Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Software Architecture of Anticipatory Harvesting Robot Teams

This paper presents a novel application of anticipatory coordination and optimization principles, previously developed for autonomous inspection robot teams. The core tenet behind our research is to build a team of agile and relatively small robots, each equipped with two synergistically operating robotic arms, one arm with a soft gripper, the other arm with a set of cameras and a fruit cutting mechanism, all controlled by innovative anticipatory network (AN) algorithms. This allows the plantation management to efficiently supervise the team, coordinated and optimized by an adaptive anticipatory decision engine. Another key component of the robotic software architecture that allows the team of robots to efficiently harvest soft fruits such as strawberries or raspberries is a package of information fusion algorithms in the world model, associated with a knowledge base and a digital twin of the cover crops. We will show that AN coordination principles ensure the reliable operation of robots in case of communication disturbances or when uncertain or incomplete instructions are entered by the operator in the management and supervision system. The flexible design of the overall robotic architecture will make it possible to further develop the robot teams to respond to additional needs such as harvesting other fruits and working in various horticultural environments.
A Time-Dependent Motion Planning System for Mobile Service Robots in Dynamic Social Environments

In this paper, we propose a time-dependent motion planning system for mobile service robots in dynamic social environments using the socio-spatio-temporal characteristics of the humans and an online trajectory planning technique. The main idea of the proposed algorithm is to incorporate the human position, motion related to the robot, time-dependent information and social cues into the conventional timed elastic band technique, and proposed the time-dependent timed elastic band (TDTEB) model. We evaluate the developed model through a series of simulation experiments. The simulation results show that the proposed framework is fully capable of autonomously driving the mobile robot to avoid the individual humans in dynamic social environments, providing the safety and comfort for the humans and the socially acceptable behaviors for the mobile robot.
BOR-BORs Antenna

A system composed of a feeding body of revolution (BOR) and parasitic BORs surrounding the feeding BOR is investigated for operation as a beam-steerable antenna. The system is called the BOR-BORs antenna. The parasitic BORs, each with an on-off switching circuit connected between the BOR bottom end and the ground plane, are indirectly excited by the feeding BOR. Investigation of the feeding BOR reveals that it radiates a conical beam. This beam is transformed into a high gain radiation beam in a specific direction by the parasitic BORs and a particular combination of states for the switching circuits (open- or closed-circuited with respect to the ground plane). Other characteristics of the beam-steerable BOR-BORs antenna, including the radiation pattern and VSWR, are also discussed.
Multi-Modality and Multi-View 2D CNN to Predict Locoregional Recurrence in Head & Neck Cancer

Locoregional recurrence (LRR) remains one of leading causes in head and neck (H&N) cancer treatment failure despite the advancement of multidisciplinary management. Accurately predicting LRR in early stage can help physicians make an optimal personalized treatment strategy. In this study, we propose an end-to-end multi-modality and multi-view convolutional neural network model (mMmV-CNN) for LRR prediction in H&N cancer. In mMmV, a dimension reduction operator is designed, projecting the 3D volume onto 2D images in different directions, and a multi-view strategy is used to replace the original 3D method, which reduces the complexity of the algorithm while preserving important 3D information. Meanwhile, multi-modal data is used for the classification by making full use of the complementary information from cross modality data. Furthermore, we design a multi-modality deep neural network which is trained in an end-to-end manner and jointly optimize the deep features of CT, PET and clinical features. A H&N dataset which consists of 206 patients was used to evaluate the performance. Experimental results demonstrated that mMm V-CNN can obtain an AUC value of 0.81 and outperform a state of the art CNN-based method.
Predicting Heating Sliver in Duplex Stainless Steels Manufacturing through Rashomon Sets

A particular challenge while designing duplex steel is the minimization of surface defects such as heating slivers, since these defects may significantly increase production costs as they remain mostly undetected, being observed only during the final product inspection. Heating slivers may be originated in different stages of the steelmaking process, and to identify their formation in duplex stainless steel, we propose a feature decomposition approach that resulted in hundreds of thousands of predictive models learned from defective and non-defective duplex plates, thus offering diverse models regarding their incidence. We grouped these models based on their competing explanations, as a means to isolate the different possible root causes for heating sliver. Finally, we search for optimal models in the graphs derived from intra-cluster feature relationships.
Interpreting Node Embedding with Text-labeled Graphs

Graph neural networks have recently received increasing attention. These methods often map nodes into latent spaces and learn vector representations of the nodes for a variety of downstream tasks. To gain trust and to promote collaboration between AIs and humans, it would be better if those representations were interpretable for humans. However, most explainable AIs focus on a supervised learning setting and aim to answer the following question: “Why does the model predict y for an input x? ”. For an unsupervised learning setting as node embedding, interpretation can be more complicated since the embedding vectors are usually not understandable for humans. On the other hand, nodes and edges in a graph are often associated with texts in many real-world applications. A question naturally arises: could we integrate the human-understandable textural data into graph learning to facilitate interpretable node embedding? In this paper we present interpretable graph neural networks (iGNN), a model to learn textual explanations for node representations modeling the extra information contained in the associated textual data. To validate the performance of the proposed method, we investigate the learned interpretability of the embedding vectors and use functional interpretability to measure it. Experimental results on multiple text-labeled graphs show the effectiveness of the iGNN model on learning textual explanations of node embedding while performing well in downstream tasks.
Real-Time Gait Phase Recognition Based on Time Domain Features of Multi-MEMS Inertial Sensors

Gait phase analysis is widely used in disease diagnosis, rehabilitation training, and other fields by studying the characteristics of human gait. It systematically evaluates human body’s skeletal muscles and nerves with combined disciplines. Microelectromechanical system (MEMS) inertial sensors are extensively used in attitude detection because of its high-precision, portability, and good real-time performance in time-domain analysis. In this article, we presented a multi-degree-of-freedom MEMS gait detection method, which resolved the problems of single sensor and limited gait phase. We designed a sensor-based gait signal acquisition system, in which gait data acquisition program and feature analysis algorithm were compiled to verify the feasibility of the proposed method. We performed coordinate transformation and corrected position information to eliminate the gait phase detection error caused by random noise interference. Acceleration and angular velocity information were collected from 20 experimenters. We applied an adaptive threshold gait phase detection algorithm to classify the gait information collected by single sensor. To improve the results of gait phase classification, we used multisensor redundant measurement to analyze characteristics of five gait phases. The acceleration and angular velocity information collected by the three sensors placed at instep, ankle, and thigh were input into support vector machine (SVM). The classification results of the five gait phases are approximately 90%. Lastly, we built a human body structure model to simulate human motion in real time, realizing the real-time gait phase detection, which proves effectiveness of the proposed algorithm.
Visual Global Localization Based on Deep Neural Netwoks for Self-Driving Cars

In this work, we present a visual global localization system based on Deep Neural Networks (DNNs) for self-driving cars, named DeepVgl(Deep Visual Global Localization). In training mode, DeepVglis trained with images and associated poses from datasets built during the mapping process; and, in operating mode, DeepVglreceives images captured online and infers the global poses of the self-driving car. To assess the performance of DeepVgl,we carried out experiments using datasets collected by experimental self-driving cars on trips made over long periods of time, thus including significant changes in the environment, traffic volume and weather conditions, as well as different times of the day and seasons of the year. Experimental results show that DeepVglis able to correctly locate the self-driving car up to 75% of the time for 0.2 m of accuracy and 96% of the time for 5 m of accuracy.
Gluten free approach in fat and sugar amended biscuits: A healthy concern for obese and diabetic individuals

Correspondence Ajay Singh, Department of Food Engineering and Technology, Sant Longowal Institute of Engineering and Technology, Sangrur, Punjab, India. Email: ajay3singh@gmail.com Abstract Flour premix of copra meal 15% with germinated foxtail millet 85% blended with amaranth in 60:40 proportions to get gluten free, calorie deficient composite mix for biscuits. Attempts were made for obese and diabetics as target population with maltodextrin and acesulfame-k to find the extent of possible partial replacement of bakery fat and table sugar, respectively. It was found that substitution feasibility to 48% fat and 47% sugar lie within the acceptable limits of physical and sensorial properties comparable to control sample. Successful calorie reduction in fat and binary (fat and sugar) amended biscuits were achieved to 364.58 kcal/g and 315.97 kcal/g, respectively, as in comparison with control (471.01 kcal/g). Low anti-nutrients (phytate and tannin) and increased polyphenols in biscuits is a function of germinated foxtail millet. Acrylamide development also restricted to 0.193 mg/g in sugar replaced and 0.32 mg/g in sugar and fat replaced biscuits comparable to control (0.5 mg/g).
On Machine Thinking

Artificial Intelligence (AI) has made much progress, but the existing paradigm for AI is still basically pattern recognition based on a human-handcrafted representation. An AI paradigm shift seems to be necessary to address the machine thinking question raised by Alan Turing over 90 years ago. As a necessary subject of our new conscious learning paradigm introduced 2020, this work deals with general-purpose machine thinking, with planning as a special case, based on new concepts of emergent Super-Turing Machines realized by our proposed neural network models-Developmental Networks (DNs) that have been mathematically proven for its optimally in the sense of Maximum Likelihood (ML). Experimental demonstrations are presented for simulated new mazes in disjoint tests.
Automated Hyper-Parameter Tuning of a Mask R-CNN for Quantifying Common Rust Severity in Maize

This study builds on previous work in which Mask R-CNN was used to quantify common rust severity on maize leaves grown in a greenhouse environment. The Mask R-CNN outperformed standard image processing techniques when applied to the rust quantification task. One of the challenges with using Mask R-CNN is the large amount of parameters that need to be tuned, this study examines automated tuning of these parameters. This study is the first to perform hyper-parameter tuning on the Mask R-CNN at this scale as well as using a genetic algorithm (GA). The results gathered from the GA implementation is compared to that of the prior results from the Mask R-CNN with manually selected hyper-parameters. This work concludes that using a genetic algorithm to determine optimal hyper-parameter values for the Mask R-CNN is comparable to the current state of the art for this problem domain. Future work includes performing a fitness landscape analysis and potential development of new genetic operators.
Design and Development of Self-Service Telemedicine Kiosk for Remote Towns

Telemedicine is one of the flag ship applications of Multimedia Super Corridor initiative in Malaysia. Although it was introduced two decades ago, many locations in Sabah and Sarawak still do not have any healthcare facilities. People are forced to take a one- hour boat trip to get the basic healthcare treatment. This proposed system attempts to address the issue through the design and develop the Telemedicine Kiosk with advance state of the art facilities and real time video conferencing facilities with the medical officer for basic healthcare treatment. Advanced sensors were used to measure the patient’s medical conditions and update the doctors in real-time for remote consultancy. These sensors would detect and send the data to a web service platform by using a ESP32 WIFI module. The results were obtained by conducting multiple experiments from different people. The proposed techniques provide promising outcomes in remote patience data monitoring and real-time consultancy services to implement the clinical operations.
A Novel High Gain Array Antenna Design for Autonomous Vehicles of 6G Wireless Systems

By 2030, 6G wireless networks will provide a fully autonomous system for many applications, one of them is fully automated vehicles. Antennas with different gains, half-power beamwidths (HPBW), and different radiation zones will be needed for different purposes such as parking assistance, lane change, emergency braking, and traffic jam assist in autonomous vehicles with integrated RADAR technologies. The Federal Communication Commission (FCC) has proposed millimeter-wave (mmW) frequency bands for vehicular RADAR systems. This paper describes the development of a new and improved 2x4 array antenna for autonomous vehicles. Development of proto-type single patch for the array is described in [1]. The array antenna resonates at7 7 GHz frequency. With a higher gain, the optimized array antenna has a lower mutual coupling effect. The gain of the 2x4 corporate feed array antenna is 18.0 dB. The return loss was calculated to be -39.8947 decibels. The array antenna designed and described in the paper can be used in autonomous vehicles.
Determining Wave Height from Nearshore Videos Based on Multi-level Spatiotemporal Feature Fusion

Wave height is one of the most significant parameters of waves, affecting marine navigation, coastal environment, and surfing activities. In this paper, we present a wave height determination network from nearshore surveillance videos. This method consists of three parts. Firstly, video frames and frame differences are extracted to represent static and dynamic characteristics of waves, respectively. Then, by taking the two types of data as different modalities, two independent Network In Network (NIN) networks are constructed to learn spatial and temporal features of wave heights. Meanwhile, the two types of features are fused across multiple feature levels using a central network, where concatenation or attention-based fusion is applied. Finally, the wave height is determined by minimizing a global loss. The proposed network for wave height determination, dubbed NINMf-WH or NINMf-A-WH (with attention), was evaluated by a set of comparative experiments from two aspects: i) the basic network of feature learning: NIN vs. CNN; ii) the spatial-temporal feature fusion strategy: multi-level fusion vs. late fusion vs. intermediate feature fusion. The results show that NIN is suited to learn wave height features due to the nonlinearity of waves in video, and the multi-level spatiotemporal feature fusion can achieve low error and good stability of wave height determination. With attention-based fusion, NINMf-A-WH can further reduce the prediction error. To verify the generalization performance of the proposed network, we applied it for estimating wave heights in new surveillant videos. The results provide a low relative error of 6.43%±4.9, which can well meet the operational requirement for nearshore wave forecasting (<=20%).
A Novel Low-Complexity GFDM Relay Communication System: Relay Selection and Filter-and-Forward

In this paper, we develop a low-complexity cooperative relay network based on generalized frequency division multiplexing (GFDM) system. In this network, multiple relay nodes (RNs) are used to relay GFDM symbols from a source node (SN) toward a destination node (DN). We propose three methods to design the RNs based on the minimization of the total power of the noise after symbol estimation at the DN. In the first method, only one node is selected among all RNs to forward the signal from the SN to the DN. In the second and third methods, a filter-and-forward (FF) scheme is used, such that at each RN, the received GFDM signal from the SN is circularly convolved with a finite impulse response (FIR) filter and then, forwarded to the DN. The FIR filter's coefficients are represented as a circulant matrix. Since a circulant matrix is characterized by its eigenvalues, we obtain the eigenvalues of the FIR coefficient matrices for all RNs. The difference between the second and third methods is that in the former, the eigenvalues of the filter matrices are restricted to be one or zero, however, in the later, the eigenvalues are determined with no restrictions. Simulation results are provided to demonstrate the performance of the proposed methods in terms of bit error rate (BER). Moreover, we show that because of using circular convolution, the proposed methods can be implemented with a low complexity.
Implementing a Ternary Inverter Using Dual-Pocket Tunnel Field-Effect Transistors

In this article, we propose a standard ternary inverter (STI) based on a dual-pocket tunnel FET (DP-TFET) for low-power applications. Using 2-D device simulations, we demonstrate that if appropriate doping concentration and length are chosen for the dual-pocket, then the device can exhibit ternary inverter voltage transfer characteristics (VTCs) with three stable output voltage levels. Ternary inverter characteristics are obtained by two tunneling mechanisms in the device: 1) gate bias-independent within-channel tunneling and 2) gate bias-dependent source-channel tunneling. We also demonstrate that the ternary inverter obtained could be operated at different supply voltages by controlling the pocket doping concentration.
Voltage-Balancing Control in Stacked Multicell Converter Based on Single Space-Vector Modulation

This article introduces voltage-balancing control methods in stacked multicell converters based on single space-vector modulation. Two different control strategies for capacitors voltage are presented. The basic vectors with the smallest common-mode voltage are chosen to accomplish the voltage-balancing control. The nearest vector is selected for the first proposed method, and the appropriate switch combinations of each phase are determined by the cost function of floating-capacitor voltages and neutral point (NP) voltage. For the second proposed method, we consider sacrificing a little current performance to provide more possibility of switching state selection. The switching state of each phase is determined according to the voltage-balancing control of floating capacitors, and the optimal switching state with the smallest NP voltage is selected from the two nearest adjacent basic vectors. Experimental results compared with the existing space vector pulsewidth modulation method are presented to verify the effectiveness of the proposed methods.
GazeFlow: Gaze Redirection with Normalizing Flows

Gaze estimation often requires a large scale datasets with well annotated gaze information to train the estimator. However, such a dataset requires costive annotation and is usually very difficult to collect. Therefore, a number of gaze redirection approaches have been proposed to address such a problem. However, existing methods lack the ability to precisely synthesize images with target gaze and head pose in complex lighting scenes. As a powerful technique to model the distribution of given data, normalizing flows have the ability to generate photo-realistic images and provide flexible latent space manipulation. In this work, we present a novel flow-based generative model, GazeFlow11The code will be made available at https://github.com/CVI-SZU/GazeFlow, for gaze redirection. The visual results of gaze redirection show that the quality of eye images synthesized by GazeFlow is significantly higher than that of other approaches like Deep Warp and PRGAN. Our approach has also been applied to augment the training data to improve the accuracy of gaze estimators and significant improvement has been achieved for both within dataset and cross dataset experiments.
Link Prediction of Directed Network Based on Node Importance

Directed link prediction aims to predict the emergence of future relations and their orientations from the structure information of current network. A lot of methods have been proposed for directed link prediction, however, most of them only consider the shared information between node pairs when calculating the node similarity, and ignore the difference in the contribution of the importance of the node itself to the formation of the link. For this reason, in this paper, we propose a method based on the importance of the node for directed link prediction. It not only considers the shared information but also the nodes' importance. The method utilizes the PageRank algorithm to calculate the PR value for every node and corrects it as the importance of the node by the network size, and then integrates the importance of the node into the adjacent matrix of the network by defining the reciprocal link weight. Then we use the improved Bifan predictor to calculate the similarity of node pairs for link prediction. To validate the introduction of the nodes' importance, we further extend our method to classic predictors and experiments results on eight real directed networks demonstrate that our method improves the prediction accuracy.