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Many social media applications use different animated or morphed images to make fake news viral. Recognition of text from images for their classification as real or fake requires a neural network. BERT (Bidirectional Encoder Representation Transformer) or MLP-based (Multi-Layer Perceptron) algorithms are successful when working with textual data alone. However, the system needs to extract the sequential text from the images to identify the semantic meaning of the content before the classification process. The dataset utilized was acquired from The Indian Fake News Dataset (IFND) contains text and visual data from 2013 to 2021. The data includes both visual and textual information, as well as 126k data points obtained from millions of users. In the proposed model, a squeezed lambda is implemented to process the data in the three forms of verbal tenses, i.e., past to future and future to past. In the lambda layer, temporal classification is performed by applying two bidirectional LSTM (Long Short Term Memory) layers based on the retuning sequences of the character list available in the dataset. It also computes the batch cost of every iteration and reduces them based on the ratio of prediction and input class labels available. To ensure that the suggested technique is more accurate than the current approach, a validation was undertaken, resulting in a +0.5 increase in accuracy over the BERT (Bidirectional Encoder Representation Transformer) model. Hence, the proposed method has achieved higher accuracy than existing algorithms. Than existing algorithms. [ABSTRACT FROM AUTHOR]

Natural language inference is critical in Natural Language Processing where semantics is involved. Also known as textual entailment recognition, it defines a directional relationship between pair of sentences, namely the text and the hypothesis. Identifying entailment, contradiction, and neutrality in text pairs is necessary for various language processing applications to reduce text redundancy. An overview of the critical works in this aspect for all languages, including the Indian language perspective, is detailed here. There is a high volume of textual entailment and related attempts in English and foreign languages. In contrast, there are only a few attempts for low resource languages. This article presents the progress in textual entailment for various languages and the development of different datasets for textual entailment. Over these years, the datasets developed differ in size and kind. Most of the datasets are raw or generated, and a few of the latest are translated datasets. The article also points to observation notes on the progress that has happened throughout these years. [ABSTRACT FROM AUTHOR]

Climate change is a significant global challenge concerning agriculture and food security. The understanding of climate change effects on crop production is necessary for developing an effective adaptation strategies and predicting a crop yield accurately. This paper suggests the combined Clustering Long Short Term Memory Transformer (CLSTMT) model for crop yield prediction. CLSTMT is a hybrid model that integrates clustering, deep learning based LSTM and Transformer techniques. The outliers from the historical crop and climate data are removed using k-means clustering. Followed by, the crop yield is predicted using Transformer-based neural network with LSTM layers and feed-forward neural network (FNN) components. The model design effectively captures climate-influenced patterns, enhances the precision and comprehensiveness of crop yield prediction. The experiment is conducted using the dataset with crop yield, climate, and pesticide details over 101 countries collected from 1990 to 2013. The comparative analysis reveals that the CLSTMT model outperforms other regression models such as SGDRegressor (SGDR), Lasso Regression (LR), Support Vector Regression (SVR), ElasticNet (EN) and Ridge Regression (RR). The proposed design effectively captures climate-influenced patterns, enhancing the precision and comprehensiveness of crop yield predictions. The findings indicate that the proposed model provides an accurate prediction of crop yield with high R2 of 0.951 and lesser Mean Absolute Percentage Error (MAPE) of 0.195. This value suggests a minimal average percentage deviation between the actual and predicted yields. The findings indicate that the CLSTMT model provides more accurate crop yield prediction compared to others. [ABSTRACT FROM AUTHOR]

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating fabrication has increased. This review discusses the latest advancements in grating manufacturing techniques, particularly highlighting laser interference lithography, which excels in sub-beam generation through wavefront and amplitude division. Techniques such as Lloyd's mirror configurations produce stable interference fringe fields for grating patterning in a single exposure. Orthogonal and non-orthogonal, two-axis Lloyd's mirror interferometers have advanced the fabrication of two-dimensional gratings and large-area gratings, respectively, while laser interference combined with concave lenses enables the creation of concave gratings. Grating interferometry, utilizing optical interference principles, allows for highly precise measurements of minute displacements at the nanometer to sub-nanometer scale. This review also examines the application of grating interferometry in high-precision, absolute, and multi-degree-of-freedom measurement systems. Progress in grating fabrication has significantly advanced spectrometer technology, with integrated structures such as concave gratings, Fresnel gratings, and grating–microlens arrays driving the miniaturization of spectrometers and expanding their use in compact analytical instruments. [ABSTRACT FROM AUTHOR]

This systematic review aimed to summarise and analyse the evidence on the reliability and validity of linear tranducers (LTs) in exercises of different nature and different modes of execution. This systematic review was carried out under PRISMA guidelines, and was carried out using three databases (PubMed, Web of Sciences, and Scopus). Of the 351 initially found, 21 were included in the qualitative synthesis. The results reflected that linear position transducers (LPTs) were valid and reliable in monitoring movement velocity in non-plyometric exercises. However, precision and reliability were lower in execution protocols without isometric phase and in the execution of exercises in multiple planes of movement, with greater measurement errors at higher sampling frequencies. On the other hand, linear velocity transducers (LVTs) proved to be valid and reliable in measuring velocity during plyometric and non-plyometric exercises performed on the Smith machine, with less variation in measurement in the latter. Finally, the use of peak values is recommended, since they are less dependent on the technological errors of LTs. Therefore, the performance of non-plyometric exercises, carried out in the Smith machine and with an isometric phase in the execution of the movement, will help to minimise the technological error of the LTs. [ABSTRACT FROM AUTHOR]

In recent years, the accuracy of visual SLAM (Simultaneous Localization and Mapping) technology has seen significant improvements, making it a prominent area of research. However, within the current RGB-D SLAM systems, the estimation of 3D positions of feature points primarily relies on direct measurements from RGB-D depth cameras, which inherently contain measurement errors. Moreover, the potential of triangulation-based estimation for ORB (Oriented FAST and Rotated BRIEF) feature points remains underutilized. To address the singularity of measurement data, this paper proposes the integration of the ORB features, triangulation uncertainty estimation and depth measurements uncertainty estimation, for 3D positions of feature points. This integration is achieved using a CI (Covariance Intersection) filter, referred to as the CI-TEDM (Triangulation Estimates and Depth Measurements) method. Vision-based SLAM systems face significant challenges, particularly in environments, such as long straight corridors, weakly textured scenes, or during rapid motion, where tracking failures are common. To enhance the stability of visual SLAM, this paper introduces an improved CI-TEDM method by incorporating wheel encoder data. The mathematical model of the encoder is proposed, and detailed derivations of the encoder pre-integration model and error model are provided. Building on these improvements, we propose a novel tightly coupled visual-inertial RGB-D SLAM with encoder integration of ORB triangulation and depth measurement uncertainties. Validation on open-source datasets and real-world environments demonstrates that the proposed improvements significantly enhance the robustness of real-time state estimation and localization accuracy for intelligent vehicles in challenging environments. [ABSTRACT FROM AUTHOR]

We present a method for improving the amplitude and angular error of inductive position sensors, by advancing the design of receiver coil systems with multiple windings on two layers of a printed circuit board. Multiple phase-shifted windings are connected in series, resulting in an increased amplitude of the induced voltage while decreasing the angular error of the sensor. The amplitude increase for a specific number of windings can be predicted in closed form. Windings are placed electrically in series by means of a differential connection structure, without adversely affecting the signal quality while requiring a minimal amount of space in the layout. Further, we introduce a receiver coil centerline function which specifically enables dense, space-constrained designs. It allows for maximization of the number of possible coil windings while minimizing the impact on angular error. This compromise can be fine-tuned freely with a shape parameter. The application to a typical rotary encoder design for motor control applications with five periods is presented as an example and analyzed in detail by 3D finite-element simulation of 18 different variants, varying both the number of windings and the type of centerline functions. The best peak-to-peak angular error achieved in the examples is smaller than 0.1° electrically (0.02° mechanically, periodicity 5) under nominal tolerance conditions, in addition to an amplitude increase of more than 170% compared to a conventional design which exhibits more than twice the angular error. Amplitude gains of more than 270% are achieved at the expense of increased angular error. [ABSTRACT FROM AUTHOR]

Sequential recommendation predicts users' future interactions by capturing dynamic sequential patterns hidden in their historical behavioral sequences. Recently, many deep neural networks have been applied to learn representations of these sequences. However, noisy interactions in user behavior data impose a negative effect on these learned representations. To address this issue, we propose a model called Hybrid Fourier Neural Filter (HFNF) for the sequential recommendation, which is a combination of two kinds of Fourier neural filters and three kinds of objective functions. In HFNF, a global filter network and an adaptive Fourier neural operator are integrated to model dynamic user behaviors by fully exploiting their respective powerful denoising and expressiveness capabilities. Furthermore, a heterogeneous loss is designed to better train HFNF by enhancing preference learning while preserving the uniformity and the alignment of the learned representations. Experiments conducted on eight benchmark datasets demonstrate the superiority of the proposed HFNF model over competing deep neural network based sequential recommendation models. [ABSTRACT FROM AUTHOR]

In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet, mainly through the addition of a unique attention-guided branch in the encoder part, aiming to enhance the model’s ability to comprehensively capture and deeply fuse contextual information. The uniqueness of CPF-UNet lies in its dual-path mechanism, which differs from the dense connectivity strategy adopted in networks such as UNet++. The dual-path structure in this study can effectively integrate deep and shallow features without relying excessively on dense connections, achieving a balanced processing of image details and overall semantic information. Experiments have shown that CPF-UNet not only slightly surpasses the segmentation accuracy of UNet++, but also significantly reduces the number of model parameters, thereby improving inference efficiency. We conducted a detailed comparative analysis, evaluating the performance of CPF-UNet against existing UNet++ and other corresponding methods on the same benchmark. The results indicate that CPF-UNet achieves a more ideal balance between accuracy and parameter quantity, two key performance indicators.

A photonic crystal is an artificial material with a periodic optical refractive index. The band structure of photons can be tailored by adjusting the geometric parameters spatially. By properly designing the configuration, the photonic crystal can generate an optical forbidden band within the structure. To choose an appropriate geometric configuration that can generate a photonic band gap in the desired frequency range, traditionally, the band structure of such structure has been obtained by applying Floquet periodic boundary conditions and performing the eigen analysis calculation. However, a great amount of testing is required and consumes a great amount of time. In our study, the top view of the unit cell is converted to a binary bitmap and encoded to downgrade the bitmap for forming the input data of the artificial neural network. We adopted the finite element method to calculate the band structure training data set, which contained various geometric parameters and corresponding band structures. Our neural network shows high accuracy and is less time-consuming. The results are useful for the inverse design of photonic crystals. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) provides services for proper traffic assistance. ITS helps in creating a transportation system that is smart, safe and efficient. Vehicular Ad-hoc Network supplies internet connectivity to vehicles and helps in traffic guidance. This paper uses a modified transformer architecture for time-series vehicular data to predict traffic flow. Time-series sequences are generated from the dataset for capturing temporal dependencies. Our proposed transformer-based model has been engineered to capture inter-feature correlations along with inter-sample correlations. The 2D-Transformers model has a significant decrease in error compared with Transformers and LSTM-based models. The prediction generated from the model can be transmitted throughout a network of vehicles. So, a holistic networking model is proposed where the vehicles will be connected to Road-side Units (RSUs) and the backbone network will be Software Defined Network (SDN). The traditional design principles, that incorporate data, control and management planes together in a network device, are incapable to adapt with this much data growth, bandwidth, speed, security, and scalability compared to SDN as it provides with centralized programmable mechanism reliably. The trained parameters learned using the transformer model can be passed throughout the network for traffic guidance.

Gastrointestinal (GI) cancer is the most common cancer in men and women. GI cancers are increasing every year worldwide. In the biomedical industry, Radiation treatment is a frequent choice for treating cancers of the GI tract in which the oncologist focuses the high range of X-ray beams on the tumor while avoiding the healthy organs. Manual segmentation of healthy organs to focus X-ray beams only on the tumor portion is very tedious and time-consuming, which can lead the treatment from a few minutes to hours. Deep learning techniques can concur with this problem by segmentation of healthy organs. This research article proposes a deep learning-based model Pyramid Scene Parsing Network (PSPNet) for segmenting organs such as the stomach, small bowel, and large bowel in the GI tract. The model has been simulated with five feature encoding networks: ResNext 50, Timm_Gernet_S, ResNet 34, EfficientNet B1, and MobileNet V2. These encoders were used for downsampling the feature map in the PSPNet architecture. The implementations have been performed using the UW Madison GI tract dataset, which contains 38,496 MRI scans of cancer patients. The model was evaluated using validation dice, Jaccard, and validation loss. The results reveal that the PSPNet model combined with ResNet 34 as encoder outperforms the other feature encoding networks with validation dice as 0.8842, validation Jaccard as 0.8531, and validation loss as 0.1365. Radiation oncologists can use the proposed model to speed up radiation therapy for cancer treatment.

In this article, we propose the design, construction, and validation of a technological solution with the ability to automate the process of reviewing and analyzing thesis paper by using natural language processing and a deep learning training algorithm. This project seeks to be a proof of concept because current solutions in the academic field focus on abstracting and analyzing scientific articles but not in theses. Another point to consider is that these solutions are in English language. The resulting language model was compared with other language models based on the Transformers architecture. The result of this comparison gives us an objective for future research on the S.A.R.A. project.

In quantum communication, the channel noise and the misalignment of the reference frames between the communication parties will lead to the failure of quantum state transmission. Here an alignment‐free spatial‐polarization hyperentanglement transmission scheme is provided for hyperentangled photons. In this scheme, before the spatial‐polarization hyperentanglement is transmitted through the fiber channel, it is first encoded as a time‐bin entanglement with the same polarization. After the photons pass through the noise channel, the polarization errors caused by reference frames misalignment and channel noise can be corrected by time‐bin entanglement. In principle, by implementing this scheme, the communication parties can share the original hyperentangled state, and the success probability can approach unity. The scheme is robust to random channel noise and reference frames misalignment, and the decoherence effect caused by the misalignment of the reference frames between the communication parties can be completely suppressed by implementing this scheme. [ABSTRACT FROM AUTHOR]

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the validity, reliability, and sensitivity of a new linear position transducer (LPT) device (RepOne) to a previously validated LPT (Tendo) during the barbell back squat and bench press exercises. Fourteen recreationally resistancetrained individuals (7 males and 7 females) performed three repetitions for the back squat and bench press at loads ranging from 30-90% 1RM. Both devices recorded average (ACV) and peak (PCV) concentric velocities concurrently for every repetition at each load. Significant correlations were observed between RepOne and Tendo during the back squat (PCV: r = 0.90-0.99, p < 0.01; ACV: r = 0.84-0.99, p < 0.01), bench press (PCV: r = 0.74-0.99, p < 0.01; ACV r = 0.81-0.99, p < 0.01). ICCs reveal good to excellent reliability between devices for back squat (PCV, 0.85-0.99; ACV, 0.83-0.99) and bench press (PCV, 0.79-0.99; ACV, 0.83-0.99). Bland-Altman plots revealed greater bias during PCV for both exercises across intensities (back squat, 0.072 to 0.110 m/s; bench press, 0.039 to 0.107 m/s), although ACV bias was lower for both exercises (back squat, -0.002 to -0.029 m/s; bench press, -0.022 to 0.015 m/s). The RepOne device generally exhibited higher smallest detectable change (SDC) values compared to the Tendo, except for specific loads in certain conditions. Additionally, the RepOne device demonstrated higher smallest worthwhile change (SWC) values than the Tendo unit for most loads in back squat ACV. Collectively, the RepOne exhibits strong validity and reliability comparable to the Tendo across both barbell back squat and bench press exercises, despite some variations in sensitivity metrics like SDC and SWC, indicating its efficacy for resistance training application.

For a lower limb exoskeleton, accurate and continuous estimation of the gait phase in real time is a fundamental requirement to provide a well-tailored assistive action at the proper time in the gait cycle (GC). This article presents a new gait phase estimator for a portable hip exoskeleton based on discrete wavelet transform (DWT) and adaptive oscillators. The algorithm is capable of continuously tracking the gait phase and identifying the relevant biomechanical gait events online, i.e., heel strike (HS) and toe-off (TO). The proposed method exploits only the hip joint angle signals measured by hip encoders, avoiding the need to use additional sensors to those already built in the exoskeleton. The novel phase estimator has been benchmarked against a state-of-the-art method, based on the maximum flexion angle (MFA), with pressure-sensitive insoles used as the reference ground truth for the event detection. To validate the method, two experimental activities were carried out. Experiments conducted with eight healthy subjects walking on a treadmill at different speeds, with and without hip assistance, demonstrated that the DWT-based method outperformed the MFA method in all operative conditions, reducing the rms of the phase reset error by 64.0% in assistive mode, and identifying the HS and TO events with low delay (0.4% and 1.1% GC, respectively, for HS and TO). Experiments carried out with three transfemoral amputees showed similar performance, paving the way for clinical applications of the method. [ABSTRACT FROM AUTHOR]

Automatic systems to monitor people and subsequently improve people’s lives have been emerging in the last few years, and currently, they are capable of identifying many activities of daily living (ADLs). An important field of research in this context is the monitoring of health risks and the identification of falls. It is estimated that every year, one in three persons older than 65 years will fall, and fall events are associated with high mortality rates among the elderly. We propose an anomaly identification framework to detect falls, which incorporates a spatial-temporal convolutional graph network (ST-GCN) as a feature extractor and uses an encoder process to reconstruct ADLs and identify falls as anomalies. As the publicly available fall datasets are few and generally unbalanced, training a reliable model using approaches that need explicit labeling is challenging. Thus, a focus on learning without external supervision is desirable. Treating a fall as an exception of ADLs allows us to recognize falls as anomalies without explicit labels. Given its modular architecture, our framework can robustly represent visual information and use the encoder’s reconstruction error to identify falls as anomalies. We assess our framework’s ability to recognize falls by training it with only ADLs. We perform three types of experiments: single dataset training and evaluation that consists of separate 90% of the data to train the model 5% to adjust the model, and the rest to the test. A joint dataset experiment, where we combine two datasets to increase the number of samples our model is trained on, and a cross-dataset evaluation, where we train on one dataset and evaluate using another one. Besides presenting state-of-the-art results on our experiments, particularly on the cross-dataset one, the model also presents a low number of false events, which makes it an ideal candidate for real-world application.

Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, we design an evaluation platform which we use to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers. We perform more than one hundred experiments using publicly available datasets, labeled by financial experts. We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers. The results show improved efficiency of contextual embeddings in sentiment analysis compared to lexicons and fixed word and sentence encoders, even when large datasets are not available. Furthermore, distilled versions of NLP transformers produce comparable results to their larger teacher models, which makes them suitable for use in production environments.

Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.

This paper presents a detailed subjective quality assessment for the ultra-high definition (UHD) videos having frame rates of 30fps and 60 fps. The subjective assessment is based on the ITU-R BT-500 recommendations, where double stimulus continuous quality scale (DSCQS-type II) test is performed for the evaluation of the perceived quality of the user’s in terms of differential mean opinion score (DMOS). Encoding of the UHD videos by opting encoders i.e. H.264/AVC, H.265/HEVC, and VP9 at five different quantization parameter (QP) levels is done to investigate the perceived user’s quality of experience (QoE) given as DMOS. Moreover, the encoding efficiency as the encoding time for each encoder and qualitative performance by employing full-reference (FR) quality metrics are presented in this work. [ABSTRACT FROM AUTHOR]

• We propose representation fusion using self-supervised learning for activity recognition (ReFuSeAct) framework. • We propose novel attention encoders for performing feature-level fusion. • We propose modality-specific encoders to extract representational features from multiple sensor modalities. • We adopt Bayesian decision-level fusion strategy on multimodal representation to improve the activity recognition performance. Over the years, wearable sensors have gained a lot of attention from the research community due to their non-invasive nature, adoption of sensors by general public, and their applicability in healthcare services. With the advancements in communication networks, machine learning methods, and wearable sensor deployment, it is essential to design a method that could accurately classify human activities while reducing the dependence on annotated data. Traditional machine learning approaches require large-scale annotated data in order to provide a reasonable recognition performance. Recently, self-supervised learning methods are proposed but they are either limited to single sensor devices or fail to model intra-modal correlations within the self-supervised learning paradigm. In this work, we propose Representation Fusion using Self-supervised learning for Activity Recognition (ReFuSeAct) framework that uses modality-specific encoders, attention encoders, and decision-level fusion strategies to address the aforementioned limitations. The self-supervised learning paradigm ensures that the method achieves better performance even with less amount of annotated data. The architecture proposed for modality-specific encoder ensures that extraction of representative features that could help in improving recognition performance. The feature-level fusion performed using the proposed attention encoders enhances the quality of representative features that could be used in supervised learning phase. Finally, the decision-level fusion strategy enhances the activity recognition accuracy in comparison to the single deep learning classifier. Our experimental analysis shows that the proposed approach records 9.1% improvement over semi-supervised learning baselines and more than 2% improvement in comparison to existing self-supervised learning approaches. [ABSTRACT FROM AUTHOR]

High precision angular actuators are used for high demanding applications such as laser steering for photolithography. Piezo technology allows developing actuators with a resolution as low as a few nanoradians, with bandwidths as high as several kilohertz. In most demanding applications, the actual performance of these instruments needs to be characterized. The best angular measurement instruments available today do not sufficient resolution and/or bandwidth to satisfy these needs. At the Istituto Nazionale di Ricerca Metrologica, INRIM a device was designed and built aiming at characterizing precision 2D angular actuators with a resolution surpassing the best devices on the market. The device is based on a multi reflection scheme that allows multiplying the deflection angle by a factor of 70. The ultimate resolution of the device is 2 prad/√Hz over a measurement range of 36 µrad with a measurement band >10 kHz. The present work describes the working principle, the practical realization, and a case study on a top-level commercial angular actuator (Nano-MTA2 produced by Mad City Labs).

The complications of identifying radiation effects in electromechanical sensors in the presence of mechanical variation are discussed. A corresponding analysis method is developed to separate radiation degradation effects from expected sensing variation due to mechanical tolerances of test fixtures. This is achieved by statistically characterizing the fixture variation for each sensor before irradiation, and evaluating the statistical significance of any sensitivity changes after irradiation. The method is demonstrated using a contactless Hall-effect rotary encoder IC commonly used in robotic systems. Sensitivity changes in measurements relevant to robotic controller design are isolated and quantified as a maximum positioning error of 0.5° and an angular velocity error of 1.5%. These measurements can be used to improve a commercial off-the-shelf robotic system’s tolerance to radiation by designing motion controllers robust to sensitivity changes prior to total device failure. [ABSTRACT FROM AUTHOR]

Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in nondestructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets. [ABSTRACT FROM AUTHOR]

In order to explore the gaps between decoder's interpretations and encoder's designing intentions with respect to the same multimodal discourses, thirty linguistic and thirty art graphic participants were chosen as decoders and encoders, respectively. The participants were required to interpret the same research data in terms of the best and the worst major colors, as well as the best and the worst synergetic patterns formed by major modes. It was found that the complete unanimity in terms of both color and spatial arrangements among the interpretations between participants only reached 43.3%. The unanimity in the interpretations from the perspective of color alone reached 46.7%. Moreover, the interpretations from the perspective of spatial arrangements present high unanimity, with a rate up to 70%. It is concluded that there are both differences and similarities between the interpretations made by encoders and decoders. The possible reasons underlying both differences and similarities are probed in the present study as well. [ABSTRACT FROM AUTHOR]

The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities is, therefore, often expressed in terms of the mutual information, which also takes into account the nonlinear effects but is not normalized. To compare data from different experiments, the information quality ratio is, therefore, in many cases, of easier interpretation. On the other hand, both mutual information and information quality ratio are always positive and, therefore, cannot provide information about the sign of the influence between quantities. Moreover, they require an accurate determination of the probability distribution functions of the variables involved. As the quality and amount of data available are not always sufficient to grant an accurate estimation of the probability distribution functions, it has been investigated whether neural computational tools can help and complement the aforementioned indicators. Specific encoders and autoencoders have been developed for the task of determining the total correlation between quantities related by a functional dependence, including information about the sign of their mutual influence. Both their accuracy and computational efficiencies have been addressed in detail, with extensive numerical tests using synthetic data. A careful analysis of the robustness against noise has also been performed. The neural computational tools typically outperform the traditional indicators in practically every respect.

Diffraction data may be measured using approaches that lead to ambiguity in the interpretation of scattering distributions. Thus, the encoding and decoding of coherent scattering distributions have been considered with a view to enabling unequivocal data interpretation. Two encoding regimes are considered, where encoding occurs between the X-ray source and sample, and where the encoder is placed between the sample and detector. In the first case, the successful recovery of diffraction data formed from the interrogation of powder samples with annular incident beams is presented using a coded aperture approach. In the second regime, encoding of Debye cones is shown to enable recovery of the sample position relative to the detector. The errors associated with both regimes are considered and the advantages of combining the two discussed. [ABSTRACT FROM AUTHOR]