Deep reinforcement learning (DRL) techniques have the potential to significantly improve the ability of Unmanned Aerial Vehicles (UAVs) for mobile device localization in disaster scenarios by optimizing flight paths and enhancing signal detection accuracy using Reference Signal Received Power (RSRP) measurements. DRL allows UAVs to learn optimal navigation strategies autonomously in dynamic and complex environments, leading to more efficient and accurate localization of mobile devices. The integration between UAVs and 4G/5G technology allows for more accurate and timely localization of mobile devices under the rubble, thereby improving the overall effectiveness of the system. Smallcells, low-power cellular base stations, are used to enhance coverage and capacity. In this study, we propose a DRL-based UAV-Smallcell system that can quickly and efficiently localize devices in large disaster areas. The performance of the proposed system is evaluated through an extensive simulation campaign to demonstrate that our approach significantly improves the effectiveness of mobile device localization compared to other state-of-the-art approaches.

This paper proposes an innovative technique for identifying any missing persons in natural disaster scenarios using an algorithm that exploits the femtocell sensor's ability to univocally hook a mobile terminal and that of a UAV system to easily approach to the monitoring area. The algorithm locates any mobile terminals with an accuracy of about 2 m.

The use of drones or UAV for civilian applications are one of the topics of great interest in recent years. The application contexts are numerous and the mobile Internet access is becoming a key component in the design of such a systems and in their deployment in critical areas or emergency scenarios typically managed by the Department of Civil Protection. The possibility of measuring data through a set of sensors or taking video images with cameras on board the drone, making information available in real time in remote command and control positions, opens new horizons in terms of methodology and accuracy in the mobile data monitoring. The paper presents a first prototype of "smart VPN bonding mechanism for drone communications" and the first tests have already confirmed the benefits of this innovative approach.

Today many applications require a high Quality of Service (QoS) to the network, especially for real time applications like VoIP services, video/audio conferences, video surveillance, high definition video transmission, etc. Besides, there are many application scenarios for which it is essential to guarantee high QoS in high speed mobility context using an Internet Mobile access.However, internet mobile networks are not designed to support the real-time data traffic due to many factors such as resource sharing, traffic congestion, radio link, coverage, etc., which affect the Quality of Experience (QoE).In order to improve the QoS in mobility scenarios, the authors propose a new technique named "Smart VPN Bonding" which is based on aggregation of two or more internet mobile accesses and is able to provide a higher end-to-end available bandwidth due to an adaptive load balancing algorithm.In this paper, in order to dynamically establish the correct load balancing weights of the smart VPN bonder, a neural network approach to predict the main Key Performance Indicators (KPIs) values in a determinate geographical point is proposed.

The performance of traditional biometric identification systems is, as yet, unsatisfactory in certain applications. For this reason, other physiological or behavioral characteristics have recently been considered, using new electrical or physical signals linked to a person's vital signs. This paper examines the biometric characteristics of phonocardiogram (PCG) signals from cardiac auscultation. The idea is that PCG signals have specific individual characteristics that can be taken into consideration as a physiological sign used in a biometric system. More specifically, the paper proposes a preliminary study related to the identification of individuals via frequency analysis of cardiac sounds. The results, obtained using a database containing several heart sound recordings from 20 different people, confirm the biometric properties of PCG signals, which can thus be included among the physiological signs used by an automatic identification system.

The recent introduction of new variable bit rate (VBR) speech coders has opened up new perspectives for the implementation of adaptive voice over IP (AVoIP) systems. The paper compares different VBR speech coding techniques in a scenario in which the rate of the single sources is dynamically adapted to the workload conditions. The coders compared are the AMR, the M/sup 3/R and the G.729. Using source and rate control mechanism models, performance was evaluated in terms of loss probability, offered throughput and mean CMOS (comparison mean opinion score) with varying numbers of sources and different background noise conditions. The use of header compression mechanisms was also evaluated.

In the near future, congestion control should be introduced for multimedia UDP-based traffic, in such a way that this traffic becomes "TCP-friendly". To this end, several TCP-friendly algorithms have been proposed in the literature. However, although these algorithms were introduced to support real-time applications on the Internet, the only target in optimizing them, until now, was to achieve fairness with TCP flows in the network. No attention has been paid to the quality of service (QoS) perceived by their users. The target of this paper is to analyze the problem of transmitting voice over IP when voice sources use two of the most promising TCP-friendly algorithms, RAP (rate adaptation protocol) and TFRC (TCP-friendly rate control). With this aim, a VoIP system architecture is introduced and the characteristics of each of its elements are discussed. To optimize the system, a voice multirate encoder, which is able to work over a TCP layer, is used, and a modification of both RAP and TFRC is proposed. Finally, in order to analyze the performance of the proposed system architecture and to compare the modified RAP and TFRC with the original algorithms, the sources have been modeled with an arrival process modulated by a Markov chain, and the model has been used to generate traffic in a simulation study performed with the ns-2 network simulator.

The paper proposes a performance evaluation and comparison of G.729, AMR, and fuzzy voice activity detection (FVAD) algorithms. The comparison was made using objective, psychoacoustic, and subjective parameters. A highly varied speech database was also set up to evaluate the extent to which VADs depend on language, the signal-to-noise ratio (SNR), or the power level.

The paper proposes a performance evaluation and comparison of recent ITU-T and ETSI voice activity detection algorithms. The comparison was made using both objective and psychoacoustic parameters, so as to have reliable judgements that were close to subjective ones. A highly varied speech database was also set up to evaluate the extent to which VAD depend on language, the signal to noise ratio, or the power level.

This paper presents a hybrid multi-mode/multi-rate, toll quality CS-ACELP coder developed for voice over IP applications. The coder uses coding modes compatible with the three 6.4, 8, and 11.8 kbit/s coding schemes standardised by ITU-T in G.729. In particular, the algorithm presents 4 coding categories, with an average bit rate ranging between about 3 and 8 kbit/s, that adapt the rate to changes in network conditions.

Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new-generation wireless communication systems. In this context robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. The fuzzy voice activity detector (FVAD) proposed by Beritelli, Casale, and Cavallaro (see Contribution ITU-T, Study Group 16, question 19/16, Washington, September 1997), shows that a valid alternative to deal with the problem of activity decision is to use methodologies like fuzzy logic. In this paper we propose a multichannel approach to activity detection using both fuzzy logic and time delay estimation. Objective and subjective tests confirm a significant improvement over traditional methods, above all in terms of a reduction in activity increase for nonstationary noise.

In the field of mobile communications new robust Voiced/Unvoiced (V/UV) classification algorithms are required in that correct voicing detection is a crucial point in the perceived quality and naturalness of a very low bit-rate speech coding system. The paper shows that a valid and more convenient alternative to deal with the problem of voicing decision is to use methodologies like fuzzy logic which are suitable for problems requiring approximate rather than exact solutions, and which can be represented through descriptive or qualitative expressions. The Fuzzy Voicing Detector proposed is based on a pattern recognition approach in which the matching phase is performed using three fuzzy rules. The rules have been obtained using FuGeNeSys, a new hybrid learning tool based on Genetic Algorithm and Neural Networks. The fuzzy classifier is computationally very simple and more efficient than traditional methods, which are affected by misclassification errors, above all in the presence of background noise.

Multimode coders are able to exploit the different characteristics of the speech waveform and to take into account the different peculiarities of background noise, thus allowing improvements in both signal reconstruction and network-offered load. In this context the variable rate code excited linear prediction (VR-CELP) coding, that is, a multimode variable bit rate (VBR) coding based on the CELP technique, has been introduced in the literature and is currently being considered for use in various applications, especially in the third-generation UMTS cellular systems. The target of the paper is to introduce an efficient and accurate framework allowing a network designer to analyze the impact of multimode VBR speech coding on the quality of service (QoS) provided by a wireless/wired ATM network. In order to capture the coder output characteristics, we propose to model a VR-CELP voice source by using a switched batch Bernoulli process (SBBP). More specifically, three models are introduced and compared in terms of accuracy and simplicity in determining network performance. As a result of the comparison, a four-state model has been chosen as the best tradeoff. The model is then used to analytically derive the loss probability and the jitter probability density function of an ATM multiplexer loaded by a number of VR-CELP sources. Finally, the proposed paradigm has been assessed in a case study where we demonstrate that, for a given output ATM link capacity and for a number of telecommunication services involving voice transmission, VR-CELP coding performs better than traditional on-off coding.

This letter deals with a variable bit-rate CS-ACELP speech coder based on new algorithms that are robust in the presence of the background noise typical of wireless communications. The coder presents eight operating modes ranging from 0-8 kbit/s with an average bit-rate of about 4 kbit/s. Subjective and objective comparisons with the recent ITU-T G.729 8 kbit/s standard, used in the discontinuous transmission mode, demonstrate that the proposed coder provides an average bit rate reduction of about 20% maintaining the same algorithmic delay and perceptive quality.

In the field of mobile communications correct voice activity detection (VAD) is a crucial point for perceived speech quality, reduction of co-channel interference, power consumption in portable equipment. This paper shows that a valid alternative to deal with the problem of activity decision is to use methodologies like fuzzy logic, which are suitable for problems requiring approximate rather than exact solutions, and which can be presented through descriptive or qualitative expressions. The fuzzy voice activity detector (FVAD) proposed uses the same set of parameters adopted by the VAD in Annex B to ITU-T G.729 and a set of six fuzzy rules automatically extracted through supervised learning. Objective and listening tests confirm a significative improvement respect the traditional methods above all for low signal-to-noise ratios.

Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new generation wireless communication systems. In this context, robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. This paper presents a voice detection algorithm which is robust to noisy environments, thanks to a new methodology adopted for the matching process. More specifically, the VAD proposed is based on a pattern recognition approach in which the matching phase is performed by a set of six fuzzy rules, trained by means of a new hybrid learning tool. A series of objective tests performed on a large speech database, varying the signal-to-noise ratio (SNR), the types of background noise, and the input signal level, showed that, as compared with the VAD standardized by ITU-T in Recommendation G.729 annex B, the fuzzy VAD, on average, achieves an improvement in reduction both of the activity factor of about 25% and of the clipping introduced of about 43%. Informal listening tests also confirm an improvement in the perceived speech quality.