pwc-archive/papers-with-abstracts huggingface dataset:
Electricity generation from renewable energy based on abandoned wind fan
Authors: Arni Munira Markom, Muhammad Hakimi Aiman Hadri, Tuah Zayan Muhamad Yazid, Zakiah Mohd Yusof, Marni Azira Markom, Ahmad Razif Muhammad
Date: 2022-04-07 00:00:00

In the 21st century, our world is facing difficult conditions for serious environmental pollution and the problem of energy shortage. An innovative idea has emerged to recycle wind energy from air conditioning condenser fans in outdoor buildings. Therefore, the main goal of this research is to develop renewable wind energy from the condenser fan of an air conditioner using Arduino as a microcontroller. This research moves towards a portable, low cost, environmentally friendly mini device that harnesses renewable energies with endless resources for future alternative power generation and reduces the burden of consumers' electricity bills
A Framework for Distributed Estimation with Reduced Communication via Event-Based Strategies
Authors: Jiaqi Yan, Yilin Mo, Hideaki Ishii
Date: 2022-04-07 00:00:00

This paper considers the problem of distributed estimation in a sensor network, where multiple sensors are deployed to infer the state of a linear time-invariant (LTI) Gaussian system. By proposing a lossless decomposition of Kalman filter, a framework of event-based distributed estimation is developed, where each sensor node runs a local filter using solely its own measurement, alongside with an event-based synchronization algorithm to fuse the neighboring information. One novelty of the proposed framework is that it decouples the local filter from synchronization process. By doing so, we prove that a general class of triggering strategies can be applied in our framework, which yields stable distributed estimators under the minimal requirements of network connectivity and collective system observability. As compared with existing works, the proposed algorithm enjoys lower data size for each transmission. Moreover, the developed results can be generalized to achieve a distributed implementation of any Luenberger observer. By solving a semi-definite programming (SDP), we further present a low-rank estimator design to obtain the optimal gain of Luenberger observer such that the distributed estimation is realized under the constraint of message complexity. Numerical examples are finally provided to demonstrate the proposed methods.
Alternating Direction Based Sequential Boolean Quadratic Programming Method for Transmit Antenna Selection
Authors: Shijie Zhu, Xu Du
Date: 2022-04-07 00:00:00

The wireless mobile communication system is updated and iterated on the whole almost every decade. It is now in the development period of the application scenarios of the fifth generation mobile communication system (5G). Unfortunately, 5G relies on plenty of small base stations with a large number of antennas that consume a lot of energy. In this paper, a novel Boolean variable quadratic programming algorithm is designed for the antenna selection optimization problem to reduce power consumption. Experiments show that the proposed algorithm achieves high complementarity satisfaction accuracy with only a few steps.
Online Adaptive Identification of Switched Affine Systems Using a Two-Tier Filter Architecture with Memory
Authors: Pritesh Patel, Sayan Basu Roy, Shubhendu Bhasin
Date: 2022-04-07 00:00:00
Tasks: parameter estimation

This work proposes an online adaptive identification method for multi-input multi-output (MIMO) switched affine systems with guaranteed parameter convergence. A family of online parameter estimators is used that is equipped with a dual-layer low pass filter architecture to facilitate parameter learning and identification of each subsystem. The filters capture information about the unknown parameters in the form of a prediction error which is used in the parameter estimation algorithm. A salient feature of the proposed method that distinguishes it from most previous results is the use of a memory bank that stores filter values and promotes parameter learning during both active and inactive phases of a subsystem. Specifically, the learnt experience from the previous active phase of a subsystem is retained in the memory and leveraged for parameter learning in its subsequent active and inactive phases. Further, a new notion of intermittent initial excitation (IIE) is introduced that extends the previously established initial excitation (IE) condition to the switched system framework. IIE is shown to be sufficient to ensure exponential convergence of the switched system parameters.
Stability and Safety through Event-Triggered Intermittent Control with Application to Spacecraft Orbit Stabilization
Authors: Pio Ong, Gilbert Bahati, Aaron D. Ames
Date: 2022-04-06 00:00:00

In systems where the ability to actuate is a scarce resource, e.g., spacecrafts, it is desirable to only apply a given controller in an intermittent manner--with periods where the controller is on and periods where it is off. Motivated by the event-triggered control paradigm, where state-dependent triggers are utilized in a sample-and-hold context, we generalize this concept to include state triggers where the controller is off thereby creating a framework for intermittent control. Our approach utilizes certificates--either Lyapunov or barrier functions--to design intermittent trigger laws that guarantee stability or safety; the controller is turned on for the period for which is beneficial with regard to the certificate, and turned off until a performance threshold is reached. The main result of this paper is that the intermittent controller scheme guarantees (set) stability when Lyapunov functions are utilized, and safety (forward set invariance) in the setting of barrier functions. As a result, our trigger designs can leverage the intermittent nature of the actuator, and at the same time, achieve the task of stabilization or safety. We further demonstrate the application and benefits of intermittent control in the context of the spacecraft orbit stabilization problem.
Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap
Authors: Alexius Wadell, Matthew Guttenberg, Christopher P. Kempes, Venkatasubramanian Viswanathan
Date: 2022-04-06 00:00:00

Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV infrastructure needs of the US at a county-level resolution. Surprisingly, we find that the current EV infrastructure deployment scales super-linearly with population, deviating from the sub-linear scaling of gasoline stations and other infrastructure. We discuss how this demonstrates the infancy of EV station abundance compared to other mature transportation infrastructures. By considering the power delivery of existing gasoline stations, and appropriate EV efficiencies, we estimate the EV infrastructure gap at the county level, providing a road map for future EV infrastructure expansion. Our reliance on scaling analysis allows us to make a unique forecast in this domain.
Control Barrier Function based Attack-Recovery with Provable Guarantees
Authors: Kunal Garg, Ricardo G. Sanfelice, Alvaro A. Cardenas
Date: 2022-04-06 00:00:00

This paper studies provable security guarantees for cyber-physical systems (CPS) under actuator attacks. In particular, we consider CPS safety and propose a new attack detection mechanism based on zeroing control barrier function (ZCBF) conditions. In addition, we design an adaptive recovery mechanism based on how close the system is to violating safety. We show that under certain conditions, the attack-detection mechanism is sound, i.e., there are no false negatives for adversarial attacks. We propose sufficient conditions for the initial conditions and input constraints so that the resulting CPS is secure by design. We also propose a novel hybrid control to account for attack detection delays and avoid Zeno behavior. Next, to efficiently compute the set of initial conditions, we propose a sampling-based method to verify whether a set is a viability domain. Specifically, we devise a method for checking a modified barrier function condition on a finite set of points to assess whether a set can be rendered forward invariant. Then, we propose an iterative algorithm to compute the set of initial conditions and input constraints set to limit the effect of an adversary if it compromises vulnerable inputs. Finally, we use a Quadratic Programming (QP) approach for online recovery (as well as nominal) control synthesis. We demonstrate the effectiveness of the proposed method in a simulation case study involving a quadrotor with an attack on its motors.
Reputation as insurance: how reputation moderates public backlash following a company's decision to profiteer
Authors: Danae Arroyos-Calvera, Nattavudh Powdthavee
Date: 2022-04-07 00:00:00
Tasks: Fairness

We examine whether a company's corporate reputation gained from their CSR activities and a company leader's reputation, one that is unrelated to his or her business acumen, can impact economic action fairness appraisals. We provide experimental evidence that good corporate reputation causally buffers individuals' negative fairness judgment following the firm's decision to profiteer from an increase in the demand. Bad corporate reputation does not make the decision to profiteer as any less acceptable. However, there is evidence that individuals judge as more unfair an ill-reputed firm's decision to raise their product's price to protect against losses. Thus, our results highlight the importance of a good reputation in protecting a firm against severe negative judgments from making an economic decision that the public deems unfair.
What is the effect of EU's fuel-tax cuts on Russia's oil income?
Authors: Johan Gars, Daniel Spiro, Henrik Wachtmeister
Date: 2022-04-07 00:00:00

Following the oil-price surge in the wake of Russia's invasion of Ukraine, many countries in the EU are cutting taxes on petrol and diesel. Using standard theory and empirical estimates, we assess how such tax cuts influence the oil income in Russia. We find that a tax cut of 20 euro cents per liter increase Russia's oil profits by around 11 million Euros per day in the short run and long run. This is equivalent to 4100 million Euros in a year, 0.3% of Russia's GDP or 7% of its military spending. We show that a cash transfer to EU citizens, with an equivalent fiscal burden as the tax cut, reduces these side effects to a fraction.
Boosting Self-Supervised Embeddings for Speech Enhancement
Authors: Kuo-Hsuan Hung, Szu-Wei Fu, Huan-Hsin Tseng, Hsin-Tien Chiang, Yu Tsao, Chii-Wann Lin
Date: 2022-04-07 00:00:00
Tasks: Self-Supervised Learning, Speech Enhancement

Self-supervised learning (SSL) representation for speech has achieved state-of-the-art (SOTA) performance on several downstream tasks. However, there remains room for improvement in speech enhancement (SE) tasks. In this study, we used a cross-domain feature to solve the problem that SSL embeddings may lack fine-grained information to regenerate speech signals. By integrating the SSL representation and spectrogram, the result can be significantly boosted. We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE. Consequently, we found that SSL representations with lower noise robustness are more important. Furthermore, our experiments on the VCTK-DEMAND dataset demonstrated that fine-tuning an SSL representation with an SE model can outperform the SOTA SSL-based SE methods in PESQ, CSIG and COVL without invoking complicated network architectures. In later experiments, the CN distance in SSL embeddings was observed to increase after fine-tuning. These results verify our expectations and may help design SE-related SSL training in the future.
Music-robust Automatic Lyrics Transcription of Polyphonic Music
Authors: Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
Date: 2022-04-07 00:00:00
Tasks: Automatic Lyrics Transcription, Language Modeling, Language Modelling

Lyrics transcription of polyphonic music is challenging because singing vocals are corrupted by the background music. To improve the robustness of lyrics transcription to the background music, we propose a strategy of combining the features that emphasize the singing vocals, i.e. music-removed features that represent singing vocal extracted features, and the features that capture the singing vocals as well as the background music, i.e. music-present features. We show that these two sets of features complement each other, and their combination performs better than when they are used alone, thus improving the robustness of the acoustic model to the background music. Furthermore, language model interpolation between a general-purpose language model and an in-domain lyrics-specific language model provides further improvement in transcription results. Our experiments show that our proposed strategy outperforms the existing lyrics transcription systems for polyphonic music. Moreover, we find that our proposed music-robust features specially improve the lyrics transcription performance in metal genre of songs, where the background music is loud and dominant.
Recursive Frequency Selective Reconstruction of Non-Regularly Sampled Video Data
Authors: Markus Jonscher, Karina Jaskolka, Jürgen Seiler, André Kaup
Date: 2022-04-07 00:00:00
Tasks: Motion Estimation, SSIM

High resolution images can be acquired using a non-regular sampling sensor which consists of an underlying low resolution sensor that is covered with a non-regular sampling mask. The reconstructed high resolution image is then obtained during post-processing. Recently, it has been shown that the temporal correlation between neighboring frames can be exploited in order to enhance the reconstruction quality of non-regularly sampled video data. In this paper, a new recursive multi-frame reconstruction approach is proposed in order to further increase the reconstruction quality. By using a new reference order, previously reconstructed frames can be used for the subsequent motion estimation and a new weighting function allows for the incorporation of multiple pixels projected onto the same position. With the new recursive multi-frame approach, a visually noticeable average gain in PSNR of up to 1.13 dB with respect to a state-of-the-art single-frame reconstruction approach can be achieved. Compared to the existing multi-frame approach, a gain of 0.31 dB is possible. SSIM results show the same behavior as PSNR results. Additionally, the pre-reconstruction step of the existing multi-frame approach can be avoided and the new algorithm is, in general, capable of real-time processing.
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction
Authors: Markus Jonscher, Jürgen Seiler, Daniela Lanz, Michael Schöberl, Michel Bätz, André Kaup
Date: 2022-04-07 00:00:00
Tasks: Super-Resolution

Both a high spatial and a high temporal resolution of images and videos are desirable in many applications such as entertainment systems, monitoring manufacturing processes, or video surveillance. Due to the limited throughput of pixels per second, however, there is always a trade-off between acquiring sequences with a high spatial resolution at a low temporal resolution or vice versa. In this paper, a modified sensor concept is proposed which is able to acquire both a high spatial and a high temporal resolution. This is achieved by dynamically reading out only a subset of pixels in a non-regular order to obtain a high temporal resolution. A full high spatial resolution is then obtained by performing a subsequent three-dimensional reconstruction of the partially acquired frames. The main benefit of the proposed dynamic readout is that for each frame, different sampling points are available which is advantageous since this information can significantly enhance the reconstruction quality of the proposed reconstruction algorithm. Using the proposed dynamic readout strategy, gains in PSNR of up to 8.55 dB are achieved compared to a static readout strategy. Compared to other state-of-the-art techniques like frame rate up-conversion or super-resolution which are also able to reconstruct sequences with both a high spatial and a high temporal resolution, average gains in PSNR of up to 6.58 dB are possible.
Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images
Authors: Markus Jonscher, Jürgen Seiler, André Kaup
Date: 2022-04-07 00:00:00

There exist many scenarios where pixel information is available only on a non-regular subset of pixel positions. For further processing, however, it is required to reconstruct such images on a regular grid. Besides many other algorithms, frequency selective reconstruction can be applied for this task. It performs a block-wise generation of a sparse signal model as an iterative superposition of Fourier basis functions and uses this model to replace missing or corrupted pixels in an image. In this paper, it is shown that it is not required to spend the same amount of iterations on both homogeneous and heterogeneous regions. Hence, a new texture-dependent approach for frequency selective reconstruction is introduced that distributes the number of iterations depending on the texture of the regions to be reconstructed. Compared to the original frequency selective reconstruction and depending on the number of iterations, visually noticeable gains in PSNR of up to 1.47 dB can be achieved.
A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks
Authors: Elaheh Vaezpour, Layla Majzoobi, Mohammad Akbari, Saeedeh Parsaeefard, Halim Yanikomeroglu
Date: 2022-04-07 00:00:00
Tasks: Fairness, Management

Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly. Utilizing the promising benefits of non-orthogonal multiple access (NOMA) for improving the throughput of cell edge users, we propose a newly NOMA-based cell outage compensation scheme. In this scheme, the compensation is formulated as a mixed integer non-linear program (MINLP) where outage zone users are associated to neighboring cells and their power are allocated with the objective of maximizing spectral efficiency, subject to maintaining the quality of service for the rest of the users. Owing to the importance of immediate management of cell outage and handling the computational complexity, we develop a low-complexity suboptimal solution for this problem in which the user association scheme is determined by a newly heuristic algorithm, and power allocation is set by applying an innovative deep neural network (DNN). The complexity of our proposed method is in the order of polynomial basis, which is much less than the exponential complexity of finding an optimal solution. Simulation results demonstrate that the proposed method approaches the optimal solution. Moreover, the developed scheme greatly improves fairness and increases the number of served users.
Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit
Authors: Joan Palacios, Nuria González-Prelcic, Cristian Rusu
Date: 2022-04-07 00:00:00
Tasks: Position

Compressive approaches provide a means of effective channel high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures. Such estimates can also be used as part of a joint channel estimation and localization solution. Achieving good localization performance, though, requires high resolution channel estimates and better methods to exploit those channels. In this paper, we propose a low complexity multidimensional orthogonal matching pursuit strategy for compressive channel estimation based by operating with a product of independent dictionaries for the angular and delay domains, instead of a global large dictionary. This leads to higher quality channel estimates but with lower complexity than generalizations of conventional solutions. We couple this new algorithm with a novel localization formulation that does not rely on the absolute time of arrival of the LoS path and exploits the structure of reflections in indoor channels. We show how the new approach is able to operate in realistic 3D scenarios to estimate the communication channel and locate devices in an indoor simulation setting.
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
Authors: Lingwei Zhu, Koki Odani, Ziwei Yang, Guang Shi, Yirong Kan, Zheng Chen, Renyuan Zhang
Date: 2022-04-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Feature Engineering

Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this study, we propose an adaptive scheme to probabilistically encode, filter and accumulate the input signals and weight the resultant features by the half-Gaussian probabilities of signal intensities. The adaptive representations are subsequently fed into a transformer model to automatically mine the relevance between features and corresponding stages. Extensive experiments on the largest public dataset against state-of-the-art methods validate the effectiveness of our proposed method and reveal promising future directions.
Barcodes distinguish morphology of neuronal tauopathy
Authors: David Beers, Despoina Goniotaki, Diane P. Hanger, Alain Goriely, Heather A. Harrington
Date: 2022-04-07 00:00:00

The geometry of neurons is known to be important for their functions. Hence, neurons are often classified by their morphology. Two recent methods, persistent homology and the topological morphology descriptor, assign a morphology descriptor called a barcode to a neuron equipped with a given function, such as the Euclidean distance from the root of the neuron. These barcodes can be converted into matrices called persistence images, which can then be averaged across groups. We show that when the defining function is the path length from the root, both the topological morphology descriptor and persistent homology are equivalent. We further show that persistence images arising from the path length procedure provide an interpretable summary of neuronal morphology. We introduce {topological morphology functions}, a class of functions similar to Sholl functions, that can be recovered from the associated topological morphology descriptor. To demonstrate this topological approach, we compare healthy cortical and hippocampal mouse neurons to those affected by progressive tauopathy. We find a significant difference in the morphology of healthy neurons and those with a tauopathy at a postsymptomatic age. We use persistence images to conclude that the diseased group tends to have neurons with shorter branches as well as fewer branches far from the soma.