Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).
Tissue optical clearing as a diagnostic tool for tissue pathology differentiation

With the objective of developing a diagnostic tool, we have used the immersion optical clearing method and studied normal and pathological tissues (cancer, diabetes) under treatment by optical clearing agents (OCAs). In order to quantify pathology status OCA diffusion properties in different tissues were measured. We have demonstrated that free water content in cancerous tissues is higher than in normal.
Single-Shot and Label-Free Refractive Index Dispersion of Single Nerve Fiber by Triple-Wavelength Diffraction Phase Microscopy

Nerve fibers play a critical role in the propagation of electrical impulses in the nervous system, and several studies have demonstrated their electrical and chemical properties. However, because of the lack of practical biophotonics techniques, most of the optical properties of these fibers are unknown. Herein, we propose and demonstrate a novel method of precisely measuring the dispersion of an individual nerve fiber in striatal neurons, using a single-shot label-free triple-wavelength diffraction phase interferometer and employing Cauchy coefficients of the refractive index. Compared with the current techniques of refractive index measurement, the proposed technique employs a common-path arrangement to achieve high temporal stability sensing of dispersion by a monochromatic detector. The dispersion spectra are obtained by reconstructing the triple-color wavefront of the specimen, extracting the thickness from the projected width of the fiber in the image, and then employing a Cauchy relation that is valid for the normal dispersions of transparent materials in the visible range. The feasibility of this technique was confirmed by analyzing the refractive indices of polystyrene microspheres. The technique was then applied to measure refractive index map and dispersion behavior of nerve fibers in medium spiny neurons with high temporal and spatial resolution.
Traffic Matching in 5G Ultra-Dense Networks

Driven by enormous traffic demand, traditional cellular networks are evolving into an ultra-dense architecture. To take full advantage of such an ultra-dense architecture and efficiently serve the traffic with spatiotemporal fluctuation, the transmission mechanisms should be redesigned under the constraints of backhaul and energy consumption. In this article, we summarize and classify the spatiotemporal arrival properties of different traffic in ultra-dense networks, and optimize several promising technologies, such as dynamic time-division duplexing and full duplex radio, to adapt the network service to match the traffic. A new approach based on combining stochastic geometry and queueing theory is proposed to provide a useful guidance for the design of ultra-dense networks when the traffic is spatiotemporally fluctuating. Successive efforts, such as the analysis of different QoS requirements and more complicated but practical scenarios, still require attention along this line of research.
The brain on opioids.

There is some logic to having acute pain. It acts as a signal for withdrawal from danger or damage; it enforces rest after injury or during illness; and it can be used in pain vs pleasure calculations of which signals are needed to avert future damage or incite beneficial behaviors. Chronic pain, on the other hand, seems to have no purpose. In fact, the more we learn about pain processes, the clearer it becomes that chronic pain differs from acute pain in ways not previously appreciated. It may be felt similarly in that it is perceived in an anatomical location, often after an acute injury or event. It is easy to assume that chronic pain is merely acute pain that has not gone away, and reflects unresolved peripheral damage. Yet, recent scientific study has revealed processes in the nervous system, and particularly the brain, which account for chronic pain, and are distinct from acute pain processes. A seemingly paradoxical role for the endogenous opioid system in the development of chronic pain is brought to light.What follows is that the actions of exogenous opioids (opioid medications) differ vastly between acute and chronic pain.
Parallel Implementation of Simeck Family Block Cipher by Using ARM NEON

In these days, it takes a long time, a lot of operations and memory to implement the existing block ciphers based on SPN architecture such as AES, and etc. on the resource-constrained IoT device environment. By this reason, there are a lot of light-weight block ciphers such as SIMON, SPECK, and Simeck support various block/key sizes. In this paper, authors propose efficient implementation methods and results of the Simeck family block cipher on the 32-bit ARM by using ARM NEON SIMD and SIMT. Proposed methods on Simeck32/64 and Simeck64/128 are 8.9 cycles/byte and 12.0 cycles/byte. Proposed methods are 41.4% and 46.9% faster than previous related work respectively.
Modelling, Printing and Validation of Dental Dry Models for Implantology Skills Training

The acquisition of procedural technical skills is essential to become a surgeon. Traditional medical training required to practice procedures in cadavers and animals. New technologies such as virtual reality and additive manufacturing can help surgeons to learn and improve their skills through physical models of the human organ to be intervened. However, shape representation is not enough to have a realistic training experience. Training models need to biomimicry the real organs in other types of properties such as texture, density, mechanical behaviour among other properties. Therefore, different 3D-processes materials, textures and densities need to be explored and tested. The goal of this work was the development and validation of a dental dry model system using additive manufacturing. The mechanical profile of the printed models by capturing the force applied by a dental micro motor attached to a CNC machine during a standardized drilling test is measured. This research work was done in collaboration with the Nancy School of Surgery to help develop a new system to guide the training process for dentistry surgical procedures.
The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy

Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.
Evaluation of Pattern Recognition in Myoelectric Signal Using Netlab GLM

The myoelectric signal that is collected from the surface of the skin can be used to construct rehabilitation systems for people who have suffered some trauma or who were born with some form of malformation. This signal is used to feed classifiers that can tell with some degree of distinction which movement each signal belongs to. Among the approaches used for this task are the use of artificial neural networks (ANN), multi-layer perceptron (MLP), linear discriminant models (LDA), among others. In this study a approach to pattern recognition called Netlab GLM that has two optimized methods for network training is evaluated in different situations. The classical algorithm LDA is used as a criterion of comparison.
Blood levels of cadmium and lead in relation to breast cancer risk in three prospective cohorts

Cadmium and lead have been classified as carcinogens by the International Agency for Research on Cancer. However, their associations with breast cancer risk are unknown despite their persistence in the environment and ubiquitous human exposure. We examined associations of circulating levels of cadmium and lead with breast cancer risk in three case–control studies nested within the Cancer Prevention Study‐II (CPS‐II) LifeLink Cohort, European Prospective Investigation into Cancer and Nutrition – Italy (EPIC‐Italy) and the Northern Sweden Health and Disease Study (NSHDS) cohorts. Metal levels were measured in stored erythrocytes from 1,435 cases and 1,433 controls using inductively coupled plasma‐mass spectrometry. Summary relative risks (RR) and 95% confidence intervals (CI) were calculated using random‐effects models with each study result weighted by the within‐ and between‐study variances. I2 values were calculated to estimate proportion of between study variation. Using common cut‐points, cadmium levels were not associated with breast cancer risk in the CPS‐II cohort (continuous RR = 1.01, 95% CI 0.76–1.34), but were inversely associated with risk in the EPIC‐ Italy (continuous RR = 0.80, 95% CI 0.61–1.03) and NSHDS cohorts (continuous RR = 0.73, 95% CI 0.54–0.97). The inverse association was also evident in the meta‐analysis (continuous RR = 0.84, 95% CI 0.69–1.01) with low between‐study heterogeneity. Large differences in lead level distributions precluded a meta‐analysis of their association with breast cancer risk; no associations were found in the three studies. Adult cadmium and lead levels were not associated with higher risk of breast cancer in our large meta‐analysis.
Towards a Resilient Openflow Channel Through MPTCP

In the recent years, Software Defined Networking (SDN) has changed the way networks are engineered, making them more flexible, programmable and dynamic. SDN advocates for the centralization of control functionalities in a central node, the so-called controller. This entity has a wide view of the entire network, including the topology, facilitating the management and decreasing the complexity. However, the existence of a single entity running the complete control plane constitutes a single point of failure, thus triggering the need of improving the resiliency and reliability of the controller and the connection between the control and the data plane. This paper presents a solution for the improvement of the resiliency and the reliability on the OpenFlow channel through the use of multipath TCP (MPTCP). The proposed solution is based on the simultaneous use of in-band and out-of-band paths for the OpenFlow control channel, and includes a first experimental evaluation of the performance gains that can be achieved.
Coordinate System Transformations for Imitation of Goal-Directed Trajectories in Virtual Humans

The goal of this work is to record human hand trajectories during goal-oriented reaching behaviors in immersive VR for later imitation by a virtual human. It is desirable that the used trajectory representation generalizes over different positions and orientations of the target object. To this end, this work investigates trajectory representations in two neurophysiologically inspired coordinate systems. Results show that natural looking movement can be preserved as real motion capture data is used as a basis for these calculations
Image-Guided Dual Master–Slave Robotic System for Maxillary Sinus Surgery

The pathway to the maxillary sinus is anatomically curved and narrow. Thus, the conventional approach using a straight endoscope and surgical tools is difficult to diagnose and treat some blind regions of the maxillary sinus through the nostrils. Such cases are usually dealt with by an approach with an external incision that causes large invasive surgery. In order to approach the blind regions without any external incision, a new bendable device and an image-guided robotic approach for the maxillary sinus surgery are required. This work reports design, development, and validation of an image-guided dual master–slave robotic system for the maxillary sinus surgery. Initially, specifications of the robotic system for sinus surgery are decided by analysis of the anatomical structure of the sinus. A method for determining the design parameters of continuum type salve robot is also presented. Based on the specifications and the design parameter determining method, a compact design of bendable dual slave robotic system for inspection and biopsy operation of the maxillary sinus area is devised and workspace analysis for verifying the robot design is conducted. The performance of the dual master–slave system equipped with flexible devices is validated through several phantom tests. The results suggest that bendable end-effectors and navigation software are useful to navigate and treat blind regions inside general sinus areas as well as the maxillary sinus.
Fully Soft-Switched Dual-Active-Bridge Series-Resonant Converter With Switched-Impedance-Based Power Control

Both conduction loss and switching loss can contribute significantly to the overall power loss of an isolated bidirectional dual-active-bridge series-resonant dc–dc converter (DABSRC) operating at high frequency. To achieve soft switching and minimum-tank-current operation under wide-range variations in output voltage and current, a switched-impedance-based DABSRC is proposed. Minimum-tank-current operation aims to reduce conduction loss arising from circulating current at the low voltage, high-current side of DABSRC. Full-range soft switching is achieved in all switches, thus, switching loss is significantly reduced. With this new topology, power control is achieved by controlling a switch-controlled capacitor in the series resonant tank while ensuring minimum-tank-current operation and soft switching in all switches. The proposed topology and modulation scheme are validated by means of a 1-kW experimental prototype of DABSRC operating at 100 kHz designed to interface a 250-V dc bus to a supercapacitor with a rated output voltage of 48 V. The effectiveness of the proposed topology for charging/discharging a supercapacitor at a maximum rated power of 1 kW is verified by simulations and experimental results with a maximum efficiency of 97.5%.
A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment

Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques.
Hamming Metric Multi-Granularity Locality-Sensitive Bloom Filter

A Bloom filter is a type of space-efficient data structure that supports membership tests in numerous network applications. Recently, emerging applications require an approximate membership test (AMT) rather than conventional (exact-matching) membership test. Some AMT problems can be effectively solved by using a locality-sensitive hashing (LSH) based Bloom filter. However, existing work cannot handle changing Hamming distances. In this paper, we present a new Hamming metric locality-sensitive Bloom filter (HLBF) to tackle the challenge. Each object of the data set is hashed by bit sampling LSH functions and encoded into a standard Bloom filter in the HLBF structure. To support AMTs with different given Hamming distances, we propose a multi-granularity test algorithm (called the M-HLBF) based on the HLBF and virtual objects which are created from the given test object. Theoretical analyses show that false positive rates and false negative rates can be controlled within low levels. To further accelerate the processing of an AMT, we also illustrate a hardware implementation. Extensive experimental results demonstrate that our method is quite promising in achieving high efficiency and flexibility for processing AMTs with different granularities/distances.
Fundamental Tradeoffs of Non-Orthogonal Multicast, Multicast, and Unicast in Ultra-Dense Networks

Ultra-dense networks (UDNs) are the promising technology for the fifth-generation wireless networks and beyond to significantly boost network capacity and improve network coverage by exploiting spatial spectrum reuse through the deployment of massive base stations (BSs). In this paper, the fundamental tradeoffs of non-orthogonal multicast, multicast, and unicast in the UDN are studied, to understand the impact of network densitification on them and provide some insights on UDN deployment. Non-orthogonal multicast with imperfect channel estimation and successive interference cancellation is also investigated. To evaluate the performance, a tractable model for performance analysis is developed by using stochastic geometry, and then the analytical expressions for downlink signal-to-interference-plus-noise ratio coverage probability, spectrum efficiency, area traffic capacity, and energy efficiency are derived. The numerical results together with the Monte Carlo simulations are also provided. The results demonstrate that non-orthogonal multicast can further improve the performance of multicast and achieve higher spectrum efficiency, area traffic capacity, and energy efficiency than unicast from the low-to-high BS density regions, but suffers from inferior performance to unicast in the very high BS density region. The results also show that the non-orthogonal multicast and multicast exhibit different performance trends from unicast.
PV System Control to Provide Active Power Reserves Under Partial Shading Conditions

The challenges of modern power systems will inevitably impose increased ancillary service requirements to photovoltaic (PV) plants in the future, including operating reserves. Recent studies have investigated methods to maintain active power reserves without energy storage in the standard case of uniform illumination. In this paper, this functionality is extended to partial shading conditions, often encountered in PV systems. A new control scheme is proposed that permits operation at a reduced power level, estimating at the same time the shading conditions and maximum available power. This is achieved by applying a least-squares curve fitting algorithm on voltage and current measurements, without relying on any irradiance or temperature sensors. To the best of our knowledge, this is the first power reserves control scheme for PV systems under partial shading presented in the literature. The robustness and effectiveness of the proposed method are validated under rapidly changing shading conditions through simulations and experimental tests on a 2-kW PV system prototype.
Delayed Installation and Expedited Eviction: An Alternative Approach to Reduce Flow Table Occupancy in SDN Switches

Limited flow table size in switches is a major concern for SDN applications. The common approach to overcome this problem is to identify elephant flows and solely focus on them. However, there is no gold standard to assess the effectiveness of such greedy solutions. In this paper, we formally define this problem by choosing a cost function (hit ratio) and an objective function to optimize the (average table occupancy) and present the optimum solution (i.e., theoretical gold standard) for it. We model the problem as a knapsack problem, analyze how its solution minimizes the table occupancy, and the similarities to and differences from the default idle timeout mechanism used in OpenFlow. We also present a new approach to minimize flow table occupancy based on the insight gained from the knapsack model analysis. Our solution expedites rule evictions by forecasting the TCP flow termination from RST/FIN packets and delays rule installation by incubating non-TCP flows. It reduces average flow table occupancy between 16%–62% in various networks with less than 1.5% reduction in hit ratio. Using three real-world packet traces, we compare the performance of our solution with the theoretically optimum solution, the static idle timeout approach used in current OpenFlow systems, and heavy hitter detection approaches that are commonly used to solve this problem. We provide in-depth analysis of when and where our approach outperforms other solutions, while discussing why it might be better to use rate-based heavy hitter detection in some scenarios.