Hasil untuk "eess.SP"

Georg Essl

Topological filters via sheaves generalize the classical linear translation-invariant filter theory by attaching the filter computation locally to a simplicial topological space. This paper develops topological filters for causal signal flow over a 2-simplex. Our construction retains the established construction for 1-simplices and we show how an apparent conflict in the sheaf assignment can be resolved by a concurrent extension, which introduces an auxiliary 1-simplex that computes the resolution. Furthermore, we discuss how singularities formed by double cone connections can be resolved.

O. S. Sunish Kumar, Lutz Lampe, Shenghang Luo et al.

We propose a fiber nonlinearity post-compensation technique using the DNN and the second-order perturbation theory. We achieve 1 dB Q-factor improvement for a 32 Gbaud PDM-64-QAM at 1200 km compared to the linear dispersion compensation.

Troels Pedersen, Ramoni Adeogun

A polarimetric model for the power delay spectrum for inroom communication is proposed. The proposed model describes the gradual depolarization of the signal with delay. The model is based on the theory of room electromagnetics, specifically the mirror source approach, which is straightforwardly generalized to the polarimetric case. Compared to the previously known unipolarized room electromagnetic models, which are contained as a special case, the new model holds one additional parameter describing the polarization leakage per wall bounce. \tprev{The proposed model is found to fit well to two sets of polarimetric data one mm-wave and one cm-wave measurements

Shiva Akbari, Shahrokh Valaee

This paper proposes a cooperative angle-of-arrival(AoA) estimation, taking advantage of co-processing channel state information (CSI) from a group of access points that receive signals of the same source. Since received signals are sparse, we use Compressive Sensing (CS) to address the AoA estimation problem. We formulate this problem as a penalized l0-norm minimization, reformulate it as an Ising energy problem, and solve it using Markov Chain Monte Carlo (MCMC). Simulation results show that our proposed method outperforms the existing methods in the literature.

Astrid Barreiro, Gabriele Liga, Alex Alvarado

We study the performance of gradient-descent optimization to estimate the coefficients of the discrete-time first-order regular perturbation (FRP). With respect to numerically computed coefficients, the optimized coefficients yield a model that (i) extends the FRP range of validity, and (ii) reduces the model's complexity.

R. Ildar

This manuscript presented an autonomous home farm for predicting metrological characteristics that can not only automate the process of growing crops but also, due to a neural network, significantly increase the productivity of the farm. The developed farm monitors and manages the following indicators: illumination, soil PH, air temperature, ground temperature, air humidity, CO2 concentration, and soil moisture. The presented farm can also be considered as a device for testing various weather conditions to determine the optimal temperature characteristics for different crops. This farm as a result is completely autonomous grows tomatoes at home.

Zijian Wang, Wen Chen

A multiantenna multiuser broadcast channel with transmitter beamforming and user selection is considered. Different from the conventional works, we consider imperfect channel state information (CSI) which is a practical scenario for multiuser broadcast channels. We propose a robust regularized zero-forcing (RRZF) beamforming at the base station. Then we show that the RRZF outperforms zero-forcing (ZF) and regularized ZF (RZF) beamforming even as the number of users grows to infinity. Simulation results validate the advantage of the proposed robust RZF beamforming.

Yang Yu, Wen Chen, Lili Wei

In this letter, we present a hybrid iterative decoder for non-binary low density parity check (LDPC) codes over binary erasure channel (BEC), based on which the recursion of the erasure probability is derived to design non-binary LDPC codes with convergence-optimized degree distributions. The resulting one-step decoding tree is cycle-free and achieves lower decoding complexity. Experimental studies show that the proposed convergence-optimization algorithm accelerates the convergence process by 33%.

Samar Elmeadawy, Raed M. Shubair

5G wireless communications technology is being launched, with many smart applications being integrated. However, 5G specifications merge the requirements of new emerging technologies forcefully. These include data rate, capacity, latency, reliability, resources sharing, and energy/bit. To meet these challenging demands, research is focusing on 6G wireless communications enabling different technologies and emerging new applications. In this report, the latest research work on 6G technologies and applications is summarized, and the associated research challenges are discussed.

Ahmed A. Al-habob, Octavia A. Dobre

This article provides an overview of mobile edge computing (MEC) and artificial intelligence (AI) and discusses the mutually-beneficial relationship between them. AI provides revolutionary solutions in nearly every important aspect of the MEC offloading process, such as resource management and scheduling. On the other hand, MEC servers are utilized to avail a distributed and parallelized learning framework, namely mobile edge learning.

Buenyamin Oezkaya, Peter A. Gloor

By measuring the electrostatic discharge of human bodies together with Mimosa Pudica and other plants in response to the human movement, we have been able to recognize (a) individuals based on their distinctive pattern of body movements with 66% accuracy as well as (b) positive or negative mood based on their gait characteristics with 85% accuracy. We use the Plant SpikerBox, a device that measures the electrical action potential while also measuring the electrostatic discharge between the electrode on the leaves of a plant and the capacitively coupled human body.

Ahmed Kord, Andrea Alu

In this paper, we discuss recent progress in magnet-free non-reciprocal structures based on a synthetic form of angular momentum bias imparted via spatiotemporal modulation. We discuss how such components can support metrics of performance comparable with traditional magnetic-biased ferrite devices, while at the same time offering distinct advantages in terms of reduced size, weight, and cost due to the elimination of magnetic bias. We further provide an outlook on potential applications and future directions based on these components, ranging from wireless full-duplex communications to metasurfaces and topological insulators.

Mario Garrido

When we study the Karnaugh map in the switching theory course, we learn that the ones in the map must be combined in groups of $a \times b$ elements, being $a$ and $b$ powers of two. The result is the logic function described as a sum of products. This paper shows that we can also make groups where $a$ and/or $b$ are equal to three. This does not result in a sum of products, but in a logic function that is simpler than the sum of products in terms of logic gates. This idea is extended later in the paper to groups of $2^n-1$ elements.

Andreas Brendel, Walter Kellermann

Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenvalue decomposition. The presented update rules are shown to outperform competing state-of-the-art methods in terms of convergence speed at a comparable runtime due to the restriction to unitary demixing matrices. This is demonstrated by experiments with recorded real-world data.

Andreas Brendel, Thomas Haubner, Walter Kellermann

We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm, a blind source separation algorithm, by incorporating a prior over the demixing matrices, relying on a free-field model. In this way, the outer permutation ambiguity of IVA is avoided. The resulting MAP optimization problem is solved by deriving majorize-minimize update rules to achieve convergence speed comparable to the well-known auxiliary function IVA algorithm. The performance of the proposed algorithm is investigated and compared to a benchmark algorithm using real measurements.

Ayan Mandal, Asish Koruprolu

This paper presents a low-power 10-bit 130-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 90 nm CMOS process. The proposed asynchronous ADC consists of a comparator, SAR logic block and two control blocks for the capacitive digital to analog converters (DAC). At a 1.2 V supply and 130 MS/s, the ADC achieves an SNDR of 55.2 dB and consumes 860 uW, resulting in a figure of merit (FOM) of 50.9 fJ/MHz. It achieves an ENOB of 8.8 bits with a differential input range of 1570 mV.

Yuji Ito, Yukihiro Tadokoro

A nanoscale receiver utilizing the cantilever of a carbon nanotube has been developed to detect phase information included in transmitted signals. The existing receiver consists of a phase detector and demodulator which employ a reference wave and carrier signal, respectively. This paper presents a design method to simplify the receiver in structure with enhancing the performance for the phase detection. The reference wave or carrier signal is not needed in the receiver via the proposed design method.

Alexandre Aksenov, Pierre-Olivier Amblard, Olivier Michel et al.

The measure timetable plays a critical role for the accuracy of the estimator. This article deals with the optimization of the schedule of measures for observing a random process in time using a Kalman filter, when the length of the process is finite and fixed, and a fixed number of measures are available. The measuring devices are allowed to differ. The mean variance of the estimator is chosen as criterion for optimality. The cases of $1$ or $2$ measures are studied in detail, and analytical formulas are provided.

Dawei Gao, Qinghua Guo

This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. In this paper, we propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference, and a circulant input weight matrix is employed, which significantly reduces the complexity of the receiver with the fast Fourier transform (FFT). It is demonstrated that the proposed receiver can efficiently handle the LED nonlinearity and cross-LED interference.

Jun Zhang, Fei-Yue Wang

Chinese power grid enterprises are in need for development of digital asset management system. The characteristics of decentralization, self-trust and self-confidence, pave a promising technical path for power grid digital assess management and Big Data applications. This article firstly introduces the state-of-the-art of power grid Big Data and digital asset management, and the related issues in power grid enterprises. The solution of blockchain based digital asset management technology and its implementation are presented in details, followed by a discussion of their future developmental directions.