Miracle-dz/UniFAR-FTUs huggingface dataset:
HARQ Assisted Short-Packet Communications for Cooperative Networks Over Nakagami-m Fading Channels

Considering the coverage and short-packet communication requirements, this paper proposes a hybrid automatic repeat request (HARQ) assisted short-packet communications for cooperative Internet of Things (IoT) networks, where the signals are transmitted through short-packets. Since the short-packet communications will bring a higher packet error rate than long-packet communications, HARQ is adopted to improve transmission reliability by allowing retransmit the signals which cannot be detected correctly. For lighting the burden of the transmitter, the retransmitted signals are only sent from the relay. According to the detection capability of the user, selection combining (SC) scheme and maximum ratio combining (MRC) scheme are designed to detect the multiple transmitted signals. Besides, a set of closed-form expressions of the average packet error rate (APER) and effective throughput (ET) are derived over Nakagami- $m$ channels in the proposed SC and MRC schemes. For providing more insights into system performance, the asymptotic results are also provided. Simulation results demonstrate that HARQ will significantly improve reliability and transmission efficiency. Also, the MRC scheme outperforms the SC scheme in terms of APER and ET metrics, however, when the transmit power at the relay is high enough or the packet length is big enough, they achieve nearly the same performance.
Skeleton data pre-processing for human pose recognition using Neural Network*

Automatic monitoring of daily living activities can greatly improve the possibility of living autonomously for frail individuals. Pose recognition based on skeleton tracking data is promising for identifying dangerous situations and trigger external intervention or other alarms, while avoiding privacy issues and the need for patient compliance. Here we present the benefits of pre-processing Kinect-recorded skeleton data to limit the several errors produced by the system when the subject is not in ideal tracking conditions. The accuracy of our two hidden layers MLP classifier improved from about 82% to over 92% in recognizing actors in four different poses: standing, sitting, lying and dangerous sitting.
Acoustic Event Detection Using Fuzzy Integral Ensemble and Oriented Fuzzy Local Binary Pattern Encoded CNN

In this paper, we propose a novel ensemble classifier using an Oriented Fuzzy Local Binary Pattern Encoded Convolutional Neural Network (CNN) for acoustic event detection (AED). The CNN has been widely used to perform acoustic event detection using a spectrogram image of the acoustic signals. The efficiency of the CNN depends on representation of the spectrogram images used during the training process. We propose the Oriented Fuzzy Local Binary Pattern (OFLBP) that extracts directional texture features from the spectrogram image by inspecting neighborhood pixels present at different angles from a central pixel. The proposed OFLBP technique is capable to deal with uncertainty present in the spectrogram image. The ensemble of the trained CNN is performed by a Fuzzy Integral method. The experiment and results show the proposed method outperforms to existing AED methods to classify the ESC-50 dataset.
A Simulation Study on Minimizing Threshold Voltage Variability by Optimizing Oxygen Vacancy Concentration Under Metal Gate Granularity

In this work, we discuss the mitigation of threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{TH}}$ </tex-math></inline-formula>) variability in nanoscale FinFET by exploiting the interaction of oxygen vacancies (OV) against metal gate granularity (MGG). Deposition of a metal gate on high-<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> dielectric HfO<sub>2</sub> is known to generate OV in the dielectric which induces variability in surface potential. A higher workfunction of metal grain increases the probability of OV formation. Interestingly, while a higher workfunction metal grain decreases the surface potential of the channel underneath, the positive charge of OV increases the potential. Therefore, the concentration of OV can be controlled to negate the surface potential variability induced by MGG. We discuss the law of mass action-based concentration models of OV under MGG to analyze this. We also present TCAD simulations supporting the above hypothesis. In our simulations, for 10-nm channel FinFET, MGG-induced <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">${V}_{{TH}}$ </tex-math></inline-formula>) can be reduced to ~20 mV from ~35 mV for four-sided planar grains with 8 nm average edge length under the optimal OV concentration.
Model-based Nonlinear Filter Design for Tower Load Reduction of Wind Power Plants with Active Power Control Capability

In the light of an increasing share in the electrical grid, wind turbines must be enabled to provide grid stabilizing behavior. This can be achieved by a variation of the turbine’s power output depending on the current state of the electrical grid. However, changes of power output excite oscillations in the turbine structure. To reduce the loading caused by the considered frequency droop scheme, in this paper a nonlinear model-based filter design in a Takagi-Sugeno structure is proposed. The design uses Lyapunov function-based linear matrix inequalities for deriving the necessary feedback gains of the filter. The results are obtained for NREL’s 5 MW reference turbine. By connecting FAST to an analytic power system model, we study the effects on turbine loading as a result of frequency stabilization in case of a load imbalance. The proposed filter is designed and implemented to reduce the damage equivalent load of the tower fore-aft motion, and its influence on the frequency trajectory is studied.
Crack Detection with Multi-task Enhanced Faster R-CNN Model

To solve the low accuracy problem when dealing with multiple or small objects detection, a Multi-task Enhanced crack detection based on Faster R-CNN (ME-Faster R-CNN) was proposed in the dam safety monitoring. MEFaster R-CNN adopts the Multi-source Adaptive Balance TrAdaBoost based on K-means (K-MABtrA) to enhance the small-scale samples. ME-Faster R-CNN uses RestNet-50 network to extract feature and apply multi-task enhanced RPN module to generate the candidate regions through adopting the appropriate size and dimension of anchor box. The features map and candidate regions are processed to detect the dam cracks. Experimental results demonstrate that the proposed ME-Faster R-CNN with K-MABtrA method can obtain 82.52% average IoU and 80.02% average precision mAP, respectively. Compared with Faster R-CNN with the same parameters, the average IoU and mAP can increase 1.06% and 1.56%, respectively.
Compressed Sensing based Image Reconstruction with Projection Recovery for Limited Angle Cone-Beam CT Imaging

This paper presents a new 3D CT image reconstruction for limited angle C-arm cone-beam CT imaging system based on total-variation (TV) regularized in image domain and L1-penalty in projection domain. This is motivated by the facts that the CT images are sparse in TV setting and their projections are sinusoid-like forms, which are sparse in the discrete cosine transform (DCT) domain. Furthermore, the artifacts in image domain are directional due to limited angle views, so the anisotropic TV is employed. And the reweighted L1penalty in projection domain is adopted to enhance sparsity. Hence, this paper applied the anisotropic TV-norm and reweighted L1-norm sparse techniques to the limited angle Carm CT imaging system to enhance the image quality in both CT image and projection domains. Experimental results also show the efficiency of the proposed method.Clinical Relevance—This new CT reconstruction approach provides high quality images and projections for practicing clinicians.
Cortical connectivity and spectral perturbations underlying TENS stimulation of hand nerves: a case study

The major challenge in upper limbs neuroprosthetic improvement is the implementation of effective sensory feedback. Transcutaneous electrical nerve stimulation (TENS) of the median and ulnar nerves confirmed, with electroencephalographic (EEG) recordings, the presence of appropriate responses in relevant cortical areas with induced sensation successfully located in the innervation regions of each nerve. The characterization of these elicited responses could be used to recreate precise somatotopic feedback from hand protheses. Using TENS and EEG, the purpose of this study was to detect distinctions in time-frequency cortical dynamics and connectivity occurring after stimulation of hand nerves. Region of interest (ROI) were selected according to topographical distributions and Somatosensory Evoked Potentials (SEP) localization and were named Contralateral Parietal (Cont P), Central Frontal (Cent F) and Superior Parietal (Sup P). The analysis of cortical oscillations showed spectral inflections in theta [4-7 Hz] and alpha [7.5-12.5 Hz] band which occurred at 60 ms in Cont P and 300 ms in Sup P and prominent for the ulnar condition over the median one. The beta band decrease [16-30 Hz] which occurred in the same ROIs was especially significant after ulnar stimulation too. Effective connectivity measures did not differ significantly across conditions but exhibited some slight difference in the alpha-band causal flow coming from Cent F in direction to Cont P and Sup P. Although pending completion of multiple-subjects study, these results already suggest magnitude differences in somatosensory spectral fluctuations and sensorimotor interactions flows.
Respuesta al uso de nitrógeno en variedades de arroz sembradas en Colombia, 1950-1999

Al asociar el cultivo de variedades modernas con un uso mayor de fertilizantes es válido preguntarse, qué porción del incremento de la productividad se debe al alza en la aplicación de fertilizantes. De igual forma es relevante saber hasta qué punto un bajo uso de insumos, como el que hacen los agricultores de menores recursos, está relacionado a mayores niveles de productividad o si, por el contrario, en esas condiciones, las variedades tradicionales son las más apropiadas.
Design of a Flexible High-Density Surface Electromyography Sensor

In recent years, high-density surface electromyography (HD-sEMG) has shown promising advantages in many robotics applications. Using HD-sEMG can not only reduce the sensitivity of the sensor position on the muscle belly but can also facilitate the acquisition of more muscle activity information due to spatial sampling. As current commercial HD-EMG systems use stationary amplifiers, leading to bulky measurements and poor portability, the interest in developing an HD-EMG sensor has increased. However, the insufficient electrode density and complicated fabrication process are challenges to overcome. In this paper, we propose a flexible HD-EMG sensor with an on-board amplifier capable of a density level of 0.53 channel/cm2, higher than those in previous works. First, we investigated the effects of different sensor parameters (i.e., the electrode material, the inter-electrode distance (IED) and the size of the electrode) on the measured signal quality. Second, a low-cost, easily fabricated, easily customized HD-EMG fabrication method was proposed based on the selected sensor parameters with a signal-to-noise ratio (SNR) comparable to those of commercial sensors. Finally, we applied a muscle activation estimation algorithm to validate the feasibility of the designed HD-EMG sensor, showing higher estimation accuracy levels. The results here demonstrate that the designed HD-EMG sensor can be used as an effective human-machine interface for robotics applications.
Identifying and Generating Missing Tests using Machine Learning on Execution Traces

Testing IT systems has become a major bottleneck for many companies. Besides the growing complexity of such systems, shorter release cycles and increasing quality requirements have led to increased verification and validation costs. However, analysis of existing testing procedures reveals that not all artifacts are exploited to tame this cost increase. In particular, customer traces are usually ignored by validation engineers. In this paper, we use machine learning from execution traces (both customer traces and test execution traces) to identify test needs and to generate new tests in the context of web services and API testing. Log files of customer traces are split into smaller traces (user sessions) then encoded into Pandas DataFrames for data analysis and machine learning. Clustering algorithms are used to analyse the customer traces and compare them with existing system tests, and machine learning models are used to generate missing tests in the desired clusters. The tool-set is implemented in an open-source library called Agilkia.
Task-irrelevant Auditory Event-related Potentials as Mental Workload Indicators: A Between-task Comparison Study*

Real-time monitoring of mental workload (MWL) is a crucial step to build closed-loop adaptive aiding systems for human-machine systems. MWL estimators based on spontaneous electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to achieve this goal. However, the previous studies show that the between-task robustness of these EEG/ERP-based MWL estimators is still an unsolved intractable question. This study attempts to examine the task-irrelevant auditory event-related potentials (tir-aERPs) as MWL indicators. A working memory task (verbal n-back) and a visuo-motor task (multi-attribute task battery, MATB), both with two difficulty levels (easy and hard), were used in the experiment, along with task-irrelevant auditory probes that did not need any response from the participants. EEG was recorded from ten participants when they were performing the tasks. The tir-aERPs elicited by the auditory probes were extracted and analyzed. The results show that the amplitudes of N1, early P3a (eP3a) and the late reorienting negativity (RON) significantly decreased with the increasing MWL in both n-back and MATB. Task type has no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERPs features. These results suggest that the tir-aERPs are potentially more constant MWL indicators across very different task types. Therefore, the tir-aERPs should be taken into consideration in future task-independent MWL monitoring studies.
A Preliminary Approach to Allocate Categories of Buildings into Lands based on Generative Design

In comparison to some other economic sectors, the productivity of workers in the construction has not increased much over the last 20 years. There are many approaches to tackle this problem, and one of them is to rethink the design process and introduce automation. In this paper, we propose a workflow that allocates houses on a plot of land. This workflow starts with GIS data and urban planning requirements and ends with a BIM model on that property. A genetic algorithm tackles the design process of allocation of houses by iterating and optimizing a parametric model. In the end, a designer chooses the final solution between the generated range. The degree of automation achieved by the algorithm will be tested by comparing the results of the algorithm with human-made designs.
Assessment of Athletes’ Attitude: Physiological Evaluation via Wearable Sensors during Grappling Competitions

Sports activity is characterised by the influence of different factors, which relate to both psychological and emotional stress of athletes. As a consequence, mental and physical preparations are fundamental in pre-competition and competition activities. In fact, being able to manage the reactions to stressful events and high demanding conditions, and adapt the strategy depending on the ongoing situation and opponent’s reactions allow the athletes to properly process the surrounding information, evaluate all the possible solutions, and finally take the right decision. In this regard, the Skin Conductance (SC), Heart Rate (HR), and Skin Temperature (ST) signals were recorded during a grappling tournament from ten athletes with the aim to investigate if physiological assessments could provide an objective measure of athletes’ attitude. The results proved that individual training programs can be tailored accordingly to the neurophysiological state of the athletes, but also that their awareness about both mental and physical preparations and attitudes could be improved.
Pricing Decisions in Closed-Loop Supply Chains With Multiple Fairness-Concerned Collectors

In the same model, both distributional fairness concern and peer-induced fairness concern are considered, so are the absolute fairness concern and the relative fairness concern. When there are multiple recycling markets, collectors attach importance not only to the fairness distribution of profit with the manufacturer (distributional fairness concern), but also to the fairness distribution of profit with the peer collector (peer-induced fairness concern). On the basis of the perspective of the collectors, this article introduces distributional and peer-induced fairness concern into the closed-loop supply chain (CLSC). The impact of the collectors’ different fairness concerns on the CLSC pricing decisions is discussed under five models.The research shows that: (1) When the manufacturer considers the collectors’ fairness concern, the manufacturer’s profit and distributional fairness concern coefficient change inversely, and the collectors’ profit and distributional fairness concern coefficient move together, that is, the distributional fairness concern is of advantage to the collectors but not good for the manufacturer. On the contrary, the manufacturer’s profit and peer-induced fairness concern coefficient move in the same direction, while the profit of the collectors and peer-induced fairness concern coefficient change in opposite direction; (2) As long as the manufacturer considers the fairness of the collectors, the profit of the collector who pays attention to both fairness is always lower than that of the collector who only concerns distributional fairness, so peer-induced fairness concern is not a self-interest behavior. However, when the appeal of the two collectors for fairness distribution is not considered by the manufacturer, peer-induced fairness concern benefits himself; (3) The two-part Tariff contract has realized the coordination of the CLSC. Additionally, our findings provide some managerial insights on the pricing decision when the collectors consider multiple fairness concerns.
Grassmannian Frames in Composite Dimensions by Exponentiating Quadratic Forms

Grassmannian frames in composite dimensions D are constructed as a collection of orthogonal bases where each is the element-wise product of a mask sequence with a generalized Hadamard matrix. The set of mask sequences is obtained by exponentiation of a q-root of unity by different quadratic forms with m variables, where q and m are the product of the unique primes and total number of primes, respectively, in the prime decomposition of D. This method is a generalization of a well-known construction of mutually unbiased bases, as well as second-order Reed-Muller Grassmannian frames for power-of-two dimension D = 2m, and allows to derive highly symmetric nested families of frames with finite alphabet. Explicit sets of symmetric matrices defining quadratic forms leading to constructions in non-prime-power dimension with good distance properties are identified.
Customized Access Technology for Children using Head Movement Recognition*

Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.6% in predicting head movements.
Topology-Morphing Photovoltaic Microconverter With Wide MPPT Voltage Window and Post-Fault Operation Capability

In this paper the concept of wide input voltage range photovoltaic microconverter with post-fault operation capability is presented. This microconverter could remain operational in the case of short- or open-circuit failure of any switch of its front-end inverter. When the fault occurs, the control system automatically identifies the fault and applies the special post-fault modulation sequence thus keeping the functionality of the microconverter unchanged. The research findings are validated by the help of a 350 W experimental prototype specially developed for integration of silicon PV modules into the residential DC nanogrids with the nominal voltage of 350 V. Paper explains the multi-mode operation principle and fault identification routine. It presents the experimental efficiencies and evaluates the maximum power point tracking performance of the microconverter prior and after the short-circuit fault of an upper switch of the inverter. The daily energy yield test conducted for both cases has confirmed that the proposed approach can extend the service life of PV microconverters with a moderate impact on their performance.
A Prototype of a Fully-Implantable Charge-Balanced Artificial Sensory Stimulator for Bi-directional Brain-Computer-Interface (BD-BCI)

Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback. Previously we developed and validated a benchtop prototype of a fully implantable BCI system for motor decoding. Here, a prototype artificial sensory stimulator was integrated into the benchtop system to develop a prototype of a fully-implantable BD-BCI. The artificial sensory stimulator incorporates an active charge balancing mechanism based on pulse-width modulation to ensure safe stimulation for chronically interfaced electrodes to prevent damage to brain tissue and electrodes. The feasibility of the BD-BCI system’s active charge balancing was tested in phantom brain tissue. With the charge-balancing, the removal of the residual charges on an electrode was evident. This is a critical milestone toward fully-implantable BD-BCI systems.