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Time-interleaved ΔΣ DACs have the potential for wideband and high-speed operation. Their SNR is limited by the timing skew between the output delays of the channels to the output. In a two-channel interleaved ΔΣ DAC, the channel skew arises from the duty cycle error in the half sample rate clock. The effects of timing skew error can be mitigated by hold interleaving, digital pre-filtering or compensation in the form of analog post-correction or digital pre-correction. This paper presents a comparative study of these techniques for two-channel interleaving and the trade-offs are investigated. First order FIR pre-filtering is found to be a suitable solution with a moderate DAC matching penalty of one bit. Higher order pre-filtering achieves a near immunity to timing skew at the cost of higher matching penalty. Correction techniques are found to be less effective than pre-filtering and not well suited for high-speed implementation.

In this paper we analyze a cognitive radio network with one primary and one secondary transmitter, in which the primary transmitter has bursty arrivals while the secondary node is assumed to be saturated (i.e. always has a packet waiting to be transmitted). The secondary node transmits in a cognitive way such that it does not impede the performance of the primary node. We assume that the receivers have multipacket reception (MPR) capabilities and that the secondary node can take advantage of the MPR capability by transmitting simultaneously with the primary under certain conditions. We obtain analytical expressions for the stationary distribution of the primary node queue and we also provide conditions for its stability. Finally, we provide expressions for the aggregate throughput of the network as well as for the throughput at the secondary node.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. Since cellular communication is ready available, LTE communication has been designed to work at high speeds for vehicular communication. The challenge is that the protocols in LTE/LTE-Advanced should not only provide good packet delivery but also adapt to changes in the network topology due to vehicle volume and vehicular mobility. It is a critical requirement to ensure a seamless quality of experience ranging from safety to relieving congestion as deployment of LTE/LTE-Advanced become common. This requires learning how to improve the LTE/LTE-Advanced model to better appeal to a wider base and move toward additional solutions. In this paper we present a feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic. A M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches. Also, when a base station (eNB) becomes overloaded with a single-hop, a multi-hop rerouting optimization approach is presented.

This paper presents the design of a sampling switch to be used in the input interface to an ultra low-power 8-bit, 1-kS/s SAR ADC in 65 nm CMOS working at a supply voltage of 0.4 V. Important design trade-offs for the sampling switch in this low-voltage and low-power scenario are elaborated upon. The design of a multi-stage charge pump which generates the requisite boosted control voltage is described. A combination of the multi-stage charge pump and a leakage-reduced transmission-gate (TG) switch meets the speed requirement while mitigating leakage without employing additional voltages. Performance of the sampling switch has been characterized over process and temperature (PT) corners. In post-layout simulation, the sampling switch provides a linearity corresponding to 9.42 bits to 13.5 bits over PT corners with a worst-case power consumption of 216 pW while occupying an area of 25.4 μm × 24.7 μm.

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

This paper presents the design of a simple emulation framework for performance evaluation and testing of mobile applications. Our testbed combines production hardware and software to allow emulation of realistic and repeatable mobility scenarios, in which the mobile user can travel long distances, while being served by an application server. The framework allows (i) geo-location information, (ii) client network conditions such as bandwidth and loss rate, as well as (iii) the application workload to be emulated synchronously. To illustrate the power of the framework we also present the design, proof-of-concept implementation, and evaluation of a geo-smart scheduler for application updates in smartphones. This geo-smart scheduler reduces the average download time by using a network performance map to schedule the downloads when at places with relatively good conditions. Our trace-driven evaluation of the geo-smart scheduler, illustrates the workings of the emulation framework, and the potential of the geo-smart scheduler.

Motivated by improved models for content workload prediction, in this paper we consider the problem of dynamic content allocation for a hybrid content delivery system that combines cloud-based storage with low cost dedicated servers that have limited storage and unmetered upload bandwidth. We formulate the problem of allocating contents to the dedicated storage as a finite horizon dynamic decision problem, and show that a discrete time decision problem is a good approximation for piecewise stationary workloads. We provide an exact solution to the discrete time decision problem in the form of a mixed integerlinear programming problem, propose computationally feasible approximations, and give bounds on their approximation ratios.Finally, we evaluate the algorithms using synthetic and measuredtraces from a commercial music on-demand service and give insight into their performance as a function of the workload characteristics.

Survivable synthetic ROADMs are equipped with redundant switching modules to support failure recovery. The paper proposes a dynamic connection provisioning strategy which exploits these idle redundant modules to provision regular traffic resulting in a substantial improvement in the blocking performance.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. Since cellular communication is ready available, LTE communication has been designed to work at high speeds for vehicular communication. The challenge is that the protocols in LTE/LTE-Advanced should not only provide good packet delivery but also adapt to changes in the network topology due to vehicle volume and vehicular mobility. It is a critical requirement to ensure a seamless quality of experience ranging from safety to relieving congestion as deployment of LTE/LTE-Advanced become common. This requires learning how to improve the LTE/LTE-Advanced model to better appeal to a wider base and move toward additional solutions. In this paper we present a feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic. A M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches. Also, when a base station (eNB) becomes overloaded with a single-hop, a multi-hop rerouting optimization approach is presented.

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. Since cellular communication is ready available, LTE communication has been designed to work at high speeds for vehicular communication. The challenge is that the protocols in LTE/LTE-Advanced should not only provide good packet delivery but also adapt to changes in the network topology due to vehicle volume and vehicular mobility. It is a critical requirement to ensure a seamless quality of experience ranging from safety to relieving congestion as deployment of LTE/LTE-Advanced become common. This requires learning how to improve the LTE/LTE-Advanced model to better appeal to a wider base and move toward additional solutions. In this paper we present a feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic. A M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches. Also, when a base station (eNB) becomes overloaded with a single-hop, a multi-hop rerouting optimization approach is presented.

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

The new market trends are very different, so it iscrucial to the companies improve the tools and capabilities thatallow themselves readjust rapidly and effectively to the newsmarket changes and to the new requirements. In order tofacilitate this process, it is proposed in this paper an agent basedimplementation that can provide to the existent systems thecapacity to quickly adapt and reconfigure using standardtechnology. The proposed framework provides an intelligent toolto autonomously help the configuration when a productionoperator pretends to introduce a new variant of product inruntime and consult important information provided by thesystem to monitor execution., Plug and produce intelligent multi-agent environment based on standard technology (PRIME FP7)

Designing decoder for forward error correction (FEC) is more and more challenging because of the requirements on simultaneous supporting of various wireless standards within one IC module. The flexibility, silicon cost and throughput efficiency are all necessary to be traded off. In this paper, by using ASIP methodology, software-hardware co-design is introduced to offer sufficient flexibility of FEC decoding. The decoding procedure can be programmable for decoding QC-LDPC, Turbo and Convolutional Codes. Firstly, the common features from all mentioned algorithms and their corresponding datapaths are analyzed and a unified multi-standard datapath is introduced. Based on it, an application specific instruction-set is proposed and an ASIP (Application Specific Instruction-set Processor) for the FEC algorithms is designed. The firmware FEC codes are developed to adapt to standards. Synthesis results show that the proposed FEC processor is 1.54mm(2) under 65nm CMOS process. It offers QC-LDPC decoding for WiMAX, Turbo decoding for 3GPP-LTE, and 64 states Convolutional code (CC) decoding at the throughput of 193 Mbps, 62 Mbps and 60 Mbps respectively under clock frequency of 200 MHz. The proposed ASIP provides programmable high throughput compared to other tri-mode hardware modules.

The paper presents performance analysis of an analog front end (AFE) for body-coupled communication (BCC) which is based on data transmission via capacitive coupling through the human body. The AFE designs by cascading differential preamplifier and Schmitt trigger. A flipped voltage follower (FVF) topology is used as part of the preamplifier design. The operation frequency of the overall system is mentioned as 10 MHz. The open loop gain is 24.01 dB and the close loop gain is 19.43 dB for this preamplifier. The input referred noise PSD is 8.69 nV/root Hz at 10 MHz. The system runs on a supply voltage of 1 V with implementation being done on 65 nm CMOS technology.

In this paper, we present LDPC decoder designs based on gear-shift algorithms, which can use multiple decoding algorithms or update rules over the course of decoding a single frame. By first attempting to decode using low-complexity algorithms, followed by high-complexity algorithms, we increase energy efficiency without sacrificing error correction performance. We present the GSP and IGSP algorithms, and ASIC designs of these algorithms for the 10 Gbps Ethernet (2048,1723) LDPC code. In 65nm CMOS, our pipelined GSP decoder achieves a core area of 5.29mm(2), throughput of 88.1 Gbps, and energy efficiency of 39.3 pJ/bit, while our IGSP decoder achieves a core area of 6.00mm(2), throughput of 100.3 Gbps, and energy efficiency of 14.6 pJ/bit. Both algorithms achieve error correction performance equivalent to the offset min-sum algorithm. The throughput per unit area and energy efficiency of these decoders improve upon state-of-the-art decoders with comparable error correction performance.

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

Being able to predict the outcome of an opinion forming process is an important problem in social network theory. However, even for linear dynamics, this becomes a difficult task as soon as non-cooperative interactions are taken into account. Such interactions are naturally modeled as negative weights on the adjacency matrix of the social network. In this paper we show how the Perron-Frobenius theorem can be used for this task also beyond its standard formulation for cooperative systems. In particular we show how it is possible to associate the achievement of unanimous opinions with the existence of invariant cones properly contained in the positive orthant. These cases correspond to signed adjacency matrices having the eventual positivity property, i.e., such that in sufficiently high powers all negative entries have disappeared. More generally, we show how for social networks the achievement of a, possibily non-unanimous, opinion can be associated to the existence of an invariant cone fully contained in one of the orthants of ℝless thansupgreater thannless than/supgreater than.

Space division multiplexing (SDM) over multi-core fiber (MCF) is advocated as a promising technology to overcome the capacity limit of the current single-core optical networks. However, employing the MCF for flexgrid networks necessitates the development of new concepts, such as routing, spectrum and core allocation (RSCA) for traffic demands. The introduction of MCF in the networks mitigates the spectrum continuity constraint of the routing and spectrum assignment (RSA) problem. In fact cores can be switched freely on different links during routing of the network traffic. Similarly, the route disjointness for demands with same allocated spectrum diminishes to core disjointness at the link level. On the other hand, some new issues such as the inter-core crosstalk should be taken into account while solving the RSCA problem. This paper formulates the RSCA network planning problem using the integer linear programming (ILP) formulation. The aim is to optimally minimize the maximum number of spectrum slices required on any core of MCF of a flexgrid SDM network. Furthermore, a scalable and effective heuristic is proposed for the same problem and its performance is compared with the optimal solution. The results show that the proposed algorithm is able to well approximate the optimal solution based on ILP model.

The spectrum overcrowding drives investigating solutions to increase the usage of underutilized spectrum bands, minimize redundant interference and maximize expected throughput. Cognitive radio (CR) has presented itself as an appealing technology for solutions in energy constrained devices. Within this context, this paper introduces a system structure which enhances wireless multi-interfaced objects with cognitive radio. We propose a modular architecture to imbue such multi-interfaced devices with cognitive radio features, and based on it we present numerical results which identify performance boundaries and potential energy savings for an adaptive uncoded modulation scheme. Our results show that the tradeoff between throughput and energy consumption can be leveraged satisfactorily to enhance the lifetime significantly in noisy environments.

In this paper, we propose improved methods to select the most suitable frequency for transmission by also including information about the local interference environment. Knowledge of the local interference is included in the frequency selection through estimation of the interference power, and in addition through estimation of the impulsiveness of the interference. We evaluate the methods for both unicast and multicast scenarios using different performance measures, and show that the suggested methods can significantly reduce the bit error probability in relevant scenarios.

A poor choice of importance density can have detrimental effect on the efficiency of a particle filter. While a specific choice of proposal distribution might be close to optimal for certain models, it might fail miserably for other models, possibly even leading to infinite variance. In this paper we show how mixture sampling techniques can be used to derive robust and efficient particle filters, that in general performs on par with, or better than, the best of the standard importance densities. We derive several variants of the auxiliary particle filter using both random and deterministic mixture sampling via multiple importance sampling. The resulting robust particle filters are easy to implement and require little parameter tuning., VPS, CADICS

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. Since cellular communication is ready available, LTE communication has been designed to work at high speeds for vehicular communication. The challenge is that the protocols in LTE/LTE-Advanced should not only provide good packet delivery but also adapt to changes in the network topology due to vehicle volume and vehicular mobility. It is a critical requirement to ensure a seamless quality of experience ranging from safety to relieving congestion as deployment of LTE/LTE-Advanced become common. This requires learning how to improve the LTE/LTE-Advanced model to better appeal to a wider base and move toward additional solutions. In this paper we present a feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic. A M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches. Also, when a base station (eNB) becomes overloaded with a single-hop, a multi-hop rerouting optimization approach is presented.

Autonomous robot teams that simultaneously dis- patch transportation tasks are playing a more and more impor- tant role in the industry. In this paper we consider the multi- robot motion planning problem in large robot teams and present a decoupled approach by combining decentralized path planning methods and swarm technologies. Instead of a central coordi- nation, a proper behavior which is directly selected according to the context is used by the robot to keep cooperating with others and to resolve path collisions. We show experimentally that the quality of solutions and the scalability of our method are significantly better than those of conventional decoupled path planning methods. Furthermore, compared to conventional swarm approaches, our method can be widely applied in large- scale environments., Accepted for publication

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

A digital-to-RF converter (DRFC) architecture for IQ modulator is proposed in this paper. The digital-RF converter utilizes the mixer DAC concept but a discrete-time oscillatory signal is applied to the digital-RF converter instead of a conventional continuous-time LO. The architecture utilizes a low pass Sigma Delta modulator and a semi-digital FIR filter. The digital Sigma Delta modulator provides a single-bit data stream to a current-mode SDFIR filter in each branch of the IQ modulator. The filter taps are realized as weighted one-bit DACs and the filter response attenuates the out-of-band shaped quantization noise generated by the Sigma Delta modulator. To find the semi-digital FIR filter response, an optimization problem is formulated. The magnitude metrics in out-of-band is set as optimization constraint and the total number of unit elements required for the DAC/mixer is set as the objective function. The proposed architecture and the design technique is described in system level and simulation results are presented to support the feasibility of the solution.

This paper presents a quadrature DLL-based architecture for WiMedia ultra-wideband (UWB) frequency synthesis. I and Q carriers are directly generated by combining the quadrature multi-phase outputs of the DLL, using separate edge combiners (EC). A variable-stage voltage-controlled delay line (VCDL) scheme is proposed to provide the corresponding output phases to each EC, without the need for multiplexing the DLL outputs for different bands. Moreover, to prevent possible synthesizer hopping time degradation due to dynamic variations in temperature and voltage, a monitoring mechanism is employed to measure the time error at the instant of band switching, and compensate for it if it is beyond a limited value. The Synthesizer is implemented in a standard 65-nm CMOS technology and the simulation results indicate a hopping time of 4.5 to 8.8 ns across process corners. Simulated phase noise at 1 MHz offset from 4488 MHz carrier is -115 dBc/Hz and the worst case spur suppression is -31 dBc. The synthesizer consumes 13.9 mA from a 1.2-V supply.

A 0.7 V 400 nW fourth-order active-passive ΔΣ modulator with one active stage is presented in this paper using standard CMOS 65 nm technology. The modulator achieves 84 dB SNR and 80.3 dB SNDR in a signal bandwidth of 500 Hz with a sampling frequency of 256 kHz. The input-feedforward architecture is used to improve the voltage swing before the comparator of the traditional passive modulators, which enables simpler comparator design with no preamplifier as well as cascading three successive power-efficient passive filters. The first active stage is used to reduce the comparator's noise and offset and to minimize the capacitive area. The modulator achieves a high power-efficiency (47 fJ/step) in terms of widely used figure of merit.

Many cyber-physical systems comprise several control applications sharing communication and computation resources. The design of such systems requires special attention due to the complex timing behavior that can lead to poor control quality or even instability. The two main requirements of control applications are: (1) robustness and, in particular, stability and (2) high control quality. Although it is essential to guarantee stability and provide a certain degree of robustness even in the worst-case scenario, a design procedure which merely takes the worst-case scenario into consideration can lead to a poor expected (average-case) control quality, since the design is solely tuned to a scenario that occurs very rarely. On the other hand, considering only the expected quality of control does not necessarily provide robustness and stability in the worst-case. Therefore, both the robustness and the expected control quality should be taken into account in the design process. This paper presents an efficient and integrated approach for designing high-quality cyber-physical systems with robustness guarantees.

In this paper, we approach embedded systems design from a new angle that considers not only quality of service but also security as part of the design process. Moreover, we also take into consideration the dynamic aspect of modern embedded systems in which the number and nature of active tasks are variable during run-time. In this context, providing both high quality of service and guaranteeing the required level of security becomes a difficult problem. Therefore, we propose a novel secure embedded systems design framework that efficiently solves the problem of run-time quality optimization with security constraints. Experiments demonstrate the efficiency of our proposed techniques.

This paper presents a reconfigurable FFT architecture for variable-length and multi-streaming WiMax wireless standard. The architecture processes 1 stream of 2048-point FFT, up to 2 streams of 1024-point FFT or up to 4 streams of 512-point FFT. The architecture consists of a modified radix-2 single delay feedback (SDF) FFT. The sampling frequency of the system is varied in accordance with the FFT length. The latch-free clock gating technique is used to reduce power consumption. The proposed architecture has been synthesized for the Virtex-6 XCVLX760 FPGA. Experimental results show that the architecture achieves the throughput that is required by the WiMax standard and the design has additional features compared to the previous approaches. The design uses 1% of the total available FPGA resources and maximum clock frequency of 313.67 MHz is achieved. Furthermore, this architecture can be expanded to suit other wireless standards.

Active living in older adults reduces disabilities and increases quality of life. Adherence to physical activity is low in older adults, therefore new possibilities to motivate to exercise must be explored. One of this new possibilities is to use exergaming. In this paper, we present an assistive tool for monitoring daily, physical activities in older adults with access to exergaming in their home environments. The proposed system employs the Kinect device and allows one to analyse the body movements. From a pilot study, we proved that the system can detect movements through exergaming. In the future we plan to implement the developed monitoring tool as well as exergaming in nursing homes.

This paper presents ePUMA, a master-slave heterogeneous DSP processor for communications and multimedia. We introduce the ePUMA VPE, a vector processing slave-core designed for heavy DSP workloads and demonstrate how its features can used to implement DSP kernels that efficiently overlap computing, data access and control to achieve maximum datapath utilization. The efficiency is evaluated by implementing a basic set of kernels commonly used in SDR. The experiments show that all kernels asymptotically reach above 90% effective datapath utilization. while many approach 100%, thus the design effectively overlaps computing, data access and control. Compared to popular VLIW solutions, the need for a large register file with many ports is eliminated, thus saving power and chip area. When compared to a commercial VLIW solution, our solution also achieves code size reductions of up to 30 times and a significantly simplified kernel implementation.

Guaranteeing stability of control applications in embedded systems, or cyber-physical systems, is perhaps the alpha and omega of implementing such applications. However, as opposed to the classical real-time systems where often the acceptance criterion is meeting the deadline, control applications do not primarily enforce hard deadlines. In the case of control applications, stability is considered to be the main design criterion and can be expressed in terms of the amount of delay and jitter a control application can tolerate before instability. Therefore, new design and analysis techniques are required for embedded control systems. In this paper, the analysis and design of such systems considering server-based resource reservation mechanism are addressed. The benefits of employing servers are manifold: (1) providing a compositional framework, (2) protection against other tasks misbehaviors, and (3) systematic bandwidth assignment. We propose a methodology for designing bandwidth-efficient servers to stabilize control tasks.

The monitoring of physical activities under realistic, everyday life conditions - thus while an individual follows his regular daily routine - is usually neglected or even completely ignored. Therefore, this paper investigates the development and evaluation of robust methods for everyday life scenarios, with focus on the task of aerobic activity recognition. Two important aspects of robustness are investigated: dealing with various (unknown) other activities and subject independency. Methods to handle these issues are proposed and compared, a thorough evaluation simulates usual everyday scenarios of the usage of activity recognition applications. Moreover, a new evaluation technique is introduced (leave-one-other-activity-out) to simulate when an activity recognition system is used while performing a previously unknown activity. Through applying the proposed methods it is possible to design a robust physical activity recognition system with the desired generalization characteristic.

The automatic, on-demand, integration of information from multiple diverse sources outside the control of the application itself is central to many fusion applications. An important problem is to handle situations when the requested information is not directly available but has to be generated or adapted through transformations. This paper extends the semantic information integration approach used in the stream-based knowledge processing middleware DyKnow with support for finding and automatically applying transformations. Two types of transformations are considered. Automatic transformation between different units of measurements and between streams of different types. DyKnow achieves semantic integration by creating a common ontology, specifying the semantic content of streams relative to the ontology and using semantic matching to find relevant streams. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is integrated in the Robot Operating System (ROS) and used in collaborative unmanned aircraft systems missions., CUAS, CADICS, CENIIT

This paper presents a reconfigurable FFT architecture for variable-length and multi-streaming WiMax wireless standard. The architecture processes 1 stream of 2048-point FFT, up to 2 streams of 1024-point FFT or up to 4 streams of 512-point FFT. The architecture consists of a modified radix-2 single delay feedback (SDF) FFT. The sampling frequency of the system is varied in accordance with the FFT length. The latch-free clock gating technique is used to reduce power consumption. The proposed architecture has been synthesized for the Virtex-6 XCVLX760 FPGA. Experimental results show that the architecture achieves the throughput that is required by the WiMax standard and the design has additional features compared to the previous approaches. The design uses 1% of the total available FPGA resources and maximum clock frequency of 313.67 MHz is achieved. Furthermore, this architecture can be expanded to suit other wireless standards.

This paper presents a reconfigurable FFT architecture for variable-length and multi-streaming WiMax wireless standard. The architecture processes 1 stream of 2048-point FFT, up to 2 streams of 1024-point FFT or up to 4 streams of 512-point FFT. The architecture consists of a modified radix-2 single delay feedback (SDF) FFT. The sampling frequency of the system is varied in accordance with the FFT length. The latch-free clock gating technique is used to reduce power consumption. The proposed architecture has been synthesized for the Virtex-6 XCVLX760 FPGA. Experimental results show that the architecture achieves the throughput that is required by the WiMax standard and the design has additional features compared to the previous approaches. The design uses 1% of the total available FPGA resources and maximum clock frequency of 313.67 MHz is achieved. Furthermore, this architecture can be expanded to suit other wireless standards.

The monitoring of physical activities under realistic, everyday life conditions - thus while an individual follows his regular daily routine - is usually neglected or even completely ignored. Therefore, this paper investigates the development and evaluation of robust methods for everyday life scenarios, with focus on the task of aerobic activity recognition. Two important aspects of robustness are investigated: dealing with various (unknown) other activities and subject independency. Methods to handle these issues are proposed and compared, a thorough evaluation simulates usual everyday scenarios of the usage of activity recognition applications. Moreover, a new evaluation technique is introduced (leave-one-other-activity-out) to simulate when an activity recognition system is used while performing a previously unknown activity. Through applying the proposed methods it is possible to design a robust physical activity recognition system with the desired generalization characteristic.

The automatic, on-demand, integration of information from multiple diverse sources outside the control of the application itself is central to many fusion applications. An important problem is to handle situations when the requested information is not directly available but has to be generated or adapted through transformations. This paper extends the semantic information integration approach used in the stream-based knowledge processing middleware DyKnow with support for finding and automatically applying transformations. Two types of transformations are considered. Automatic transformation between different units of measurements and between streams of different types. DyKnow achieves semantic integration by creating a common ontology, specifying the semantic content of streams relative to the ontology and using semantic matching to find relevant streams. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is integrated in the Robot Operating System (ROS) and used in collaborative unmanned aircraft systems missions., CUAS, CADICS, CENIIT

The monitoring of physical activities under realistic, everyday life conditions - thus while an individual follows his regular daily routine - is usually neglected or even completely ignored. Therefore, this paper investigates the development and evaluation of robust methods for everyday life scenarios, with focus on the task of aerobic activity recognition. Two important aspects of robustness are investigated: dealing with various (unknown) other activities and subject independency. Methods to handle these issues are proposed and compared, a thorough evaluation simulates usual everyday scenarios of the usage of activity recognition applications. Moreover, a new evaluation technique is introduced (leave-one-other-activity-out) to simulate when an activity recognition system is used while performing a previously unknown activity. Through applying the proposed methods it is possible to design a robust physical activity recognition system with the desired generalization characteristic.

The automatic, on-demand, integration of information from multiple diverse sources outside the control of the application itself is central to many fusion applications. An important problem is to handle situations when the requested information is not directly available but has to be generated or adapted through transformations. This paper extends the semantic information integration approach used in the stream-based knowledge processing middleware DyKnow with support for finding and automatically applying transformations. Two types of transformations are considered. Automatic transformation between different units of measurements and between streams of different types. DyKnow achieves semantic integration by creating a common ontology, specifying the semantic content of streams relative to the ontology and using semantic matching to find relevant streams. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is integrated in the Robot Operating System (ROS) and used in collaborative unmanned aircraft systems missions., CUAS, CADICS, CENIIT

This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time., ELLIIT, ETT, CUAS, UK EPSRC: EP/H023135/1

Nonuniform sampling occurs in time-interleaved analog-to-digital converters (TI-ADC) due to timing mismatches between the individual channel analog-to-digital converters (ADCs). Such nonuniformly sampled output will degrade the achievable resolution in a TI-ADC. To restore the degraded performance, digital time-varying reconstructors can be used at the output of the TI-ADC, which in principle, converts the nonuniformly sampled output sequence to a uniformly sampled output. As the bandwidth of these reconstructors increases, their complexity also increases rapidly. Also, since the timing errors change occasionally, it is important to have a reconstructor architecture that requires fewer coefficient updates when the value of the timing error changes. Multivariate polynomial impulse response reconstructor is an attractive option for an M-channel reconstructor. If the channel timing error varies within a certain limit, these reconstructors do not need any online redesign of their impulse response coefficients. This paper proposes a technique that can be applied to multivariate polynomial impulse response reconstructors in order to further reduce the number of fixed-coefficient multipliers, and thereby reduce the implementation complexity.

In this paper we present novel algorithms and data structures for capturing, processing and rendering with real world lighting conditions based on high dynamic range video sequences. Based on the captured HDR video data we show how traditional image based lighting can be extended to include illumination variations in both the temporal as well as the spatial domain. This enables highly realistic renderings where traditional IBL techniques using a single light probe fail to capture important details in the real world lighting environment. To demonstrate the usefulness of our approach, we show examples of both off-line and real-time rendering applications., VPS