Classification of Sensory Neural Signals through Deep Learning Methods

The recording and analysis of peripheral neural signals can be beneficial to provide feedback to prosthetic limbs and recover the sensory functionality in people with nerve injuries. Nevertheless, the interpretation of sensory recordings extracted from the nerve is not trivial, and only few studies have applied classifiers on sequences of neural signals without previous feature extraction. This paper evaluates the classification performance of two deep learning (DL) models (CNN and ConvLSTM) applied to the electroneurographic (ENG) activity recorded from the sciatic nerve of rats. The ENG signals, available from two public datasets, were recorded using multi-channel cuff electrodes in response to four sensory inputs (plantarflexion, dorsiflexion, nociception, and touch) elicited in response to mechanical stimulation applied to the hind paw of the rats. Different temporal lengths of the signals were considered (2.5 s, 1 s, 500 ms, 200 ms, and 100 ms), Both the two DL models proved to correctly discriminate sensory stimuli without the need of hand-engineering feature extraction. Moreover, ConvLSTM outperformed state-of-the-art results in classifying sensory ENG activity (more than 90% F1-score for sequences greater than 500 ms), and it showed promising results for real-time application scenarios

LTE transmission exploiting pulse width modulation in fibre optic links

Recently it has been demonstrated that pulse width modulation (PWM) can represent a viable solution for the analog optical fronthaul alternative to standard radio over fibre, which still avoids the bandwidth expansion of the digital fronthaul. The PWM encodes the analog samples at the transmitter onto the duration of the on/off keyed optical signal, splitting the sampling and quantization of the radio signal between remote radio units (RRUs) and baseband units (BBUs). In particular in this contribution we demonstrate the capabilities of optical PWM for the transport of LTE signals to support the centralized access network (C-RAN) fronthaul in fibre optic link up to 10-km of standard single mode fibre. The generation and analysis of the radio signals is provided by software modules compliant with the LTE standard which allowed to analyse performance results for the different LTE carriers, channels and services. The PWM optical signal connecting RRUs to BBUs is generated by either directly modulating a DFB laser or an externally seeded reflective semiconductor optical amplifier (RSOA). Both devices could be exploited inside a wavelength division multiplexed passive optical network (WDM PON) architecture where the various RRU-to-BBU links are pooled through virtual point-to-point connections at different wavelengths

Deployment and design of multiantenna solutions for fixed WiMAX systems

WiMax has already attracted the attention of operators and manifacturing industries for its promise of large throughput and coverage in broadband wireless access. However, towards the goal of an efficient deployment of this technology, a thorough analysis of its performance in presence of frequency reuse under realistic traffic conditions is mandatory. In particular, an important performance limiting factor is the inter-cell interference, which has strong non-stationary features. This paper investigates the deployment of multi-antenna base stations and the related design of signal processing algorithms for interference mitigation, for the uplink of IEEE 802.16-2004 systems. Extensive numerical results for realistic interference models show the advantages of the proposed multi-antenna system

Data mining tool for academic data exploitation: publication report on engineering students profiles

This report summarizes the findings of the project SPEET. It relies on the initial document generated as Intellectual Output #1 and the results obtained by application of the IT tools developed in Intellectual Output #2, and Intellectual Output #3 to the academic data provided by the partner institutions. The main objectives of applying analytic techniques to evaluate the academic data sources can be categorized as follows: Improve Student Results; Create Mass-customized Programs; Improve the Learning Experience in Real-time; Reduce Dropouts and Increase Results.info:eu-repo/semantics/publishedVersio

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Analog MIMO Radio-Over-Copper Downlink with Space-Frequency to Space-Frequency Multiplexing for Multi-User 5G Indoor Deployments

Radio access network (RAN) centralization is at the basis of current mobile networks, in which BaseBand Units (BBUs) and radio antenna units (RAUs) exchange over the FrontHaul (FH) digitized radio-frequency signals through protocols such as the common public radio interface. However, such architecture, as it stands, does not scale to the demands of multiple-antennas 5G systems, thus leading to drastic RAN paradigm changes. Differently from digital RAN architectures, we propose to overcome bandwidth/latency issues due to digitization by employing an all-analog FH for multiple-antenna RAUs based on the analog radio-over-copper (A-RoC) paradigm. The A-RoC is an alternative/complementary solution to FH for the last 200 m, such as for indoor, to reuse existing local area network (LAN) cables with remarkable economic benefits. Although LAN cables contain 4 twisted-pairs with up to 500 MHz bandwidth/ea., their usage is limited by cable attenuation and crosstalk among pairs. This paper demonstrates that a judicious mapping of each radio-frequency signal of each antenna onto a combination of cable pair-frequency allocations, referred to as space-frequency to space-frequency multiplexing, optimized together with the design of the digital precoding at the BBU, substantially mitigates the cable impairments. The LAN cables can be exploited for last 100-200 m analog transport FH to meet the requirements of 5G indoor networks

Seamless LTE connectivity in high speed trains

High speed train (HST) is becoming one of the preferred mid-range transportation and the on-board Internet service is a must for train operators. LTE networks paved the road toward high quality and cost effective on-board Internet in HSTs. However, frequent handovers (HO) for several onboard users overload sequentially cells along the train-track and increases the service interruptions, that in turn degrade untolerably the quality of service (QoS). HO service interruptions could be largely mitigated if HST could be virtually longer to access multiple cells from multiple devices coordinated in bundle. This is the paradigm of multi-cell access investigated here. More specifically, we propose a novel (and practically viable) onboard architecture for train-to-ground LTE backhauling. Multiple directional antennas with fixed-beams are deployed along the train to provide multi-cell access to distribute on-board traffics over at least three cells. Multiple antennas are paired with a load balancing mechanism for seamless on-board Internet. The combined use of multi-cell access and distributed load balancing mechanism provide a balanced QoS across all carriages that heavily mitigates the service discontinuities due to LTE HOs. The proposed architecture does not imply any change on network side. Conclusions are supported by numerical results for realistic LTE parameters in current HST settings. © 2014 IEEE