A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder

Turbo-Codes (TC) are a family of convolutional codes enabling Forward-Error-Correction (FEC) while approaching the theoretical limit of channel capacity predicted by Shannons theorem. One of the bottlenecks of a Turbo Encoder (TE) lies in the non-uniform interleaving stage. Interleaving algorithms require stalling the input vector bits before the bit rearrangement causing a delay in the overall process. This paper presents performance enhancement via a parallel algorithm for the interleaving stage of a Turbo Encoder application compliant with the DVB-RCS2 standard. The algorithm efficiently implements the interleaving operation while utilizing attributes of a given DSP. We will discuss and compare a serial model for the TE, with the presented parallel processed algorithm. Results showed a speed-up factor of up to 3.4 Total-Cycles, 4.8 Write and 7.3 Read

Matrix columns allocation problems

AbstractOrthogonal Frequency Division Multiple Access (OFDMA) transmission technique is gaining popularity as a preferred technique in the emerging broadband wireless access standards. Motivated by the OFDMA transmission technique we define the following problem: Let M be a matrix (over R) of size a×b. Given a vector of non-negative integers C→=〈c1,c2,…,cb〉 such that ∑cj=a, we would like to allocate a cells in M such that (i) in each row of M there is a single allocation, and (ii) for each element ci∈C→ there is a unique column in M which contains exactly ci allocations. Our goal is to find an allocation with minimal value, that is, the sum of all the a cells of M which were allocated is minimal. The nature of the suggested new problem is investigated in this paper. Efficient algorithms are suggested for some interesting cases. For other cases of the problem, NP-hardness proofs are given followed by inapproximability results

Container Allocation in Cloud Environment Using Multi-Agent Deep Reinforcement Learning

Nowadays, many computation tasks are carried out using cloud computing services and virtualization technology. The intensive resource requirements of virtual machines have led to the adoption of a lighter solution based on containers. Containers isolate packaged applications and their dependencies, and they can also operate as part of distributed applications. Containers can be distributed over a cluster of computers with available resources, such as the CPU, memory, and communication bandwidth. Any container distribution mechanism should consider resource availability and their impact on overall performance. This work suggests a new approach to assigning containers to servers in the cloud, while meeting computing and communication resource requirements and minimizing the overall task completion time. We introduce a multi-agent environment using a deep reinforcement learning-based decision mechanism. The high action space complexity is tackled by decentralizing the allocation decisions among multiple agents. Considering the interactions among the agents, we introduce a new cooperative mechanism for a state and reward design, resulting in efficient container assignments. The performances of both long short term memory (LSTM) and memory augmented-based agents are examined, for solving the challenging container assignment problem. Experimental results demonstrated an improvement of up to 28% in the execution runtime compared to existing bin-packing heuristics and the common Kubernetes industrial tool

Unsupervised Drones Swarm Characterization Using RF Signals Analysis and Machine Learning Methods

Autonomous unmanned aerial vehicles (UAVs) have attracted increasing academic and industrial attention during the last decade. Using drones have broad benefits in diverse areas, such as civil and military applications, aerial photography and videography, mapping and surveying, agriculture, and disaster management. However, the recent development and innovation in the field of drone (UAV) technology have led to malicious usage of the technology, including the penetration of secure areas (such as airports) and serving terrorist attacks. Autonomous weapon systems might use drone swarms to perform more complex military tasks. Utilizing a large number of drones, simultaneously increases the risk and the reliability of the mission in terms of redundancy, survivability, scalability, and the quality of autonomous performance in a complex environment. This research suggests a new approach for drone swarm characterization and detection using RF signals analysis and various machine learning methods. While most of the existing drone detection and classification methods are typically related to a single drone classification, using supervised approaches, this research work proposes an unsupervised approach for drone swarm characterization. The proposed method utilizes the different radio frequency (RF) signatures of the drone’s transmitters. Various kinds of frequency transform, such as the continuous, discrete, and wavelet scattering transform, have been applied to extract RF features from the radio frequency fingerprint, which have then been used as input for the unsupervised classifier. To reduce the input data dimension, we suggest using unsupervised approaches such as Principal component analysis (PCA), independent component analysis (ICA), uniform manifold approximation and projection (UMAP), and the t-distributed symmetric neighbor embedding (t-SNE) algorithms. The proposed clustering approach is based on common unsupervised methods, including K-means, mean shift, and X-means algorithms. The proposed approach has been evaluated using self-built and common drone swarm datasets. The results demonstrate a classification accuracy of about 95% under additive Gaussian white noise with different levels of SNR

Time-Frequency Analysis for Feature Extraction Using Spiking Neural Network

Time-frequency analysis plays a crucial role in various fields, including signal processing and feature extraction. In this article, we propose an alternative and innovative method for time-frequency analysis using a biologically inspired spiking neural network (SNN), encompassing both specific spike-continuous-time-neuron (SCTN) based neural architecture and an adaptive learning rule.  We aim to efficiently detect frequencies embedded in a given signal for the purpose of feature extraction. To achieve this, we suggest using an SN-based network functioning as a resonator for the detection of specific frequencies. We developed a modified supervised Spike-Timing-Dependent Plasticity (STDP) learning rule to effectively adjust the network parameters.  Unlike traditional methods for time-frequency analysis, our approach obviates the need for segmenting the signal into several frames, resulting in a streamlined and more effective frequency analysis process. Simulation results demonstrate the efficiency of the proposed method, showcasing its ability to detect frequencies and  generate a Spikegram akin to the Fast Fourier Transform (FFT) based spectrogram. The proposed approach is applied to analyzing EEG signals demonstrating an accurate correlation to the equivalent FFT transform.</p

Can laboratory evaluation differentiate between coronavirus disease-2019, influenza, and respiratory syncytial virus infections? A retrospective cohort study

Aim To identify clinical and laboratory parameters that can assist in the differential diagnosis of coronavirus disease 2019 (COVID-19), influenza, and respiratory syncytial virus (RSV) infections. Methods In this retrospective cohort study, we obtained basic demographics and laboratory data from all 685 hospitalized patients confirmed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus, or RSV from 2018 to 2020. A multiple logistic regression was employed to investigate the relationship between COVID19 and laboratory parameters. Results SARS-CoV-2 patients were significantly younger than RSV (P=0.001) and influenza virus (P=0.022) patients. SARS-CoV-2 patients also displayed a significant male predominance over influenza virus patients (P=0.047). They also had significantly lower white blood cell count (median 6.3×106 cells/μ) compared with influenza virus (P<0.001) and RSV (P=0.001) patients. Differences were also observed in other laboratory values but were insignificant in a multivariate analysis. Conclusions Male sex, younger age, and low white blood cell count can assist in the diagnosis of COVID-19 over other viral infections. However, the differences between the groups were not substantial enough and would probably not suffice to distinguish between the viral illnesses in the emergency departmen