Model of Signal Mark Detection in a Passive Sonar System

Modeling of broadband and narrowband signal mark detection is widely used for sonar and radar systems. In this work, the problem is considered in relation to hydroacoustics. The paper describes the formation of a stream of correctly detected and false signal marks and calculation of estimates of their parameters, taking into account the antenna characteristics as well as the processing parameters of the system being simulated. Also considered are the realistic distribution of false signal marks by heading angles and the influence of the Doppler effect on the estimation of the mark parameters. The resulting model can be used in simulation systems, in the formation of a stream of detected signal marks, and the development of tracking algorithms. The model can be also used for predictive calculations that determine the probability of detecting signal sources and their characteristics

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

Advanced Watermarking o f Digital Images Showing Robust, Semi Fragile a nd Fragile Behaviour

Digital Image Watermarking plays an important role when it comes to maintaining digital color picture authentication information. The proposed paper consists mainly of a digital watermarking scheme consisting of discrete wavelet transformation and involving the generation of pn sequence number to embed the watermark and also to extract the watermark from the host image. The technique suggested includes both embedding the watermark and removing it from the host file. Both the method of embedding and extraction consists of generating the pn sequence number values using the random numbers. The technique for all three of the RGB signal sources is included. The watermark symbol is located independently within the RGB image's red, green, and blue channels. The suggested technique further reveals the improved mode of digital watermarking of images through fragile watermarking of images and semi-fragile digital watermarking of images.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain machine interfaces (BMI), which harness neural signals to reanimate the limbs via electrical activation of the muscles, or to control extra-corporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements using bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. While the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and non-invasive strategies for sensory restoration.

Neuroorganoleptics: Organoleptic Testing Based on Psychophysiological Sensing

There is an increasing interest, in consumer behaviour research related to food and beverage, in taking a step further from the traditional self-report questionnaires and organoleptic properties assessment. With the growing availability of psychophysiological data acquisition devices, and advancements in the study of the underlying signal sources seeking affective state assessment, the use of psychophysiological data analysis is a natural evolution in organoleptic testing. In this paper we propose a protocol for what can be defined as neuroorganoleptic analysis, a method that combines traditional approaches with psychophysiological data acquired during sensory testing. Our protocol was applied to a case study project named MobFood, where four samples of food were tested by a total of 83 participants, using preference and acceptance tasks, across three different sessions. Best practices and lessons learned regarding the laboratory setting and the acquisition of psychophysiological data were derived from this case study, which are herein described. Preliminary results show that certain Heart Rate Variability (HRV) features have a strong correlation with the preferences self-reported by the participants.

Inference of time series components by online co-evolution

Time series data is often composed of a multitude of individual, superimposed dynamics. We propose a novel algorithm for inferring time series compositions through evolutionary synchronization of modular networks (ESMoN). ESMoN orchestrates a set of trained dynamic modules, assuming that some of those modules’ dynamics, suitably parameterized, will be present in the targeted time series. With the help of iterative co-evolution techniques, ESMoN optimizes the activities of its modules dynamically, which effectively synchronizes the system with the unfolding time series signal and distributes the dynamic subcomponents present in the time series over the respective modules. We show that ESMoN can adapt modules of different types. Moreover, it is able to precisely identify the signal components of various time series dynamics. We thus expect that ESMoN will be useful also in other domains—including, for example, medical, physical, and behavioral data domains—where the data is composed of known signal sources.