Neighboring Signals Research Articles

DNTFE-Net: Distant Neighboring-Temporal Feature Enhancement Network for side scan sonar small object detection

Side-scan sonar small target detection becomes fundamental work for side-scan sonar applications which holds vital importance for marine engineering, maritime military, and so on. However, existing algorithms suffer from performance degradation and poor portability when sonar data is limited. We find that benefit from the strong temporal correlation of sonar images, background information from the long-term images and foreground information from the short-term images can be used to enhance small targets in the current image. To fully utilize these two cues, this paper presents the Distant Neighboring-Temporal Feature Enhancement Network (DNTFE-Net) for detecting small objects in sonar images. The algorithm is based on the signal acquisition process of sonar devices, which is considered a continuous time sequence. Firstly, a flexible foreground feature alignment module is developed to align the foreground information between neighboring signals and the current signal so that foreground information can be used to enhance small target features. Then, we propose a background comparison module for selecting distant-temporal signals that provide the most contextual information in order to improve the ability of migrating to different domains. Finally, a foreground background feature amalgamation module is designed to fuse neighboring and distant-temporal signals for enhancing small target features. Furthermore, real sonar data is collected to compose the real continuous sonar dataset. Our DNTFE-Net exhibits superior recognition performance on the real sonar dataset compared to other popular methods, with an improvement of at least 27%. Our algorithms are publicly available at https://github.com/zbyhnu/DNTFE-Net.git.

A Local Self-Attention Sentence Model for Answer Selection Task in CQA Systems

Current evidence indicates that the semantic representation of question and answer sentences is better generated by deep neural network-based sentence models than traditional methods in community answer selection tasks. In particular, as a widely recognized language model, the self-attention model computes the similarity between the specific word and the whole sets of words in the same sentence and generates new semantic representation through the similarity-weighted summation of semantic representations of the whole words. However, the self-attention operation entirely considers all the signals with a weighted sum operation, which disperses the distribution of attention, which may result in overlooking the relation of neighboring signals. This issue becomes serious when applying the self-attention model to online community question answering platforms because of the varied length of the user-generated questions and answers. To address this problem, we introduce an attention mechanism enhanced local self-attention (LSA), which restricts the range of original self-attention by a local window mechanism, thereby scaling linearly when increasing the sequence length. Furthermore, we propose stacking multiple LSA layers to model the relationship of multiscale <inline-formula> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula>-gram features. It captures the word-to-word relationship in the first layer and then captures the chunk-to-chunk (such as lexical <inline-formula> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula>-gram phrases) relationship in its deeper layers. We also test the effectiveness of the proposed model by applying the learned representation through the LSA model to a Siamese and a classification network in community question answer selection tasks. Experiments on the public datasets show that the proposed LSA achieves a good performance.

Adjusting the range of cell–cell communication enables fine-tuning of cell fate patterns from checkerboard to engulfing

During development, spatio-temporal patterns ranging from checkerboard to engulfing occur with precise proportions of the respective cell fates. Key developmental regulators are intracellular transcriptional interactions and intercellular signaling. We present an analytically tractable mathematical model based on signaling that reliably generates different cell type patterns with specified proportions. Employing statistical mechanics, We derived a cell fate decision model for two cell types. A detailed steady state analysis on the resulting dynamical system yielded necessary conditions to generate spatially heterogeneous patterns. This allows the cell type proportions to be controlled by a single model parameter. Cell–cell communication is realized by local and global signaling mechanisms. These result in different cell type patterns. A nearest neighbor signal yields checkerboard patterns. Increasing the signal dispersion, cell fate clusters and an engulfing pattern can be generated. Altogether, the presented model allows us to reliably generate heterogeneous cell type patterns of different kinds as well as desired proportions.

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing.

Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.

Enhancing context representations with part-of-speech information and neighboring signals for question classification

Question classification is an essential task in question answering (QA) systems. An effective and efficient question classification model can not only restrict the search space for answers, but also guide the QA system in selecting the optimal knowledge base and search strategy. In recent years, self-attention mechanism has been widely used in question classification for its strength of capturing global dependencies. However, it models all signals with weighted averaging, which is prone to overlooking the relation of neighboring signals. Furthermore, recent research has revealed that part-of-speech (POS) information can be used to determine and reinforce the semantics in sentence representation. In this paper, we propose a POS-aware adjacent relation attention network (POS-ARAN) for question classification, which enhance context representations with POS information and neighboring signals. To consider the local context, we propose an adjacent relation attention mechanism, which incorporates a Gaussian bias via a dynamic window to revise the vanilla self-attention mechanism. Thus, it can capture both the long-term dependency and local representation of semantic relations among words in different sentences. In addition, a POS-aware embedding layer is proposed, which helps to locate the appropriate headwords by syntactic information. Extensive experiments are conducted on Experimental Data for Question Classification (EDQC) dataset and Yahoo! Answers Comprehensive Questions and Answers 1.0, the results demonstrate that our model significantly outperforms the existing methods, achieving 95.59% in coarse-grained level accuracy and 92.91% in fine-grained level accuracy, respectively.

NIMG-29. ELEVATION OF GLUTAMINE AND CITRATE BY MR SPECTROSCOPY IS AN IMAGING BIOMARKER OF RAPID CELL PROLIFERATION IN GLIOMAS

Abstract Prior studies suggested glutamine metabolism in cancers may be altered to meet the high demands of nucleotide biosynthesis in cancers. We conducted 1H MR spectroscopy in 18 glioma patients and analyzed the metabolic data together with post-gadolinium MRI and cell proliferation rate (MIB-1 label index). The optimized MRS (TE 97 ms PRESS) provided well discernible signal of glutamine at 2.45 ppm and a negative-polarity signal of citrate at 2.6 ppm, without considerable overlaps with neighboring signals. The 18 patients had biopsy-proven anaplastic gliomas or glioblastomas. Concurrent elevation of glutamine and citrate was identified in 15 gliomas. These gliomas presented with enhancement in post-gadolinium MRI, indicative of broken blood-brain barrier (BBB). The 15 gliomas were grouped as subset-1 while the other 3 gliomas that had elevated citrate without elevation of glutamine were grouped as subset-2. Citrate level was significantly different between the two subsets of gliomas (1.4+/-0.5 vs. 1.6+/-0.4 mM). However, subst-1 had significantly higher choline (4.7+/-1.8 vs. 1.6+/-0.3 mM, p&amp;lt; 0.01) and higher MIB-1 compared with subset-2. Given that choline is a cellularity marker, a finding of high choline and high MIB-1 with elevation of glutamine and citrate suggests that the tumors have high tumor cellularity and cell multiplication competence. Of the 18 gliomas, 13 were IDH mutated with elevated 2HG. The glutamine and citrate levels in IDH-mutant gliomas were not significantly different from those in IDH wildtype gliomas. In conclusion, high-grade gliomas undergo a metabolic rearrangement of concurrent elevation of glutamine and citrate to attain malignant characters such as high cellularity, rapid cell proliferation, and BBB breakdown. IDH mutant and IDH wildtype gliomas may share a common metabolic rearrangement of glutamine-mediated citrate elevation to attain malignancy. Measurements of glutamine and citrate may serve a potential imaging biomarker for aggressive gliomas.

Role of Self Phase Modulation and Cross Phase Modulation on Quality of Signal in Optical Links

Background: In WDM networks, there is a crucial need to monitor signal degradation factors in order to maintain the quality of transmission. This is more critical in dynamic optical networks as non-linear impairments are network state dependent. Moreover, PLIs are accumulative in nature, so the overall impact is increased tremendously as the length of signal path is increased. The interactions between different impairments along the path also influence their overall impact. Objective: Among the different impairments, the present work focuses on phase modulations owing to the intensities of signals themselves as well as the neighboring signals. It includes the influence of SPM, SPM and XPM, system parameters like signal power, wavelength and fiber parameters like attenuation coefficient, dispersion coefficient and their influence on Q-value and BER. Methods: The analysis is done through a single and two-channel transmitter system with varied power, wavelengths and system parameters. The corresponding optical spectrums are analysed. Results &amp; Conclusion: It has been found that SPM and XPM pose broadening effect on spectrum without any effect on temporal distributions. The magnitude of signal power is among the parameters significantly influencing the broadening of spectrum. The higher the power, the more the magnitude of broadening. It has been found that in order to neglect the impact of input power; its magnitude must be kept below 20 mW. Also, the dispersion and attenuation value need to be carefully as they pose counteracting effect to SPM and XPM for certain values and hence can be used as compensation measures without any additional cost.

Real-time optical spectrum monitoring in filterless optical metro networks

Filterless optical networks (FONs) have been proposed as a feasible solution for optical metro networks. In addition, as a result of the shorter distance compared to core optical networks, direct detection optical transmission systems can be used in metro FONs, which reduces costs, as compared to coherent detection systems. In this work, we present two optical signal tracking approaches for FONs that allow to detect small frequency laser drift problems and enable safely reducing channel spacing. Additionally, we study the most proper resolution for optical spectrum analyzers continuously scanning the whole C-band. Proposed approaches are: (1) feature-based tracking and (2) residual-based tracking. Experiments are carried out to compare the performance of the approaches for both direct detection and coherent detection systems, in the case of a single optical signal with no neighboring signals nearby in the spectrum, as well as in the case of a group of optical signals closely allocated in the spectrum. The results show that the residual-based approach enriched with contextual information enables to better track and correct the wavelength drifts before they reach their limits (e.g., ± 2.5 GHz), leading to the guard band reduction for what concerns wavelength drifts.

Comprehensive Document Summarization with Refined Self-Matching Mechanism

Under the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization system at the encoder side, which allows the encoder to optimize the encoding information at the global level and effectively improves the memory capacity of conventional LSTM. Inspired by human coarse-to-fine understanding mode, localness is modeled by Gaussian bias to improve contextualization for each sentence, and merged into the self-matching energy. The refined self-matching mechanism not only establishes global document attention but perceives association with neighboring signals. At the decoder side, the pointer network is utilized to perform a two-hop attention on context and extraction state. Evaluations on the CNN/Daily Mail dataset verify that the proposed model outperforms the strong baseline models and statistical significantly.

Granger Causality-Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotational Drivers.

The mechanisms sustaining myocardial fibrillation remain disputed, partly due to a lack of mapping tools that can accurately identify the mechanism with low spatial resolution clinical recordings. Granger causality (GC) analysis, an econometric tool for quantifying causal relationships between complex time-series, was developed as a novel fibrillation mapping tool and adapted to low spatial resolution sequentially acquired data. Ventricular fibrillation (VF) optical mapping was performed in Langendorff-perfused Sprague-Dawley rat hearts (n=18), where novel algorithms were developed using GC-based analysis to (1) quantify causal dependence of neighboring signals and plot GC vectors, (2) quantify global organization with the causality pairing index, a measure of neighboring causal signal pairs, and (3) localize rotational drivers (RDs) by quantifying the circular interdependence of neighboring signals with the circular interdependence value. GC-based mapping tools were optimized for low spatial resolution from downsampled optical mapping data, validated against high-resolution phase analysis and further tested in previous VF optical mapping recordings of coronary perfused donor heart left ventricular wedge preparations (n=12), and adapted for sequentially acquired intracardiac electrograms during human persistent atrial fibrillation mapping (n=16). Global VF organization quantified by causality pairing index showed a negative correlation at progressively lower resolutions (50% resolution: P=0.006, R2=0.38, 12.5% resolution, P=0.004, R2=0.41) with a phase analysis derived measure of disorganization, locations occupied by phase singularities. In organized VF with high causality pairing index values, GC vector mapping characterized dominant propagating patterns and localized stable RDs, with the circular interdependence value showing a significant difference in driver versus nondriver regions (0.91±0.05 versus 0.35±0.06, P=0.0002). These findings were further confirmed in human VF. In persistent atrial fibrillation, a positive correlation was found between the causality pairing index and presence of stable RDs (P=0.0005,R2=0.56). Fifty percent of patients had RDs, with a low incidence of 0.9±0.3 RDs per patient. GC-based fibrillation analysis can measure global fibrillation organization, characterize dominant propagating patterns, and map RDs using low spatial resolution sequentially acquired data.

Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer

Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations. However, the tracking regularizers are often based on the $\ell _p$ -norm which cannot account for important geometrical relationships between neighboring signal elements. We propose a practical approach to using the earth mover's distance (EMD) via the earth mover's distance dynamic filtering (EMD-DF) algorithm for causally tracking time-varying sparse signals when there is a natural geometry to the coefficient space that should be respected (e.g., meaningful ordering). Specifically, this letter presents a new Beckmann formulation that dramatically reduces computational complexity, as well as an evaluation of the performance and complexity of the proposed approach in imaging and frequency tracking applications with real and simulated neurophysiology data.

Sensors of Encoding and Decoding Based on Superstructure Fiber Bragg Gratings Modulated in Amplitude and Phase for applications in systems OCDMA - 63 chip

ABSTRACTIn this paper, we report a series of incremental optical coding and decoding sensors using their respective fiber Bragg grating superstructure (SS-FBG). We performed a comparative study of the encoding/decoding bipolar and unipolar sequences using these grids. The modulation in the refractive index can be changed by discrete jumps in phase (± π) or variations in the amplitude of the specific grid regions. The aim of this study was to evaluate the performance of SS-FBG in autocorrelation and cross-correlation studies. This approach allows us to measure the quality of unipolar and bipolar coding and understand how the decoded signal can be affected by the presence of multiple users on the network. Each generated code is a Gold sequence for bipolar code 63chips and an M sequence for unipolar code 63chips. We use five different codes to represent the users, and we calculate through the signal/noise power ratio (SRNe) the energy used by a certain signal to interfere with a neighboring signal. Our results emphasize the accuracy and flexibility of Bragg gratings (FBG-SS) and showed that this technology is promising not only to Optical Code Division Multiple Access (OCDMA) but also for an extended variety of other optical processing applications.

P6594Granger Causality-based analysis to accurately identify specific electrophenotypes of myocardial fibrillation

Abstract Background Clinical identification of fibrillation drivers remains challenging in both atrial and ventricular fibrillation (VF). In this study, we developed novel tools using granger causality (GC) analysis for quantifying the causal relationship between neighbouring fibrillatory signals. We tested whether it was adaptable to low resolution, limited coverage and sequentially acquired data for quantifying global organisation of VF and mapping regions with stable rotational drivers (RDs). Methods Eighteen Sprague-Dawley rat hearts were perfused ex vivo for optical mapping studies. VF with differing degrees of organisation was induced with carbenoxolone (10–50μM, n=8), or prior maturation of patchy ventricular fibrosis (n=10) generated by ischaemia-reperfusion. After phase mapping, the data was downsampled to 25% of full resolution to develop validated GC-based tools. The causality pairing index (CPI), a global measure of organisation, quantified propagational effects between all neighboring signals. Low-resolution GC-vector maps localized areas harboring RDs and quantified the prevalence of RDs over time using a novel index called circular interdependence value (CIV). These GC-based tools were then adapted to analyze low-resolution multi-electrode electrograms of sixteen persistent-AF (psAF) patients presenting for a first ablation procedure. Results A spectrum of fibrillatory organisation and mechanisms in VF was observed. In rat VF there was a positive correlation between CPI and the number of stable RDs (R2=0.41, p=0.004), and CIV showed a significant difference in driver vs non-driver regions (0.91±0.05 vs 0.35±0.06, p=0.0002). Similarly, in psAF patients, there was a positive correlation between CPI and the number of stable RDs (R2=0.56, p≤0.001). GC vector mapping showed that 8/16 of patients had at least one RD area, and 8/16 had chaotic activity with no RDs. Conclusion Mechanisms of myocardial fibrillation occurs along a spectrum between organized activity with discrete areas harboring RDs and disorganised myocardial activation with no RDs. GC maps can be utilised for identifying regions localising RDs with sequential mapping in limited spatial resolution and coverage. In psAF GC-based analysis accurately identified specific fibrillatory mechanisms from low-resolution mapping. GC vector mapping holds potential for development with human fibrillation data as a mapping tool for driver guided ablation. Acknowledgement/Funding BHF Programme Grant PG/16/17/32069

Neighbour signals perceived by phytochrome B increase thermotolerance in Arabidopsis.

Due to the preeminence of reductionist approaches, understanding of plant responses to combined stresses is limited. We speculated that light-quality signals of neighbouring vegetation might increase susceptibility to heat shocks because shade reduces tissue temperature and hence the likeness of heat shocks. In contrast, plants of Arabidopsis thaliana grown under low-red/far-red ratios typical of shade were less damaged by heat stress than plants grown under simulated sunlight. Neighbour signals reduce the activity of phytochrome B (phyB), increasing the abundance of PHYTOCHROME-INTERACTING FACTORS (PIFs). The phyB mutant showed high tolerance to heat stress even under simulated sunlight, and a pif multiple mutant showed low tolerance under simulated shade. phyB and red/far-red ratio had no effects on seedlings acclimated with nonstressful warm temperatures before the heat shock. The phyB mutant showed reduced expression of several fatty acid desaturase (FAD) genes and less proportion of fully unsaturated fatty acids and electrolyte leakage of membranes exposed to heat shocks. Red-light-activated phyB also reduced thermotolerance of dark-grown seedlings but not via changes in FADs expression and membrane stability. We propose that the reduced photosynthetic capacity linked to thermotolerant membranes would be less costly under shade, where the light input limits photosynthesis.

Role of developmental venous anomalies in etiopathogenesis of demyelinating diseases

Objective: Multiple sclerosis (MS) is a demyelinating autoimmune disease of the central nervous system. T2W-hyperintense demyelinating lesions are detected in cranial magnetic resonance imaging (MRI). Developmental venous anomalies (DVAs) have frequently been detected in enhanced cranial MRI images, and are generally accepted as normal variants of venous development. The aim of the present study was to investigate whether there was an association between demyelinating diseases and venous anomalies.Methods: One hundred five patients who were diagnosed as having MS in accordance with the McDonald diagnostic criteria, and 105 patients who were diagnosed as having vascular headache who had no lesions similar to MS were included in the present retrospective study.Results: DVAs were detected in 31 of the study group and in 14 patients in the control group. A statistically significant higher rate of DVAs and abnormal signal increase in the neighboring tissue was detected in the study group (p = 0.004) (p = 0.006). The DVA was superficially localized in the RRMS, It was deeply located in RIS.Conclusion: Recent studies have emphasized the association of the central vein and the lesion severity of MS with the detection of the central collecting vein in MS lesions. In our study, DVAs, which are generally regarded as innocent developmental anomalies, and neighboring signal increase were found significantly higher in the MS group compared with the control group. The role of DVAs in the etiology of demyelinating lesions must be clarified through comprehensive future studies that use more advanced techniques.

ICA Based Semi-Blind Decoding Method for a Multicell Multiuser Massive MIMO Uplink System in Rician/Rayleigh Fading Channels

A massive multiple-input multiple-output (MIMO) system, which utilizes a large number of antennas at base stations to communicate with multiple user terminals each with a single antenna, is one of the most promising techniques for future wireless communications systems. A successful massive MIMO implementation relies on accurate channel estimation, which is typically performed through pilot sequences. However, the channel estimation performance or massive MIMO performance is limited by pilot contamination due to unavoidable reuse of pilot sequences from terminals in neighboring cells. In this paper, a semi-blind decoding method based on independent component analysis (ICA), channel energy levels, and reference bits is proposed. Specifically, the proposed decoding method uses ICA to separate and decode the received signals and to estimate the channels. The estimated channel energy is used to differentiate the in-cell signals and the neighboring cell signals, and reference bits are applied to identify a desired signal among signals within a cell. The analytical performance results of the proposed decoding method are derived. Numerical results show that the proposed semi-blind decoding method has better bit error rate performance and higher transmit efficiency than the traditional minimum mean-square error decoding method and the singular value decomposition-based decoding method.

Multiple links between shade avoidance and auxin networks.

Auxin has emerged as a key player in the adjustment of plant morphology to the challenge imposed by variable environmental conditions. Shade-avoidance responses, including the promotion of stem and petiole growth, leaf hyponasty, and the inhibition of branching, involve an intimate connection between light and auxin signalling. Low activity of photo-sensory receptors caused by the presence of neighbouring vegetation enhances the activity of PHYTOCHROME INTERACTING FACTORs (PIFs), which directly promote the expression of genes involved in auxin biosynthesis, conjugation, transport, perception, and signalling. In seedlings, neighbour signals increase auxin levels in the foliage, which then moves to the stem, where it reaches epidermal tissues to promote growth. However, this model only partially accounts for shade-avoidance responses (which may also occur in the absence of increased auxin levels), and understanding the whole picture will require further insight into the functional significance of the multiple links between shade and auxin networks.

Low-Complexity Subband Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access

Noncontiguous transmission schemes combined with high power-efficiency requirements pose big challenges for radio transmitter and power amplifier (PA) design and implementation. Due to the nonlinear nature of the PA, severe unwanted emissions can occur, which can potentially interfere with neighboring channel signals or even desensitize the own receiver in frequency division duplexing (FDD) transceivers. In this article, to suppress such unwanted emissions, a low-complexity sub-band DPD solution, specifically tailored for spectrally noncontiguous transmission schemes in low-cost devices, is proposed. The proposed technique aims at mitigating only the selected spurious intermodulation distortion components at the PA output, hence allowing for substantially reduced processing complexity compared to classical linearization solutions. Furthermore, novel decorrelation based parameter learning solutions are also proposed and formulated, which offer reduced computing complexity in parameter estimation as well as the ability to track time-varying features adaptively. Comprehensive simulation and RF measurement results are provided, using a commercial LTE-Advanced mobile PA, to evaluate and validate the effectiveness of the proposed solution in real world scenarios. The obtained results demonstrate that highly efficient spurious component suppression can be obtained using the proposed solutions.

Blind Decoding Based on Independent Component Analysis for a Massive MIMO Uplink System in Microcell Rician/Rayleigh Fading Channels

In a massive multiple-input–multiple-output (MIMO) uplink system, the pilot sequence reuse in neighboring cells causes pilot contamination, causing the decoding performance to degrade significantly. In this paper, a blind decoding method based on independent component analysis (ICA) is proposed without using pilot sequences. The proposed blind decoding method uses ICA to separate the received signals (from in-cell and neighboring cells) and estimate channels. The energy levels of the estimated channels are used to differentiate an in-cell signal from neighboring cell signals. The analytical performance results of the blind decoding method are derived. Numerical results show that the proposed blind decoding scheme outperforms minimum-mean-square-error (MMSE) decoding and zero-forcing (ZF) decoding with imperfect channel state information (CSI). The proposed scheme has only a negligible performance loss compared with MMSE decoding and ZF decoding with perfect CSI.

Cross-Talk Characterization in Passive Neutron Coincidence Counting of Radioactive Waste Drums With Plastic Scintillators

This paper reports on a numerical feasibility study of a passive neutron coincidence counting system for radioactive waste drums with plastic scintillators. The motivation is to replace $^{3}\text{He}$ gas counters generally used for this type of measurement. Indeed, plastic scintillators present several advantages for the measurement of neutron coincidences such as a good efficiency for detecting fast neutrons, short detection time, and low cost comparatively to $^{3}\text{He}$ . However, unlike $^{3}\text{He}$ counters, their high sensitivity to gamma rays and cross talk constitutes a drawback as parasite random and true coincidences are detected together with the useful signal of plutonium. Simulations are performed using the Monte Carlo transport code MCNPX-PoliMi v2.0 coupled to data processing algorithms developed with ROOT data analysis software. Performances of the coincidence counting system are studied for the case of a vitrified waste drum containing Pu and $^{241}\text{Am}$ , focusing particularly on multiplicity 1 and 2, i.e., 2 or 3 pulses recorded in a short time gate in different detectors. Cross talk induced by neutrons and gamma rays has been characterized in terms of time and distance between detectors, and strategies to limit this phenomenon are reported, consisting of ignoring neighboring detectors signal. A significant improvement of the Pu to $^{241}\text{Am}$ ratio for multiplicity 2 coincidences has thus been obtained, at the expense of counting statistics. Alternative case studies with organic and metallic matrixes of technological wastes are also reported, for which the part of useful signal of plutonium is significantly higher, showing the feasibility of the measurement method.