Round-trip time (RTT) is an important performance metric. Traditional RTT estimation methods usually depend on the cooperation of other networks and particular active or passive measurement platforms, whose global deployments are costly and difficult. Thus a new RTT estimation algorithm, ME algorithm, is introduced. It can estimate the RTT of two hosts communicating through border routers by using TCP CUBIC bulk flow data from those routhers without the use of extra facilities, which makes the RTT estimation in large-scale high-speed networks more effective. In addition, a simpler and more accurate algorithm — AE algorithm — is presented and used when the link has large bandwidth and low packet loss rate. The two proposed algorithms suit sampled flow data because only duration and total packet number of a TCP CUBIC bulk flow are inputs to their calculations. Experimental results show that both algorithms work excellently in real situations. Moreover, they have the potential to be adapted to other TCP versions with slight modification as their basic idea is independent of the TCP congestion control mechanism.

We consider a chain of integrators system that has an uncertain delay in the input. Also, there is a measurement noise in the feedback channel that only noisy output is available. We develop a new output feedback control scheme along with amplification such that the ultimate bounds of all states and output of the controlled system can be made arbitrarily small. We note that the condition imposed on the sensor noise is quite general over the existing results such that the sensor noise is uncertain and is only required to be bounded by a known bound. The benefit of our control method is shown via an example.

For systems with a delay in the input, the predictor method has been often used in state feedback controllers for system stabilization or regulation. In this letter, we show that for a chain of integrators with even an unknown input delay, a much simpler and memoryless controller is a good candidate for system regulation. With an adaptive gain-scaling factor, the proposed state feedback controller can deal with an unknown time-varying delay in the input. An example is given for illustration.

In deep-submicron era, interconnection delays are not negligible even in high-level synthesis and regular-distributed-register architectures (RDR architectures) have been proposed to cope with this problem. In this paper, we propose a high-level synthesis algorithm using operation chainings which reduces the overall latency targeting RDR architectures. Our algorithm consists of three steps: The first step enumerates candidate operations for chaining. The second step introduces maximal chaining distance (MCD), which gives the maximal allowable inter-island distance on RDR architecture between chaining candidate operations. The last step performs list-scheduling and binding simultaneously based on the results of the two preceding steps. Our algorithm enumerates feasible chaining candidates and selects the best ones for RDR architecture. Experimental results show that our proposed algorithm reduces the latency by up to 40.0% compared to the original approach, and by up to 25.0% compared to a conventional approach. Our algorithm also reduces the number of registers and the number of multiplexers compared to the conventional approaches in some cases.

In this letter, we derive two lower bounds for the number of terms in a double-base number system (DBNS), when the digit set is {1}. For a positive integer n, we show that the number of terms obtained from the greedy algorithm proposed by Dimitrov, Imbert, and Mishra [1] is $Thetaleft(rac{log n}{log log n} ight)$. Also, we show that the number of terms in the shortest double-base chain is Θ(log n).

In this short correspondence, (1+uv)-constacyclic codes over the finite non-chain ring R[v]/(v2+v) are investigated, where R=F2+uF2 with u2=0. Some structural properties of this class of constacyclic codes are studied. Further, some optimal binary linear codes are obtained from these constacyclic codes.

LED (Light Encryption Device) block cipher, one of lightweight block ciphers, is very compact in hardware. Its encryption process is composed of AES-like rounds. Recently, a scan-based side-channel attack is reported which retrieves the secret information inside the cryptosystem utilizing scan chains, one of design-for-test techniques. In this paper, a scan-based attack method on the LED block cipher using scan signatures is proposed. In our proposed method, we focus on a particular 16-bit position in scanned data obtained from an LED LSI chip and retrieve its secret key using scan signatures. Experimental results show that our proposed method successfully retrieves its 64-bit secret key using 36 plaintexts on average if the scan chain is only connected to the LED block cipher. These experimental results also show the key is successfully retrieved even if the scan chain includes additional 130,000 1-bit data.

Sensor noise prevents the exact measurement of output, which makes it difficult to guarantee the ultimate bound of the actual output and states, which is smaller than the sensor noise amplitude. Even worse, the time-varying delay in the input does not guarantee the boundedness of the actual output and states under sensor noise. In this letter, our considered system is a chain of integrators in which time-varying delay exists in the input and there is an additive form of sensor noise in the output measurement. To guarantee the arbitrarily small ultimate bound of the actual output and states, we newly propose an adaptive output feedback controller whose gain is tuned on-line. The merits of our control method over the existing results are clearly shown in the example.

Trivium is a synchronous stream cipher using three shift registers. It is designed to have a simple structure and runs at high speed. A scan-based side-channel attack retrieves secret information using scan chains, one of design-for-test techniques. In this paper, a scan-based side-channel attack method against Trivium using scan signatures is proposed. In our method, we reconstruct a previous internal state in Trivium one by one from the internal state just when a ciphertext is generated. When we retrieve the internal state, we focus on a particular 1-bit position in a collection of scan chains and then we can attack Trivium even if the scan chain includes other registers than internal state registers in Trivium. Experimental results show that our proposed method successfully retrieves a plaintext from a ciphertext generated by Trivium.

In this paper, we focus on a centralized spectrum access strategy in a cognitive radio network, in which a single licensed spectrum with one primary user (PU) and several secondary users (SUs) (multiple input streams) are considered. We assume the spectrum can be divided into multiple channels and the packets from variable SUs can arrive at the system simultaneously. Taking into account the priority of the PU, we suppose that one PU packet can occupy the whole licensed spectrum, while one SU packet will occupy only one of the channels split from the licensed spectrum when that channel is not used. Moreover, in order to reduce the blocking ratio of the SUs, a buffer with finite capacity for the SUs is set. Regarding the packet arrivals from different SUs as multiple input streams, we build a two-dimensional Markov chain model based on the phase of the licensed spectrum and the number of SU packets in the buffer. Then we give the transition probability matrix for the Markov chain. Additionally, we analyze the system model in steady state and derive some important performance measures for the SUs, such as the average queue length in the buffer, the throughput and the blocking ratio. With the trade-off between different performance measures, we construct a net benefit function. At last, we provide numerical results to show the change trends of the performance measures with respect to the capacity of the SU buffer under different network conditions, and optimize the capacity of the SU buffer accordingly.

We consider an output feedback control problem of a chain of integrators under sensor noise. The sensor noise enters the output feedback channel in an additive form. A similar problem has been addressed most recently in [9], but their result has been developed only under AC sensor noise. We generalize the result of [9] by allowing the sensor noise to include both AC and DC components. With our new output feedback controller, we show that the ultimate bounds of all states can be made arbitrarily small. We show the generality of our result over [9] via an example.

A mobile hotspot is a moving vehicle that hosts an Access Point (AP) such as train, bus and subway where users in these vehicles connect to external cellular network through AP to access their internet services. To meet Quality of Service (QoS) requirements, typically throughput and/or delay, a Call Admission Control (CAC) is needed to restrict the number of users accepted by the AP. In this paper, we analyze a modified guard channel scheme as CAC for mobile hotspot as follows: During a mobile hotspot is in the stop-state, we adopt a guard channel scheme where the optimal number of resource units is reserved for vertical handoff users from cellular network to WLAN. During a mobile hotspot is in the move-state, there are no handoff calls and so no resources for handoff calls are reserved in order to maximize the utility of the WLAN capacity. We model call's arrival and departure processes by Markov Modulated Poisson Process (MMPP) and then we model our CAC by 2-dimensional continuous time Markov chain (CTMC) for single traffic and 3-dimensional CTMC for two types of traffic. We solve steady-state probabilities by the Quasi-Birth and Death (QBD) method and we get various performance measures such as the new call blocking probabilities, the handoff call dropping probabilities and the channel utilizations. We compare our CAC with the conventional guard channel scheme which the number of guard resources is fixed all the time regardless of states of the mobile hotspot. Finally, we find the optimal threshold value on the amount of resources to be reserved for the handoff call subject to a strict constraint on the handoff call dropping probability.

Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed reads and ghost reads. In this letter, we propose two effective models, a Bayesian inference-based decision model and a path-based detection model, to increase the accuracy of RFID data cleaning in RFID based supply chain management. In addition, the maximum entropy model is introduced for determining the value of sliding window size. Experiment results validate the performance of the proposed method and show that it is able to clean raw RFID data with a higher accuracy.

A wireless emergency communication network with a fixed allocation of spectrum resources cannot meet the tremendous demand for spectrum access when a crisis occurs. It is necessary to develop an effective spectrum access scheme to improve the performance of emergency communication systems. In this paper, we study a new emergency communication system combines cognitive radio technology and an emergency communication network. Emergency users can utility resources in a general network when traffic becomes congested in an emergency network. Non-reciprocal spectrum access scheme (NRA) and reciprocal spectrum access scheme (RA) for two heterogeneous cognitive networks, namely emergency network and general network are proposed to compare with traditional spectrum access scheme (TA). User behavior with each scheme is modeled by continuous-time Markov chains. Moreover, the blocking and dropping probabilities of users in two heterogeneous cognitive networks are derived as the performance metrics. In addition, the throughput and the spectrum utilization rate of the system are evaluated. Finally, we compare the performance of three dynamic spectrum access schemes. The simulation results show that the RA scheme is an effective scheme to enhance the performance of emergency systems.

It is well known that cognitive radio (CR) techniques have great potential for supporting future demands on the scarce radio spectrum resources. For example, by enabling the utilization of spectrum bands temporarily not utilized by primary users (PUs) licensed to operate on those bands. Spectrum sensing is a well-known CR technique for detecting those unutilized bands. However, the spectrum sensing outcomes cannot be perfect and there will always be some misdetections and false alarms which will affect the performance thereby degrading the quality of service (QoS) of PUs. Continuous time Markov chain (CTMC) based modeling has been widely used in the literature to evaluate the performance of CR networks (CRNs). A major limitation of the available literature is that all the key factors and realistic elements such as the effect of imperfect sensing and state dependent transition rates are not modeled in a single work. In this paper, we present a CTMC based model for analyzing the performance of CRNs. The proposed model differs from the existing models by accurately incorporating key elements such as full state dependent transition rates, multi-channel support, handoff capability, and imperfect sensing. We derive formulas for primary termination probability, secondary success probability, secondary blocking probability, secondary forced termination probability, and radio resource utilization. The results show that incorporating fully state dependent transition rates in the CTMC can significantly improve analysis accuracy, thus achieving more realistic and accurate analytical model. The results from extensive Monte Carlo simulations confirm the validity of our proposed model.

IEEE802.11 Wireless Local Area Networks (WLANs) are becoming more and more pervasive due to their simple channel access mechanism, Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), but this mechanism provides all nodes including Access Point and other Stations with the same channel access probability. This characteristic does not suit the infrastructure mode which has so many downlink flows to be transmitted at the Access Point that congestion at the Access Point is more likely to occur. To resolve this asymmetry traffic problem, we develop an Optimal Contention Window Adjustment method assuming the condition of erroneous channels over WLANs. This method can be easily implemented and is compatible with the original CSMA/CA mechanism. It holds the ratio of downlink and uplink flows and at the same time achieves the maximum saturation throughput in the WLANs. We use the Markov Chain analytical model to analyze its performance and validate it through the simulations.

Random telegraph noise (RTN) is a phenomenon that is considered to limit the reliability and performance of circuits using advanced devices. The time constants of carrier capture and emission and the associated change in the threshold voltage are important parameters commonly included in various models, but their extraction from time-domain observations has been a difficult task. In this study, we propose a statistical method for simultaneously estimating interrelated parameters: the time constants and magnitude of the threshold voltage shift. Our method is based on a graphical network representation, and the parameters are estimated using the Markov chain Monte Carlo method. Experimental application of the proposed method to synthetic and measured time-domain RTN signals was successful. The proposed method can handle interrelated parameters of multiple traps and thereby contributes to the construction of more accurate RTN models.

In this letter, we consider a control problem of a chain of integrators where there is an uncertain delay in the input and sensor noise. This is an output feedback control result over [10] in which a state feedback control is suggested. The several generalized features are: i) output feedback control is developed instead of full state feedback control, ii) uncertain delay in the input is allowed, iii) all states are derived to be arbitrarily small under uncertain sensor noise.

In this letter, we consider a control problem of a chain of integrators by output feedback under sensor noise. First, we introduce a measurement output feedback controller which drives all states and output of the considered system to arbitrarily small bounds. Then, we suggest a measurement output feedback controller coupled with a switching gain-scaling factor in order to improve the transient response and retain the same arbitrarily small ultimate bounds as well. An example is given to show the advantage of the proposed control method.

This paper proposes the test case prioritization in regression testing. The large size of a test suite to be executed in regression testing often causes large amount of testing cost. It is important to reduce the size of test cases according to prioritized test sequence. In this paper, we apply the Markov chain Monte Carlo random testing (MCMC-RT) scheme, which is a promising approach to effectively generate test cases in the framework of random testing. To apply MCMC-RT to the test case prioritization, we consider the coverage-based distance and develop the algorithm of the MCMC-RT test case prioritization using the coverage-based distance. Furthermore, the MCMC-RT test case prioritization technique is consistently comparable to coverage-based adaptive random testing (ART) prioritization techniques and involves much less time cost.