Signals最新文献

{"title":"WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping","authors":"J. Holtom, A. Herschfelt, Isabella Lenz, O. Ma, Hanguang Yu, D. Bliss","doi":"10.3390/signals3040041","DOIUrl":"https://doi.org/10.3390/signals3040041","url":null,"abstract":"Validating RF applications is traditionally time consuming, even for relatively simple systems. We developed the WISCA Software-Defined Radio Network (WISCANet) to accelerate the implementation and validation of radio applications over-the-air (OTA). WISCANet is a hardwareagnostic control software that automatically configures and controls a software-defined radio (SDR) network. By abstracting the hardware controls away from the user, WISCANet allows a non-expert user to deploy an OTA application by simply defining a baseband processing chain in a high level language. This technology reduces transition time between system design and OTA deployment, accelerates debugging and validation processes, and makes OTA experimentation more accessible to users that are not radio hardware experts. WISCANet emulates real-time RF operations, enabling users to perform real-time experiments without the typical restrictions on processing speed and hardware capabilities. WISCANet also supports multiple RF front-ends (RFFEs) per compute node, allowing sub-6 and mmWave systems to coexist on the same node. This coexistence enables simultaneous baseband processing that simplifies and enhances advanced algorithms and beyond-5G applications. In this study, we highlight the capabilities of WISCANet in several sub-6 and mmWave over-the-air demonstrations. The open source release of this software may be found on the WISCA GitHub page.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45270442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation","authors":"Fei He, Andrew Harms, L. Yang","doi":"10.3390/signals3040040","DOIUrl":"https://doi.org/10.3390/signals3040040","url":null,"abstract":"This paper presents a novel method of tensor rank regularization with bias compensation for channel estimation in a hybrid millimeter wave MIMO-OFDM system. Channel estimation is challenging due to the unknown number of multipath components that determines the channel rank. In general, finding the intrinsic rank of a tensor is a non-deterministic polynomial-time (NP) hard problem. However, by leveraging the sparse characteristics of millimeter wave channels, we propose a modified CANDECOMP/PARAFAC (CP) decomposition-based method that jointly estimates the tensor rank and channel component matrices. Our approach differs from most existing works that assume the number of channel paths is known and the proposed method is able to estimate channel parameters accurately without the prior knowledge of number of multipaths. The objective of this work is to estimate the tensor rank by a novel sparsity-promoting prior that is incorporated into a standard alternating least squares (ALS) function. We introduce a weighting parameter to control the impact of the previous estimate and the tensor rank estimation bias compensation in the regularized ALS. The channel information is then extracted from the estimated component matrices. Simulation results show that the proposed scheme outperforms the baseline l1 strategy in terms of accuracy and robustness. It also shows that this method significantly improves rank estimation success at the expense of slightly more iterations.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45727197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lunar Backup Record of Humanity","authors":"Carson Ezell, A. Lazarian, A. Loeb","doi":"10.3390/signals3040049","DOIUrl":"https://doi.org/10.3390/signals3040049","url":null,"abstract":"The risk of a catastrophic or existential disaster for our civilization is increasing this century. A significant motivation for a near-term space settlement is the opportunity to safeguard civilization in the event of a planetary-scale disaster. A catastrophic event could destroy the significant cultural, scientific, and technological progress on Earth. However, early space settlements can preserve records of human activity by maintaining a backup data storage system. The backup can also store information about the events leading up to the disaster. The system would improve the ability of early space settlers to recover our civilization after collapse. We show that advances in laser communications and data storage enable the development of a data storage system on the lunar surface with a sufficient uplink data rate and storage capacity to preserve valuable information about the achievements of our civilization and the chronology of the disaster.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48199835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GRAPE: Grammatical Algorithms in Python for Evolution","authors":"Allan de Lima, Samuel Carvalho, D. Dias, Enrique Naredo, Joseph P. Sullivan, C. Ryan","doi":"10.3390/signals3030039","DOIUrl":"https://doi.org/10.3390/signals3030039","url":null,"abstract":"GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49424918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verilog Design, Synthesis, and Netlisting of IoT-Based Arithmetic Logic and Compression Unit for 32 nm HVT Cells","authors":"Raj Mouli Jujjavarapu, Alwin Poulose","doi":"10.3390/signals3030038","DOIUrl":"https://doi.org/10.3390/signals3030038","url":null,"abstract":"Micro-processor designs have become a revolutionary technology almost in every industry. They brought the reality of automation and also electronic gadgets. While trying to improvise these hardware modules to handle heavy computational loads, they have substantially reached a limit in size, power efficiency, and similar avenues. Due to these constraints, many manufacturers and corporate entities are trying many ways to optimize these mini beasts. One such approach is to design microprocessors based on the specified operating system. This approach came to the limelight when many companies launched their microprocessors. In this paper, we will look into one method of using an arithmetic logic unit (ALU) module for internet of things (IoT)-enabled devices. A specific set of operations is added to the classical ALU to help fast computational processes in IoT-specific programs. We integrated a compression module and a fast multiplier based on the Vedic algorithm in the 16-bit ALU module. The designed ALU module is also synthesized under a 32-nm HVT cell library from the Synopsys database to generate an overview of the areal efficiency, logic levels, and layout of the designed module; it also gives us a netlist from this database. The synthesis provides a complete overview of how the module will be manufactured if sent to a foundry.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48998564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Language Inference Using Elman Networks with Evolutionary Training","authors":"Nikolaos P. Anastasopoulos, I. Tsoulos, E. Dermatas, E. Karvounis","doi":"10.3390/signals3030037","DOIUrl":"https://doi.org/10.3390/signals3030037","url":null,"abstract":"In this paper, a novel Elman-type recurrent neural network (RNN) is presented for the binary classification of arbitrary symbol sequences, and a novel training method, including both evolutionary and local search methods, is evaluated using sequence databases from a wide range of scientific areas. An efficient, publicly available, software tool is implemented in C++, accelerating significantly (more than 40 times) the RNN weights estimation process using both simd and multi-thread technology. The experimental results, in all databases, with the hybrid training method show improvements in a range of 2% to 25% compared with the standard genetic algorithm.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42883997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Network Service Optimization in the Context of 5G/NFV","authors":"P. Karkazis, Konstantinos Railis, Stelios Prekas, P. Trakadas, H. Leligou","doi":"10.3390/signals3030036","DOIUrl":"https://doi.org/10.3390/signals3030036","url":null,"abstract":"Our contemporary society has never been more connected and aware of vital information in real time, through the use of innovative technologies. A considerable number of applications have transitioned into the cyber-physical domain, automating and optimizing their routines and processes via the dense network of sensing devices and the immense volumes of data they collect and instantly share. In this paper, we propose an innovative architecture based on the monitoring, analysis, planning, and execution (MAPE) paradigm for network and service performance optimization. Our study confirms distinct evidence that the utilization of learning algorithms, consuming datasets enriched with the users’ empirical opinions as input during the analysis and planning phases, contributes greatly to the optimization of video streaming quality, especially by handling different packet loss rates, paving the way for the achievable provision of a resilient communications platform for calamity assessment and management.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45122742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform","authors":"Maximilian Grobbelaar, Souvik Phadikar, Ebrahim Ghaderpour, A. Struck, N. Sinha, Rajdeep Ghosh, Md. Zaved Iqubal Ahmed","doi":"10.3390/signals3030035","DOIUrl":"https://doi.org/10.3390/signals3030035","url":null,"abstract":"Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.","PeriodicalId":93815,"journal":{"name":"Signals","volume":"325 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41290454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compressive Sensing Based Space Flight Instrument Constellation for Measuring Gravitational Microlensing Parallax","authors":"Asmita Korde-Patel, R. Barry, T. Mohsenin","doi":"10.3390/signals3030034","DOIUrl":"https://doi.org/10.3390/signals3030034","url":null,"abstract":"In this work, we provide a compressive sensing architecture for implementing on a space based observatory for detecting transient photometric parallax caused by gravitational microlensing events. Compressive sensing (CS) is a simultaneous data acquisition and compression technique, which can greatly reduce on-board resources required for space flight data storage and ground transmission. We simulate microlensing parallax observations using a space observatory constellation, based on CS detectors. Our results show that average CS error is less than 0.5% using 25% Nyquist rate samples. The error at peak magnification time is significantly lower than the error for distinguishing any two microlensing parallax curves at their peak magnification. Thus, CS is an enabling technology for detecting microlensing parallax, without causing any loss in detection accuracy.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47365557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case Report: Modulation of Effective Connectivity in Brain Networks after Prosthodontic Tooth Loss Repair","authors":"A. Muroni, Daniel Barbar, M. Fraschini, M. Monticone, G. Defazio, F. Marrosu","doi":"10.3390/signals3030033","DOIUrl":"https://doi.org/10.3390/signals3030033","url":null,"abstract":"INTRODUCTION. Recent neuroimaging studies suggest that dental loss replacements induce changes in neuroplasticity as well as in correlated connectivity between brain networks. However, as the typical temporal delay in detecting brain activity by neuroimaging cannot account for the influence one neural system exerts over another in a context of real activation (“effective” connectivity), it seems of interest to approach this dynamic aspect of brain networking in the time frame of milliseconds by exploiting electroencephalographic (EEG) data. MATERIAL AND METHODS. The present study describes one subject who received a new prosthodontic provisional implant in substitution for previous dental repairs. Two EEG sessions led with a portable device were recorded before and after positioning the new dental implant. By following MATLAB-EEGLAB processing supported by the plugins FIELDTRIP and SIFT, the independent component analysis (ICA) derived from EEG raw signals was rendered as current density fields and interpolated with the dipoles generated by each electrode for a dynamic study of the effective connectivity. One more recording session was undertaken six months after the placement of the final implant. RESULTS. Compared to the baseline, the new prosthodontic implant induced a novel modulation of the neuroplasticity in sensory-motor areas which was maintained following the definitive implant after six months, as revealed by changes in the effective connectivity from the basal strong enslavement of a single brain area over the others, to an equilibrate inter-related connectivity evenly distributed along the frontotemporal regions of both hemispheres. CONCLUSIONS. The rapid shift of the effective connectivity after positioning the new prosthodontic implant and its substantial stability after six months suggest the possibility that synaptic modifications, induced by novel sensory motor conditions, modulate the neuroplasticity and reshape the final dynamic frame of the interarea connectivity. Moreover, given the viability of the EEG practice, this approach could be of some interest in assessing the association between oral pathophysiology and neuronal networking.","PeriodicalId":93815,"journal":{"name":"Signals","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44723498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}