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Thanks to its non-destructive, high-resolution imaging possibilities and its sensitivity to both conductive and dielectric subsurface structures, Ground-Penetrating Radar (GPR) has become a widely recognized near-surface geophysical tool, routinely adopted in a wide variety of disciplines. Since its first development almost 100 years ago, the domain in which the methodology has been successfully deployed has significantly expanded from ice sounding and environmental studies to precision agriculture and infrastructure monitoring. While such expansion has been clearly supported by the evolution of technology and electronics, the operating principles have always secured GPR a predominant position among alternative inspection approaches. The aim of this contribution is to provide a large-scale survey of the current areas where GPR has emerged as a valuable prospection methodology, highlighting the reasons for such prominence and, at the same time, to suggest where and how it could be enhanced even more. Xunta de Galicia | Ref. ED431F 2021/08 Agencia Estatal de Investigación | Ref. RYC2019-026604-I

We report the synthesis, magnetic and conducting properties of a new manganese complex [MnII(hfac)2(IMHPhOPh)]2[MnII(hfac)3](IMH2PhOPh) (1), containing the reduced forms (IMHPhOPh and IMH2PhOPh) of the NIT-PhOPh radical (NIT-PhOPh = 2,4‘-benzoxo-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide). Direct current magnetic susceptibility studies revealed the presence of weak antiferromagnetic exchange interactions between the metal ions, within the [MnII(hfac)2(IMHPhOPh)]2 unit. The EPR spectra of 1 exhibit simultaneous contributions of both the monomeric [MnII(hfac)3] and dimeric [MnII(hfac)2(IMHPhOPh)]2 species. Conductivity measurements shown that the electrical conduction of 1 is dominated by Ohm’s law, with a conductance value of 1.3 (6) × 10−9 S/cm.

Summary Open-shell molecular radicaloids could constitute the key to molecular quantum information and quantum sensing technologies. The effect of their morphology on the quantum properties is anyway unknown, hampering the development of synthetic strategies. Herein, we establish the links between morphology and quantum properties, using three related radicaloids based on meta-quinodimethane. We unravel the roles of the π-conjugated backbone and those of the side groups on the spin-flip and quantum coherence times. The temperature regions are identified where different structural parts of the molecule or solvent become the dominant decoherence channel. The record quantum coherence values obtained at room temperature are still well below the intrinsic limits of radicaloids, and we discuss the directions to optimize the quantum performance.

Objectives The DETECT algorithm has been developed to identify SSc patients at risk for pulmonary arterial hypertension (PAH) yielding high sensitivity but low specificity, and positive predictive value. We tested whether cardiopulmonary exercise testing (CPET) could improve the performance of the DETECT screening strategy. Methods Consecutive SSc patients over a 30-month period were screened with the DETECT algorithm and positive subjects were referred for CPET before the execution of right-heart catheterization. The predictive performance of CPET on top of DETECT was evaluated and internally validated via bootstrap replicates. Results Out of 314 patients, 96 satisfied the DETECT application criteria and 54 were positive. PAH was ascertained in 17 (31.5%) and pre-capillary pulmonary hypertension in 23 (42.6%) patients. Within CPET variables, the slope of the minute ventilation to carbon dioxide production relationship (VE/VCO2 slope) had the best performance to predict PAH at right-heart catheterization [median (interquartile range) of specificity 0.778 (0.714–0.846), positive predictive value 0.636 (0.556–0.750)]; exploratory analysis on pre-capillary yielded a specificity of 0.714 (0.636–0.8) and positive predictive value of 0.714 (0.636–0.8). Conclusion In association with the DETECT algorithm, CPET may be considered as a useful tool in the workup of SSc-related pulmonary hypertension. The sequential determination of the VE/VCO2 slope in DETECT-positive subjects may reduce the number of unnecessary invasive procedures without any loss in the capability to capture PAH. This strategy had also a remarkable performance in highlighting the presence of pre-capillary pulmonary hypertension.

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction., 21 pages Published in Sensors: https://www.mdpi.com/1424-8220/20/19/5450

Mild cognitive impairment (MCI) is a heterogeneous syndrome with two main clinical subtypes, amnestic (aMCI) and non-amnestic (naMCI). The analysis of heart rate variability (HRV) is a tool to assess autonomic function. Cognitive and autonomic processes are linked via the central autonomic network. Autonomic dysfunction entails several adverse outcomes. However, very few studies have investigated autonomic function in MCI and none have considered MCI subtypes or the relationship of HRV indices with different cognitive domains and structural brain damage. We assessed autonomic function during an active orthostatic challenge in 253 oupatients aged ≥ 65, [n = 82 aMCI, n = 93 naMCI, n = 78 cognitively normal (CN), neuropsychologically tested] with power spectral analysis of HRV. We used visual rating scales to grade cerebrovascular burden and hippocampal/insular atrophy (HA/IA) on neuroimaging. Only aMCI showed a blunted response to orthostasis. Postural changes in normalised low frequency (LF) power and in the LF to high frequency ratio correlated with a memory test (positively) and HA/IA (negatively) in aMCI, and with attention/executive function tests (negatively) and cerebrovascular burden (positively) in naMCI. These results substantiate the view that the ANS is differentially impaired in aMCI and naMCI, consistently with the neuroanatomic substrate of Alzheimer's and small-vessel subcortical ischaemic disease.

Among all the forensic applications in which it has become an important exploration tool, ground penetrating radar (GPR) methodology is being increasingly adopted for buried landmine localisation, a framework in which it is expected to improve the operations efficiency, given the high resolution imaging capability and the possibility of detecting both metallic and non-metallic landmines. In this context, this study presents landmine detection equipment based on multi-polarisation: a ground coupled GPR platform, which ensures suitable penetration/resolution performance without affecting the safety of surveys, thanks to the inclusion of a flexible ballistic shielding for supporting eventual blasts. The experimental results have shown that not only can the blanket absorb blast-induced flying fragments impacts, but that it also allows for the acquisition of data with the accuracy required to generate a correct 3D reconstruction of the subsurface. The produced GPR volume is then processed through an automated learning scheme based on a Convolutional Neural Network (CNN) capable of detecting buried objects with a high degree of accuracy.

Objectives: The goal of this study was to assess the diagnostic performance of coronary computed tomography angiography (CTA) alone, adenosine-stress myocardial perfusion assessed by computed tomography (CTP) alone, and coronary CTA + CTP by using a 16-cm Z-axis coverage scanner versus invasive coronary angiography (ICA) and fractional flow reserve (FFR) as the clinical standard. Background: Diagnostic performance of coronary CTA for in-stent restenosis detection is still challenging. Recently, CTP showed additional diagnostic power over coronary CTA in patients with suspected coronary artery disease. However, few data are available on CTP performance in patients with previous stent implantation. Methods: Consecutive stable patients with previous coronary stenting referred for ICA were enrolled. All patients underwent stress myocardial CTP and rest CTP + coronary CTA. Invasive FFR was performed during ICA when clinically indicated. The diagnostic rate and diagnostic accuracy of coronary CTA, CTP, and coronary CTA + CTP were evaluated in stent-, territory-, and patient-based analyses. Results: In the 150 enrolled patients (132 men; mean age 65.1 ± 9.1 years), the CTP diagnostic rate was significantly higher than that of coronary CTA in all analyses (territory based [96.7% vs. 91.1%; p < 0.0001] and patient based [96% vs. 68%; p < 0.0001]). When ICA was used as gold standard, CTP diagnostic accuracy was significantly higher than that of coronary CTA in all analyses (territory based [92.1% vs. 85.5%, p < 0.03] and patient based [86.7% vs. 76.7%, p < 0.03]). The concordant coronary CTA + CTP assessment exhibited the highest diagnostic accuracy values versus ICA (95.8% in the territory-based analysis). The diagnostic accuracy of CTP was significantly higher than that of coronary CTA (75% vs. 30.5%; p < 0.001). The radiation exposure of coronary CTA + CTP was 4.15 ± 1.5 mSv. Conclusions: In patients with coronary stents, CTP significantly improved the diagnostic rate and accuracy of coronary CTA alone compared with both ICA and invasive FFR as gold standard.

Summary This paper presents the results from a Ground Penetrating Radar experimental campaign aiming at characterising the thickness of principal concrete elements of a subway tunnel, following relevant water infiltration phenomena that might have affected their integrity and possibly have degraded their structural stability. Given the requirements of penetration and image quality, the main purpose of the study was to identify an optimal equipment design capable of accurately determine thickness variations of the subsurface structures by the joint analysis of different operational frequency, particularly a 200 MHz and a 600 MHz equipment, and antenna polarisation. While suitable depth performance has been achieved for both frequency bands, the image quality is notably higher when the interference from the metallic elements is minimised, and hence the 600 MHz equipment oriented perpendicular to the armour level has proven to be the best option. Obtained results allow a centimetric characterisation of the vertical extension of the investigated structures, and confirm their deterioration.

Summary The advent of deep learning techniques had a huge impact in the geophysical community. Convolutional Neural Networks have been investigated for interpretation tasks and, lately, for seismic imaging problems traditionally approached with analytical methods. In this manuscript we employ a state-of-the-art CycleGAN for processing migrated images. We show an application scenario in which this post-processing machine transforms migrated images into corresponding reflectivity model of the subsurface. The proposed methodology achieves promising results in both synthetic and field data. The reconstructed reflectivity model exhibits a meaningful topology. However, the results suggest that computer-vision techniques need to be adapted to tackle the complexity of seismic imaging problems. For this purpose, we believe that geophysical knowledge can be embedded into more accurate network design.

Soil morphology plays a fundamental role in the vertical and lateral movements of solutes and water transport, providing knowledge regarding spatial distribution of its textural properties and subsurface dynamics. In this framework, the measured values of electrical conductivity are able to reveal the heterogeneity of soil that is present in a particular agricultural field and they are affected by more than one important physical characteristic: soil texture, organic matter, moisture content, and the depth of the clay pan. In the microwave region, these dynamics are known to exhibit a frequency dependent behaviour. This study explores the application of a Step Frequency Continuous Wave Ground Penetrating Radar (SFCW GPR) to shed light on the practical impact that these dependencies have on the imaging results, not only regarding the electrical characterisation of the subsurface morphology, but also in its correct interpretation. This information is of notable importance for determining water-use efficiency and planning precision-agriculture programs. The results clearly show visible and significant fluctuations of the amplitude levels, depending on the considered central frequency, demonstrating that the frequency dependence of electromagnetic properties of heterogeneous soil are significant and cannot be ignored if the aim is to properly define the subsurface attributes. The measurements also suggest that correlating the delineated variations might help in the identification of extended features and the classification of areas that possess similar properties in order to increase the confidence in monitoring soil resources.

The effectiveness of the ground penetrating radar (GPR) imaging process and its capability of correctly reconstructing buried objects is strictly bounded to a correct acquisition strategy, both in terms of data density and regularity. In some GPR applications, such as landmine detection, these requirements may not be fulfiled due to logistical limitations and environmental obstacles. In the light of autonomous platform, possibly driven by a positioning device, the knowledge of the maximum affordable grid irregularity is essential. This experimental work, employing a data set acquired at a landmine test site, provides a demonstration that the same information content could be maintained even with a sparser data grid, compared to the commonly adopted requirements, mitigating the pressing demand for a precise samples positioning.

The PREdicting bleeding Complications In patients undergoing Stent implantation and subsEquent Dual Antiplatelet Therapy (PRECISE-DAPT) score has been validated to predict bleeding complications in patients undergoing stent implantation and dual antiplatelet therapy. This score does not include the platelet count (PC), which has been shown to be an independent marker of mortality in patients with acute coronary syndrome (ACS). We assessed the role of the PRECISE-DAPT score calculated on admission for mortality risk prediction and evaluated whether the predictive accuracy of this score improved by adding the PC. In a retrospective cohort study of 1000 patients with ACS, after adjustment for relevant covariates, a PRECISE-DAPT score ≥25 was independently associated with mortality (hazard ratio [HR]: 7.91; 95% confidence interval [CI]: 4.37-14.30). When this score was combined with PC, compared to patients with PRECISE-DAPT 9/L, the adjusted HR was 7.2 (95% CI 2.4-21.6) for those with PRECISE-DAPT 9/L; 10.7 (95% CI: 5.2-21.9) for those with PRECISE-DAPT ≥25 and PC ≥150 × 109/L; and 17.9 (95% CI 7.0-45.4) for those with PRECISE-DAPT ≥25 and PC 9/L. Selecting thresholds for high-risk designation, the PRECISE-DAPT score integrated with PC had a higher prediction value, compared to the PRECISE-DAPT and Global Registry of Acute Coronary Events scores.

One of the main characteristics that today makes cloud services so popular is their ability to be elastic, i.e., they can adapt their provisioning to variable workloads, thus increasing resource utilization and reducing operating costs. At the core of any elastic service lies an automatic scaling mechanism that drives provisioning on the basis of a given strategy. In this paper we propose PASCAL , an architecture for Proactive Auto-SCALing of generic distributed services. PASCAL combines a proactive approach, to forecast incoming workloads, with a profiling system, to estimate required provision. Scale-in/out operations are decided according to an application-specific strategy, which aims at provisioning the minimum number of resources needed to sustain the foreseen workload. The main novelties introduced with PASCAL architecture are: (i) a strategy to proactively auto-scale a distributed stream processing system (namely, Apache Storm) with the aim of load balancing operators through an accurate system performance estimation model, and (ii) a strategy to proactively auto-scale a distributed datastore (namely, Apache Cassandra), focused on how to choose when executing scaling actions on the basis of the time needed for the activation/deactivation of storage nodes so as to have the configuration ready when needed. We provide a prototype implementation of PASCAL for both use cases and, through an experimental evaluation conducted on a private cloud, we validate our approach and demonstrate the effectiveness of the proposed strategies in terms of saved resources and response time.

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

Federating Cloud systems is an urgent need of the Public Sector. In this paper, we showcase a recent Cloud Federation-as-a-Service solution empowered by blockchain technology. This solution is used by the Italian Ministry of Economy and Finance to realise a cross-Cloud application for payslip calculation of Police Forces. Blockchain offers decentralised means to conciliate the need of keeping data protected while ensuring certified computation on it. This solution has been adopted as part of the project CloudifyNoiPA to re-engineer the whole payroll system of the Italian Public Administration.

One of the main trends in the evolution of smart grids is transactive energy, where distributed energy resources, e.g. smart meters, develop towards Internet-of-Things (IoT) devices enabling prosumers to trade energy directly among each other, without the need of involving any centralised third party. The expected advantages in terms of cost-effectiveness would be significant, indeed technical solutions are being investigated and large-scale deployment are planned by major utilities companies. However, introducing transactive energy in the smart grid entails new security threats, such as forging energy transactions.This paper introduces an infrastructure to support reliable and cost-effective transactive energy, based on blockchain and smart contracts, where functionalities are implemented as fully decentralised applications. Energy transactions are stored in the blockchain, whose high replication level ensures stronger guarantees against tampering. Energy auctions are carried out according to transparent rules implemented as smart contracts, hence visible to all involved actors. Threats deriving from known vulnerabilities of smart meters are mitigated by temporarily keeping out exposed prosumers and updating their devices as soon as security patches become available.

Reliable landmine detection is still an unresolved problem. Demining operations are complex activities because of the large variety of existing landmine types, many different possible soil and terrain conditions, and environmental circumstances. Due to its ability of detecting both metallic and non-metallic objects, ground penetrating radar (GPR) is a promising method for detecting landmines that may allow faster and safer operations. As the performance of GPR is mainly governed by the target signature, the potential of discriminating target based on the presence of internal reflections could be a valuable advantage for identification and recognition process. This study demonstrates that from a set of high resolution GPR slices the internal design of the landmine can be properly imaged and characterised, confirming the applicability of the methodology and the validity of such an approach.

Distributed stream processing frameworks are designed to perform continuous computation on possibly unbounded data streams whose rates can change over time. Devising solutions to make such systems elastically scale is a fundamental goal to achieve desired performance and cut costs caused by resource over-provisioning. These systems can be scaled along two dimensions: the operator parallelism and the number of resources. In this paper, we show how these two dimensions, as two symbiotic entities, are independent but must mutually interact for the global benefit of the system. On the basis of this observation, we propose a fine-grained model for estimating the resource utilization of a stream processing application that enables the independent scaling of operators and resources. A simple, yet effective, combined management of the two dimensions allows us to propose ELYSIUM, a novel elastic scaling approach that provides efficient resource utilization. We implemented the proposed approach within Apache Storm and tested it by running two real-world applications with different input load curves. The outcomes backup our claims showing that the proposed symbiotic management outperforms elastic scaling strategies where operators and resources are jointly scaled.

Permissioned blockchains are arising as a solution to federate companies prompting accountable interactions. A variety of consensus algorithms for such blockchains have been proposed, each of which has dierent benets and drawbacks. Proof-of-Authority (PoA) is a new family of Byzantine fault-tolerant (BFT) consensus algorithms largely used in practice to ensure better performance than traditional Practical Byzantine Fault Tolerance (PBFT). However, the lack of adequate analysis of PoA hinders any cautious evaluation oftheir eectiveness in real-world permissioned blockchains deployed over the Internet, hence on an eventually synchronous network experimenting Byzantine nodes. In this paper, we analyse two of the main PoA algorithms, named Aura and Clique, both in terms of provided guarantees and performances. First, we derive their functioning including how messages are exchanged, then we weight, by relying on the CAP theorem, consistency, availability and partition tolerance guarantees. We also report a qualitative latency analysis based on message rounds. The analysis advocates that PoA for per-missioned blockchains, deployed over the Internet with Byzantine nodes, do not provide adequate consistency guarantees for scenarios where data integrity is essential. We claimthat PBFT can t better such scenarios, despite a limited loss in terms of performance.

Buried unexploded landmines are a serious threat in many countries all over the World. As many landmines are nowadays mostly plastic made, the use of ground penetrating radar (GPR) systems for their detection is gaining the trend. However, despite several techniques have been proposed, a safe automatic solution is far from being at hand. In this paper, we propose a landmine detection method based on convolutional autoencoder applied to B-scans acquired with a GPR. The proposed system leverages an anomaly detection pipeline: the autoencoder learns a description of B-scans clear of landmines, and detects landmine traces as anomalies. In doing so, the autoencoder never uses data containing landmine traces at training time. This allows to avoid making strong assumptions on the kind of landmines to detect, thus paving the way to detection of novel landmine models.

Ground Penetrating Radar (GPR) is widely recognised as an operationally useful sensor for mine detection as it can offer better detection performance than the ubiquitous metal detector in the presence of low-metal content mines. However, GPR has to overcome many potential sources of false alarm due to clutter and battlefield debris, which lower the efficiency of the sensor. This paper analyses a set of experimental data collected in a recent multi-offset GPR measurement campaign with inert landmines composed of different assemblies buried in sandy soil. The aim of the work is to evaluate the key differences observed by a radar system when the transmitter and the receiver are moved apart, as a function of their distance and hence when the illuminated section of the target is diversified. The results of the comparison between the collected multi-offset profiles show that using a bistatic geometry could represent a strategy to reconstruct composite objects with finer and better details.

Water front in concrete exposed to rapid heating is the layer where water vaporization and the subsequent pore pressure rise take place. Pore pressure is one of the main triggering factors in heat-induced explosive spalling (relevant for structures, such as tunnels, exposed to fire), while moisture migration influences concrete radiation shielding capability (important in containment shells of nuclear power plants and radioactive waste repositories). Hence, the experimental monitoring of water front in concrete at high temperature is a very interesting – though challenging – task. In a recent experimental campaign carried out at Politecnico di Milano, promising results have been obtained by coupling pore pressure-temperature measurements and water front monitoring through Ground-Penetrating Radar. This technique was implemented in a fire test performed on a concrete slab heated at the bottom face and proved to be effective in detecting the position of the water front during heating. The combination with pressure measurement allowed to confirm that pressure peaks are achieved in correspondence of the water front.

Undiagnosed atrial fibrillation (AF) patients are at high risk of cardioembolic stroke or other complications. The aim of this study was to analyze the blood volume pulse (BVP) signals obtained from a wristband device and develop an algorithm for discriminating AF from normal sinus rhythm (NSR) or from other arrhythmias (ARR).Thirty patients with AF, 9 with ARR and 31 in NSR were included in the study. The recordings were obtained at rest from Empatica E4 wristband device and lasted 10 min. The analysis, on a 2 min segment, included spectral, variability and irregularity analysis performed on the inter-diastolic interval series, and similarity analysis performed on the BVP signal. Main results and Significance: Variability parameters were the highest in AF, the lowest in NSR and intermediate for ARR, as an example pNN50 values were, respectively, [Formula: see text], [Formula: see text], [Formula: see text] (p 0.05). The similarity parameters were the highest in NSR, the lowest in AF and intermediate for ARR, as an example using a threshold for assessing similarity of [Formula: see text]: [Formula: see text], [Formula: see text], [Formula: see text], all p 0.05. The rhythm classification was preceded by over-sampling (using synthetic minority over-sampling technique) the class of ARR, being it the smallest class. Then, the features selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (pNN70 and pNN40) as the best selection. The classification by the k-nearest neighbor classifier reached an accuracy of about 0.9 for NSR and AF, and 0.8 for ARR. Using pNN70 and pNN40, the specificity for the three rhythms was Spnsr = 0.928, Spaf = 0.963, Sparr = 0.768, while the sensitivity was Spnsr = 0.773, Spaf = 0.754, Sparr = 0.758.

The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to ground-penetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles.

With the proliferation of low-intensity conflict, landmines have proven to be one of the weapons of choice for both government and guerrilla forces around the world. Recent improvements to mine technology pose increasingly significant problems for demining operations, requiring the constant upgrading of countermine technologies. Ground Penetrating Radar (GPR) is one of the most exhaustively researched topics in the detection of buried mines as it can be used to detect non-metallic and plastic mines. However, identification and recognition are still unsolved problems, due to the scattering similarity between mines and clutter objects. This study provides an experimental evaluation of the improvements that a bistatic approach could yield and what can be gained from investigating the angular dependencies of the landmine radar signature.

Background: To evaluate the changes in autonomic neural control mechanisms before malignant ventricular arrhythmias, we measured heart rate variability (HRV) and heart rate turbulence (HRT) in patients with ventricular tachycardia or fibrillation (Group I; n=6), non sustained ventricular tachycardia (Group II; n=32), frequent premature ventricular beats (Group III; n=26) and with ICD implantation (Group IV; n=11). Methods: Time domain parameters of HRV and turbulence onset (TO) and slope (TS) were calculated on 24 hour Holter recordings. Normal values were: SDNN > 70 msec for HRV, TO 2.5 msec/RR-I for HRT. Results: Whereas SDNN was within normal range and similar in all study groups, HRT parameters were significantly different in patients who experienced VT/VF during Holter recording. Abnormal TO and/or TS were present in 100% of Group I patients and only in about 50% of Group II and IV. On the contrary, normal HRT parameters were present in 40-70% of Group II, III and IV patients and none of Group I. Conclusions: These data suggest that HRT analysis is more suitable than HRV to detect those transient alterations in autonomic control mechanisms that are likely to play a major trigger role in the genesis of malignant cardiac arrhythmias. (Heart International 2007; 3: 51-7)

Landmine clearance is a severe and unresolved humanitarian issue. The development of low-cost, smaller, faster and lighter Ground Penetrating Radars (GPR), which can be mounted on unmanned platforms, will allow faster and safer 24/7 operations. This technology will make it possible to survey affected areas with more flexible trajectories and these will provide measurements of landmine signatures from many different aspect angles. As a result, multi-perspective information over wide angular windows and the behaviour of the signature as a function of the angle of illumination can be exploited. Landmine signatures are expected to present features that are less sensitive to the angle of illumination with respect to those of common cluttered objects, and this can lead to an improvement in detection and discrimination performance. In this paper, we present the results of an experimental trial carried out to collect the High Range Resolution Profiles (HRRPs) of two landmines, the SB-33 and the VS-50, off the ground. An analysis of the auto-correlation function of each range bin as a function of the aspect angle is presented together with that of the cross-correlation between profiles collected from different aspect angles.

BACKGROUND AND METHODS In order to study fractal HR dynamics in Chagas disease, we performed detrended fluctuation analysis (DFA)-along with analysis of power-law beta slope (beta index) and standard deviation of N-N intervals (SDNN)-in edited and unedited (with ventricular premature beats - VPBs, only in DFA analysis) series of R-R intervals from Holter monitoring of healthy controls (Group 0, n = 27) and Chagas disease patients with left ventricular (LV) ejection fraction >50% (Group 1, n = 137) and with LV ejection fraction

The highly irregular ventricular rate during atrial fibrillation (AF) represents a unique and physiological experimental model to eliminate the influence of rhythmical components of RR variability on arterial pressure variability for investigating the origin of low frequency (LF) component in arterial pressure. Surface ECG, blood pressure and respiratory signals were recorded in thirty patients with persistent AF, at rest and during a passive orthostatic stimulus ("tilt test"). Short-term systolic (SAP) and diastolic arterial pressure (DAP) variability was estimated by autoregressive method. In 15 patients (group A), SAP significantly increased during tilt (from 98±16 to 114±18mmHg, p

This paper presents the results obtained from the combination of co-pole GPR data collected along perpendicular directions. The scope is to demonstrate how this approach can efficiently overcome pitfalls of traditional single orientation surveys and ensure target detection regardless of their geometrical and physical properties. This is of high importance especially when acquiring across targets that show directional dependencies of the preferential scattering components. This work relies on four field examples, each of them illustrating in detail the improvements and the advantages that a single image resulting from the stack of the two volumes can show, in particular what concerns target imaging.