Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Classification of Rubber Wood Boards Using an SSR-Based CNN

The classification of wood types plays an important role in many fields, especially in construction industry and furniture manufacturing. In order to manufacture rubber wood furniture with highly uniform color and texture, wood boards of different colors and textures should be classified elaborately. Many traditional methods have been applied in wood classification relying on extracting features using handcrafted descriptors designed by experienced experts, but it is not easy to construct robust features in various conditions. In this article, we present a split-shuffle-residual (SSR)-based CNN that can learn features automatically from wood images for real-time classification of rubber wood boards. Specifically, we introduce an SSR module that combines channel split and shuffle operations with residual structure to reduce the computation cost while maintaining high classification accuracy. In each module, the input is split into two low-dimensional branches, and the channel shuffle operation is used to enable the information communication between the input and the two separated branches, which is regarded as the feature reuse that enlarges network capacity without increasing complexity. The comprehensive experiments demonstrate that our algorithm outperforms other traditional classification methods and the state-of-the-art deep learning classification networks, yielding an accuracy of 94.86%. Furthermore, the analysis of running time indicates that the SSR-based CNN can be employed for wood classification in real time, which takes only 26.55 ms to handle a single image.
NoRBERT: Transfer Learning for Requirements Classification

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results F1-scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying non-functional requirements subclasses. The most frequent classes are classified with an average F1-score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of 15 percentage points in average F1 score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an F1-score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results.
Multi-Antenna Two-Way Relay Based Cooperative NOMA

In this paper, we investigate a non-orthogonal multiple access (NOMA) assisted multi-antenna two-way relay system, where multi-antenna users apply NOMA to support bidirectional superposition transmission via multiple multi-antenna relays. Specifically, we propose two cooperative strategies, namely multiple-access broadcast NOMA and time division broadcast NOMA. For each of the two cooperative strategies, we devise a joint antenna-and-relay selection scheme to enhance the transmission reliability. Analytical expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed cooperative strategies with the corresponding joint antenna-and-relay selection schemes. To further reduce the outage probability, we use the derived analytical results as objective functions to optimize the transmit power allocation under both cooperative strategies. Finally, extensive simulations are carried out to validate the accuracy of the derived analytical results. Our simulation results indicate that the proposed strategies significantly outperform existing benchmark strategies in terms of outage probability and diversity order.
Sensor Data Reconstruction in Industrial Environments with Cellular Connectivity

The reliable acquisition of monitoring information is critical for several industrial use cases relying on wireless sensor network deployments. However, missing sensor measurements are typical in industrial systems empowered by cellular connectivity due to the stochastic nature of the wireless channel. In this paper, we propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements. Based on an analytical framework for the network model and a closed-form expression for the outage probability, the impact on the reconstruction error performance is thoroughly explored. Considering a dataset with high spatiotemporal correlation in the sensor observations, our proposed scheme is shown to outperform two baseline data recovery methods in terms of reconstruction error for various network configurations. In addition, despite the presence of imperfect cellular connectivity, our proposed scheme exhibits high event-detection accuracy.
FVRAS-Net: An Embedded Finger-Vein Recognition and AntiSpoofing System Using a Unified CNN

Despite claims that finger-vein biometrics can detect aliveness, recent research has shown that current systems can be fooled by forged vein patterns printed on a distinctive paper, raising considerable security concerns regarding the identification authenticity of these systems. Additionally, finger-vein identification exhibits low accuracy rates in real-world applications due to the inferior image quality caused by varied finger thicknesses and vein pattern variations caused by finger axial rotation. To address these issues, we propose a lightweight convolutional neural network (CNN) called the Finger-Vein Recognition and AntiSpoofing Network (FVRAS-Net), which integrates the recognition task and the antispoofing task into a unified CNN model by utilizing a multitask learning (MTL) approach and achieves high security and strong real-time performance. Then, a multi-intensity illumination strategy is introduced into the embedded biometric system to automatically select the most informative image for finger-vein identification, which can effectively improve the recognition performance of the real system. Finally, a challenging finger-vein database with images depicting severe axial finger rotation is built for more rigorous validation of the proposed system, which enriches the database resources for the finger-vein recognition community. Experiments demonstrate that the proposed FVRAS-Net achieves excellent performance in both recognition and antispoofing tasks on public data sets, especially on challenging databases with images depicting axial rotation.
Fall Detection using Wi-Fi Signals and Threshold-Based Activity Segmentation

We present the design and implementation of a low-cost, accurate and non-invasive wireless fall detection system utilising commercial off-the-shelf (COTS) 802.11n WLAN network interface cards (NICs). The system utilises the channel state information (CSI) of the wireless channel between a transmitter and a receiver. Notably, in addition to the CSI amplitude, the proposed system exploits the phase difference over 2 receiving antennas to detect patterns uniquely attributed to a human falling. Our extensive experimental results show that the CSI phase difference is a more granular measure at 5 GHz rather than the amplitude. The proposed method for fall detection consists of two stages. In the first stage, we quickly segment two types of actions, fall-like activities and falling activities to reduce the computational power required. In the second stage, we build a classification algorithm with newly defined features to detect three types of falls, namely walking-falls, standing-falls and sitting-falls. The concept of a sitting-fall is introduced whereby a person falls as they are standing up or sitting down. This is much more subtle than a walking-fall or standing-fall. To this end we introduce new features for signal classification such as the velocity of change of the standard deviation of the CSI phase difference. We also improve on existing features such as TimeLag proposed in [1]. We carry out extensive experiments to evaluate the performance of the proposed fall detection system. Particularly, the results demonstrate a balanced accuracy of 96 % for the proposed system, compared to 91 % for the top state-of-the-art solution [1].
A Reconfigurable SOI CMOS Doherty Power Amplifier Module for Broadband LTE High-Power User Equipment Applications

A reconfigurable broadband Doherty PA module for LTE HPUE (High Power User Equipment) applications is presented, which is the first to be based on an SOI-CMOS PA without predistortion and supply modulation. The PA die (1.3×1.7mm2) is fabricated in a 130nm SOI-CMOS process and assembled, using flip-chip, on a 3.2×3.7mm2 laminate package. From 1.9GHz to 2.7GHz, the PA provides 28dBm of output power (Pout) under 3.4V supply voltage (Vdd), with a PAE higher than 35% and an E-UTRA ACLR lower than -35dBc when using a 10MHz-50RB QPSK LTE uplink signal, without predistortion. At 2.3GHz, the proposed PA achieves 43.5% of PAE and -39.6dBc of ACLR at 28dBm of Pout. When operating at Vdd=5V (HPUE mode), the PA reaches a saturated power of 4W with a maximum PAE of 57% and delivers a Pout of 31dBm with 42.6% of PAE and -35.7dBc of ACLR using a 20MHz-100RB QPSK LTE signal.
The Use of Mutual Information to Improve Value-at-Risk Forecasts for Exchange Rates

In this paper, we show a simple but novel approach in an attempt to improve value-at-risk forecasts. We use mutually dependent covariate returns to create exogenous break variables and jointly use the variables to augment GARCH models to account for time-variations and breaks in the unconditional volatility processes simultaneously. A study of hypothetical mutual dependencies between volatility and the covariates is first carried out to investigate the levels of the shared mutual information among the variables before using the augmented models to forecast 1% and 5% value-at-risks. The results provide evidence of some substantial exchange of information between volatility and the lagged exogenous covariates. In addition, the results show that the estimated augmented models have lower volatility persistence, reduced information leakages, and improved explanatory powers. Furthermore, there is evidence that our approach leads to fewer violations, improved 1% value-at-risk forecasts, and optimal daily capital requirements for all the models. There is, however evidence of relative superiority of the majority of the models for the 5% value-at-risks forecasts from our approach, although they have relatively higher failure rates. Based on these results, we recommend the incorporation of our approach to existing risk modeling frameworks. It is believed that such models may lead to fewer bank failures, expose banks to optimal market risks, and assist them in computing optimal regulatory capital requirements and minimize penalties from regulators.
Outage Analysis for Terrestrial-Satellite Spectrum Sharing

This letter investigates the effect of the interference due to spectrum sharing between terrestrial and satellite systems. Based on a realistic coexistence model, we provide an exact closed-form outage probability expression of the satellite link in the presence of the terrestrial interference. We assume Nakagami fading both for terrestrial and satellite links, with an additional consideration for the shadowed Rician fading satellite link. Numerical results show that uncontrolled terrestrial interference can significantly degrade the outage performance of the satellite link. The obtained formulas are useful in predicting and preventing harmful interference when designing coexistence mechanisms for terrestrial and satellite systems.
Towards a Taxonomy for Eliciting Design-Operation Continuum Requirements of Cyber-Physical Systems

Software systems that are embedded in autonomous Cyber-Physical Systems (CPSs) usually have a large life-cycle, both during its development and in maintenance. This software evolves during its life-cycle in order to incorporate new requirements, bug fixes, and to deal with hardware obsolescence. The current process for developing and maintaining this software is very fragmented, which makes developing new software versions and deploying them in the CPSs extremely expensive. In other domains, such as web engineering, the phases of development and operation are tightly connected, making it possible to easily perform software updates of the system, and to obtain operational data that can be analyzed by engineers at development time. However, in spite of the rise of new communication technologies (e.g., 5G) providing an opportunity to acquire Design-Operation Continuum Engineering methods in the context of CPSs, there are still many complex issues that need to be addressed, such as the ones related with hardware-software co-design. Therefore, the process of Design-Operation Continuum Engineering for CPSs requires substantial changes with respect to the current fragmented software development process. In this paper, we build a taxonomy for Design-Operation Continuum Engineering of CPSs based on case studies from two different industrial domains involving CPSs (elevation and railway). This taxonomy is later used to elicit requirements from these two case studies in order to present a blueprint on adopting Design-Operation Continuum Engineering in any organization developing CPSs.
High-Field Coherent Terahertz Radiation Generation From Chirped Laser Pulse Interaction With Plasmas

This article presents a theoretical analysis of high-field terahertz (THz) radiation generation from chirped laser pulse interaction with a plasma of slanting density modulation. The mechanism is based on strong nonlinear transverse current produced by the beating of two chirped laser pulses, where the plasma density modulation is inclined at angle to the propagation of the lasers. We claim that oblique spatial density ripple plays a crucial role in field enhancement of THz. At resonance (due to the phase-matching), the field strength of THz becomes maximum for a particular slating angle of the plasma density modulation. We claim that the oblique spatial density ripple and laser frequency-chirp combinedly play a crucial role in field enhancement of THz field. We observe that stable THz is generated with a maximum field strength of 100 MV/cm and a relatively broad spectrum extending out to 2.4 THz, with a strong dependence on the selection of slating angle of plasma density ripple and laser frequency chirp parameter, which corresponds to the conversion efficiency in order of 10−2. The efficiency of the process may be optimized and controlled by the laser and plasma parameters, which may be useful in various applications of these kind of high-field THz.
MOCZ for Blind Short-Packet Communication: Practical Aspects

We investigate practical aspects of a recently introduced blind (noncoherent) communication scheme, called modulation on conjugate-reciprocal zeros (MOCZ). MOCZ is suitable for a reliable transmission of sporadic and short-packets at ultra-low latency and high spectral efficiency via unknown multipath channels, which are assumed to be static over the receive duration of one packet. The information is modulated on the zeros of the transmitted discrete-time baseband signal’s $z-$ transform. Because of ubiquitous impairments between the transmitter and receiver clocks, a carrier frequency offset occurs after down-conversion to the baseband. This results in a common rotation of the zeros. To identify fractional rotations of the base angle in the zero-pattern, we propose an oversampled direct zero-testing decoder to identify the most likely one. Integer rotations correspond to cyclic shifts of the binary message, which we determine by cyclically permutable codes (CPC). Additionally, the embedding of CPCs into cyclic codes, enables additive error-correction which reduces the bit-error-rate tremendously. Furthermore, we exploit the trident structure in the signal’s autocorrelation for an energy based detector to estimate timing offsets and the effective channel delay spread. We finally demonstrate how this joint data and channel estimation can be largely improved by receive antenna diversity at low SNR.
Influence of Vibrotactile Feedback on the Motor Evoked Potentials (MEPs) Induced by Motor Imagery

Motor imagery brain-computer interface (MI BCI) is a rapidly developing method of rehabilitation after a stroke. The presence of feedback is an essential point in closing the disturbed sensorimotor loop for the patients. In this work, we investigate motor cortex excitability changes after training with BCI, resulting in motor evoked potentials (MEPs). Seven healthy subjects took part in three experimental conditions: I) direct vibrotactile feedback while training; II) control session with constant vibration; III) BCI training without feedback Special attention was paid to classification accuracy, degree of desynchronization, and MEP measurements. The study has shown that the absence of the feedback does not influence the classification accuracy. The desynchronization degree was significantly higher (p<0,05) for all the subjects in the experiments with the feedback, meanwhile, there was no significant difference in MEPs.
Preliminary findings on tools for the analysis of mental activity of programmers using EEG data from portable devices

Developers are indeed the most important resource in software production, and the individual developers are hard to substitute. The core of the work of the developers of knowledge intensive systems is in their mind, and this now a growing interest in understanding how to detect and model the state of their mind. Such analysis would enable to determine and model the optimal situation to develop in terms, for instance, of speed of development, minimization of errors, etc., and, to the extent possible, to recreate or to get close to such situation while organizing the work, the processes, the tools, and so on. The problem of performing such analysis is that the most refined equipment to model the work of the mind, like the fMRI, is very expensive and not movable. However, a tool, MNE, has been developed who is able to recreate accurate approximations using EEG of the data coming from an accurate wearable device that would come from the fMRI. This is a major enabler of the research in this area and in this paper details on why to select it and how to use it are provided. Current paper makes proof-of-concept to show that EEG is applicable for for retrieving information about functional state of the brain especially with a help of MNE tool.
A Reliable Industrial Wireless Sensor and Actor Network Based on CDMA-OQAM-OFDM

In this paper, the performance of a Wireless Sensor and Actor Network (WSAN) based on the combination of Multicarrier Code Division Multiple Access (MC-CDMA) with Orthogonal Frequency Division Multiplexing (OFDM) and Offset Quadrature Amplitude Modulation (OQAM)-OFDM using the PHYDYAS prototype filter is investigated and compared under Barrage Noise Jamming (BNJ), Partial Band Jamming (PBJ), and Multi-tone Jamming (MTJ). Different spreading methods were applied to both transmission techniques. Under a Line-Of-Sight (LOS) indoor industrial channel with Additive White Gaussian Noise (AWGN), it is proven that CDMA-OQAM-OFDM variants outperform CDMA-OFDM in terms of Bit Error Ratio (BER) unless they use Time Spreading (TS) under the influence of MTJ.
An Easy-to-Use Socket-Suspension System Monitor for Lower Limb Amputees

Prosthetic sockets and the related suspension systems play key roles in functional restoration for individuals with limb loss; a good fit of the socket-suspension systems (SSSs) is critical to avoid unexpected skin breakdown. However, maintaining a good fit between the limb and SSS is challenging due to daily volume changes in the residual limb. In clinical practice, the fit of the SSS is monitored only through haptic feedback from the user. While important, this subjective feedback is often unreliable and inaccurate. Several automatic SSS monitoring approaches have been proposed, but widespread adoption has been limited by related high costs and difficulty of use. To address these issues, we designed a wearable SSS monitor that can be adopted for any SSS using liners without requiring permanent modifications of the SSS. Using a tiny magnetic disk as a marker, we were able to monitor the relative displacement between the residual limb (represented by the liner) and the prosthetic socket along the proximal-distal direction using field orientation measurement sensors. We conducted bench tests to validate the high accuracy of the wearable SSS monitor and demonstrated the capability of tracking SSS fit changes during locomotion without permanent modification of the SSS.
Deep Q-learning for the Control of PLC-based Automated Production Systems

This paper evaluates the use of Deep Reinforcement Learning to control Programmable Logic Controller-based automated Production Systems, which are characterized by multiple end-effectors that are actuated in only one or two axes. Due to the large number of actuators of which only a few affect the processing of a workpiece at a given time, these systems are challenging to learn. In this paper, Deep Q-learning is applied to a small use case focusing on sorting workpieces by color in a simulation of such a production system. The basic algorithm is hereby compared to four commonly used extensions: Double Q-learning, Dueling Networks, Prioritized Experience Replay, and Hindsight Experience Replay. For the scope of this paper, simplifications are applied to the state and action space. While the baseline implementation of Deep Q-learning is able to correctly sort 30 previously seen workpiece combinations, it does not reliably generalize to unseen ones within 45,000 training episodes. In contrast, the algorithm using all four considered extensions is able to reliably generalize to all 81 possible workpiece combinations.
Broadband Light Amplitude Tuning Characteristics of SnSe2 Coated Microfiber

Broadband light amplitude tuning characteristics are demonstrated in a 2-D tin diselenide (SnSe2) nanosheets coated microfiber (MF) structure (SnSe2CMF). Due to the strong absorption characteristics of SnSe2 in broadband wavelengths, the transmitted optical power (TOP) of SnSe2CMF can be tuned by external pump lights with different wavelengths (405, 532, and 660 nm) over a broadband spectrum (1520–1620 nm). Five wavelengths are extracted in order to investigate the tuning performance. The TOP of SnSe2CMF increases with the pump light power and a maximum tuning efficiency of ∼0.3074 dB/mW is achieved. In terms of tuning speed, the minimum response time is ∼3.7/3.9 ms. The experimental results have qualitatively good agreement with the simulations predicted by FEM models. The demonstrated structure has the advantages of broad tuning bandwidth and high tuning efficiency. Therefore, it might be a promising candidate for building high performance broadband fiber-optic sensors and modulators.