Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive Transmit Nulling Beamforming via Manifold Optimization Approach

In this paper, we deal with the design of the constant modulus weight vector in an effort to achieving a desired transmit beampattern with the nulling in the spatial of interest. The design model is formulated accounting for maximizing the ratio of the mainlobe level to the weighted integrated sidelobe level in the presence of the constant modulus restriction. To handle the resulting non-convex (in general NP-hard) problem, an iterative approach resorting to the complex circle manifold is proposed. By deriving Riemannian gradient and retraction operator, each iteration of the proposed algorithm invokes the Broyden-Fletcher-Goldfarb-Shanno (BFGS) in complex circle manifold instead of Euclidean space to ensure the monotonic increase of the objective function. Finally, numerical examples are provided to verify the capability of manifold optimization approach over some counterparts.
Engaging Females with a Model for Affective Storytelling

Female gamers have been underrepresented in some game genres. One of the reasons for this is the gender difference in players' emotional experience. According to psychological studies, females are more likely to be concerned with other's emotions and tend to involve their emotions in the storytelling by empathising with the characters and experiencing other-directed emotions [12]. Given these considerations, this paper proposes a model for affective storytelling which aims to create an engaging emotional experience and involve the player's emotion with the characters. More importantly, the player's emotions, evoked by appraising the story and empathizing with the characters, determine the character's behaviors and therefore have an impact on the whole storyline. Since the model for affective storytelling aims to create an engaging emotional experience for the player with-out following a goal-oriented narrative approach, it appears that males and females might have a different user experience in this model for affective storytelling. Future work therefore will be carried out to develop an affective storytelling system and test if females are more engaged and satisfied with the affective story-telling system.
Deep Multi-task Cascaded Acoustic Echo Cancellation and Noise Suppression

With the growing need for online communication, removing the acoustic echo and background noise during voice and video calls has become a major problem. Recent studies show that deep learning based algorithms can successfully be applied to acoustic echo cancellation. These algorithms usually use one mask to remove acoustic echo and noise at the same time. Considering the patterns of acoustic echo and noise are different, hence we propose a multi-task cascaded framework with multiple masks named DMC-AEC to ease the difficulty of removing different interference, i.e., acoustic echo and noise. The DMC-AEC consists of three cascaded blocks, each block containing one mask. The first block takes mic and far-end signals to learn the auxiliary task of estimating the echo. The second block utilizes the estimated echo, far-end and mic signals to cancel acoustic echo. And in the third block, it takes the output of the second block and further suppresses noise. The DMC-AEC is trained on a synthetic dataset of ICASSP AEC Challenge.
A Low-Power Counter-based Digital CDR

This paper presents a counter-based digital CDR. For frequency acquisition, frequency direction is determined by comparing the number of edges of input data and recovered data. And for fine frequency acquisition, FLL gain is reduced by counting the number of times as the direction signal changes. The proposed digital CDR is designed and simulated in a 28nm CMOS technology and consumes 6.5mW in 1V supply voltage with 10Gb/s input data.
Accelerating Numerical Inverse Kinematics Methods with the Lookup Table

Inverse kinematics (IK) for a robotic arm solves for the joint variables to move its end-effector to a target position with an optional orientation. IK can be solved by iterative numerical methods. However, as the degree-of-freedom (DoF) of the robotic arm increases or the applications impose extra constraints, the iterative process will be lengthened dramatically. In this paper, we study the idea of using a lookup table to accelerate the numerical IK methods. Given a target position, the table outputs a joint configuration that can move the end-effector to a position near the target, from where the numerical method can start the iterative process. This significantly decreases the number of iterations and the time in solving IK. In addition, extra constraints of the applications can be checked after table lookup, instead of “formulating into” the numerical formulations. This further simplifies the formulations and reduces the computation complexity. The proposed lookup table only needs to be built once when the robotic arm is developed and can be coupled with any numerical IK methods. Experiments based on the IKpy package show that the proposed method can accelerate different numerical IK methods up to 33% if only end-effector position is required and to 70% if both position and orientation are required.
Attention-based Mechanism of LSTM network for Speech Emotion Recognition

The context of speech signals has certain relevance, and a long short-term memory (LSTM) model that introduces an attention mechanism is proposed to address the problems of insufficient relevance of feature information and low recognition rate in traditional speech emotion recognition (SER) research. Using the Mel-Frequency Cepstral Coefficient (MFCC) extracted features as the input of the long and short-term memory network, the LSTM model can learn the long period contextual information and improve the utilization of data, while the embedded attention mechanism can effectively capture the temporal features of speech signals and get the distinguished emotion features, and accurate classification of emotion with the help of softmax function. This experiment is conducted on a self-built electric power customer service corpus, and the average recognition rate is 66%. Comparing the results of several sets of experiments, the introduction of attention mechanism makes the model have better recognition performance, and this model also has good recognition rate on the self-built database.
Matching of Scattering Centers Based on 3-D Scattering Center Model with Application to SAR ATR

This letter proposes a scattering center matching method based on 3-D scattering center model with application to synthetic aperture radar (SAR) automatic target recognition (ATR). The 2-D scattering centers predicted by 3-D scattering center model are matched with the extracted scattering centers from the test image using a neighbor matching algorithm. The matching result is quantitatively evaluated by five indexes. The neighbor matching efficiently builds robust correspondences between the model-predicted and extracted scattering center sets. And the five indexes comprehensively evaluate the similarity between the two scattering center sets. Afterwards, a one-against-one voting strategy is performed based on the five indexes for target recognition. To evaluate the effectiveness and robustness of the proposed method, experiments are conducted on the simulated data of three ground targets by electromagnetic (EM) code under various conditions.
Moving Target Tracking Based on Shadow with Unsupervised Deep Track in Video-SAR

The moving target is always defocused and shifted out of the scene in image sequences in video Synthetic Aperture Radar (video-SAR), which makes moving target tracking be a formidable challenge. In this paper, a new moving target tracking method based on shadow with unsupervised deep track (UDT) is proposed. Our track network is built on the Siamese network framework and is trained on a series of unlabeled SAR image sequences with unsupervised learning way. Meanwhile, we use the trajectory detection technology to get more accurate tracking results. Finally. , the processing results of the real data are provided to verify the effectiveness of the proposed method.
Depressive Tendency Recognition by Fusing Speech and Text Features: A Comparative Analysis

Depression will be accompanied by long-term depression, loss of interest, excessive guilt, and other states, seriously affecting people’s physical and mental health, causing certain harm to the individual family and society. Depressed people speak slowly, single tone, and the pause time is longer. At the same time, the expression of emotion is often accompanied by many negative words. Therefore, the combination of speech and text information using Gated Recurrent Neural Network for depression prediction, this fusion method from the signal layer to the language layer to analyze the data. It is more comprehensive than only use a single speech feature or text feature model. At the same time, the comparative analysis of different speech types, three kinds of emotional stimulus corpus, and gender was carried out. The multimodal system is more effective than a single modality, and speech type, emotional stimulus, and gender have certain effects on depressive recognition.
A New Feature Extraction Method Based on Micro-Doppler Signature of Ground Moving Targets

Since some discriminative features are required for ground moving targets classification, we propose a new feature extraction method based on micro-Doppler signature. Firstly, the time-frequency analysis of measured data indicates that the time-frequency spectrograms of the three kinds of ground moving targets, i.e., single walking person, two people walking and a moving wheeled vehicle, are discriminative. Then, a 3-dimensional time-frequency feature vector is extracted from the time-frequency spectrograms to depict these differences. At last, a Support Vector Machine (SVM) classifier is trained with the proposed 3-dimensional feature vector. The classification accuracy to categorize ground moving targets into the three kinds on the measured data is found to be over 96%, which demonstrates the good discriminative ability of the proposed micro-Doppler feature.
$L_{1/2}$-norm Regularization Based SAR Interferogram Generation

Synthetic aperture radar interferometry (InSAR) is a key technology developed in recent years. It extends two-dimensional synthetic aperture radar (SAR) imaging to a higher dimension by obtaining digital elevation model (DEM). It is known that high-quality DEM depends on the well processed interferogram. However, typical phase noise reduction technologies such as various filters will cause a decrease of image spatial resolution. To solve this problem, in this paper, an $L_{1/2}$-norm regularization based InSAR processing algorithm is proposed and introduced to generate the high-quality interferogram. In the proposed method, we first construct the model of interferogram generation procedure based on the relationship between two used images and discrete wavelet transform (DWT). Then the interferogram will be recovered by solving an $L_{1/2}$-norm regularization problem. Compared with typical matched filtering (MF) image based result, the interferogram reconstructed by the proposed method has better performance, especially less phase noise and reduced number of residue. Experimental results based on simulated and real data validate the proposed method.
MOOCs Learning Pathways Recommendation Based on Sequence Alignment

In MOOC education, users often have difficulty understanding courses due to insufficient prerequisite knowledge, which makes it easy to give up learning. Therefore, when recommending MOOCs to users, they also want to be told in what order these courses should be taken. However, the learning pathways of courses generally need to be determined by professionals, which is lacking in online education such as MOOC. In this paper, we propose a MOOCs learning pathways recommendation method based on sequence alignment, which runs without any prior knowledge from professionals. The method analyzes historical behavior data of users in the learning platform, obtains the common choice of multiple users on the order of course learning through sequence alignment, and constructs a graph of MOOCs learning pathways. We verify that our work is useful by conducting experiments based on real data from an online learning platform, and the results show that the learning pathways recommended by our method are in line with experts' teaching experience.
TK-BERT: Effective Model of Language Representation using Topic-based Knowledge Graphs

Recently, the K-BERT model was proposed to add knowledge for language representation in specialized fields. The K-BERT model uses a knowledge graph to perform transfer learning on the pre-trained BERT model. However, the K-BERT model adds the knowledge that exists in the knowledge graph rather than the data relevant to the topic of the input data when using the knowledge graph of the corresponding field. Hence, the K-BERT model can cause confusion in the training. To solve this problem, this study proposes a topic-based knowledge graph BERT (TK-BERT) model, which uses the topic modeling technique. The TK-BERT model divides the knowledge graph by topic using the knowledge graph's topic model and infers the topic for the input sentence, adding only knowledge relevant to the topic. Therefore, the TK-BERT model does not add unnecessary knowledge to the knowledge graph. Moreover, the proposed TK-BERT model outperforms the K-BERT model.
OFDM Chirp Waveform Design for Clutter Suppression

Orthogonal frequency division multiplexing (OFDM) chirp waveform has attracted much attention for its excellent characteristics such as large time-bandwidth product, high Doppler tolerance, low range-Doppler coupling and high degree of freedom in waveform design. To suppress clutter more effectively from the transmitter of radar system, a method of designing the OFDM chirp waveform based on the maximum output signal-to-clutter-plus-noise ratio (SCNR) is proposed in this paper. First, the relationship between the optimal energy spectral density (ESD) corresponding to the maximum SCNR and the waveform parameters of OFDM chirp is analyzed. Then, the OFDM chirp waveform design with sub-bandwidths diversity is presented. Last, the designed waveform is analyzed and validated by numerical results. The simulation results demonstrate that the designed waveform has an output SCNR and ESD close to the optimal values so as to suppress clutter in frequency domain.
Ionospheric Clutter Image Segmentation Method of High Frequency Surface Wave Radar Based on the Improved Deeplab Network

High Frequency Surface Wave Radar(HFSWR) can achieve over-the-horizon detection, which effectively detects and tracks the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to resolve the automatic detection of ionospheric clutter that uses the Range-Doppler spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional segmentation methods that require a large amount of a priori experience.
A joint spatial-polarization adaptation method for DOA estimation under mainlobe jamming

Aiming at the DOA estimation problem under mainlobe jamming, a joint spatial-polarization adaptation method with polarization sensitive array is proposed in this paper. First, in order to suppress the mainlobe jamming, each row (or column) of array data is adaptively processed in spatial-polarization domain to obtain a column (or row) of adaptive channel data. Then, the maximum likelihood algorithm is used to estimate the elevation (or azimuth) of target. Numerical simulations are performed to demonstrate the effectiveness of the proposed method.
The Importance of Medical Robots in Improving Healthcare Services

Medical robots and computer-assisted therapy both play a crucial role in Information and Communication Technology (ICT) and mechatronics. In fact, ICT technology has made a significant impact on the healthcare industry during the past three decades by introducing new methods for maintaining a patient's therapy. As surgical navigation has been adopted, particularly in minimally invasive surgery, medical robotics is a development of exam and professional robots as well as other technologies. The majority of existing and future uses and applications of robotics in healthcare are briefly summarized in this article. The Healthcare industry is growing every year and the need for investment in that field also increasing. Owing to the development in the healthcare sector, the need for medical robots came to improve healthcare services with their different applications. Medical robots play an essential role in developing healthcare and have proven their efficiency
VICSORT - A Virtualised ICS Open-source Research Testbed

Industrial Control Systems (ICS) are at the forefront of most, if not all the critical infrastructure and critical service delivery. ICS underpin modern manufacturing and utility processes and greatly contribute to our day-to-day livelihoods. However, there has been a significant increase in the number and complexity of cyberthreats specifically targetted at ICS, facilitated by increased connectivity in an effort to improve production efficiency. Furthermore, the barriers of entry to ICS cybersecurity are still high given the limited skills base, expensive and proprietary hardware and software as well as the inherent dangers of manipulating real physical processes. This greatly inhibits the practical application of cybersecurity tools in ICS environments and therefore the opportunity for practitioners to gain valuable ICS cybersecurity experience. ICS Testbeds are often either expensive and are not necessarily holistic enough to provide learners with the complete breadth of ICS. This paper introduces VICSORT, a open-source virtualised ICS testbed that provides a platform for ICS cybersecurity learners and practitioners to interface with an ICS environment that closely emulates a real-world ICS, as well as explore and practice techniques for attack and consequently defence of an ICS. VICSORT builds upon the Graphical Realism Framework for Industrial Control Systems (GRFICS) to offer an easier to deploy environment with greater flexibility, whilst requiring significantly less resources all reducing the cost to the learner.
TomoSAR 3D Focusing Method Via Linear Error Estimation Based on Mulipass Subimage Registration

In this paper, a high-precision linear error estimation method based on subimage registration is proposed, to solve the problem that the linear error between tracks affects the focusing effect of multipass TomoSAR. Firstly, establish a multipass TomoSAR echo structure, analyze the effects of linear errors between different tracks on two-dimensional imaging algorithm. Secondly get the image offset between the sub-images to rebuild the trajectory. Finally use of the tomographic three-dimensional imaging algorithm to process the echo, and introduced a three-dimensional auto-focusing algorithm to remove residual motion errors. The effectiveness of the method is verified by simulation experiments.
Automation Framework for Digital Circuit Design and Verification

Due to the demands for various custom digital circuitry, it is very important to reduce the effort of circuit design in the semiconductor industry. The automation framework proposed in this paper can greatly reduce unnecessary design effort in general digital circuits while maintaining the power and performance. The area, time, and power of tri-state inverter-based D Flip-Flop designed using the layout automation and hand-drawn were measured with the Python-based measurement automation tool developed for digital circuits. And by comparing and analyzing these results, the performance and limitations of the automation framework were confirmed, and conclusions were drawn comprehensively.
Secrecy transmission of NOMA-OTFS based multicast-unicast streaming

This paper investigates the security performance of a cooperative multicast-unicast system, where the users present the feature of high mobility. Specifically, we develop the non-orthogonal multiple access (NOMA) based orthogonal time frequency space (OTFS) transmission scheme, namely NOMA-OTFS, in order to combat Doppler effect as well as to improve the spectral efficiency. Further, we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming. Based on that, we utilize the relay selection strategy, to improve the security of unicast streaming. In the context of multicast-unicast streaming, our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission, which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.
Modified Demand Response Market for Carbon Emission Control

Low-carbon guidance on the demand side of the power system is helpful to reduce the carbon emission of the power system. The existing demand-side market lacks an effective guidance mechanism for carbon emission reduction. This paper modifies the existing demand response market by introducing carbon emission control, including the carbon emission constraint and the CO2 price from the carbon market. The OPF-based acquisition of marginal emission factors is used to quantify the carbon emission reduction. Case studies show that the modified demand response market can not only achieve demand-side carbon reduction, but also increase the surplus of demand response resources. The introduction of the CO2 price can reduce the payment from the market to demand response resources, which provides a viable source of funding to facilitate the participation of demand response.
Text-Informed Knowledge Distillation for Robust Speech Enhancement and Recognition

Most existing speech enhancement (SE) approaches heavily depend on simulated data for training, leading to performance degradation on realistic data and subsequent speech recognition task. One of the main reasons is that SE models cannot be trained on real data due to the absence of reference signals. In this paper, we aim to tackle this problem by exploiting transcribed real data to mitigate the mismatch between training and evaluation. A text-informed SE teacher is first trained to provide “reference” signals for the transcribed real data. Then a SE student is trained on both simulated and real data, where the supervision comes from the simulated ground truth and the teacher, respectively. Finally, a speech recognition model is trained on enhanced signals from the SE student. Our experimental results show that the proposed method can not only improve the speech enhancement performance, but also reduce the word error rate on the downstream speech recognition task.