Miracle-dz/UniFAR-FTUs huggingface dataset:
RIBoNN: Designing Robust In-Memory Binary Neural Network Accelerators

RRAM crossbar-based accelerators show promise to execute compute intensive Deep Learning applications at the edge. For highly energy-constrained systems, Binary Neural Networks (BNNs) have gained momentum in recent times as the reduced precision alleviates the costs associated with storage, compute and communication. However, faults manifested in a unit bitcell of a RRAM crossbar-based accelerator may lead to drastic degradation in accuracy of the BNN, resulting in unintended system behavior. In this paper, we propose RIBoNN, a robust RRAM-based in-memory BNN accelerator, that consists of a 2T2R differential bitcell as the basic element of the crossbar. By leveraging the inherent characteristics of the proposed bitcell, RIBoNN is capable of achieving in-situ fault tolerance, thus circumventing the need to stall the deployed application for detection or diagnosis at the edge. RIBoNN, when evaluated on image-based datasets yields up to 96.57 % improvement in BNN classification accuracy, at a fault rate of 5 %; thereby demonstrating significant fault-tolerance over the state-of-the-art XNOR-RRAM BNN accelerator. Even though RiBoNN furnishes a negligible energy overhead of 2.62% over XNOR-RRAM, our proposed accelerator significantly reduces the inference latency by performing 24.4 % faster MAC operations with identical area footprint, while providing immense fault tolerance at the edge.
Toxic chromium in water and the effects on the human body: a systematic review

Both in developed and developing countries, there is a growing concern about the dangerous hexavalent chromium, in the consumption of drinking water. Hexavalent chromium is recognized to have a negative impact on the human body, both in the short and long term. The purpose of this study is to evaluate the relationship between hexavalent chromium in water and its impact on human health. A systematic review of the international literature is applied, according to the instructions of PRISMA protocol, in the databases PubMed and Scopus, for the years 2005–2022. The systematic literature review was conducted using inclusion and exclusion criteria, and appropriate keywords for exposure and outcome. A total of 18 studies met all inclusion criteria. Most research concludes that hexavalent chromium is a risk factor for humans, as opposed to trivalent chromium who is a protective factor. A study indicates the consumption of drinking water with high concentrations of hexavalent chromium, as a protective factor for human life, while eight of the total studies suggest hexavalent chromium as a potential risk factor. It is necessary to clarify the origin, the mode of action, and the negative impact has on human health, to create appropriate preventive and intervention measures.
A Drone-Based Multisensory Payload for Maritime Pollutants Detections

Surface floating marine pollution is responsible for a wide range of environmental hazards for marine fauna. Surface sea pollution monitoring is usually carried out by visual inspection from aircrafts or ships. However, human inspection is costly and results can vary from observer to observer. Remote sensing technology is a valid alternative to in situ measurements that can reduce costs and risks compared to in situ missions. In this context, aerial platforms provide a convenient tradeoff between coverage and resolution, both spatial and temporal, compared to ship or satellite based surveys. As well, small, unmanned aircrafts have the potential to be very inexpensive to operate, offer more flexibility of operation, and will benefit from active research on reducing size, weight, and power (SWAP) requirements of various sensors. However, the use of a multisensory system on board a commercial small drone is hard to implement considering that it is desirable to make a system reliable and robust. This article shows the main results of POSEIDON Project. Within this project, a commercial drone has been equipped with a radar system and a multispectral camera jointly observing a common area of interest. Flights have been performed in Spain in February 2021 and the main achievements are reported in this article.
The Predictive Kinematic Control Tree: Enhancing Teleoperation of Redundant Robots through Probabilistic User Models

When teleoperating complex robotic manipula-tors, operators often find it most natural to issue commands that dictate end effector movements in task space. If the robot has redundant degrees of freedom, the translation of this com-mand from task space into configuration space can affect the robot's maneuverability, smoothness of motion, and the general precision of the teleoperated system. In this paper, we propose a novel method for performing this translation that predicts future operator commands in order to choose joint motions that maintain maneuverability in future timesteps. We introduce a Predictive Kinematic Control Tree (PrediKCT) that optimizes joint movement in the nullspace of the Jacobian over multiple future timesteps by reasoning over probabilistic models of the human operator. In essence, PrediKCT builds out and evaluates a tree of possible future commands. We implement this system on two simulated and one physical 7 -degree-of-freedom robotic arms and characterize performance by analyzing robot motions produced through multiple command trajectories with differing user model accuracies and tree parameters, demonstrating benefits to path accuracy over both a minimum-norm joint velocity solution and local optimization of joint movement.
Learning Light Fields for Improved Lane Detection

Robust lane detection is imperative for the realization of intelligent transportation. Recently, vision-based systems that employ deep convolution neural networks (CNNs) for lane detection have made considerable progress. However, for better generalization under various road conditions learning-based methods require excessive training data, which becomes non-trivial in challenging conditions such as illumination variation, shadows, false lane lines, and worn lane markings, etc. In this paper, we propose a light field (LF) based lane detection method that utilizes the additional angular information for improved prediction and increased robustness. Two different LF representations are investigated to study the possibility of maximum performance improvement and minimal additional computation cost and data labeling efforts. Experimental results successfully demonstrate that the proposed approach improves the prediction of the lane line point coordinates and is significantly robust against the aforementioned adverse conditions.
Deficiency indices and spectrum of self-adjoint extensions of some classes of differential operators

Problems relating to the asymptotic behaviour in the neighbourhood of the point and in the neighbourhood of the origin of a solution of an equation of arbitrary (even or odd) order with complex-valued coefficients are studied. It is assumed here that the coefficients of the quasidifferential expression have the following property: if one reduces the equation to a system of first-order differential equations, then one can transform that system to a system of differential equations with regular singular point at or . The results obtained allow one to determine the deficiency indices of the corresponding minimal symmetric differential operators and the structure of the spectrum of self-adjoint extensions of these operators.In addition, on the basis of refined asymptotic formulae for solutions to the equation the deficiency numbers of a certain differential operator generated by a differential expression with leading coefficient vanishing in the interior of the interval in question are found.
Effects of Melatonin Dose on Fruit Yield, Quality, and Antioxidants of Strawberry Cultivars Grown in Different Crop Systems

Melatonin acts as a seed germination activator, plant growth regulator, leaf senescence retardant, and, in general, has a multifunctional role as a ‘defence molecule’; furthermore, by interacting with other molecules, such as phytohormones and gaseous molecules, it greatly enhances plant adaptation to different environments. However, there are not enough studies about the use of melatonin on horticultural crops, and even fewer studies have outlined the differences related to this phytohormone use between protected environment and in open field. The two latter systems have different growing conditions that could lead to diversified application doses. As the choice of melatonin dose depends on all crop system components, the present research aimed to assess the effects of three melatonin concentrations (1 ppm, 5 ppm and 10 ppm) plus an untreated control, on yield, quality, and antioxidants of four strawberry cultivars (i.e., Kabarla, Fortuna, Sweet Ann, Festival) grown either in greenhouse or in open field. Research was conducted to assess the yield parameters were better affected by greenhouse than open field, and mean fruit weight was the highest in cultivar Sweet Ann. In open field 10 ppm showed the highest values of fruit number and yield, but in greenhouse did not differ from 5 ppm which led to the highest fruit number. At all melatonin doses, cultivar Kabarla demonstrated the highest yield, compared to the other cultivars, with the maximum value of about 46 t·ha−1. Plant dry weight was 90% higher under greenhouse than in open field conditions, and 52% or 132% higher with cultivar Kabarla in comparison with Fortuna and Sweet Ann, respectively. The melatonin dose of 10 p.p.m showed 56% higher plant dry weight in comparison to the untreated control. Fortuna showed higher values of fruit dry matter, soluble solids, and glucose than Sweet Ann. The fruit dry matter was 7% lower at 10 ppm melatonin than in the untreated control. Fructose was higher under 1 ppm melatonin with 245 mg·g−1 d.w. compared to the untreated control with 220 mg·g−1 d.w. in Festival, whereas in Fortuna was the highest in the control fruits, the latter also showing the highest titratable acidity in Fortuna and Sweet Ann. The highest phenolics content was recorded under 10 ppm melatonin in open field, and with 5 ppm in greenhouse; the phenolics content was the highest under 1 ppm melatonin dose in Kabarla and 5 ppm in Fortuna. Under the protected environment 5 and 10 p.p.m. melatonin elicited the highest accumulation of ascorbic acid; 10 ppm were more effective in Fortuna and Sweet Ann, and 5 ppm in Festival. The most enhanced antioxidant activity was recorded under 5 p.p.m. melatonin dose in Fortuna and Festival. The present study confirms that the dose of melatonin to apply to strawberry crop closely relates both to cultivar and crop system.
3D CAD-based object recognition for a flexible assembly cell

Current developments in the field of automated assembly systems show an increasing interest in systems that are flexible in both CAD based product design and CAD based assembly. For the application addressed in this paper, coupling the vision system and a CAD database is of prime importance in order to achieve the required automatic reconfiguration of the assembly cell when new parts are defined. This paper presents a 3D CAD-based vision system for obtaining 3D data about the scene. After the images are acquired, edge detection is preformed and the detected edges are stored as chaincodes. Following that, a stereo vision algorithm is applied for finding the recognition features. The output are lists of features that are combined into a 3D wireframe representing the scene. The recognition algorithm takes the observed wireframe outputs from the stereo vision system, and compares them with a set of model wireframes derived from previous models, in order to select the 'best match', where the previous models used for recognition are derived from a product data model (PDM). The PDM is an interface between the CAD database and the recognition system, which allows the automatic generation of new models when new parts are introduced into the system. The vision system described in this paper is part of an intelligent robotic assembly cell, where the aim is to build a flexible intelligent robotic assembly cell, such that robots would be able to automatically assemble a random variety of small- batch products.
Generalized transport-band field-effect mobility in disordered organic and inorganic semiconductors

Field-effect mobility mu(FE) and its activation energy in disordered inorganic and organic semiconductor thin-film transistors is strongly dependent on bias conditions. This implies a nonlinear dependence of conductivity on carrier concentration, which stems from the high density of trapped carriers while only a few contribute to conduction. When mu(FE) is extracted from measurement data, the nonlinear conductivity-concentration dependence is averaged over the semiconducting film. Consequently, mu(FE) becomes mingled with device attributes such as gate capacitance in addition to terminal bias, which undermines the physical interpretation of mu(FE) and subsequent comparison of measured values for different devices and different semiconductors. This paper presents an effective mobility mu(eff) description at a reference carrier concentration, which separates the physical conductivity-concentration dependence from the device and bias attributes, enabling comparison of carrier transport in disordered semiconductors. In particular, by using the generalized concept of mobility edge and exponential band tails we show that mu(eff) can be applied to a wide range of inorganic and organic semiconductors. Indeed, three parameters, viz., mu(eff), exponential band tail slope T-t, and bias-independent activation energy E-a0 of mu(eff), can describe carrier transport in the transistor together with its bias and temperature dependence.
Functional reach test performance in distance and velocity – A pilot study

Abstract Aims: To describe the difference in functional reach test (FRT) distance and velocity during “self-selected” and “as-fast-as-possible” reach velocities, and to describe the age-related differences associated distance and velocity in the two tests. Methods: A cross-sectional study included 73 healthy volunteers, 20–95 years of age. Reach ability while standing was measured during three trials performed at a “comfortable speed”, and three performed as fast as possible. A web camera was used to record the tests at a frequency of 15 Hz. Major findings: The forward reach was by mean longer in 1.2 cm (± 4.9 cm) (F1;70 = 4.9, p = 0.03) in the self-selected speed condition. In addition, values were significantly higher among the younger group; during the self-selected speed by 5.8 ± 2.1 cm and by 7.2 ± 2.1 cm during the as-fast-as-possible condition (F1;70 = 11.6, p < 0.001), the backward movement in the self-selected speed was smaller by 1.2 cm (± 4.9 cm) (F1;70 = 6.3, p = 0.01),The mean reach backward in both condition was significantly different between age groups (F1;70 = 15.3, p < 0.001), values were significantly higher among the older participants. In addition, a significant interaction effect was noted (F1;70 = 3.8, p = 0.05); combination of older age and as-fast-as-possible condition was associated with increased backward movement. Principal conclusion: By using simple technology and a new measure of balance assessment that measure the quality of functional reach, therapists may discover new information regarding balance performance and falls.
Electro-Adhesive Tubular Clutch for Variable-Stiffness Robots

Electro-adhesive clutches have become effective tools for variable stiffness functions in many robotic systems due to their light weight, high speed and strong brake force. In this paper, we present a novel, tubular design of an electro-adhesive clutch. Our clutch consists of flexible electrode sheets rolled into a tubular structure. This design allows encapsulating large electrode areas in a compact size for strong brake force. Additionally, the tubular structure acts as a guide for directional sliding without external guides. The structure also ensures that the electrode surfaces are encapsulated, preventing the accumulation of dust and thus leading to reliable performance. This structure is therefore an improvement over the commonly used planar designs. The characterization of the electro-adhesive tubular clutch shows that the frictional force increases with the increase of the electrode contact area, the decrease of the roll diameter and the dielectric layer thickness. A retractable tubular clutch is made by fixing an elastic cable along the clutch axis and achieves a stiffness change factor up to 260. Applications of this retractable clutch in robotics to achieve variable stiffness are demonstrated in two systems: a tensegrity structure and a wing skeleton. Changes in stiffness by 13.2 and 30.2 times are achieved for the two systems, respectively. The proposed tubular clutch is an effective means of achieving variable stiffness, particularly in the case of robotic systems that transmit forces through tensioned cables.
Multiple sulphur isotope record of Paleoarchean sedimentary rocks across the Onverwacht Group, Barberton Greenstone Belt, South Africa

This study presents multiple sulphur isotope (32S, 33S, 34S, 36S) data on pyrites from silicified volcano‐sedimentary rocks of the Paleoarchean Onverwacht Group of the Barberton greenstone belt, South Africa. These rocks include seafloor cherts and felsic conglomerates that were deposited in shallow marine environments preserving a record of atmospheric and biogeochemical conditions on the early Earth. A strong variation in mass independent sulphur isotope fractionation (MIF‐S) anomalies is found in the cherts, with Δ33S ranging between −0.26‰ and 3.42‰. We explore possible depositional and preservational factors that could explain some of this variation seen in MIF‐S. Evidence for microbial activity is recorded by the c. 3.45 Ga Hooggenoeg Formation Chert (HC4) preserving a contribution of microbial sulphate reduction (−Δ33S and –δ34S), and a c. 3.33 Ga Kromberg Formation Chert (KC5) recording a possible contribution of microbial elemental sulphur disproportionation (+Δ33S and –δ34S). Pyrites from a rhyo‐dacitic conglomerate of the Noisy Formation do not plot along a previously proposed global Felsic Volcanic Array, and this excludes short‐lived pulses of intense felsic volcanic gas emissions as the dominant control on Archean MIF‐S. Rather, we suggest that the MIF‐S signals measured reflect dilution during marine deposition, early diagenetic modification, and mixing with volcanic/hydrothermal S sources. Given the expanded stratigraphic interval (3.47–3.22 Ga) now sampled from across the Barberton Supergroup, we conclude that large MIF‐S exceeding >4‰ is atypical of Paleoarchean near‐surface environments on the Kaapvaal Craton.
GaSLAM: An Algorithm for Simultaneous Gas Source Localization and Gas Distribution Mapping in 3D

Chemical gas dispersion poses considerable threat to humans, animals and the environment. The research areas of gas source localization and gas distribution mapping aim to localize the source of gas leaks and map the gas plume respectively, in order to help the coordination of swift rescue missions. Although very similar, these two areas are often treated separately in literature. In some cases, inferences on the gas distribution are made a posteriori from the source location, or vice-versa. In this paper, we introduce GaSLAM, a methodology that couples the estimation of the gas map and the source location using two state of the art algorithms with a novel navigation strategy based on informative quantities. The synergistic approach allows our algorithm to achieve a good estimation of both objectives and push the navigation strategies towards informative areas of the experimental volume. We validate the algorithm in simulation and with physical experiments in varying environmental conditions. We show that the algorithm improves on the source location estimate compared to a similar approach found in literature, and is able to deliver good quality maps of the gas distribution.
Prospects for Improving Subseasonal Predictions

AbstractExtending atmospheric prediction skill beyond the predictability limit of about 10 days for daily weather rests on the hope that some time-averaged aspects of anomalous circulations remain predictable at longer forecast lead times, both because of the existence of natural low-frequency modes of atmospheric variability and coupling to the ocean with larger thermal inertia. In this paper the week-2 and week-3 forecast skill of two global coupled atmosphere–ocean models recently developed at NASA and NOAA is compared with that of much simpler linear inverse models (LIMs) based on the observed time-lag correlations of atmospheric circulation anomalies in the Northern Hemisphere and outgoing longwave radiation (OLR) anomalies in the tropics. The coupled models are found to beat the LIMs only slightly, and only if an ensemble prediction methodology is employed. To assess the potential for further skill improvement, a predictability analysis based on the relative magnitudes of forecast signal and forecas.
Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
Processing in Memory for Mathematics of Arrays Operations

Processing In Memory (PIM), also referred to as Processor in Memory, is the integration of a processor with RAM (Random Access Memory) on a single Integrated Circuit (IC). In this paper, the concept of PIM is developed to support memory read/write requirements for Mathematics of Arrays (MoA) operations that implement tensor inner and outer products. The concept for MoA in PIM supports the integration of the processing operations and data storage memory, allowing for increases in processing speed and memory data transfer rate, and can support decreases in memory access latency and power usage.