Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity planning for 5G packet-based front-haul

Packet switched transport technologies have been suggested to be used in 5G front-haul networks. Front-haul bandwidth and packet latency are two major challenges to be addressed in these networks. This paper focuses on capacity planning for packet-based front-haul technologies considering the latency requirements of the 5G front-haul flows. More specifically, we answer three main questions for Constant Bit Rate (CBR) and Variable Bit Rate (VBR) packet front-haul networks: First, is statistical multiplexing gain always achievable in packet front-haul networks? Second, what is the minimum bandwidth required to accommodate a set of front-haul flows on a front-haul link? Third, what is the proper bandwidth allocation and the scheduling algorithm among the packets of different antennas to guarantee the latency requirements of the flows? It is shown that for CBR traffic no statistical multiplexing gain is achievable and the Earliest Deadline First (EDF) is the optimal packet scheduler. For VBR traffic, statistical multiplexing gain is achievable only when the latency requirement of at least one flow is greater than the packet inter-arrival time. The optimal bandwidth allocation is also determined for a Weighted Fair Queueing (WFQ) scheduler. Finally, simulation is used to validate the theoretical results for both CBR and VBR front-haul traffic.
Novel key roles for structural maintenance of chromosome flexible domain containing 1 (Smchd1) during preimplantation mouse development

Structural maintenance of chromosome flexible domain containing 1 (Smchd1) is a chromatin regulatory gene for which mutations are associated with facioscapulohumeral muscular dystrophy and arhinia. The contribution of oocyte‐ and zygote‐expressed SMCHD1 to early development was examined in mice ( Mus musculus) using a small interfering RNA knockdown approach. Smchd1 knockdown compromised long‐term embryo viability, with reduced embryo nuclear volumes at the morula stage, reduced blastocyst cell number, formation and hatching, and reduced viability to term. RNA sequencing analysis of Smchd1 knockdown morulae revealed aberrant increases in expression of a small number of trophectoderm (TE)‐related genes and reduced expression of cell proliferation genes, including S‐phase kinase‐associated protein 2 ( Skp2). Smchd1 expression was elevated in embryos deficient for Caudal‐type homeobox transcription factor 2 ( Cdx2, a key regulator of TE specification), indicating that Smchd1 is normally repressed by CDX2. These results indicate that Smchd1 plays an important role in the preimplantation embryo, regulating early gene expression and contributing to long‐term embryo viability. These results extend the known functions of SMCHD1 to the preimplantation period and highlight important function for maternally expressed Smchd1 messenger RNA and protein.
Multiresolution CNN for reverberant speech recognition

The performance of automatic speech recognition (ASR) has been greatly improved by deep neural network (DNN) acoustic models. However, DNN-based systems still perform poorly in reverberant environments. Convolutional neural network (CNN) acoustic models showed lower word error rate (WER) in distant speech recognition than fully-connected DNN acoustic models. To improve the performance of reverberant speech recognition using CNN acoustic models, we propose the multiresolution CNN that has two separate streams: one is the wideband feature with wide-context window and the other is the narrowband feature with narrow-context window. The experiments on the ASR task of the REVERB challenge 2014 showed that the proposed multiresolution CNN based approach reduced the WER by 8.79% and 8.83% for the simulated test data and the real-condition test data, respectively, compared with the conventional CNN based method.
A hybrid TSA-fuzzy logic approach to detect induction motor rotor faults

This paper treats the broken rotor bar fault detection and diagnosis in induction motors. Indeed, the broken rotor bar fault do not have an initial effect can lead an induction motor to fail, there can be serious secondary effects. In this context, a new fault detection and diagnosis approach, namely the TSA-Fuzzy are presented. This technique uses the residual current, obtained by the TSA method. In fact, this current enables to detect faults that cannot be detected by analyzing the stator current, especially in the low load motor case. The RMS of residual current and the load will be used as inputs for the fuzzy logic bloc, where the decision about the state of the rotor is made. The results show the reliability and the efficiency of the proposed approach.
Performance analysis of multiantenna two-way relay network with co-channel interference

This paper presents the performance analysis of the multiple antenna two-way amplify and-forward (AF) relay network in an interference-limited environment. Multiple antenna system is considered to overcome the fading effect. To avoid the high feedback overhead and fully exploit multiple antenna diversity, we employ transmit antenna selection (TAS) at the user node and analog network coding (ANC) at relaying node. We are assuming the presence of multiple co-channel interference (CCI) at the AF relay and noisy sources, an approximate closed-form expression for the overall outage probability (OOP) is derived. To gain more insight into system performance high SNR analysis is done and asymptotic expression for OOP is obtained. Furthermore, upper bound on ergodic capacity (EC) and approximate expression for symbol error probability(SEP) are derived. The tightness of our analysis is attested through Monte Carlo simulation and provides the insight into the impact of CCI under the general operating conditions and the key system parameters on overall system performance.
A High-Voltage-Tolerant and Power-Efficient Stimulator With Adaptive Power Supply Realized in Low-Voltage CMOS Process for Implantable Biomedical Applications

A high-voltage-tolerant and power-efficient stimulator with adaptive power supply is proposed and realized in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1.8-V/3.3-V CMOS process. The self-adaption bias technique and stacked MOS configuration are used to prevent issues of electrical overstress and gate-oxide reliability in low-voltage transistors. The on-chip high-voltage generator uses a pulse-skip regulation scheme to generate a variable dc supply voltage for the stimulator by detecting the headroom voltage on the electrode sites. With a dc input voltage of 3.3 V, the on-chip high-voltage generator provides an adjustable dc output voltage from 6.7 to 12.3 V at a step of 0.8 V, which results in a maximal system power efficiency of 56% at a 2400-<inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> stimulus current. The charge mismatch of the stimulator is down to 1.7% in the whole stimulus current range of 200–<inline-formula> <tex-math notation="LaTeX">$3000~\mu \text{A}$ </tex-math></inline-formula>. The in vivo experiments verified that epileptic seizures could be suppressed by the electrical stimulation provided by the proposed stimulator. In addition, the reliability measurements verified that the proposed stimulator is robust for electrical stimulation in medical applications.
Accurate Quantile Estimation for Skewed Data Streams Using Nonlinear Interpolation

Quantile estimation is a fundamental method to generate the descriptions of the distribution of data for data management and analysis. Although the investigation and design of efficient quantile estimation algorithm has attracted much study, the problem of accurately finding quantiles in the case of skewed data streams, which are prevalent in many data sources like text data and IP traffic streams, is still not well addressed. In this paper, we specifically address the problem of estimating the quantiles of skewed data streams by designing and implementing an incremental quantile estimation with nonlinear-interpolation algorithm. The comprehensive experimental evaluation results demonstrate that the estimated quantiles of the proposed algorithm are more accurate than the existing methods in the literature on both synthetic and real-world datasets, especially on important extreme quantiles.
A static hand gesture recognition system to recognize the total number of fingers

The main purpose of gesture recognition systems is to understand important expressions of motion by humans which involve hands, head, face, arms, or body. It is of major importance in designing an expert interface between humans and computers. We take into account the two possible fixed geometries to work on. The proposed methods follow the procedure of Preprocessing which helps with noise removal and image enhancement; Segmentation of hand region uses skin likelihood method to extract skin color; Feature extraction uses morphological and geometry based functions to extract the fingers; and active fingers are counted by method of Rule based Classification. In order to test performance, an experiment is conducted using standard and a self-generated dataset of images. The accuracy achieved on these dataset is greater than the similar state-of-the-art.
New concatenated code schemes for data gathering in WSN's using rank metric codes

In wireless sensor networks (WSNs), data produced by sensors are usually routed through several intermediate nodes to reach the sink Base Station (BS). In fact, since a WSN is usually composed of low-cost and limited capability sensors, their transmission range prevents the establishment of a reliable communication with the sink. When an intermediate node fails, errors may occur and the message is not delivered to the sink. The reliability of the system can be increased by using Network Coding (NC) techniques. In this paper, we consider the problem of data gathering in WSNs and we propose a novel error correction mechanism using Low Rank Parity Check code (LRPC), which is known to be good at correcting burst errors, as an outer code and a convolutional code as an inner code to correct sparse errors. Furthermore, we investigate the performance of the proposed system in terms of the packet error probability and the decoding complexity. We also propose a theoretical approximation of the decoding probability for LRPC codes in the case of network coding for binary and non-binary fields. We show, through Matlab simulations, that the proposed concatenated code outperforms the proposed coding schemes in the literature for data gathering in terms of decoding rate and complexity.
Self-Supervised Feature Learning by Learning to Spot Artifacts

We introduce a novel self-supervised learning method based on adversarial training. Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts, and then to extract features from its intermediate layers that can be transferred to other data domains and tasks. To generate images with artifacts, we pre-train a high-capacity autoencoder and then we use a damage and repair strategy: First, we freeze the autoencoder and damage the output of the encoder by randomly dropping its entries. Second, we augment the decoder with a repair network, and train it in an adversarial manner against the discriminator. The repair network helps generate more realistic images by inpainting the dropped feature entries. To make the discriminator focus on the artifacts, we also make it predict what entries in the feature were dropped. We demonstrate experimentally that features learned by creating and spotting artifacts achieve state of the art performance in several benchmarks.
Formation Control of Teleoperating Cyber-Physical System With Time Delay and Actuator Saturation

Teleoperating cyber-physical system (TCPS) is referred to as a promising technology to extend human actions and intelligences to remote locations. Many applications of TCPS demand operator and slaves to keep a desired formation. However, the cyber- and physical-constrained characteristics on TCPS make it challenging to achieve such a formation task. This brief is concerned with a formation control problem for multislave TCPS, subject to time-varying delays in cyber channels and actuator saturation in physical channels. We design a decentralized controller to enforce the formation of master and slave robots. Sufficient conditions for stability are provided to show that the formation controller can stabilize the master-slave TCPS. In order to estimate the domain of attraction, the stability conditions are rearranged into a form of linear matrix inequalities (LMIs), and then, the required initial stability conditions for master and salves are developed. The transparent interaction between human operator and environment is also provided. Finally, simulations and experiments are performed for illustration and validation purposes.
Survey on research of driver fatigue detection technology

This paper reviewed driver fatigue detection technologies based on development in this field in recent decades.Summarized the related research methods of driver fatigue detection research and provided categories based on driver’s feature and vehicle performance.The detection based on driver feature was to subtract the features which including fatigue information, which including psychology feature parameter and visual feature.The method based on vehicle performance was according the performance of car tracing to judge whether existed driver fatigue indirectly.Introduced some typical driver detection systems and discussed the challenges and developments of fatigue detection.
AINDA É PRECISO TER CUIDADO: ESCOLA?!

Neste ensaio, buscou-se recuperar indignacoes presentes na obra “Cuidado: Escola”, publicada em 1980. Com o apoio de Alexander Neill, Donald Schon e Georges Snyders, as ideias do referido livro foram cotejadas com a (pretendida) pratica docente do autor do artigo, como uma forma de conhecer-na-acao. Ao final, espera-se demonstrar que ha algo de arcaico na educacao escolar, que cada vez mais se fortalece.
3D-FilterMap: A Compact Architecture for Deep Convolutional Neural Networks

We present a novel and compact architecture for deep Convolutional Neural Networks (CNNs) in this paper, termed 3D-FilterMap Convolutional Neural Networks (3D-FM-CNNs). The convolution layer of 3D-FM-CNN learns a compact representation of the filters, named 3D-FilterMap, instead of a set of independent filters in the conventional convolution layer. The filters are extracted from the 3D-FilterMap as overlapping 3D submatrics with weight sharing among nearby filters, and these filters are convolved with the input to generate the output of the convolution layer for 3D-FM-CNN. Due to the weight sharing scheme, the parameter size of the 3D-FilterMap is much smaller than that of the filters to be learned in the conventional convolution layer when 3D-FilterMap generates the same number of filters. Our work is fundamentally different from the network compression literature that reduces the size of a learned large network in the sense that a small network is directly learned from scratch. Experimental results demonstrate that 3D-FM-CNN enjoys a small parameter space by learning compact 3D-FilterMaps, while achieving performance compared to that of the baseline CNNs which learn the same number of filters as that generated by the corresponding 3D-FilterMap.
Salivary Exosomes: Emerging Roles in Systemic Disease

Saliva, which contains biological information, is considered a valuable diagnostic tool for local and systemic diseases and conditions because, similar to blood, it contains important molecules like DNA, RNA, and proteins. Exosomes are cell-derived vesicles 30-100 nm in diameter with substantial biological functions, including intracellular communication and signalling. These vesicles, which are present in bodily fluids, including saliva, are released upon fusion of multivesicular bodies (MVBs) with the cellular plasma membrane. Salivary diagnosis has notable advantages, which include noninvasiveness, ease of collection, absence of coagulation, and a similar content as plasma, as well as increased patient compliance compared to other diagnostic approaches. However, investigation of the roles of salivary exosomes is still in its early years. In this review, we first describe the characteristics of endocytosis and secretion of salivary exosomes, as well as database and bioinformatics analysis of exosomes. Then, we describe strategies for the isolation of exosomes from human saliva and the emerging role of salivary exosomes as potential biomarkers of oral and other systemic diseases. Given the ever-growing role of salivary exosomes, defining their functions and understanding their specific mechanisms will provide novel insights into possible applications of salivary exosomes in the diagnosis and treatment of systemic diseases.
Evaluation of the bioactivity of a mastoparan peptide from wasp venom and of its analogues designed through targeted engineering

Mastoparan is a typical cationic and amphipathic tetradecapeptide found in wasp venom and exhibits potent biological activities. Yet, compared with other insect-derived peptides, such as melittin from the bee venom, this family have been underrated. Herein, we evaluated the biological activities of mastoparan-C (MP-C), which was identified from the venom of the European Hornet (Vespa crabro), and rationally designed two analogues (a skeleton-based cyclization by two cysteine residues and an N-terminal extension via tat-linked) for enhancing the stability of the biological activity and membrane permeability, respectively. Three peptides possessed broadly efficacious inhibiting capacities towards common pathogens, resistant strains, as well as microbial biofilm. Although, cyclized MP-C showed longer half-life time than the parent peptide, the lower potency of antimicrobial activity and higher degree of haemolysis were observed. The tat-linked MP-C exhibited more potent anticancer activity than the parent peptide, but it also loses the specificity. The study revealed that MP-C is good candidate for developing antimicrobial agents and the targeted-design could improve the stability and transmembrane delivery, but more investigation would be needed to adjust the side effects brought from the design.
Stability of three classification systems for cerebral palsy: benefits from multicenter collaboration

1 year. Now that more mobility options are available (such as toy cars and specialized devices), it is easier to implement these recommendations. Over the last years, the possibilities to accurately identify and predict the risk for cerebral palsy (CP) before the age of 5 months corrected age has increased dramatically. According to Novak et al., methods to identify children at risk (such as the Hammersmith Infant Neurological Examination [HINE] and the Prechtl Qualitative Assessment of General Movements) show a sensitivity of 90% to 98%, while magnetic resonance imaging shows a sensitivity of 86% to 89%. Even at the age of 3 months, the HINE can indicate which children are at risk of higher and lower levels of the Gross Motor Function Classification System (GMFCS). We also know that in order to optimize motor and cognitive plasticity and prevent secondary complications we need to start diagnosis-specific, evidence-based interventions early. We can no longer spend time and resources on outdated theories, beliefs, and concepts. We need to implement diagnostic tools, outcome measures, and interventions based on the best available knowledge now. Considering the importance of early self-initiated active movements, and exploring the data of actual mobility in children with CP, calls for reflection. Palisano et al. illustrated that many children with CP are not walking and have no independent mobility at the age of 4 years. Of the children classified in GMFCS level II almost half walk at home at the age of 4 years; the other half lack independent mobility and are mostly pushed, carried around, or take some steps with adult assistance. For children in GMFCS level III, 1 in 10 walks at the age of 4 years; while 9 of 10 rely on adults for mobility at home. Twice as many children in GMFCS level IV have independent mobility at the age of 4 years compared to children in GMFCS level III, where 2 of 10 have independent mobility in a wheelchair and 8 of 10 rely on adults. Children who are not provided the opportunity to experience self-initiated mobility are at a higher risk of cognitive and developmental delay. This calls for immediate action. Kenyon et al. highlight the gap between research and clinical practice in childhood disability. They provide us with facts that compel us to shift current practice and ensure children experience different mobility options in different settings to enhance activity and participation. Now it is time for the next step. What will it take to get us to provide children with greater opportunities to experience the joy of independent mobility?
Modeling, Control, and Numerical Simulations of a Novel Binary-Controlled Variable Stiffness Actuator (BcVSA)

This research work aims at realizing a new compliant robotic actuator for safe human-robotic interaction. In this paper, we present the modeling, control, and numerical simulations of a novel Binary-Controlled Variable Stiffness Actuator (BcVSA) aiming to be used for the development of a novel compliant robotic manipulator. BcVSA is the proof of concept of the active revolute joint with the variable recruitment of series-parallel elastic elements. We briefly recall the basic design principle which is based on a stiffness varying mechanism consisting of a motor, three inline clutches, and three torsional springs with stiffness values (K0, 2K0, 4K0) connected to the load shaft and the motor shaft through two planetary sun gear trains with ratios (4:1, 4:1 respectively). We present the design concept, stiffness and dynamic modeling, and control of our BcVSA. We implemented three kinds of Multiple Model Predictive Control (MPC) to control our actuator. The main motivation of choosing this controller lies in the fact that working principle of multiple MPC and multiple states space representation (stiffness level) of our actuator share similar interests. In particular, we implemented Multiple MPC, Multiple Explicit MPC, and Approximated Multiple Explicit MPC. Numerical simulations are performed in order to evaluate their effectiveness for the future experiments on the prototype of our actuator. The simulation results showed that the Multiple MPC, and the Multiple Explicit MPC have similar results from the robustness point of view. On the other hand, the robustness performance of Approximated Multiple Explicit MPC is not good as compared to other controllers but it works in the offline framework while having the capability to compute the sub-optimal results. We also performed the comparison of MPC based controllers with the Computed Torque Control (CTC), and Linear Quadratic Regulator (LQR). In future, we are planning to test the presented approach on the hardware prototype of our actuator.