Miracle-dz/UniFAR-FTUs huggingface dataset:
CZT Algorithm for Multiple-Receiver Synthetic Aperture Sonar

The compensation of slant range motion errors needs to be carried out after range pulse compression in the signal processing of multiple-receiver synthetic aperture sonar (SAS). Whereas range pulse compression cannot be performed as the first step in multiple-receiver SAS chirp scaling (CS) algorithm, as it requires the transmitted signal to be linearly frequency modulated. For this reason, it is difficult to combine CS algorithm with motion compensation. Besides, those imaging algorithms based on interpolation, such as range-Doppler algorithm and wavenumber domain algorithm, have plenty of computation load and large phase errors. Aimed at these problems, a CZT algorithm for multiple-receiver SAS in the non-stop-hop-stop mode is proposed in this paper. The proposed algorithm can correct range cell migration utilizing chirp-z transform, which avoids interpolation. Furthermore, the algorithm is easily combined with motion compensation as it can process pulse-compressed data directly. This paper describes the algorithm in 9 key steps, including the principle of correcting range cell migration utilizing chirp-z transform in discrete domain. In the simulation and sea trial data processing, the proposed algorithm provides image quality equal to the CS algorithm, and the algorithm is slightly more efficient than CS algorithm when combined with motion compensation.
Dual-Wideband Patch-Slot Loop Textile Antenna for WBAN/WiFi/LTE Applications

This work presents a textile antenna with dual- wideband performance for wearable wireless communications. The patch-slot loop structure was developed to obtain two wide operating bands, in which a slot loop was cut on the ground plane underneath the patch. The top patch acts as a wideband radiating element at the upper operating band, while the slot loop is fed by the top patch to excite a wideband resonance at the lower operating band. A copper-plated polyester fabric was used for fabricating antenna radiator and ground plane. An insulating neoprene fabric with a permittivity of 1.5 was used for preparing the substrates. The proposed antenna can achieve gains of 4.21/6.45 dBi at 2.58/5.34 GHz with wide bandwidths of 15.9/11.4 %, respectively.
In-run Self-calibration of Scale Factor Temperature Drifts for MEMS Gyroscope

This paper presents an in-run self-calibration of scale factor temperature drifts for MEMS gyroscope. The causes of scale factor drifts are analyzed. Also, the scale factor temperature drifts of open-loop detection (Mode I) and closed-loop detection (Mode II) are discussed. The linearized transfer function of closed-loop detection is studied. Based on the characteristics of scale factor under closed-loop detection, a method of temperature drifts compensation is proposed (Mode III), which independent on system demarcation and data fitting. The experimental results show that the temperature stability of Mode II and Mode III is improved by 10 times and 19 times over −20°C to 40°C, compared with Mode I. Moreover, the scale factor temperature sensitivity of Mode III is less than 10 ppm/°C.
Resource-Efficient Object Detection by Sharing Backbone CNNs

The detection of objects in image and video has made huge progress in recent years due to the use of deep convolutional neural networks (DNNs), with some network architectures becoming de-facto standards. This paper addresses the problem of sharing a backbone CNN for different tasks, for example, to enable detection of additional classes when an already trained network is available. When using multiple such neural networks, sharing a backbone can save inference time and memory consumption. We study sharing a common backbone between neural networks trained for different tasks (logoness and text block detection) based on Yolo v3. We provide results on the impact of different lengths of the shared backbone on performance and resource efficiency.
Cluster-Based Load Balancing Algorithm for Ultra-Dense Heterogeneous Networks

In a highly dense heterogeneous cellular network, the loads across cells are uneven due to random deployment of cells and the mobility of user equipments (UEs). Such unbalanced loads result in performance degradation such as throughput and handover success. In order to solve the uneven load problem for better network performance, we propose a cluster-based mobility load-balancing algorithm for heterogeneous cellular networks. Traditional mobility load balancing (MLB) schemes that consider only the adjacent neighbors cannot provide enough improvement in network performance. On the other hand, the previous MLB schemes consider neighbors in the entire network suffer from unnecessary MLB actions. However, in the load balancing process, the proposed algorithm considers overloaded cells and their neighbors within the $n$ -tiers. First, the algorithm models the network as a directed multi-graph and constructs clusters taking the overloaded cells and their $n$ -tier neighbors. Therefore, by adjusting cell individual offset parameters of the cells in the clusters the algorithm achieves load balancing locally. Since load balancing is performed inside the clusters, the network can be optimized more efficiently by avoiding unnecessary MLB actions. Simulations show that the proposed algorithm distributes the load across the network more evenly than other MLB algorithms, and in a low UE velocity scenario, it increases the overall network throughput by 6.42% compared to a non-optimized network without an MLB algorithm.
Learning Long-Term Temporal Features With Deep Neural Networks for Human Action Recognition

One of challenging tasks in the field of artificial intelligence is the human action recognition. In this paper, we propose a novel long-term temporal feature learning architecture for recognizing human action in video, named Pseudo Recurrent Residual Neural Networks (P-RRNNs), which exploits the recurrent architecture and composes each in different connection among units. Two-stream CNNs model (GoogLeNet) is employed for extracting local temporal and spatial features respectively. The local spatial and temporal features are then integrated into global long-term temporal features by using our proposed two-stream P-RRNNs. Finally, the Softmax layer fuses the outputs of two-stream P-RRNNs for action recognition. The experimental results on two standard databases UCF101 and HMDB51 demonstrate the outstanding performance of proposed method based on architectures for human action recognition.
3D Rigid Registration of Patient Body Surface Point Clouds by Integer Linear Programming

Surface point cloud matching is a useful technique for patient positioning during radiation therapy, system registering of surgical navigation system, etc. A common method for 3D point cloud registration is to estimate the registration function based on the 3D keypoint feature correspondences. However, the feature distance of correct correspondence is usually not the closest, but it is hidden in the k-nearest correspondences generally. This makes the rate of false correspondences too high for registration. In this paper, we convert the 3D point clouds rigid registration problem to a new graph matching model which combines the k-nearest feature information and the geometric constraints. The key idea is that the distance between 2 model feature points does not change after rigid transformation. This is a quadratic programming problem which could be transformed into an integer linear programming problem and thus could be efficiently solved. The final output is the 3D keypoint correspondences set which are accurate enough for registration. Our experimental results on point cloud datasets, generated from TCIA CT images datasets, show the advantages of the proposed algorithm.
Voltage Stability Constrained Optimal Power Flow Study Based on P-Index

Recently voltage instability is one of the crucial issues in the heavily stressed and large interconnected power system. This paper proposes a study of Voltage Stability Constrained Optimal Power Flow (VSC-OPF) using a new bus voltage stability index named P-index. It has been concluded that VSC-OPF with the inclusion of P-index as an inequality constraint results in enhancement of static voltage stability and also reduction in active power losses of the system compared to conventional Optimal Power Flow. The proposed VSC-OPF is tested on IEEE 14 bus test system. OPF results are validated with that of MATPOWER for the IEEE 14 bus system.
Designing child-robot interaction with Robotito*

Computational thinking is a skill that is considered essential for the future generations. Because of this it should be incorporated into the curricula as soon as possible. An interesting option to work on computational thinking with children is by means of robots. Here, we present Robotito, a robot that can be programmed by arranging its environment, intended to help the development of computational thinking in preschool children. We describe its hardware and software environment, and hierarchical state machines used to implement two modes of interaction with environment-first based on color detection and the second sensible to the surrounding objects. We also present activities that we developed to work on abstraction, generalization, decomposition, algorithmic thinking, and debugging-skills related to computational thinking.
A Robust Position Estimation Algorithm under Unusual Large Range Errors

This paper provides a robust fusion algorithm for accurate position estimation under uncertain large errors in range measurements. Many researchers have studied UWB, IMU, odometry integration algorithms for estimating position, velocity, attitude and IMU biases. However, the fused results of these conventional algorithms are likely to be affected by the unusual ranging measurement errors. Therefore, in order to improve the positioning accuracy under the large errors of the UWB range, a robust fusion algorithm is required. In this paper, instead of using the range directly, the estimated range that the errors is reduced is used. Reducing the errors is possible by using the odometry velocity that is relatively accurate and is independent of range measurement. The robustness and accuracy of the proposed algorithm is verified by a mobile robot with real-time positioning and trajectory control under large range errors which are occurred randomly.
Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning

Sensory processing is increasingly conceived in a predictive framework in which neurons would constantly process the error signal resulting from the comparison of expected and observed stimuli. Surprisingly, few data exist on the accuracy of predictions that can be computed in real sensory scenes. Here, we focus on the sensory processing of auditory and audiovisual speech. We propose a set of computational models based on artificial neural networks (mixing deep feedforward and convolutional networks), which are trained to predict future audio observations from present and past audio or audiovisual observations (i.e., including lip movements). Those predictions exploit purely local phonetic regularities with no explicit call to higher linguistic levels. Experiments are conducted on the multispeaker LibriSpeech audio speech database (around 100 hours) and on the NTCD-TIMIT audiovisual speech database (around 7 hours). They appear to be efficient in a short temporal range (25–50 ms), predicting 50% to 75% of the variance of the incoming stimulus, which could result in potentially saving up to three-quarters of the processing power. Then they quickly decrease and almost vanish after 250 ms. Adding information on the lips slightly improves predictions, with a 5% to 10% increase in explained variance. Interestingly the visual gain vanishes more slowly, and the gain is maximum for a delay of 75 ms between image and predicted sound.
Intersection Dynamic Signal Control Method based on Road-segment Characteristics and Time-period Characteristics

The distribution of traffic flow at intersection is an important basis for traffic signal control, based on the characteristics of time and the characteristics of road segments that affect the dynamic changes of traffic flow, this paper proposes an adaptive control strategy for road traffic lights. Firstly, neural network technology is applied to traffic flow state analysis, scientifically define traffic flow patterns based on road-segment information and completes fast and accurate short-term traffic flow distribution state identification online. Secondly, according to the traffic distribution status of intersections, the signal design process is proposed to improve the traffic signal control efficiency. Finally, the verification of the actual traffic flow of Jingming South Road and Juxian Street in Kunming shows that the adaptive traffic signal control method proposed in this paper can effectively reduce intersection delay, number of stops and queue length.
Deep Packet Inspection Through Virtual Platforms using System-On-Chip FPGAs

Virtual platforms provide a full hardware/software platform to study device limitations in an early stages of the design flow and to develop software without requiring a physical implementation. This paper describes the development process of a virtual platform for Deep Packet Inspection (DPI) hardware accelerators by using Transaction Level Modeling (TLM). We propose two DPI architectures oriented to System-on-Chip FPGA. The first architecture, CPU-DMA based architecture, is a hybrid CPU/FPGA where the packets are filtered in the software domain. The second architecture, Hardware-IP based architecture, is mainly implemented in the hardware domain. We have created two virtual platforms and performed the simulation, the debugging and the analysis of the hardware/software features, in order to compare results for both architectures.
An Inductive Power Transfer System With Adjustable Compensation Network For Implantable Medical Devices

A reliable wireless power transfer (WPT) system with constant voltage output is necessary for power charging of implantable medical devices (IMDs). This paper proposes a single-loop WPT system with an adjustable compensation network in which the capacitor array can be altered. It can provide constant voltage supply without regulating circuit in the receiver, which benefits for IMDs. A prototype is built up and the measurement results validate the effectiveness of the proposed method. Typically, this system works properly at various values of load resistance and coupling coefficient. The measurement results show that when the coils' distance ranges from 32 mm to 40 mm and the load resistance varies from 100 Ω to 500 Ω, the capacitor array can be adjusted automatically to offer the load almost constant voltage at 3.4 V.
Perspective Transformation Data Augmentation for Object Detection

One major reason for the success of convolutional neural networks (CNNs) is the availability of large-scale labeled data. Effective training of CNNs relies on large annotated data. Unfortunately, large amounts of data with corresponding annotations are too expensive to obtain in some real-world applications. One reasonable alternative is to use data augmentation techniques to automatically generate annotated samples. In this paper, a novel data augmentation framework based on perspective transformation is proposed. This method automatically generates new annotated data without extra manual labeling, thus effectively extends the inadequate dataset. Perspective transformation can produce new images captured from any cameras viewpoints. Therefore, our method can mimic images taken at the angle that the camera cannot reach. Extensive experimental results on several datasets have demonstrated that our perspective transformation data augmentation strategy is an effective tool when using deep CNNs on small or imbalance datasets.
A Resource Allocation Scheme Based on Least Squares Support Vector Machine and Particle Swarm Optimization Algorithm

In this paper, a classification evaluation model is designed by least squares support vector machine (LS-SVM). And then, the particle swarm optimization algorithm (PSO) is used to construct the resource allocation scheme. Finally, we solve a reasonable resource development problem according to the existing school evaluation standards. This paper takes the schools in Xi'an as an example to expound the present situation of basic resource allocation in schools.
Investigating Soft Skills Development through Peer Reviews Assessments in an Entrepreneurship Course

In this paper we present an approach to stimulate the development of soft skills in tertiary education students, which are seen as critical skills for active citizenship, personal fulfillment and employability in a knowledge-driven society. We developed an online learning setting where technical graduate students practice and gain knowledge on Innovation and Entrepreneurship themes. We present the early results of a peer assessment exercise where students submit assignments and evaluate other assignments made by their colleagues. We employ textual similarity measures and other effort related metrics to understand different learner types emerging in the exercise. Our results foster future directions of research on how soft skills can be studied in online environments. This study contributes to the ongoing debate on how to understand and visualize data coming from online courses and how to further the students’ assessment of soft skills development.
Generate Modern Chinese Poems from News Based on Text Style Transfer Using GAN

In this paper, we investigate techniques that can transfer a news story into a poem. We train cycle-GAN that can conduct text style transfer from news style to poem style even lack of parallel corpus. We compare teacher forcing and free-running modes of training as well as different attention mechanisms in the GAN and cycle-GAN architectures. We found that there is a trade-off between degree of style transfer and content preserving that can be controlled by the ratio of reconstruction and transfer using different training modes of the discriminator and the generator. We show that both GAN and cycle-GAN can be trained to convert news into poems to some extent using non-parallel corpus.
Enhancing Tracking Accuracy with Exploitation of Mobile Unit Orientation and Antenna Pattern

Localization of Mobile Unit (MU) is essential in many applications. Outdoor localization has an accuracy that degrades rapidly with scattered dynamic environment and in indoor environment, and sometimes due to lack of GPS connectivity. Current methods for tracking are based on time of flight or power measurements and usually are split into range estimation from fixed anchor points, and then aggregation of the range estimation to estimate the MU location. The antenna pattern of the MU is assumed erroneously as non-directional (isomorphic). Recently, with more accurate inertial sensor available in MUs, the mobile node orientation can be estimated. In this work, we suggest to enhance wireless tracking performance by exploitation of the instantaneous MU’s orientation as perceived by its inertial sensor and prior knowledge about the MU’s antenna directionality. We develop a CRLB bound for the directional case, in relation with the common Omni assumption, and incorporate into the solution different error sources. Then we suggest directions for implementation scheme of the new technology.
Acoustic Emissions Sensor and Fuzzy C-mean Clustering Based Break Detection in Post-Tensioning Tendons

Steel tendons have a vital role in prestressed concrete members and the stability of the members depends on tendons. The steel tendons corrode over time or when encased in grouts with excessive levels of chloride. The steel tendons are pre-tensioned or post-tensioned to 80 percent of their ultimate tensile strength, and significant corrosions can increase tensile stress leading to tendon breakage. For the purpose of safety and maintaining schedule, it is critical to develop a wire break monitoring methods for post-tensioning bridges. In this work, Fuzzy C-means clustering technique was employed to detect acoustic emissions released from breaking wires of post-tensioning steel tendons bounded in grout. To collect acoustic emissions of wire break and grout cracks, a constant rate tensile loads were applied to tendons embedded in grouts until a wire broke, and piezoelectric transducers attached to the tendons were used pick up the released acoustic signals. To improve the robustness of the clustering method, environmental acoustic noises were collected from a bridge and added to the database of the tensile tests. Wire break detection using acoustic emissions and Fuzzy C-mean clustering achieved 100 percent accuracy in detecting wire breaking acoustic signals while the acoustic signals of grout cracks and environmental noises were not detected as a wire break.
Modeling and Optimization of Vortex Modes Propagation in Rectangular Dielectric Waveguides

We study analytically and numerically the possibility of vortex modes propagation over planar dielectric rectangular waveguides, and consider the problem of waveguide geometry optimization for the support of vortex modes. The results show, that theoretically rectangular waveguides can provide transmission of quasi-TE and quasi-TM modes with high purity states of orbital angular momentum (OAM) in the dominant field component. However, only for the quasi-generate mode of azimuthal order ±1 the constituent eigenmodes can propagate in a phase-matched regime, and the vortex modes of higher azimuthal orders can propagate only with a certain beat length. Moreover, as the target azimuthal order increases, the normalized power of the corresponding OAM state in the modal superposition decreases. The analytical predictions have been verified by numerical electromagnetic simulations of silicon nitride waveguides providing field distributions and OAM spectra of the corresponding modal superpositions.
Characterization of Coated Piezo-resistive Fabric for Respiration Sensing

Electro-resistive band (ERB) or conductive elastomer can be also used as non-invasive bio-potential sensors. This stretchable conductive material gained acceptance in health monitoring systems due to their low cost, low power consumption and high flexibility. ERBs can be polarized with a current source to generate a voltage signal directly proportional to the change of length. However, the sensors require close contact with the patient's skin to monitor bio-mechanical movements adequately. Direct contact with human skin causes inconsistent current leakage and result in low SNR. Isolating the ERB is important to avoid current leakage, provide protection by overstretching and easiness of wearing. However, not all stretchable material suitable to use as ERB substrate. In this work, we have compared two different conductive elastomers (rubber and fabric) stretched using a breathing simulator machine to compare the material performance. We have coated the ERBs with 3 different coating materials: Pinkysil, Transil and silicone tape, as well as raw bands. We have processed and analyzed all the collected signals using a wide range of measurements and calculations such as linearity, baseline change, Total Harmonic Distortion (THD), Signal to Noise Ratio (SNR), correlation coefficient and phase difference. We have discov-ered from the final results that the silicone tape serves as a good isolator material and shows reliable and consistent results. The conductive rubber also shows less noise and distortion across stretching in shorter stretch applications compared to the conductive fabric.