pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Drone Transformer Network for Robust Single Object Tracking
Authors: Guanlin Chen, Pengfei Zhu, Bing Cao, Xing Wang, QinGhua Hu
Date: 2023-06-05 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2023 6
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

Drones have been widely used in a variety of applications, e.g., aerial photography and military security, because of their high maneuverability and broad views compared with fixed cameras. Multi-drone tracking systems can provide rich information about targets by collecting complementary video clips from different views, especially when targets are occluded or disappear in some views. However, it is challenging to handle cross-drone information interaction and multi-drone information fusion in multi-drone visual tracking. Recently, Transformer has shown significant advantages in automatically modeling the correlation between templates and search regions for visual tracking. To leverage its potential in multi-drone tracking, we propose a novel cross-drone Transformer network (TransMDOT) for visual object tracking tasks. The self-attention mechanism is used to automatically capture the correlation between multiple templates and the corresponding search region to achieve multi-drone feature fusion. During the tracking process, a cross-drone mapping mechanism is proposed by using the surrounding information of the drone with promising tracking status as reference, assisting drones that lost targets to re-calibrate, which implements real-time cross-drone information interaction. As the existing multi-drone evaluation metrics only consider spatial information while ignore temporal information, we further present a system perception index (SPFI) that combines both temporal and spatial information to evaluate the tracking status of multiple drones. Experiments on the MDOT dataset prove that TransMDOT greatly surpasses the state-of-the-art methods in both single-drone performance and multi-drone system fusion performance. Our code will be available on https://github.com/cgjacklin/transmdot.
GLIPv3
Authors: Jiaxing Zhao
Date: 2020-02-02 00:00:00
Proceeding: CVPR 2020 2
Tasks: object-detection, Object Detection

In the open-set object detection, the alignment of visual and text features is one of the most important factors affecting the final detection performance.  This paper proposed a enhanced language and vision feature fusion module, which includes a multi-level test-image cross-attention,  a text-image  cross-attention and an adapted deformable self-attention.  Besides, we added the deep supervison in the multi-modal task training, which is effective for the alignment of visual and text features.  Experimental results show that our method performs remarkably well on COCO and LVIS datasets. Specifically, our method achieves ***
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Improved Neural Radiance Fields Using Pseudo-depth and Fusion
Authors: Jingliang Li, Qiang Zhou, Chaohui Yu, Zhengda Lu, Jun Xiao, Zhibin Wang, Fan Wang
Date: 2023-07-27 00:00:00
Tasks: Depth Estimation, Depth Prediction, NeRF, Novel View Synthesis

Since the advent of Neural Radiance Fields, novel view synthesis has received tremendous attention. The existing approach for the generalization of radiance field reconstruction primarily constructs an encoding volume from nearby source images as additional inputs. However, these approaches cannot efficiently encode the geometric information of real scenes with various scale objects/structures. In this work, we propose constructing multi-scale encoding volumes and providing multi-scale geometry information to NeRF models. To make the constructed volumes as close as possible to the surfaces of objects in the scene and the rendered depth more accurate, we propose to perform depth prediction and radiance field reconstruction simultaneously. The predicted depth map will be used to supervise the rendered depth, narrow the depth range, and guide points sampling. Finally, the geometric information contained in point volume features may be inaccurate due to occlusion, lighting, etc. To this end, we propose enhancing the point volume feature from depth-guided neighbor feature fusion. Experiments demonstrate the superior performance of our method in both novel view synthesis and dense geometry modeling without per-scene optimization.
Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-induced Changes in ECG
Authors: S. Anas Ali, M. Saqib Niaz, Mubashir Rehman, Ahsan Mehmood, M. Mahboob Ur Rahman, Kashif Riaz, Qammer H. Abbasi
Date: 2023-08-08 00:00:00
Tasks: Binary Classification, Denoising, Transfer Learning

Vascular age is traditionally measured using invasive methods or through 12-lead electrocardiogram (ECG). This paper utilizes a low-cost single-lead (lead-I) ECG module to predict the vascular age of an apparently healthy young person. In addition, we also study the impact of smoking on ECG traces of the light-but-habitual smokers. We begin by collecting (lead-I) ECG data from 42 apparently healthy subjects (smokers and non-smokers) aged 18 to 30 years, using our custom-built low-cost single-lead ECG module, and anthropometric data, e.g., body mass index, smoking status, blood pressure, etc. Under our proposed method, we first pre-process our dataset by denoising the ECG traces, followed by baseline drift removal, followed by z-score normalization. Next, we create another dataset by dividing the ECG traces into overlapping segments of five-second duration. We then feed both segmented and unsegmented datasets to a number of machine learning models, a 1D convolutional neural network, and ResNet18 model, for vascular ageing prediction. We also do transfer learning whereby we pre-train our models on a public PPG dataset, and later, fine-tune and evaluate them on our unsegmented ECG dataset. The random forest model outperforms all other models and previous works by achieving a mean squared error (MSE) of 0.07 and coefficient of determination R2 of 0.99, MSE of 3.56 and R2 of 0.26, MSE of 0.99 and R2 of 0.87, for segmented ECG dataset, for unsegmented ECG dataset, and for transfer learning scenario, respectively. Finally, we utilize the explainable AI framework to identify those ECG features that get affected due to smoking. This work is aligned with the sustainable development goals 3 and 10 of the United Nations which aim to provide low-cost but quality healthcare solutions to the unprivileged. This work also finds its applications in the broad domain of forensic science.
Achieving Covert Communication With A Probabilistic Jamming Strategy
Authors: Xun Chen, Fujun Gao, Min Qiu, Jia Zhang, Feng Shu, Shihao Yan
Date: 2023-08-08 00:00:00

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden's detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution, and Alice's transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice's perspective, the jammer's perspective, and the global perspective. Our analysis reveals that the minimum jamming power should not always be zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. In addition, we prove that the minimum jamming power should be the same as Alice's covert transmit power, depending on the covertness and average jamming power constraints. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert throughput under the same covertness and average jamming power constraints.
Finding Globally Optimal Configuration of Active RIS in Linear Time
Authors: Heedong Do, Namyoon Lee
Date: 2023-08-08 00:00:00

This paper presents an algorithm for finding the optimal configuration of active reconfigurable intelligent surface (RIS) when both transmitter and receiver are equipped with a single antenna each. The resultant configuration is globally optimal and it takes linear time for the computation. Moreover, there is a closed-form expression for the optimal configuration when the direct link vanishes, which enables further analysis.
Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks
Authors: Miaoyi Tang, Meiqin Liu, Senlin Zhang, Ronghao Zheng, Shanling Dong
Date: 2023-08-08 00:00:00

This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous real-world scenarios, e.g., tracking a maneuvering underwater target with acoustic sensors. Simulation results demonstrate the effectiveness of the proposed filtering algorithm. In addition, considering the constraints of network energy resources, the issue of investigating a trade-off between tracking performance and energy consumption is discussed accordingly.
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review
Authors: Sajjad Emdadi Mahdimahalleh
Date: 2023-08-01 00:00:00
Tasks: Federated Learning, Scheduling

These days with the rising computational capabilities of wireless user equipment such as smart phones, tablets, and vehicles, along with growing concerns about sharing private data, a novel machine learning model called federated learning (FL) has emerged. FL enables the separation of data acquisition and computation at the central unit, which is different from centralized learning that occurs in a data center. FL is typically used in a wireless edge network where communication resources are limited and unreliable. Bandwidth constraints necessitate scheduling only a subset of UEs for updates in each iteration, and because the wireless medium is shared, transmissions are susceptible to interference and are not assured. The article discusses the significance of Machine Learning in wireless communication and highlights Federated Learning (FL) as a novel approach that could play a vital role in future mobile networks, particularly 6G and beyond.
Target Speech Extraction with Conditional Diffusion Model
Authors: Naoyuki Kamo, Marc Delcroix, Tomohiro Nakatani
Date: 2023-08-08 00:00:00
Tasks: Denoising, model, Speech Denoising, Speech Enhancement, Speech Extraction

Diffusion model-based speech enhancement has received increased attention since it can generate very natural enhanced signals and generalizes well to unseen conditions. Diffusion models have been explored for several sub-tasks of speech enhancement, such as speech denoising, dereverberation, and source separation. In this paper, we investigate their use for target speech extraction (TSE), which consists of estimating the clean speech signal of a target speaker in a mixture of multi-talkers. TSE is realized by conditioning the extraction process on a clue identifying the target speaker. We show we can realize TSE using a conditional diffusion model conditioned on the clue. Besides, we introduce ensemble inference to reduce potential extraction errors caused by the diffusion process. In experiments on Libri2mix corpus, we show that the proposed diffusion model-based TSE combined with ensemble inference outperforms a comparable TSE system trained discriminatively.
Flocking control against the malicious agent
Authors: Chencheng Zhang, Hao Yang, Bin Jiang, Ming Cao
Date: 2023-08-08 00:00:00

This paper investigates the flocking control of a swarm with a malicious agent that falsifies its controller parameters to cause collision, division, and escape of agents in the swarm. A novel geometric flocking condition is established by designing the configuration of the malicious agent and its neighbors, under which we propose a hierarchal geometric configuration-based flocking control method. To help detect the malicious agent, a parameter estimate mechanism is also provided. The proposed method can achieve the flocking control goal and meanwhile contain the malicious agent in the swarm without removing it. Experimental result shows the effectiveness of the theoretical result.
Generalized Forgetting Recursive Least Squares: Stability and Robustness Guarantees
Authors: Brian Lai, Dennis S. Bernstein
Date: 2023-08-08 00:00:00
Tasks: parameter estimation

This work presents generalized forgetting recursive least squares (GF-RLS), a generalization of recursive least squares (RLS) that encompasses many extensions of RLS as special cases. First, sufficient conditions are presented for the 1) Lyapunov stability, 2) uniform Lyapunov stability, 3) global asymptotic stability, and 4) global uniform exponential stability of parameter estimation error in GF-RLS when estimating fixed parameters without noise. Second, robustness guarantees are derived for the estimation of time-varying parameters in the presence of measurement noise and regressor noise. These robustness guarantees are presented in terms of global uniform ultimate boundedness of the parameter estimation error. A specialization of this result gives a bound to the asymptotic bias of least squares estimators in the errors-in-variables problem. Lastly, a survey is presented to show how GF-RLS can be used to analyze various extensions of RLS from the literature.
Flexible Distributed Flocking Control for Multi-agent Unicycle Systems
Authors: Tinghua Li, Bayu Jayawardhana
Date: 2023-08-08 00:00:00

Currently, the general aim of flocking and formation control laws for multi-agent systems is to form and maintain a rigid configuration, such as, the alpha-lattices in flocking control methods, where the desired distance between each pair of connected agents is fixed. This introduces a scalability issue for large-scale deployment of agents due to unrealizable geometrical constraints and the constant need of centralized orchestrator to ensure the formation graph rigidity. This paper presents a flexible distributed flocking cohesion algorithm for nonholonomic multi-agent systems. The desired geometry configuration between each pair of agents is adaptive and flexible. The distributed flocking goal is achieved using limited information exchange (i.e., the local field gradient) between connected neighbor agents and it does not rely on any other motion variables measurements, such as (relative) position, velocity, or acceleration. Additionally, the flexible flocking scheme with safety is considered so that the agents with limited sensing capability are able to maintain the connectedness of communication topology at all time and avoid inter-agent collisions. The stability analysis of the proposed methods is presented along with numerical simulation results to show their effectiveness.
Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty
Authors: Jiantao Shi, Ye Guo, Wenchuan Wu, Hongbin Sun
Date: 2023-08-08 00:00:00

In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations. A look-ahead energy-reserve co-optimization model is proposed for the rolling-window dispatch, where possible contingencies and load/renewable forecast errors over the look-ahead window are modeled as several scenario trajectories, while generation, especially its ramp, is jointly scheduled with reserve to minimize the expected system cost considering these scenarios. Based on the proposed model, marginal prices of energy and reserve are derived, which incorporate shadow prices of generators' individual ramping capability limits to eliminate their possible ramping-induced opportunity costs or arbitrages. We prove that under mild conditions, the proposed market design provides dispatch-following incentives to generators without the need for out-of-the-market uplifts, and truthful-bidding incentives of price-taking generators can be guaranteed as well. Some discussions are also made on how to fit the proposed framework into current market practice. These findings are validated in numerical simulations.
Safe Control Synthesis for Multicopter via Control Barrier Function Backstepping
Authors: Jinrae Kim, Youdan Kim
Date: 2023-08-08 00:00:00
Tasks: Position

A safe controller for multicopter is proposed using control barrier function. Multicopter dynamics are reformulated to deal with mixed-relative-degree and non-strict-feedback-form dynamics, and a time-varying safe backstepping controller is designed. Despite the time-varying variation, it is proven that the control input can be obtained by solving quadratic programming with affine inequality constraints. The proposed controller does not utilize a cascade control system design, unlike existing studies on the safe control of multicopter. Various safety constraints on angular velocity, total thrust direction, velocity, and position can be considered. Numerical simulation results support that the proposed safe controller does not violate all safety constraints including low- and high-level dynamics.
A Projection-Based Approach for Distributed Energy Resources Aggregation
Authors: Yiran Wang, Haiwang Zhong, Guangchun Ruan
Date: 2023-08-08 00:00:00

Aggregating distributed energy resources (DERs) is of great significance to improve the overall operational efficiency of smart grid. The aggregation model needs to consider various factors such as network constraints, operational constraints, and economic characteristics of the DERs. This paper constructs a multi-slot DER aggregation model that considers the above factors using feasible region projection approach, which achieved the protection of DERs data information and the elimination of internal variables. A system economic dispatch (ED) model is established for the operators to make full use of the DER clusters. We calculate the feasible regions with temporal coupling by extending the Progressive Vertex Enumeration (PVE) algorithm to high dimension by the Quickhull algorithm. Finally, an IEEE 39-bus distribution network is simulated with DERs to verify the effectiveness of the proposed model. Results show that the two-step ED derives the same results as the centralized ED.
An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks
Authors: Xi Xiong, Maonan Wang, Dengfeng Sun, Li Jin
Date: 2023-08-08 00:00:00
Tasks: Autonomous Vehicles, Travel Time Estimation

Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume the availability of pre-determined plans, which is not feasible in real-time scenarios. In this paper, we address this issue in time-dependent networks by formulating a Markov decision process at each junction, aiming to minimize travel time and fuel consumption. Initially, we analyze coordinated platooning without routing to explore the cooperation among controllers on an identical path. We propose two novel approaches based on approximate dynamic programming, offering suboptimal control in the context of a stochastic finite horizon problem. The results demonstrate the superiority of the approximation in the policy space. Furthermore, we investigate platooning in a network setting, where speed profiles and routes are determined simultaneously. To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation. We subsequently employ the aforementioned policy approximation to determine speed profiles, considering essential parameters such as travel times. Our simulation results in SUMO indicate that our method yields better performance than conventional approaches, leading to potential travel cost savings of up to 40%. Additionally, we evaluate the resilience of our approach in dynamically changing networks, affirming its ability to maintain efficient platooning operations.
Review of Contemporary Energy Harvesting Techniques and Their Feasibility in Wireless Geophones
Authors: Naveed Iqbal, Mudassir Masood, Ali Nasir, Khurram Karim Qureshi
Date: 2023-08-08 00:00:00

Energy harvesting converts ambient energy to electrical energy providing numerous opportunities to realize wireless sensors. Seismic exploration is a prime avenue to benefit from it as energy harvesting equipped geophones would relieve the burden of cables which account for the biggest chunk of exploration cost and equipment weight. Since numerous energies are abundantly available in seismic fields, these can be harvested to power up geophones. However, due to the random and intermittent nature of the harvested energy, it is important that geophones must be equipped to tap from several energy sources for a stable operation. It may involve some initial installation cost but in the long run, it is cost-effective and beneficial as the sources for energy harvesting are available naturally. Extensive research has been carried out in recent years to harvest energies from various sources. However, there has not been a thorough investigation of utilizing these developments in the seismic context. In this survey, a comprehensive literature review is provided on the research progress in energy harvesting methods suitable for direct adaptation in geophones. Specifically, the focus is on small form factor energy harvesting circuits and systems capable of harvesting energy from wind, sun, vibrations, temperature difference, and radio frequencies. Furthermore, case studies are presented to assess the suitability of the studied energy harvesting methods. Finally, a design of energy harvesting equipped geophone is also proposed.
Causal Interpretation of Linear Social Interaction Models with Endogenous Networks
Authors: Tadao Hoshino
Date: 2023-08-08 00:00:00
Tasks: Selection bias, valid

This study investigates the causal interpretation of linear social interaction models in the presence of endogeneity in network formation under a heterogeneous treatment effects framework. We consider an experimental setting in which individuals are randomly assigned to treatments while no interventions are made for the network structure. We show that running a linear regression ignoring network endogeneity is not problematic for estimating the average direct treatment effect. However, it leads to sample selection bias and negative-weights problem for the estimation of the average spillover effect. To overcome these problems, we propose using potential peer treatment as an instrumental variable (IV), which is automatically a valid IV for actual spillover exposure. Using this IV, we examine two IV-based estimands and demonstrate that they have a local average treatment-effect-type causal interpretation for the spillover effect.