Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Coloration in Young Cuttlefish (Sepia Officinalis L.): The Morphology and Development of Body Patterns and Their Relation to Behaviour

Young Sepia officinalis (0-5 months) were studied in the laboratory and in the sea, and their appearance and behaviour compared with that of adult animals. Cuttlefish lay large eggs and the hatchlings are miniature replicas of the adults. From the moment of hatching they show body patterns as complex as those of adults and far more elaborate than those shown by most juvenile cephalopods. There are 13 body patterns: 6 of these are \`chronic' (lasting for minutes or hours) and 7 are \`acute' (lasting for seconds or minutes). The patterns are built up from no fewer than 34 chromatic, 6 textural, 8 postural and 6 locomotor components, used in varying combinations and intensities of expression. Nearly all these components occur in young animals: 26 of the chromatic, all the textural and locomotor, and 6 of the postural components. Nevertheless, patterning does change with age and we have recorded this and correlated the changes with behaviour. The components are built up from units, which themselves comprise four elements organized in precise relation to one another: chromatophores, iridophores, leucophores and skin muscles. The chromatophores are always especially important: they are muscular organs innervated directly from the brain and controlled ultimately by the highest centres (optic lobes). The areas in the Sepia brain that control patterning are already well developed at hatching, for the appearance of the skin is as much part of the brain's motor program as is the attitude of the arms or fins, or the posture of the entire animal. The iridophores and leucophores develop later and are especially important constituents of many adult patterns, notably the Intense Zebra of the mature male. Experiments confirm that patterning is neurally controlled and apparently mediated exclusively by the visual system. Young cuttlefish use patterning primarily for concealment, utilizing such strategies as general colour resemblance, disruptive coloration, obliterative shading, shadow elimination, disguise and adaptive behaviour. Older animals also conceal themselves but increasingly use patterns for signalling, both interspecifically (warning or `deimatic' displays) and intraspecifically (sexual signalling). Laboratory-reared cuttlefish were released in the sea and observed underwater. They quickly and effectively concealed themselves on the substrate; it was easy for the human observer to lose them and many passing fish behaved as if they were not there. One local predator, Serranus cabrilla, was observed to attack them and no fewer than 35 attacks were recorded, only six of which were successful. Laboratory-reared cuttlefish apparently distinguished between these predators and other, non-predatory, fish the first time they encountered them in nature.
Hybrid Watermarking Algorithm Using Clifford Algebra With Arnold Scrambling and Chaotic Encryption

With the widespread use of color images, the copyright protection of those images using watermarks is one of the latest research topics. The use of color images as watermarks has advantages over binary and irreplaceable grayscale images. Color images are intuitive, rich, and lively; they have large amounts of copyright protection information and more easily recognized by human vision. To improve the security of watermark information and embedding positions and improve the algorithm’s robustness against various attacks, a Quaternion Fourier transform (QFT) based algorithm, based on Arnold transform and chaotic encryption, is proposed in this paper. Geometric algebra (GA) can deal with color images in vector form with each component of RGB handled individually. We used Quaternion, which is a sub-algebra of GA, and effectively handled color image processing by using Fourier transformation. After deriving the calculation process of the QFT with strong security by Arnold scrambling and chaotic encryption, this paper proposes a digital watermarking algorithm that resists geometric attacks by using color images as carriers. The robustness and quality of the proposed watermarking algorithm is tested with different with many statistical measures. Experimental outcomes show that the proposed approach is the best to solve conflict problems between quality and robustness. Also, the proposed approach exhibits worthy robustness against many attacks, such as, conventional attacks, and geometrical attacks.
An On-Line Battery Parameter Detection Algorithm for Sinusoidal Ripple Charge (SRC) Method

Sinusoidal Ripple Current (SRC) charge method is a new technique which has been more developed among fast charge algorithms due to higher charge speed, lower temperature rise and adverse effects on battery lifetime. Studies have been focused on presenting an appropriate algorithm to find optimal charge frequency, which minimizes battery impedance. Some of proposed methods suffer from accuracy and other methods increase complexity in practice. In this paper, a new simple online method based on battery parameters detection has been developed to overcome complexity of previous works. Although this method is simple, it has enough accuracy to extract battery parameters and find the optimal charge frequency. In order to show the performance of proposed algorithm, a laboratory battery charger consists of a Phase Shifted Full Bridge (PSFB) DC/DC converter has been employed to test proposed algorithm. In addition, results of the proposed algorithm have been compared with conventional CC/CV algorithm and fixed optimal frequency charge algorithm. Experimental results show, proposed algorithm to find optimal frequency reduces charge time about 9.25% in comparison with conventional CC/CV algorithm and 0.25% compared to fixed optimal frequency charge algorithm.
A Neural-Network-Based Non-linear Interference Cancellation Scheme for Wireless IoT Backhaul with Dual-Connectivity

In this paper, we consider an Internet of Things (IoT) wireless network using Long Term Evolution (LTE) cellular system as backhaul. To provide high throughput, by using dual-connectivity technique, the IoT gateway simultaneously connects to two evolved Node Bs (eNBs) on two carriers, one for downlink and the other for uplink. As a result, the receive link will be severely interfered by the harmonic interference (HI) and inter-modulation (IM) components caused by the imperfections of power amplifier (PA) and in-phase/quadrature (I/Q) modulator. To solve this problem, in this paper, an neural-network (NN)based non-linear interference cancellation scheme is proposed for dual-connectivity IoT gateway. In the proposed scheme, the nonlinear interference is first reconstructed by using the transmit signal and the trained NN in baseband, and then subtracted from the received signal in digital domain at receiver. The NN precisely models the link behavior from the baseband transmitter to the baseband receiver, including all the linear and non-linear effect. Additionally, the NN can be used to reconstruct and cancel not only the HI, but also the IM components of the mirror-frequency interference (MFI) caused by I/Q imbalance, and direct current (DC) bias caused by local oscillator (LO) leakage. To evaluate the performance of the proposed scheme, a hardware prototype is designed and implemented. Experimental results show that the proposed scheme has a superior performance in dual-connectivity system compared with the traditional non-linear interference cancellation scheme using polynomial (PM) model.
Early mother–young interactions in California bighorn sheep, Ovis Canadensis californiana

All except 2 of 22 pregnant females isolated themselves from conspecifics to give birth. Isolation is probably an antipredator strategy, although isolation in the 1st day after birth may also help the development of the mother–young bond. Even after they rejoined the herd, the mother and her lamb stayed close together until the lamb was 4 days old when mother–young distances increased sharply, only to decrease again when the lamb began to graze for short periods next to its mother. Neonates were very active, particularly playing, during the 1st day of life. It is suggested that this high level of activity may help develop the motor skills necessary for "follower" young born in steep terrain. Synchrony of activities between a female and her lamb was high when she rested, but when she grazed, her lamb could be engaged in one of many different activities. Suckling behaviour during active and bedding periods differed significantly, with the longest mean suckle durations occurring during or immediately after r.
Geometric Calibration of a Time-of-Flight Depth Sensor and a Colour Camera Pair

The joint information provided by the depth sensor and the colour camera has many applications in the field of computer vision. These two imaging devices require preliminary calibration (both intrinsic and extrinsic) to be used for accurate measurements. In this paper, we propose a novel approach to calibrate Intel Creative Senz3D depth sensor that is based on Time-of-Flight (ToF) technology. Since identifying feature points in-depth maps is difficult, we have designed a 2.5D checkerboard pattern with systematically placed holes to be accurately detected from low-resolution depth maps of a ToF camera as well as from high-resolution images of a colour camera. Based on the identified feature points we establish an accurate correspondence between world coordinate and camera coordinate system using the obtained intrinsic and extrinsic parameters of the respective cameras. Evaluation of the proposed method indicates an improvement of 27.13% in average pixel re-projection error with respect to the manufacturer’ s calibration.
Null-Space based Robust Controller for Quadcopter's formation in windy environments

This work presents the design, developing and simulation of a Null-Space based robust controller for a quadcopter formation in a windy environment. The proposed controller is used to perform trajectory tracking of a formation of three quadcopters through wind gusts combining the advantages of the Null-Space based controller with a SMC controller, allowing to merge the capabilities of these two control structures; the flexibility of the Null-Space Controller and the Robustness of the SMC controller. The proposed control system is validated through simulations and with the Lyapunov stability criteria analysis.
Pollen and inorganic particles in the air of climatically very different places in switzerland

Abstract Soot, dust, occasionally industrial pollution and even dust from the Sahara are sometimes or often found as inorganic floating particles in the ‘Burkard’ pollen-and-spore trap. The time of their occurrence can be determined fairly accurately, as 2 mm of the exposed tape corresponds to one hour. It is possible to evaluate this kind of aerial pollution semiquantitatively with photometric equipment for electropherograms found at every modern hospital. In addition the flowering calendars of two places with very differing climate are shown (Basle and Davos). Therefore, several years of pollen collection can be compared. In the mountains there are fewer pollen grains and also grains of not so many species. But there are also fewer inorganic particles in the air of Davos than in a city such as Basle.
Deep Learning based Affective Sensing with Remote Photoplethysmography

Recent studies show that heart rate variability (HRV) is an important physiological characteristic that reflects physiological and affective states of a person. Advancements in the field of remote camera-based photoplethysmography has made possible measurement of cardiac signals using just the raw face videos. Most of existing studies of camera-based cardiovascular monitoring focus on just heart rate (HR) estimation, leaving more interesting case of remote HRV estimation out of scope. However, knowing only the average HR is not enough for affective sensing applications, and measurement of HRV is beneficial. We propose a new framework, which uses deep spatiotemporal networks for contactless HRV measurements from raw facial videos. The proposed framework employs data augmentation technique. It was evaluated on two multimodal databases that consists face videos with synchronized physiological signals. Experiments demonstrate the advantage of our deep learning based approach for HRV estimation. We also achieved promising results for inclusion remote HRV estimation in affective sensing applications.
DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.
Understanding Deep Learning Algorithms for Object Detection and Recognition

In the current state-of-art, computer vision is one of the emerging areas of artificial intelligence. Among the various branches of computer vision, object detection and recognition are the challenging ones which has a wide range of applications in various areas like self-driving cars, facial recognition, pedestrian’s detection, and in surveillance system. With great improvement in deep learning algorithms for detection tasks it is been widely used for object detection and recognition tasks. In this survey, we have explained various deep learning algorithms and their applications in various fields. Both classification and regression algorithms for detecting and recognition tasks are discussed elaborately. Finally based upon certain parameter, the algorithms are compared, and the results are inferred.
Scalable Video Caching for Information Centric Wireless Networks

Recently, information centric wireless network (ICWN) has been concerned due to its flexible network structure and high efficiency of content delivery. Meanwhile, scalable video coding (SVC) is a promising solution to provide high quality of video services. Combining ICWN and SVC is expected to improve the performance of wireless video delivery services. Since caching strategy plays a significant role in ICWN, in this paper, we address the caching problem for scalable videos over ICWN in mobile scenarios. By jointly considering the layered structure of SVC and the hierarchical architecture of ICWN, we formulate an optimization problem to minimize the average download delay. A novel layered hierarchical caching method is proposed for solving the problem. Furthermore, we focus on a special but common case in which the above delay minimization problem is equivalently transformed into a cache hit ratio maximization problem. A simplified algorithm with 1/2 approximation ratio is provided. Finally, simulation results show that our proposed caching schemes outperform baseline methods in cache hit rate and delay performance.
Parkinson’s Disease Detection by Using Machine Learning Algorithms and Hand Movement Signal from LeapMotion Sensor

This work is devoted to the detection of Parkinson’s disease (PD) by the kinematic parameters of hand movements using machine learning methods. Hand movements of PD patients (N16) and control group (N16) were recorded using a Leap Motion sensor. Three motor tasks were chosen based on MDS-UPDRS part 3: finger tapping (FT), pronation- supination of the hand (PS), opening-closing hand movements (OC). For the signal received from the sensor, 25 kinematic parameters were calculated by key points. The key point determination was carried out with maximums and minimums finder algorithm, as well as manual marking, using a specially designed user application. For the binary classification (PD or non-PD), for each motor task separately and for three combined, various feature extraction options were used. Four classifiers: kNN, SVM, Decision Tree (DT), Random Forest (RF) were trained. Testing was carried out in the 8-fold cross-validation mode. The best results were obtained using the combination of the most significant features of both hands. The results for each task were the following: for FT 95.3%, for OC 90.6%, for PS 93.8%. The combined features result of all motor tasks was 98.4%.
Soft Haptic Interface based on Vibration and Particle Jamming

Whilst common in devices ranging from smart-phones to game controllers, vibrotactile feedback has generally been limited to providing a uniform sensation across a tactile surface. We propose a haptic interface based on the emerging physical effect of particle jamming with both vibrotactile and shape changing outputs, which can be extended in space to create haptic surfaces and devices with shape and vibrotactile responses localised to one part of the device. This paper gives an overview of the physical principles behind this technology and presents detailed performance metrics obtained from a working prototype. These include experimental characterization of the relationships between air pressure and electric motor power and vibration amplitude and frequency which show that it is possible to control vibrotactile amplitude and frequency independently.
EFFETS DU MAGNÉSIUM SUR DES MEMBRANES LIQUIDES ET BIMOLÉCULAIRES PHOSPHOLIPIDIQUES. L'ION MAGNÉSIUM COMPÉTITEUR DES CATIONS MONOVALENTS

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. EFFETS DU MAGNÉSIUM SUR DES MEMBRANES LIQUIDES ET BIMOLÉCULAIRES PHOSPHOLIPIDIQUES. L’ION MAGNÉSIUM COMPÉTITEUR DES CATIONS MONOVALENTS M. Bara
A Blockchain Privacy Protection Scheme Based on Ring Signature

Blockchain is a point-to-point distributed ledger technology based on cryptographic algorithms. However, the open and transparent blockchain ledger supplemented by statistical methods such as sociological mining and data mining has caused users’ privacy to face major threats. Therefore, privacy protection has become a focus of current blockchain technology research. Ring signature technology is a commonly used encryption technology in the field of privacy protection. Therefore, this paper constructs a blockchain privacy protection scheme based on ring signature. This solution built a privacy data storage protocol based on the ring signature on the elliptic curve, and used the complete anonymity of the ring signature to ensure the security of data and user identity privacy in blockchain applications. The correctness and safety proof analysis of the proposed scheme were also carried out.
The Application of Reconfigurable Filtennas in Mobile Satellite Terminals

Reconfigurable antennas play an important role in wireless communication systems as the physical and electrical properties of their structures can be modified, allowing them to be used in different applications. In this paper two reconfigurable filtennas are presented to integrate mobile devices that communicate (uplink and downlink) with satellites in low-orbit constellations. A microstrip patch antenna in uplink mode (29 GHz), is surrounded by copper structure that, once connected via a PIN diode, takes the antenna to resonate in a lower frequency band (20 GHz). To deal with both bands, two filters were integrated into the radiating structure: edge-coupled and hairpin filters. The antennas were characterized and compared through simulation and measurements, in terms of impedance adaptation, radiation pattern and isolation between operating bands, validating their operability. Their small size and low cost allow these antennas to be integrated into mobile terminals that communicate with satellites or 5G systems.
Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.