parameter’s initial values, to obtain reasonable estimates of model initial values, a system identification procedure based on frequency domain analysis and curve-fitting was employed. The proposed method’s performance was analyzed on simulated data with and without the proposed system identification procedure. A mean absolute percentage error of 2.68% was achieved in estimating OMW when using the proposed system identification approach. The proposed approach shows promise toward beat-to-beat tracking of cardiovascular parameters in oscillometric devices.
PIMCaffe: Functional Evaluation of a Machine Learning Framework for In-Memory Neural Processing Unit

The large amount of memory usage in recent machine learning applications imposes a significant system burden with respect to power and processing speed. To cope with such a problem, Processing-In-Memory (PIM) techniques can be applied as an alternative solution. Especially, the recommendation system, which is one of the major machine learning applications used in data centers, requires a large memory capacity and therefore represents a suitable candidate application that could be helped by the PIM technique. In this paper, we introduce a machine learning framework, PIMCaffe, designed for in-memory neural processing units and its evaluation environment. PIMCaffe consists of two components: a Caffe2-based deep learning framework that supports PIM acceleration and a PIM-emulating hardware platform. We develop a suite of functions, libraries, application programming interfaces, and a device driver to support the framework. In addition, we implement a prototype Neural Processing Unit (NPU) in PIMCaffe to evaluate the performance of our platform with machine learning applications. Our prototype NPU design includes a vector processor for parallel vector processing and a systolic array unit for matrix multiplication. Using the proposed software framework, we perform a detailed analysis of the in-memory neural processing unit. PIMCaffe supports evaluations of recommendation systems and various convolutional neural network models on the in-memory neural processing unit. PIMCaffe with the NPU shows up to <inline-formula> <tex-math notation="LaTeX">$2.26\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5.99\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.71\times $ </tex-math></inline-formula> speedup, compared to the ARM Cortex-A53 CPU, for the recommendation system, AlexNet, and ResNet-50, respectively.
Strigolactone regulates plant architecture by inhibiting lateral branch growth in Quercus mongolica seedlings

ABSTRACT Mongolian oak (Quercus mongolica) is an important timber species in northeast China, but overdeveloped lateral branches during the seedling stage often result in poor plant architecture and reduce the growth potential. Strigolactones (SLs) were proposed to play an important role in plant architecture by inhibiting the growth of lateral branches; however, there is no knowledge on the strigolactone effects in Mongolian oak. In this study, 0.3, 3, 10, and 30 mg/L of GR24 (analogue of SLs) and 1 mg/L Thidiazuron (TDZ) were applied to Mongolian oak seedlings by the lanolin method. All GR24 treatments significantly reduced the number and biomass of lateral branches; in addition, the length increment of lateral branches was decreased by 3, 10 and 30 mg/L GR24. In addition, the auxin content in lateral branches was significantly increased by the 10 mg/L GR24 treatment, which might be one of the main reasons for the inhibition of lateral branch growth. Increased sucrose content in apex, combined with a decrease of sucrose content in lateral branches, further demonstrated the important role of GR24 in inhibiting lateral branch growth.
Using an Immersive Virtual Reality Design Tool to Support Cognitive Action and Creativity: Educational Insights from Fashion Designers

Abstract This study explores how a virtual reality (VR) design tool can enhance designers' cognitive action and creativity in the design process. Ten fashion designers participated in cognitive protocol research and in-depth interviews. Regarding the idea generation process's cognitive action, we limited the experiment to the sketch phase early in the design process and selected a 2D digital design tool for comparison. Expert reviews were also conducted to evaluate the creativity of design outcomes. As a result, we found that the immersive VR design tool activated physical and perceptual action in design cognition and enhanced flexible cognitive action amongst different cognitive action levels compared to the 2D digital design tool. Consequently, we found that the enhanced flexibility in the cognitive process in VR reinforced divergent design thinking in design concept generation and was connected with creative design outcomes.
Intersexual segregation in winter foraging of great spotted woodpecker Dendrocopos major in riparian forests infested with invasive tree species

ABSTRACT We investigated the pre-breeding intersexual foraging habitat preferences of the great spotted woodpecker Dendrocopos major in six old, unmanaged riparian forest stands in Central Hungary, which are composed mainly of the invasive North American green ash Fraxinus pennysylvanica and boxelder Acer negundo, and native poplar Populus sp. and willow Salix sp. trees in lower abundances. With the influence of river management, the two invasive species have usurped native tree species in numerous Central European riparian forests. We studied the intersexual utilization of tree species, tree condition, stem diameter, foraging height, horizontal sections, substrate diameter, substrate condition, and foraging technique. The birds showed intersexual segregation in numerous studied variables despite the high niche overlap between sexes. Similar patterns were also found in other woodpecker species as well. We think that the most important part of this segregation could be the difference revealed in the choice of tree species, as males tended to be more specialized for native softwood tree species with rougher bark structure. Despite the high abundance of invasive tree species, neither sex preferred them. The further population decrease of native tree species could increase within-species competition and negatively affect the studied bird species.
Effect of Deep Brain Stimulation Frequency on Gait Symmetry, Smoothness and Variability using IMU

Deep brain stimulation (DBS) implant represents an appropriate treatment for motor symptoms typical of Parkinson’s Disease (PD). However, little attention has been given to the effects of different DBS stimulation frequencies on gait outcomes. Accordingly, the aim of this pilot study was to evaluate the effects of two different DBS stimulation frequencies (60 and 130 Hz) on gait spatio-temporal parameters, symmetry, smoothness, and variability in PD patients. The analysis concentrated on acceleration signals acquired by a magnetic inertial measurement unit placed on the trunk of participants. Sessions of gait were registered for three PD patients, three young and three elderly healthy subjects. Gait outcomes revealed a connection with both age and pathology. Values of the Harmonic Ratio (HR) estimated for the three-axis acceleration signals showed subjective effects provoked by DBS stimulation frequencies. Consequently, HR turned out to be suitable for depicting gait characteristics, but also as a monitoring parameter for the subjective adaptation of DBS stimulation frequency. Concerning the Poincaré analysis of vertical acceleration signal, PD patients showed a greater dispersion of data compared to healthy subjects, but with negligible differences between the two stimulation frequencies. Overall, the presented analysis represented a starting point for the objective evaluation of gait performance and characteristics in PD patients with a DBS implant.