Miracle-dz/UniFAR-FTUs huggingface dataset:
MapSegNet: A Fully Automated Model Based on the Encoder-Decoder Architecture for Indoor Map Segmentation

Image segmentation in robotics is an ongoing research field in which neural networks have shown promising performance. In this paper, we introduce MapSegNet, a deep convolutional neural network for indoor map segmentation, which is able to segment the indoor maps into smaller units, including rooms, corridors, windows, and furniture. The proposed model consists of an encoder phase to capture context and a corresponding decoder phase to increase the resolution of feature maps to the original resolution. A design for skip connections is introduced with fused multi-scale feature maps between the encoder and the decoder phases. The proposed skip connection increases the flow of information between the phases and improves the model generalization. We evaluate empirical studies based on abstract maps and detailed maps. While abstract maps include empty rooms and corridors, the detailed maps contain indoor space with more objects such as furniture. We investigate the effectiveness of the proposed method by employing various indoor maps and compare its performance with similar neural network models on multiple datasets. The results show that the proposed model is able to achieve more accurate recognition or lower computation cost compared to other state-of-the-art segmentation techniques.
A 290 GHz Low Noise Amplifier Operating above $f_{max}/2$ in 130 nm SiGe Technology for Sub-THz/THz Receivers

This paper presents the design of a low noise amplifier (LNA) operating at center frequency 290 GHz in 130 nm SiGe BiCMOS technology with $f_{t}/f_{max}$ of 300 GHz/450 GHz. The LNA consists of four stages of pseudo-differential cascode topology. Each stage is tuned and matched at different resonant frequency to obtain broadband frequency response around center frequency. This LNA provides 12.9 dB of gain at center frequency 290 GHz and 11.2 dB at 300 GHz. The 3-dB bandwidth is measured to be 23 GHz and simulated noise figure is 16 dB. The LNA draws 68 mA current from 2V supply. It shows the potential of silicon technologies to operate as high as $2/3(f_{max})$ with decent gain and linearity at 300 GHz range. To the authors' knowledge, this LNA achieves, without any gain-boosting technique, the highest gain at $2/3(f_{max})$ in SiGe technology.
Deep-Learning-Based Registration of Diagnostic Angiogram and Live Fluoroscopy for Percutaneous Coronary Intervention

Percutaneous coronary intervention (PCI) is an effective treatment for increasing blood flow in narrowed coronary arteries by implanting stents. When inserting a guidewire during PCI, a cardiologist watches two X-ray videos, a cine loop of angiography and a real-time video of live fluoroscopy to navigate the guidewire to the target location while avoiding entering wrong branches of blood vessels. However, watching the two separate X-ray videos increases a cardiologist’s mental efforts and intervention time. Moreover, more contrast agents may need to be injected to validate the correct insertion. To address this problem, dynamic coronary roadmapping (DCR) has been suggested because it provides registered images of two different X-ray images. In this study, we propose a novel patient-specific deep-learning-based approach for DCR that uses only two types of X-ray images without using an electrocardiogram (ECG). In the proposed approach, a trained deep generative model generates a registered image from the guidewire images in live fluoroscopy and angiographic sequence from diagnostic coronary angiography. Because the proposed approach can implicitly compensate for both cardiac and respiratory motions, it can be applied to arrhythmia patients with irregular ECG signals, unlike conventional image registration through ECG. Qualitative evaluation of 34 PCI cases, including three irregular heartbeat cases, showed low registration errors (26% lower than that of the latest work) and high image quality (more than 30 dB peak signal-to-noise ratio (PSNR)). All registration results were classified as “fit for use” (70.6% “Good” and 29.4% “Acceptable”) by three cardiologists. Furthermore, three irregular beating cases were classified as having “Good” registration quality.
Towards Power Efficient 6G Sub-THz Transmission

Extreme high rate demands for 6G can be addressed by communication technologies in the millimeter wave and Terahertz bands. In these high carrier frequencies, power consumption can become a key limiting factor and thus has to be assessed thoroughly. This paper considers the power consumption of the RF frontend of a transceiver for the use in a hot spot scenario. A power consumption model for the main functional blocks has been derived for different frequencies and array architectures, both at transmit and receive side. The impact of the EIRP reduction over the frequency range from 90 GHz up to 230 GHz due to increased path loss and reduced output power of the power amplifiers has been assessed and the related power consumption has been evaluated. Further, the impact of analog-to-digital converters on the overall power consumption of the receive part have been carved out. Key dominating components have been identified indicating the number of antenna elements needed for different architectures and the related power consumption.
Kernel Joint Non-Negative Matrix Factorization for Genomic Data

The multi-modal or multi-view integration of data has generated a wide range of applicability in pattern extraction, clustering, and data interpretation. Recently, variants of the Non-negative Matrix Factorization (NMF), such as joint NMF (jNMF), have allowed the integration of data from different sources and have facilitated the incorporation of prior knowledge such as the interactions between variables from different sources. However, in both NMF and jNMF, the factorization is carried out as a linear system, which does not identify non-linear patterns present in most real-world data. Therefore, we propose a new variant of jNMF called Kernel jNMF. This new method incorporates the factorization of the original matrices into a high-dimensional space. Applying our method to synthetic data and biological cancer data, we found that the method performed better in clustering and interpretation than the jNMF methods.
Identification and Classification of Incipient Discharges in GIS Adopting Machine Learning Techniques

In the present study, the PD due to particle movement, surface discharge, floating particle and corona defects in GIS are detected by adopting UHF sensor. The FFT analysis of UHF signal for all four defects shows the dominant frequency around 1GHz. To identify and classify the various PD source in GIS, multi classifier machine learning techniques are adopted. The spectral data of the obtained UHF PD signals for each of the four types of discharge signals were obtained after performing the Fast Fourier transform of the UHF signals frequency data. Random Forest and K Nearest Neighbour classifiers are adopted in the present study for PD classification. In order to improve the performance of RF classifier, the inbuilt hyperparameters are optimized for a better performance and accuracy, using a technique called Random Search CV. The modified Random Forest classifier showed significant improvement in the classification accuracy.
Interval Analysis of Group Poisson Models of Video Traffic Streams

The algorithms of streaming video traffic control are considered. The peculiarities of video codec traffic, which has a clearly defined batch character, are analyzed. The characteristics of such flows and their impact on the size of queues and delays in telecommunication network nodes are examined. A generalized Khinchin-Pollaczek formula is given and the linear dependence of the numerator of the given formula on the load factor is shown. The main causes of packet delays in telecommunication network queues are considered, and the impact of these delays on video traffic flow control processes is shown. A video stream control algorithm based on measuring buffer memory sizes in client equipment is proposed. It is shown that such an algorithm eliminates the impact of delays on the feedback loop of the control system. The results of simulation modeling of the processes under consideration are given.
Deep Learning Approach for Negation Handling in Sentiment Analysis

Negation handling is an important sub-task in Sentiment Analysis. Negation plays a significant role in written text. Negation terms in sentence often changes the polarity of entire sentence from positive to negative or vice versa, resulting in the opposite meaning of the sentence than what is observed by the machine learning based linguistic model. As automatic opinion mining has become very important in this digital era, proper handling of negation term is the need of the hour. In any natural language negations can be formulated both explicitly or implicitly while their use is very much domain-specific. Existing negation handling techniques follow rule-based approach and mainly used in medical domain. Due to the complex syntactic structure of negation, it is hard to build general purpose machine learning based negation handling model on user review or conversational text data. In this paper, we investigate negation components i.e., cue and scope in a sentence which determine the polarity shift in sentence. We propose LSTM based deep neural network model for negation handling task where the model automatically learns the negation features from labeled input training dataset. We used ConanDoyle story corpus for model training and testing, which is pre-annotated with negation information. The proposed model first identify negation cues in each sentence and then using bidirectional LSTM extracts the relationship between cue and other words to identify scope of the cue in sentences. We derived word level features for model training to determine correct polarity of the sentence. Result shows that the LSTM based nonlinear language models perform comparatively better than the traditional state of the art SVM, HMM or CRF based models. BiLSTM achieved best result, F1 measures 93.34%, outperform traditional rule based model in negation handling task.
Byzantine-resilient distributed learning under constraints

We consider a class of convex distributed statistical learning problems with inequality constraints in an adversarial scenario. At each iteration, an $\alpha$-fraction of $m$ machines, which are supposed to compute stochastic gradients of the loss function and send them to a master machine, may act adversarially and send faulty gradients. To guard against defective information sharing, we develop a Byzantine primal-dual algorithm. For $\alpha\in[0,0.5)$, we prove that after $T$ iterations the algorithm achieves $\tilde{\check{O}}(1/T+1/\sqrt{mT}-+\alpha/\sqrt{T})$ statistical error bounds on both the optimality gap and the constraint violation. Our result holds for a class of normed vector spaces and, when specialized to the Euclidean space, it attains the optimal error bound for Byzantine stochastic gradient descent.
Reinforcement learning based dynamic spectrum access in cognitive Internet of Vehicles

Cognitive Internet of Vehicles (CIoV) can improve spectrum utilization by accessing the spectrum licensed to primary user (PU) under the premise of not disturbing the PU's transmissions. However, the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments. In this paper, a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum, and avoid causing harmful interference to the PU. The frame structure of the CIoV is separated into sensing period and access period, whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period. Considering both detection probability and false alarm probability, a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel, bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance. The simulations have shown that compared with the traditional non-learning spectrum access algorithm, the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.
Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

The data-driven model predictive control (DDMPC) scheme is proposed to obtain fast convergence to a desired reference and to be utilised to mitigate the destabilising effects that a DC-to-DC buck-boost converter (BBC) with an active load experiences. The DDMPC strategy uses the observed state to derive an optimal control policy using a reinforcement learning (RL) algorithm. The employed Proximal Policy Optimisation (PPO) algorithm’s performance is benchmarked against the PI controller. From the simulated results obtained using the MATLAB Simulink solver, the most robust methods for short settling time and stability were the hybrid methods. These methods take advantage of the short settling time provided by the PPO algorithm and the stability provided by the PI controller or the filtering mechanism over the transient time. The source code for this study is available on GitHub to support reproducible research in industrial electronics society.
Making Multi-String Pattern Matching Scalable and Cost-Efficient with Programmable Switching ASICs

Multi-string pattern matching is a crucial building block for many network security applications, and thus of great importance. Since every byte of a packet has to be inspected by a large set of patterns, it often becomes a bottleneck of these applications and dominates the performance of an entire system. Many existing works have been devoted to alleviate this performance bottleneck either by algorithm optimization or hardware acceleration. However, neither one provides the desired scalability and costs that keep pace with the dramatic increase of the network bandwidth and network traffic today. In this paper, we present BOLT, a scalable and cost-efficient multi-string pattern matching system leveraging the capability of emerging programmable switches. BOLT combines the following two techniques, a smart state encoding scheme to fit a large number of strings into the limited memory on the programmable switch, and a variable k-stride transition mechanism to increase the throughput significantly with the same level of memory costs. We implement a prototype of BOLT and make its source code publicly available. Extensive evaluations demonstrate that BOLT could provide orders of magnitude improvement in throughput which is scalable with pattern sets and workloads, and could also significantly decrease the number of entries and memory requirement.
A CMOS LiDAR Sensor with Pre-Post Weighted-Histogramming for Sunlight Immunity Over 105 klx and SPAD-based Infinite Interference Canceling

This paper presents a CMOS LiDAR sensor with high background noise (BGN) immunity. The sensor has on-chip pre-post weighted histogramming to detect only time-correlated time-of-flight (TOF) out of BGN from both sunlight and exponentially increased dark noise while enhancing sensitivity through higher excess voltage (Vex) of SPADs. The sensor also employs a SPAD-based random number generator (SRNG) for canceling interference (IF) from an infinite number of LiDARs. The sensor shows 8.08 cm accuracy for the range of 32 m under high BGN (105 klx sunlight and 48.72 kcps dark-count rate with increased Vex).
PostMan: Rapidly Mitigating Bursty Traffic via On-Demand Offloading of Packet Processing

Unexpected bursty traffic brought by certain sudden events, such as news in the spotlight on a social network or discounted items on sale, can cause severe load imbalance in backend services. Migrating hot data—the standard approach to achieve load balance—meets a challenge when handling such unexpected load imbalance, because migrating data will slow down the server that is already under heavy pressure. This article proposes PostMan, an alternative approach to rapidly mitigate load imbalance for services processing small requests. Motivated by the observation that processing large packets incurs far less CPU overhead than processing small ones, PostMan deploys a number of middleboxes called helpers to assemble small packets into large ones for the heavily-loaded server. This approach essentially offloads the overhead of packet processing from the heavily-loaded server to helpers. To minimize the overhead, PostMan activates helpers on demand, only when bursty traffic is detected. The heavily-loaded server determines when clients connect/disconnect to/from helpers based on the real-time load statistics. To tolerate helper failures, PostMan can migrate connections across helpers and can ensure packet ordering despite such migration. Driven by real-world workloads, our evaluation shows that, with the help of PostMan, a Memcached server can mitigate bursty traffic within hundreds of milliseconds, while migrating data takes tens of seconds and increases the latency during migration.
SODA: Similar 3D Object Detection Accelerator at Network Edge for Autonomous Driving

Offloading the 3D object detection from autonomous vehicles to MEC is appealing because of the gains on quality, latency, and energy. However, detection requests lead to repetitive computations since the multitudinous requests share approximate detection results. It is crucial to reduce such fuzzy redundancy by reusing the previous results. A key challenge is that the requests mapping to the reusable result are only similar but not identical. An efficient method for similarity matching is needed to justify the use case. To this end, by taking advantage of TCAM’s ap-proximate matching capability and NMC’s computing efficiency, we design SODA, a first-of-its-kind hardware accelerator which sits in the mobile base stations between autonomous vehicles and MEC servers. We design efficient feature encoding and partition algorithms for SODA to ensure the quality of the similarity matching and result reuse. Our evaluation shows that SODA significantly improves the system performance and the detection results exceed the accuracy requirements on the subject matter, qualifying SODA as a practical domain-specific solution.
A 27.5dBm EIRP D-Band Transmitter Module on a Ceramic Interposer

We present a fully packaged D-band direct conversion transmitter module on a low-permittivity ceramic carrier (Kyocera GL771, $\varepsilon_{r}=\mathrm{5.2}, \mathrm{\delta}=\mathrm{0.003}$). The module has a broadband 135GHz 22FDX CMOS direct conversion transmitter, a medium-power (100mW Psat) high-efficiency power amplifier implemented in Teledyne 250nm InP HBT technology, and a series-fed microstrip patch antenna array. The CMOS IC is flip-chip bonded to the carrier using $\mathrm{50}\ \mu\mathrm{m}$ diameter copper studs, while the InP IC is wire bonded, with impedance-matching networks on the carrier compensating for the wire bonds' parasitic impedances. The module has 6GHz 3-dB modulation bandwidth and 27.5dBm measured effective isotropic radiated power (EIRP) at saturation. Under 5Gbaud, 64QAM modulation (30Gbps), the module shows 8.5% RMS error vector magnitude (EVM) at 21.5dBm EIRP. To our knowledge, the module has the highest reported EIRP and efficiency among D-band single channel transmitters.
Trajectory Tracking of a Novel Underactuated AUV via Nonsingular Integral Terminal Sliding Mode Control

This paper addresses the 3D trajectory tracking problem of a novel underactuated underwater vehicle with 4 propellers. A 5-DOF kinematic and dynamic model of the quadrotor underwater vehicle (QUV) is established based on the underwater vehicle and quadrotor unmanned aerial vehicle movement mechanism. A double-loop control structure is then developed. By constructing a Lyapunov function for the outer-loop controller, a 5-DOF trajectory tracking error equation and a velocity virtual control law are obtained. The inner-loop controller is built based on nonsingular integral terminal sliding mode control (NITSMC). Finally, the effectiveness and robustness of our control algorithm are demonstrated through numerical simulations. In particular, given a smooth, second-order derivable 3D reference trajectory, the QUV can quickly track the trajectory and satisfactorily converges in the neighborhood of the expected value in a finite time via NTISMC, which verifies its superiority compared with the other popular backstepping control (BSC) method.
Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM

Sleep apnea, a severe sleep disorder, is a clinically complicated disease that requires timely diagnosis for proper treatment. In this paper, an automated deep learning-based approach is proposed for the detection of sleep apnea frames from electroencephalogram (EEG) signals. Unlike conventional methods of direct feature extraction from EEG signals, the variational mode decomposition (VMD) algorithm is utilized in the proposed method to decompose the EEG signals into a number of modes. Use of such decomposed EEG signals for feature extraction offers efficient processing of the variations introduced in the frequency spectrum during apnea events irrespective of particular patients. Afterward, a fully convolutional neural network (FCNN) is proposed to separately extract the temporal features from each VMD mode in parallel while maintaining their temporal dependencies. The FCNN block utilizes causal dilated convolutions with increasing dilation rates along with multiple kernel operations in convolutions. Subsequently, for further exploration of the inter-modal temporal variations, these extracted features from different EEG-modes are jointly optimized with a stack of bi-directional long short term memory (LSTM) layers. Hence, the trained and optimized network is capable of generating predictions of apnea frames during the evaluation phase. Contrary to other studies, this study is carried out in a subject independent manner where separate subjects are considered for training and testing. Additionally, a semi-supervised approach is explored where for facilitating better classification performance on a subject’s frames, a small portion of the patient’s data is included in training to leverage insight regarding the possible environmental variations. Extensive experimentations on three publicly available datasets provide average accuracy of 93.22%, 93.25% and 89.41% in the subject-independent cross-validation scheme.
Chinese stock market prediction based on multifeature fusion and TextCNN

Stock trend forecasting plays a great role in maximizing the profit of stock investment. However, due to the high volatility and non-stationarity of the stock market, accurate trend prediction is very difficult. With the development of the Internet and deep learning technology, people can use deep learning methods to reveal market trends and volatility from the explosive information on the Internet. Unfortunately, there is a large amount of content related to the stock market, and a large part of it is useless information. As a result, how to extract the effective information and combine this information as different characteristics to effectively predict stock trends has become the biggest challenge. In order to cope with these challenges, we use TextCNN as the news text feature extractor for feature extraction of news information, and propose a prediction method based on multi-feature fusion: Bi-LSTNAA, to predict the Chinese stock market. Extensive experiments on actual stock market data show that the our method has a greater improvement in the accuracy of stock trend prediction.