Dynamic Adaptive Streaming over HTTP (DASH) is the standard for video streaming applications such as YouTube and Netflix. According to DASH, the video player must include an Adaptive Bit-Rate (ABR) controller designed to maximize users' Quality of Experience (QoE). The controller must avoid playback interruptions, due to buffer underflow, while at the same time selecting the maximum video encoding quality compatible with the available time-varying bandwidth. This paper proposes a controller designed using a nicely constrained Model Predictive Control (MPC) which employs Bellman Dynamic Programming (DP) to reduce the computational cost of the algorithm from exponential to polynomial. Compared with state-of-the-art ABR algorithms, the proposed Real-Time MPC (RT-MPC) improves QoE while remarkably reducing the computational time, so that the algorithm can be used for both live-video streaming and real-time conferencing.

Type 1 diabetes is a growing global health challenge. Standard clinical practice often relies on manual insulin injections, which can lead to suboptimal glucose regulation. Recent advancements have shifted focus towards Artificial Pancreas systems, integrating continuous glucose monitoring with automated insulin delivery. This work presents a quantitative comparison of four Deep Reinforcement Learning algorithms for autonomous glycemic regulation via insulin injection: DDPG, PPO, SAC, and TD3. The validation is conducted using the Hovorka model, in presence of uncertainties on number, time and amount of meals. Results show that all four controllers are able to maintain blood glucose levels within the target range. The TD3 algorithm outperforms the others in terms of several key performance indicators such as time in range, time in hypo/hyperglycemia and total insulin usage, while also exhibiting fewer hyperglycemic episodes compared to prior works in academic literature.

Autonomous mobile robots (AMRs) are increasingly deployed for remote inspection in various scenarios. While AMRs can perform missions autonomously, human intervention becomes necessary in cases where safety risks or potential damage to the robot arise. Since these platforms are often deployed in remote or potentially dangerous areas, physical access is not always available. In such situations, teleoperation provides an effective solution to resolve issues. The navigation stack of AMRs include obstacle avoidance and mapping functionalities that rely on the environmental feedback, typically acquired with stereo cameras which represent depth and color information using Point Clouds (PCs). In addition to obstacles mapping, PCs provide operators and algorithms with real-time environmental data improving situational awareness and supporting AI-based downstream tasks. However, the large volumes of data inherent to PCs pose challenges for real-time transmission. For this reason, dedicated transmission pipelines and data compression algorithms compatible with the limited computational capabilities of the on-board computer are required. This paper demonstrates such a scenario, equipping a differential drive ground robot with a stereo camera that produces PCs that are streamed to a remote operator wearing a standalone VR Headset. To accomodate the limited computational capabilities of the on-board computer and reduce bandwidth requirements, a distance-based filtering and quantization encoding is applied. Users will step in to assist the robot, with remote real-time teleoperation, when the AMR issues a "cry for help" signal during an autonomous mission.

Adaptive video streaming systems are expected to provide the best user experience to improve service engagement. To the purpose, video players host a controller that dynamically chooses the most suitable video representation to be downloaded. It is well-known that finding one tuning of the controller’s parameters which performs satisfactorily in a wide range of scenarios is very challenging. This paper studies the problem of providing users with (near) optimal Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) systems. We present ERUDITE, a closed-loop system to optimally tune – at run-time – the adaptive streaming controller’s parameters to adapt to changing scenario’s parameters. ERUDITE employs a Deep Neural Network (DNN) which continuously provides the streaming controller with estimates of optimal parameters based on measured metrics such as bandwidth samples and overall obtained QoE. The DNN is trained using a dataset that we have built by finding, for thousands of realistic scenarios, robust optimal adaptive streaming controller’s parameters using a Bayesian optimization algorithm. Results, gathered considering a large number of diverse scenarios, show that ERUDITE is able to provide near optimal performances by reducing impairments due to rebuffering and video level switching.

Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multi-disciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/.

Millimeter-wave (mmWave) radar sensors produce Point Clouds (PCs) that are much sparser and noisier than other PC data (e.g., Li-DAR), yet they are more robust in challenging conditions such as in the presence of fog, dust, smoke, or rain. This paper presents MilliNoise, a point cloud dataset captured in indoor scenarios through a mmWave radar sensor installed on a wheeled mobile robot. Each of the 12M points in the MilliNoise dataset is accurately labeled as true/noise point by leveraging known information of the scenes and a motion capture system to obtain the ground truth position of the moving robot. Each frame is carefully pre-processed to produce a fixed number of points for each cloud, enabling classification tools which require data with a fixed shape. Moreover, MilliNoise has been post-processed by labeling each point with the distance to its closest obstacle in the scene, which allows casting the denoising task into the regression framework. Along with the dataset, we provide researchers with the tools to visualize the data and prepare it for statistical and machine learning analysis. MilliNoise is available at: https://github.com/c3lab/MilliNoise

A steadily increasing number of users consume videos over the Internet. In current video platforms, players run a control algorithm that dynamically chooses the video bitrate to match the time-varying network bandwidth. Such an algorithm strives to improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users rather than to a quality-fair distribution of resources, possibly leading to user abandonment for those users receiving a lower quality. Therefore, we argue that well-designed video delivery networks should gracefully degrade the perceived quality equally for all users when resources become scarce. In this paper, we propose the Multi-Commodity Flow Problem (MCFP) optimization framework to address the issue of designing a QoE-fair optimal allocation strategy. We show how to make the resulting problem tractable for video platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is tested through realistic simulations involving thousands of concurrent users on two real networks by varying both the total load on the network and the system parameters.

Video streaming platforms are required to innovate their delivery pipeline to allow new and more immersive video content to be supported. In particular, Omnidirectional videos enable the user to explore a 360° scene by moving their heads using Head Mounted Display devices. Viewport adaptive streaming allows changing dynamically the quality of the video falling in the user's field of view. In this paper, we present TAPAS-360°, an open-source tool that enables designing and experimenting all the components required to build omnidirectional video streaming systems. The tool can be used by researchers focusing on the design of viewport-adaptive algorithms and also to produce video streams to be employed for subjective and objective Quality of Experience evaluations.

Adaptive video streaming systems are expected to provide the best user experience to improve service engagement. To this purpose, the video player implements a controller to dynamically choose the most suitable video representation to be downloaded. It is well-known that finding one tuning of the controller's parameters which performs satisfactorily in a wide range of scenarios is very challenging. This paper studies the problem of providing users with (near) optimal Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) systems. We present ERUDITE, a closed-loop system to optimally tune - at run-time - the adaptive streaming controller's parameters to adapt to changing scenario's parameters. The proposed system is based on a Deep Neural Network (DNN) which continuously provides the streaming controller with estimates of optimal parameters based on measured metrics such as bandwidth samples and overall obtained QoE. The DNN is trained using a dataset that we have built by finding, for thousands of scenarios, the optimal adaptive streaming controller's parameters using a Bayesian optimization algorithm. Results, gathered considering a large number of diverse scenarios, show that ERUDITE is able to provide near optimal performances by reducing impairments due to rebuffering and video level switching.

Virtual Reality/Augmented Reality applications require streaming 360° videos to implement new services in a diverse set of fields such as entertainment, art, e-health, e-learning, and smart factories. Providing a high Quality of Experience when streaming 360° videos is particularly challenging due to the very high required network bandwidth. In this paper, we showcase a proof-of-concept implementation of a complete DASH-compliant delivery system for 360° videos that: 1) allows reducing the required bitrate, 2) is independent of the employed encoder, 3) leverages technologies that are already available in the vast majority of mobile platforms and devices. The demo platform allows the user to directly experiment with various parameters, such as the duration of segments, the compression scheme, and the adaptive streaming algorithm parameters.

This article investigates several network-assisted streaming approaches that rely on active cooperation between video streaming applications and the network. We build a Video Control Plane that enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. For this purpose, a max-min fairness optimization problem is solved at runtime. We compare two approaches to actuate the optimal solution in an Software Defined Networking network: The first one allocates network bandwidth slices to video flows, and the second one guides video players in the video bitrate selection. We assess performance through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. The impact of client-side adaptation algorithms is also investigated.

Applications requiring real-time communication (RTC) between Internet peers are ever increasing. RTC requires not only congestion control but also minimization of queuing delays to provide interactivity. It is known that the well-established transmission control protocol congestion control is not suitable for RTC due to its retransmissions and in-order delivery mechanisms, which induce significant latency. In this paper, we propose a novel congestion control algorithm for RTC, which is based on the main idea of estimating-using a Kalman Filter-the end-to-end one-way delay variation which is experienced by packets traveling from a sender to a destination. This estimate is compared with a dynamic threshold and drives the dynamics of a controller located at the receiver, which aims at maintaining queuing delays low, while a loss-based controller located at the sender acts when losses are detected. The proposed congestion control algorithm has been adopted by Google Chrome. Extensive experimental evaluations have shown that the algorithm contains queuing delays while providing intra and inter protocol fairness along with full link utilization.

Video conferencing applications require low latency and high bandwidth. Standard TCP is not suitable for video conferencing since its reliability and in order delivery mechanisms induce large latency. Recently the idea of using the delay gradient to infer congestion is appearing again and is gaining momentum. In this paper we present an algorithm that is based on estimating through a Kalman filter the end-to-end one way delay variation which is experienced by packets traveling from a sender to a destination. This estimate is compared to an adaptive threshold to dynamically throttle the sending rate. The control algorithm has been implemented over the RTP/RTCP protocol and is currently used in Google Hangouts and in the Chrome WebRTC stack. Experiments have been carried out to evaluate the algorithm performance in the case of variable link capacity, presence of heterogeneous or homogeneous concurrent traffic, and backward path traffic.

In this paper we investigate several network-assisted streaming approaches which rely on active cooperation between video streaming applications and the network. We build a Video Control Plane which enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. To the purpose, a max-min fairness optimization problem is solved at run-time. We compare two approaches to actuate the optimal solution in an SDN network: the first one allocating network bandwidth slices to video flows, the second one guiding video players in the video bitrate selection. Performance is assessed through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. The impact of client-side adaptation algorithms is also investigated.

Adaptive video streaming is a relevant advancement with respect to classic progressive download streaming a la YouTube. Among the different approaches, the video stream-switching technique is getting wide acceptance, being adopted by Microsoft, Apple, and popular video streaming services such as Akamai, Netflix, Hulu, Vudu, and Livestream. In this paper, we present a model of the automatic video stream-switching employed by one of these leading video streaming services along with a description of the client-side communication and control protocol. From the control architecture point of view, the automatic adaptation is achieved by means of two interacting control loops having the controllers at the client and the actuators at the server: One loop is the buffer controller, which aims at steering the client playout buffer to a target length by regulating the server sending rate; the other one implements the stream-switching controller and aims at selecting the video level. A detailed validation of the proposed model has been carried out through experimental measurements in an emulated scenario.

The existence of excessively large and too filled network buffers, known as bufferbloat, has recently gained attention as a major performance problem for delay-sensitive applications. One important network scenario where bufferbloat may occur is cellular networks. This paper investigates the interaction between TCP congestion control and buffering in cellular networks. Extensive measurements have been performed in commercial 3G, 3.5G and 4G cellular networks, with a mix of long and short TCP flows using the CUBIC, NewReno and Westwood+ congestion control algorithms. The results show that the completion times of short flows increase significantly when concurrent long flow traffic is introduced. This is caused by increased buffer occupancy from the long flows. In addition, for 3G and 3.5G the completion times are shown to depend significantly on the congestion control algorithms used for the background flows, with CUBIC leading to significantly larger completion times.

Multimedia content feeds an ever increasing fraction of the Internet traffic. Video streaming is one of the most important applications driving this trend. Adaptive video streaming is a relevant advancement with respect to classic progressive download streaming such as the one employed by YouTube. It consists in dynamically adapting the content bitrate in order to provide the maximum Quality of Experience, given the current available bandwidth, while ensuring a continuous reproduction. In this paper we propose a Quality Adaptation Controller (QAC) for live adaptive video streaming designed by employing feedback control theory. An experimental comparison with Akamai adaptive video streaming has been carried out. We have found the following main results: 1) QAC is able to throttle the video quality to match the available bandwidth with a transient of less than 30s while ensuring a continuous video reproduction; 2) QAC fairly shares the available bandwidth both in the cases of a concurrent TCP greedy connection or a concurrent video streaming flow; 3) Akamai underutilizes the available bandwidth due to the conservativeness of its heuristic algorithm; moreover, when abrupt available bandwidth reductions occur, the video reproduction is affected by interruptions.

End-to-end rate-based congestion control algorithms are advocated for audio/video transport over the Internet instead of window-based protocols. Once the congestion controller has computed the sending rate, all rate-based algorithms proposed in the literature schedule packets to be sent spaced at intervals that are equal to the inverse of the desired sending rate. In this paper we show that such an implementation exhibits a fundamental flaw. In fact, scheduling the sending time of a packet is affected by significant uncertainty due to the fact that it is handled by the Operating System, which manages a CPU shared by other processes. To overcome this problem, the Rate Mismatch Controller (RMC) is designed aiming at counteracting the disturbance on the effective sending time due to the CPU time-varying load. Experimental results using Linux OS highlight the effectiveness of the proposed controller.

The TCP/IP stack has been extremely successful for reliable delivery of best-effort, time insensitive elastic type data traffic. Nowadays, the Internet is rapidly evolving to become an equally efficient platform for multimedia content delivery. Key examples of this evolution are, to name few, YouTube, Skype Audio/Video, IPTV, P2P video distribution such as Coolstreaming or Joost. While YouTube streams videos using the Transmission Control Protocol (TCP), applications that are time-sensitive such as Skype VoIP or Video Conferencing employ the UDP because they can tolerate small loss percentages but not delays due to TCP recovery of losses via retransmissions. Since the UDP does not implement congestion control, these applications must implement those functionalities at the application layer in order to avoid congestion and preserve network stability. In this paper we investigate Skype Video in order to discover to what extent this application is able to throttle its sending rate to match the unpredictable Internet bandwidth while preserving resource for co-existing best-effort TCP traffic.

In order to provide service differentiation in 802.11 wireless local area networks, the 802.11e working group has recently proposed the hybrid coordination function (HCF) along with a HCF controlled channel access (HCCA). However, the 802.11e proposal does not specify an effective bandwidth allocation algorithm. Recently, to overcome this limitation, a control theoretic framework has been proposed in literature. Within such a framework, this paper proposes a new allocation algorithm based on a proportional-integral-derivative (PID) controller. Moreover, a performance comparison among the simple Scheduler introduced by 802.11e standard and three bandwidth allocations algorithms, designed using different dynamic regulators, has been carried out. Ns-2 simulation results showed that the best trade-off between packet delays and number of admitted flows is achieved by a properly tuned PID regulator.

Modeling TCP is fundamental for understanding Internet behavior. The reason is that TCP is responsible for carrying a huge quota of the Internet traffic. During last decade many analytical models have attempted to capture dynamics and steady-state behavior of standard TCP congestion control algorithms. In particular, models proposed in literature have been mainly focused on finding relationships among the throughput achieved by a TCP flow, the segment loss probability, and the round trip time (RTT) of the connection, which the flow goes through. Recently, Westwood+ TCP algorithm has been proposed to improve the performance of classic New Reno TCP, especially over paths characterized by high bandwidth-delay products. In this paper, we develop an analytic model for the throughput achieved by Westwood+ TCP congestion control algorithm when in the presence of paths with time-varying RTT. The proposed model has been validated by using the ns-2 simulator and Internet-like scenarios. Validation results have shown that this model provides relative prediction errors smaller than 10%. Moreover, it has been shown that a similar accuracy is achieved by analogous models proposed for New Reno TCP.

In recent years, issues regarding the behavior of TCP in high-speed and long-distance networks have been extensively addressed in the networking research community, both because TCP is the most widespread transport protocol in the current Internet and because bandwidth-delay product continues to grow. The well known problem of TCP in high bandwidthdelay product networks is that the TCP Additive Increase Multiplicative Decrease AIMD probing mechanism is too slow in adapting the sending rate to the end-to-end available bandwidth. To overcome this problem, many modifications have been proposed such as FAST TCP [1], STCP [2], HSTCP [3], HTCP [4], BIC TCP [5] and CUBIC TCP [6]. The goal of this work is to investigate, by using both analytical models and simulation results, optimal sizing of the retransmission and out-of-order TCP buffers in case of modified TCP congestion control settings and to highlight differences between NewReno TCP and SACK TCP packet loss recovery schemes in terms of TCP internal buffers requirements. An important result is that the SACK option turns out to be particularly effective in reducing buffer requirements in the case of very high bandwidthdelay product links.

The additive increase/multiplicative decrease probing paradigm is at the core of TCP congestion control. To improve the classic Reno/New Reno congestion control algorithms, the recent Westwood+ TCP proposes to substitute the multiplicative decrease phase with an adaptive decrease phase, which takes into account an end-to-end estimate of the available bandwidth obtained by filtering the stream of returning ACKs. This paper aims at evaluating the performance of Westwood+ TCP over the real Internet. For that purpose, a Linux 2.4.19 implementation of Westwood+ TCP has been developed and compared with an implementation of New Reno. More than 4000 files, with different sizes, have been uploaded via ftp from a host at the Politecnico of Bari (South of Italy) to three remote servers, which are located at Parma (North of Italy), Uppsala University (Sweden) and University of California Los Angeles (UCLA, California). Experimental results indicate that Westwood+ TCP improves the goodput with respect to New Reno TCP over paths with a bandwidth delay product larger than few segments. In particular, goodput improvements up to 40-50% have been measured when transmitting data from Politecnico of Bari to Uppsala or UCLA servers. Currently, Westwood+ TCP support is available in the official Linux kernel. It was included both in the kernel 2.4.x from version 2.4.26-prel on and in the kernel 2.6.x from version 2.6.3-rcl on.

In this paper, we introduce a control theoretical analysis of the closed-loop congestion control problem in packet networks. The control theoretical approach is used in a proportional rate controller, where packets are admitted into the network in accordance with network buffer occupancy. A Smith Predictor is used to deal with large propagation delays, common to high speed backbone networks. The analytical approach leads to accurate predictions regarding both transients as well as steady-state behavior of buffers and input rates. Moreover, it exposes tradeoffs regarding buffer dimensioning, packet loss, and throughput.

Link layer error control is widely adopted in wireless LANs (WLANs) to hide the unreliability of the wireless channel to higher level protocols. This allows classic end-to-end congestion control algorithms to achieve acceptable throughput even in the presence of radio links. The present work investigates the performance of Westwood+ and NewReno TCP over a noisy wireless LAN channel. In particular, the effect of local error control persistency on the end-to-end performances of Westwood+ and NewReno TCP is addressed. The investigation has been carried out by using ns-2 computer simulation. Simulation results show that (1) in the presence of uniformly distributed packet losses a well tuned local error control leads both Westwood+ and NewReno TCP to full network utilization; (2) in the presence of bursty losses Westwood+ TCP improves the good-put with respect to NewReno TCP also in the presence of local error control; in particular, Westwood+ TCP requires a smaller number of retransmissions at the link layer than New Reno to achieve full utilization of the wireless channel.

Westwood+ TCP is a sender-side only modification of the classic TCP that is based on end-to-end estimation of the bandwidth available to the connection in order to shrink adaptively the TCP congestion window and the slow start threshold after congestion. We report measurements obtained using Linux implementations of Westwood+, Westwood and Reno to FTP data over Internet connections spanning continental and intercontinental distances. In particular, we show that the bandwidth estimation algorithm employed by Westwood+ nicely tracks the available bandwidth, whereas the previous bandwidth estimation algorithm used by TCP Westwood fails to work in the real Internet due to ACK compression. Collected live Internet measurements also show that Westwood+ improves the goodput, from 10% to 46%, with respect to TCP Reno.

Due to the fundamental end-to-end design principle of the TCP/IP for which the network cannot supply any explicit feedback, today the TCP congestion control algorithm implements an additive increase multiplicative decrease (AIMD) algorithm. It is widely recognized that the AIMD mechanism is at the core of the stability of end-to-end congestion control. In this paper we describe a new mechanism we call additive increase adaptive decrease (AIAD). The key concept of the adaptive decrease mechanism is to adapt congestion window reductions to the bandwidth available at the time the congestion is experienced. We propose Westwood++ TCP as an implementation of the AIAD paradigm, and we consider Reno TCP as an example of the AIMD mechanism for comparison. We derive a mathematical model of the throughput of the AIAD mechanism that shows that Westwood++ is stable, is friendly to Reno and increases the fairness in bandwidth utilization. To confirm the validity of the theoretical model Internet measurements are reported.

We study the performance of TCP Westwood (TCPW), a new TCP protocol with a sender-side modification of the window congestion control scheme. TCP Westwood controls the window using end-to-end rate estimation in a way that is totally transparent to routers and to the destination. Thus, it is compatible with any network and TCP implementation. The key innovative idea is to continuously estimate, at the TCP sender, the packet rate of the connection by monitoring the ACK reception rate. The estimated connection rate is then used to compute congestion window and slow start threshold settings after a congestion episode. Resetting the window to match available bandwidth makes TCPW more robust to sporadic losses due to wireless channel problems. These often cause conventional TCP to overreact, leading to unnecessary window reduction. Experimental studies of TCPW show significant improvements in throughput performance over Reno and SACK, particularly in mixed wired/wireless networks over high-speed links. The contributions of this paper include a model for fair and friendly sharing of the bottleneck link and a Markov Chain performance model in presence of link errors/loss. TCPW performance is compared to that of TCP Reno, and analytic results are validated against simulation results. Internet and laboratory measurements using a Linux TCPW implementation are also reported, providing further evidence of the gains achievable via TCPW.

TCP Westwood (TCPW) is a sender-side modification of the TCP congestion window algorithm that improves upon the performance of TCP Reno in wired as well as wireless networks. The improvement is most significant in wireless networks with lossy links, since TCP Westwood relies on end-to-end bandwidth estimation to discriminate the cause of packet loss (congestion or wireless channel effect) which is a major problem in TCP Reno. An important distinguishing feature of TCP Westwood with respect to previous wireless TCP “extensions” is that it does not require inspection and/or interception of TCP packets at intermediate (proxy) nodes. Rather, it fully complies with the end-to-end TCP design principle. The key innovative idea is to continuously measure at the TCP source the rate of the connection by monitoring the rate of returning ACKs. The estimate is then used to compute congestion window and slow start threshold after a congestion episode, that is, after three duplicate acknowledgments or after a timeout. The rationale of this strategy is simple: in contrast with TCP Reno, which “blindly” halves the congestion window after three duplicate ACKs, TCP Westwood attempts to select a slow start threshold and a congestion window which are consistent with the effective bandwidth used at the time congestion is experienced. We call this mechanism faster recovery. The proposed mechanism is particularly effective over wireless links where sporadic losses due to radio channel problems are often misinterpreted as a symptom of congestion by current TCP schemes and thus lead to an unnecessary window reduction. Experimental studies reveal improvements in throughput performance, as well as in fairness. In addition, friendliness with TCP Reno was observed in a set of experiments showing that TCP Reno connections are not starved by TCPW connections. Most importantly, TCPW is extremely effective in mixed wired and wireless networks where throughput improvements of up to 550% are observed. Finally, TCPW performs almost as well as localized link layer approaches such as the popular Snoop scheme, without incurring the O/H of a specialized link layer protocol.

In this paper a method for designing cryptosystems driven by hyperchaotic signals is developed. The idea is to transmit a proper hyperchaotic signal, so that the receiver behaves as a linear observer for the state of the transmitter. The proposed tool proves to be: (i) rigorous, since some propositions are given for obtaining the plaintext at the receiver in a rigorous way; (ii) flexible, since a wide class of cryptosystems is designed by exploiting different hyperchaotic transmitting circuits; (iii) efficient, since the hyperchaotic carrier masks the encrypted signal, which in turn hides the message signal. The combination of hyperchaos, cryptography and complex transmitted signal makes a contribution to the development of communication systems with higher security.

Modern production facilities (i.e. flexible manufacturing systems) exhibit a high degree of resource sharing, a situation in which deadlocks (circular waits) can arise. Using digraph theoretic concepts we derive necessary and sufficient conditions for a deadlock occurrence and rigorously characterize highly undesirable situations (second level deadlocks), which inevitably evolve to circular waits in the next future. We assume that the system dynamics is described by a discrete event dynamical model, whose state provides the information on the current interactions job-resources. This theoretic material allows us to introduce some control laws (named restriction policies) which use the state knowledge to avoid deadlocks by inhibiting or by enabling some transitions. The restriction policies involve small on-line computation costs, so they are suitable for real-time implementation. For a meaningful class of systems one of these policies is the least restrictive deadlock-free policy one can find, namely it inhibits resource allocation only if leads directly to a deadlock. Finally, the paper discusses the computational complexity of all the proposed restriction policies and shows some examples to compare their performances.

Presents a feedback control algorithm for ATM congestion control in which source rates are adjusted according to VC queue lengths at intermediate nodes along the path. The goal is to "fill in" the residual bandwidth, without exceeding a specified queue threshold. In order to obtain this, we propose a simple and classical proportional controller, plus a Smith predictor to overcome instabilities due to large propagation delays, as well as to avoid cell loss. We propose an effective EPRCA implementation in which each source computes its input rate based on the maximum VC queue length along the path. Theoretical and experimental results show that high throughput is achieved even with queue sizes independent of the round trip delay.