Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Space Analysis for a Digital Lock-In Amplifier for Infrared Gas Sensor Signal Acquisition

Climatological conditions study has gained interest nowadays due to their effect in human life. Air quality evaluation, both indoors and outdoors, is an important concern in cities, industries and workplaces. Sensors used for this task deliver small signals that need to be cleaned and processed on-the-edge. In this paper, the authors present a processing system based on a digital Lock-In amplifier for an NDIR CO2 sensor as part of a multi-sensor instrument capable of measuring different meteorological magnitudes.Both in space applications or IoT systems, factors such as maximum area, processing time and power consumption must be considered. To this purpose, two different implementations for the same processing system are presented, a purely hardware architecture and a software implementation on a soft-core microprocessor, based on the RISC-V, both onto a Microsemi FPGA. Performance, resources usage and processing time are compared in both implementations in order to study their scalability towards a multi-sensor system.
Whole-Body Torque Control Without Joint Position Control Using Vibration-Suppressed Friction Compensation for Bipedal Locomotion of Gear-Driven Torque Sensorless Humanoid

Humanoids operate in repeated contact and non-contact with their environment and so the motion of humanoids such as walking on uneven terrain or in a narrow space requires the accurate force and position control. Joint torque control systems are suitable for position and force control, but are prone to friction and other modeling errors. To solve this problem, methods have been proposed to realize torque control in combination with joint position control systems or by improving joint structures such as sensors and actuators, but these methods have problems such as response delay and increased weight and volume. Thus, it is difficult to achieve motion of life-sized humanoids by whole-body torque control. In this paper, we solve challenges not with one specific layer, but rather with multiple layers that complement each other. We propose a hierarchical whole-body torque control method using four layers: friction compensation based on a vibration-suppressed model, whole-body resolved acceleration control using priority, center-of-gravity acceleration control based on foot-guided control, and landing position time modification based on capture point. We verify through walking experiments that the proposed methods can control the life-sized humanoid robot driven by high-reduction ratio joints by whole-body torque control without a torque sensor or joint position control, and that it enables the robot to move and even transport an object on outdoor uneven terrain.
HistoDepth - Novel Depth Perception for Safe Collaborative Robots

The trend towards Industry 4.0 demands an increasing flexibility of system configurations including more agile collaborative human-robot systems that can quickly adapt to new tasks and missions. At the same time, state-of-the-art safety solution for many industrial robots is the use of barriers around the robot to limit the exposure to human co-workers. As these barriers cannot be reconfigured easily, it considerably limits the flexibility of many robotic solutions. Thus, new more agile safety solutions are required, and these require new, robust perception systems that can ensure detection of all safety relevant objects. In this paper, we propose a novel depth perception approach called HistoDepth, which addresses this need. It uses depth sensors that are mounted at fixed, static positions outside of the workspace and tracks each pixel measurement statistically over time. This enables our solution to differentiate static scene elements from dynamic (potentially hazardous) elements and to enforce a robot safety maneuver upon detection of a critical object. Moreover, it can adapt automatically to changing noise or scene configurations without user input or the need for setup changes. Using an UR5 robot arm, we demonstrate that this solution can enable new agile and flexible safety concepts for collaborative robots.
Analysing heart rate variability in preterm infants: the effect of temporal adjustment of NN peaks and missing data

The measurement of heart rate variability (HRV) in preterm infants provides important information on function to clinicians. Measuring the underlying electrocardiogram (ECG) in the neonatal intensive care unit is a challenge and there is a trade off between extracting accurate measurements of the HRV and the amount of ECG processed due to contamination. Knowledge on the effects of 1) quantization in the time domain and 2) missing data on the calculation of HRV features will inform clinical implementation. In this paper, we studied multiple 5 minute epochs from 148 ECG recordings on 56 extremely preterm infants. We found that temporal adjustment of NN peaks improves the estimate of the NN interval resulting in HRV features (m = 9) that are better correlated with age (median percentage increase in correlation of individual features: 0.2%, IQR: 0.0 to 5.6%; correlation with age predictor and age from 0.721 to 0.787). Improved (sub-sample) quantization of the NN intervals (via interpolation) reduced the overall value of HRV features (median percentage reduction in feature value: -1.3%, IQR: -18.8 to 0.0; m = 9), primarily through a reduction in the energy of high-frequency oscillations. HRV features were also robust to missing data, with measures such as mean NN, fractal dimension and the smoothed nonlinear energy operator (SNEO) less susceptible to missing data than features such as VLF, LF, and HF. Furthermore, age predictions derived from a combination of HRV measures were more robust to missing data than individual HRV measures.Clinical Relevance—Poor quantization in time when estimating the NN peak and the presence of missing data confound HRV measures, particularly spectral measures.
Entropy-driven Adversarial Training For Source-free Medical Image Segmentation

Although traditional unsupervised domain adaptation (UDA) methods have proven effective in reducing domain gaps, their reliance on source domain data during adaptation often proves unfeasible in real-world applications. For instance, data access in a hospital setting is typically constrained due to patient privacy regulations. To address both the need for privacy protection and the mitigation of domain shifts between source and target domain data, we propose a novel two-step adversarial Source-Free Unsupervised Domain Adaptation (SFUDA) framework in this study. Our approach involves dividing the target domain data into confident and unconfident samples based on prediction entropy, using the Gumbel softmax technique. Confident samples are then treated as source domain data. In order to emulate adversarial training from traditional UDA methods, we employ a min-max loss in the first step, followed by a consistency loss in the second step. Additionally, we introduce a weight to penalize the L2−SP regularizer, which prevents excessive loss of source domain knowledge during optimization. Through extensive experiments on two distinct domain transfer challenges, our proposed SFUDA framework consistently outperforms other SFUDA methods. Remarkably, our approach even achieves competitive results when compared to state-of-the-art UDA methods, which benefit from direct access to source domain data. This demonstrates the potential of our novel SFUDA framework in addressing the limitations of traditional UDA methods while preserving patient privacy in sensitive applications.
Automatic Exposure and Pose Estimation Error

Visual odometry estimates a robot’s pose by analysing images it captured and is integral to autonomous navigation. However, when devices operate outdoors, the large changes in brightness may cause localisation failure. This shortcoming motivates us to find methods that ensure visual odometry can function in high dynamic range environments.To mitigate the negative impact of operating in high dynamic range environments, researchers proposed automatic exposure algorithms to select a camera exposure time that maximises their selected image quality metric. However, direct comparisons between the selected image metric and visual odometry performance is scarce. Many of the proposed algorithms go through multiple steps, making it difficult to determine whether the improved pose estimation accuracy is the result of optimising the metric.In this submission, we generated image sequences in a simulation environment and captured real-world photos at different exposure levels. We estimate trajectories for each data set and compare the resulting errors with the image quality metrics. Our tests showed that none of the selected image quality metrics had a strong correlation with absolute translation error. The findings suggest that the current automatic exposure algorithms have yet to find an image quality metric that truly impacts visual odometry performance. Ongoing research should consider alternative metrics or metric combinations.
An Affordances and Electromyography Based Telemanipulation Framework for Control of Robotic Arm-Hand Systems

Over the last decades, significant research effort has been put into creating Electromyography (EMG) based controllers for intuitive, hands-free control of robotic arms and hands. To achieve this, machine learning models have been employed to decode human motion and intention using EMG signals as input and to deliver several applications, such as prosthesis control using gesture classification. Despite the advances introduced by new deep learning techniques, real-time control of robot arms and hands using EMG signals as input still lacks accuracy, especially when a plethora of gestures are included as labels in the case of classification. This has been observed to be due to the noise and non-stationarity of the EMG signals and the increased dimensionality of the problem. In this paper, we propose an intuitive, affordances-oriented EMG-based telemanipulation framework for a robot arm-hand system that allows for dexterous control of the device. An external camera is utilized to perform scene understanding and object detection and recognition, providing grasping and manipulation assistance to the user and simplifying control. Object-specific Transformer-based classifiers are employed based on the affordances of the object of interest, reducing the number of possible gesture outputs, dividing and conquering the problem, and resulting in a more robust and accurate gesture decoding system when compared to a single generic classification model. The performance of the proposed system is experimentally validated in a remote telemanipulation setting, where the user successfully performs a set of dexterous manipulation tasks.
A First Study on the Spectrum Needs for Release 2 V2X Services

The European Telecommunications Standards Institute (ETSI) is nearing the completion of the standardization process for Release 2 V2X Services. These services transmit messages such as the Cooperative Awareness, Vulnerable Road User Awareness, and Collective Perception Message, with the latter being considered the flagship message of Release 2. While there is a significant amount of research on the functionality and benefits of these messages, it is essential to acknowledge the different assumptions made by various authors regarding message sizes. Our objective is to address these discrepancies by conducting a comprehensive analysis of messages’ sizes. We define the anticipated size of the standardized messages using three different configurations. The first two configurations include or exclude the optional fields of the messages, respectively. The third configuration is determined through extensive consultation rounds with field experts. We believe that this study will establish a strong foundation, addressing the need for researchers to make significant assumptions when considering message sizes in these services. Moreover, using our custom real-data-driven simulator, we analyze the average transmission frequencies of these three messages in both urban and highway scenarios. In conclusion, we present the reader with the necessary spectral bandwidth requirements for these messages.
Analog/Mixed-Signal Standard Cell Based Approach for Automated Circuit Generation of Neural Network Accelerators

Analog and mixed-signal neural network accelerators are a promising solution to apply deep learning methods to edge applications where high energy and area efficiency are required. Such in-memory computing implementations use regular and repetitive circuit structures that take great advantage of design automation. An analog/mixed-signal standard cell design approach in combination with an automation framework has been developed to ease the design of such systems. The framework discussed here provides the basic functionality such as schematic and layout creation. It is based on manually designed standard cells and technology and topology parameters to steer the automation. The presented methodology drastically reduces the (re-)design time and engineering effort leading to a reduced time-to-market whilst errors occurring in manual executed circuit design can be avoided.
Dielectric Confined Nickel-Titanium Germano-Silicide Junctions to SiGe Nanochannels

The silicidation process, used for the fabrication of contacts in MOSFETs, has met some challenges, when transferred to SiGe channel material. In this paper, the formation of germano-silicides in SiGe nano-channels is investigated. A wide range of temperatures were tested and both pure Ni and Ni90Ti10 alloy were used as the diffusive metal. A repeatable process flow was established, with which a long intruded germano-silicide of around 350 nm with little to no imperfections can be achieved. A nanochannel with (NiTi)(SiGe)/SiGe junctions was gated to form an ambipo-lar transistor with clear n- and p-branches in its transfer characteristics.
Analyzing the Impact of Image Denoising and Segmentation on Melanoma Classification Using Convolutional Neural Networks

Early skin cancer detection and its treatment are crucial for reducing death rates worldwide. Deep learning techniques have been used successfully to develop an automatic lesion detection system. This study explores the impact of pre-processing steps such as data augmentation, contrast enhancement, and segmentation on improving the convolutional neural network (CNN) performance for lesion classification. The classification network was designed from scratch by uniquely organizing its layers and using a different number of kernels, depth of the network, size, and hyperparameters. In addition, the network’s performance was improved by pre-processing and segmentation steps. The proposed network was compared with the current state-of-the-art to demonstrate its best performance on the benchmark HAM10000 lesion dataset. The experimental study revealed that the classification network using denoised+segmented data achieved an accuracy (ACC), precision (PRE), recall (REC), specificity (SPE), and F-score of 93.40%, 93.45%, 94.51%, 92.08%, and 93.98%, respectively. To conclude, classification performance can be improved by incorporating pre-processing and segmentation steps.
Rollvox: Real-Time and High-Quality LiDAR Colorization with Rolling Shutter Camera

In this study, we propose a novel system for real-time coloring LiDAR point clouds with a low-cost RS camera. The main challenges are dealing with the motion distortion of the RS camera and the multi-sensor time synchronization. To tackle these challenges, we carefully design a hardware synchronizer to ensure the strict alignment of the LiDAR, inertial measurement unit, and RS camera. With accurate timestamps, we first use LiDAR-inertial odometry (LIO) for pose estimation, and the poses of image line exposure are calculated by forward propagation based on a constant velocity motion model. Then, we propose our method based on the RS constraint for colorizing the LiDAR point cloud. For comparison, we colorize the LiDAR point cloud with conventional rolling shutter image undistortion. In the real-world tests, The results show that our proposed method produces more accurate and efficient colorization of point clouds. Besides, considering the situation of readout time not being provided, we propose a method to calibrate the readout time by minimizing the reprojection error of LIO's inter-frame pose and image optical flows. We release our code and self-collected datasets on Github33https://github.com/sheng00125/Rollvox to benefit the community.
One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance— These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.
The effect of deep brain structure modeling on transcranial direct current stimulation-induced electric fields: An in-silico study

To study transcranial direct current stimulation (tDCS) and its effect on the brain, it could be useful to predict the distribution of the electric field induced in the brain with given tDCS parameters. As a solution, simulation with realistic computational models using magnetic resonance images (MRIs) have been widely used in the fields. With the recent advance of deep learning-based segmentation techniques of the brain, questions have been raised about if tDCS-induced electric field is affected by the deep brain structures. This study aimed to investigate the effect of the deep brain structure modeling on the induced electric field. To this end, we generated models with and without the deep brain structures by using an open MRI dataset comprising tDCS parameters, electric field simulation results and in-vivo intracranial recordings in the deep brain structures. We investigated the difference between the simulation results of the two models with a statistical analysis. Our results indicated that tDCS-induced electric fields and current flow in the brain are significantly different when the deep brain structures are considered.
Integrating biplane information and context for spine landmark detection

Spine landmark detection is of great significance for spinal morphological parameter assessment and three-dimensional reconstruction of the human spine. This detection task generally involves locating spine landmarks in the anterior-posterior (AP) and lateral (LAT) X-rays of the spine. Recently, the two-stage methods for AP spine landmark detection achieve better performance. However, these methods perform poorly in LAT landmark detection because of poor detection accuracy of LAT vertebra due to occlusion. To solve this problem, this paper proposes a new two-stage spine landmark detection method. In the first stage, this paper propose a biplane vertebra detection network for vertebra detection on AP and X-rays simultaneously. Then an epipolar module and a context enhancement module are proposed to assist LAT vertebra detection by using the biplane information and the context information of the vertebrae respectively. In the second stage, the landmarks can be obtained in the detected vertebrae area. Extensive experiment results conducted on a dataset containing 328 pairs of X-rays demonstrate that our method improves the vertebra and landmark detection accuracy.
Improving Spiking Neural Network Performance Using Astrocyte Feedback for Farsi Digit Recognition

The third generation of neural networks is called spiking neural networks. Spiking neural networks can not only answer all the problems that can be solved by common neural networks, they can also be computationally more powerful than them. Also, these networks are closer to the biological reality of neurons. According to these reasons, spiking neural networks have received much attention in recent years. In addition to neurons, other elements are also used in spiking neural networks, whose connection with neurons has been proven biologically. These elements are called astrocytes. In this study, we have used astrocyte local feedback in a spiking neural network to improve the accuracy of the network in a digit recognition process. To reach this goal, we use the network proposed by Diehl et al in 2015, then we added astrocyte local feedback to this network. We run the network in both states. The classification accuracy of the network in the absence of astrocytes was 88.48% on Farsi handwritten digits. After adding astrocyte local feedback, it is observed that astrocytes control the activity of neurons and modified the weight of connections based on neuron activity. In this state classification accuracy reached 90.26%. The idea was to create a neural network for learning, where, inspired by the physiological role of astrocytes at the tripartite synapse improves the performance of the neural network and we demonstrate that systems that include neurons and astrocytes perform better than systems incorporating only neurons.
Research Issues in False Information Broadcasting Rogue Node Detection for Green VANETs

Vehicular Ad-hoc Networks (VANETs) serve as the basis of Intelligent Transportation Systems (ITS). Green communication for VANETs emphasizes reducing the harmful impacts of vehicular communication on the environment through energy-efficient communication, reduced fuel consumption, alleviation of road congestions, and reduced emission of harmful compounds, etc. In VANETs, the participating nodes (vehicles) benefit from the broadcasted beacon messages among themselves that can aid in achieving the objectives of green communication. However, a rogue node in this setup may broadcast false information that may lead to potential hazards and nullify the desirable properties of green communication. Hence the detection of such rogue nodes is critical. Different techniques such as cryptography, trust score evaluation, statistical techniques, AI-based methods, etc., are used to detect false information broadcasting rogue nodes. The characteristics of VANETs and the detection of false information broadcasting rogue nodes introduce unique research issues to the problem. In this paper, we discuss these research issues that should be addressed by a technique for detecting false information broadcasting rogue nodes and highlight their importance in fulfilling green communication in VANETs.