With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.

Tracking-by-detection methods consider tracking task as a continuous detection problem applied over video frames. Modern tracking-by-detection trackers have online learning ability; the update stage is essential because it determines how to modify the classifier inherent in a tracker. However, most trackers search for the target within a fixed region centered at the previous object position; thus, they lack spatiotemporal consistency. This becomes a problem when the tracker detects an incorrect object during short-term occlusion. In addition, the scale of the bounding box that contains the target object is usually assumed not to change. This assumption is unrealistic for long-term tracking, where the scale of the target varies as the distance between the target and the camera changes. The accumulation of errors resulting from these shortcomings results in the drift problem, i.e. drifting away from the target object. To resolve this problem, we present a drift-free, online learning-based tracking-by-detection method using a single static camera. We improve the latent structured support vector machine (SVM) tracker by designing a more robust tracker update step by incorporating two Kalman filter modules: the first is used to predict an adaptive search region in consideration of the object motion; the second is used to adjust the scale of the bounding box by accounting for the background model. We propose a hierarchical search strategy that combines Bhattacharyya coefficient similarity analysis and Kalman predictors. This strategy facilitates overcoming occlusion and increases tracking efficiency. We evaluate this work using publicly available videos thoroughly. Experimental results show that the proposed method outperforms the state-of-the-art trackers.

This paper presents a fully integrated yet compact receiver front-end for Sub-GHz applications such as Internet-of-Things (IoT). The low noise amplifier (LNA) matching network leverages an inductance boosting technique. A relatively small on-chip inductor with a compact area achieves impedance matching in such a low frequency. Moreover, a passive-mixer-first mode bypasses the LNA to extend the receiver dynamic-range. The passive mixer provides matching to the 50Ω antenna interface to eliminate the need for additional passive components. Therefore, the receiver can be fully-integrated without any off-chip matching components. The flipped-voltage-follower (FVF) cell is adopted in the low pass filter (LPF) and the variable gain amplifier (VGA) for its high linearity and low power consumption. Fabricated in 65nm LP CMOS process, the proposed receiver front-end occupies 0.37mm2 core area, with a tolerable input power ranging from -91.5dBm to -1dBm for 500kbps GMSK signal at 924MHz frequency. The power consumption is 1mW power under a 1.2V supply.

In this paper, we propose a face representation approach using multi-orientation Log-Gabor local binary pattern (MOLGLBP) for realizing face recognition under facial expressions, illuminations and partial occlusions. Log-Gabor filters with different scales (frequencies) and orientations are applied on Y, I, and Q channel image in the YIQ color space respectively. Then Log-Gabor images of different orientations at the same scale are combined to form a multi-orientation Log-Gabor image (MOLGI) and two LBP operators are applied to it. For face recognition, histogram intersection metric is utilized to measure the similarity of faces. The proposed approach is evaluated on the CurtinFaces database and experiments demonstrate that the proposed approach is effectiveness against two simultaneous variations: expression & illumination, and illumination & occlusion.

A single-electron turnstile and electrometer circuit was fabricated on a silicon-on-insulator substrate. The turnstile, which is operated by opening and closing two metal-oxide-semiconductor field-effect transistors (MOSFETs) alternately, allows current quantization at 20 K due to single-electron transfer. Another MOSFET is placed at the drain side of the turnstile to form an electron storage island. Therefore, one-by-one electron entrance into the storage island from the turnstile can be detected as an abrupt change in the current of the electrometer, which is placed near the storage island and electrically coupled to it. The correspondence between the quantized current and the single-electron counting was confirmed.

Serial RapidIO (SRIO) is a high-performance interconnection standard for embedded systems. Cyclic Redundancy Check (CRC) provides protection for packet transmissions and impacts the device performances. In this paper, two CRC calculation strategies, based on adjustable slicing parallelization and simplified calculators, are proposed. In the first scheme, the temporary CRC result of the previous cycle (CPre) is considered as a dependent input for the new cycle and is combined with a specific segment of packet data before slicing parallelization. In the second scheme, which can reach a higher maximum working frequency, CPre is considered as an independent input and is separated from the calculation of packet data for further parallelization. Performance comparisons based on ASIC and FPGA implementations are demonstrated to show their effectiveness. Compared with the reference designs, more than 34.8% and 13.9% of average power can be improved by the two proposed schemes at 156.25MHz in 130nm technology, respectively.

The demand for wireless traffic is increasing rapidly, which has posed huge challenges to mobile network operators (MNOs). A heterogeneous network (HetNet) framework, composed of a marcocell and femtocells, has been proved to be an effective way to cope with the fast-growing traffic demand. In this paper, we assume that both the macrocell and femtocells are owned by the same MNO, with revenue optimization as its ultimate goal. We aim to propose a pricing strategy for macro-femto HetNets with a user centric vision, namely, mobile users would have their own interest to make rational decisions on selecting between the macrocell and femtocells to maximize their individual benefit. We formulate a Stackelberg game to analyze the interactions between the MNO and users, and obtain the equilibrium solution for the Stackelberg game. Via extensive simulations, we evaluate the proposed pricing strategy in terms of its efficiency with respect to the revenue optimization.

Network traffic load usually differs significantly at different times of a day due to users' different time-preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all the studies on TDP schemes consider only the monopoly or duopoly NSP case. In our previous work, the duopoly NSP case has been studied with the assumption that each NSP has complete information of quality of service (QoS) of the other NSP. In this paper, an oligopoly NSP case is studied. NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSPs by considering their own time preference, congestion status in the networks and the price set by the NSPs. The interactions among NSPs are modeled as an oligopoly Bertrand game. Firstly, assuming that each NSP has complete information of QoS of all NSPs, a unique Nash equilibrium of the game is established under the assumption that users' valuation of QoS is uniformly distributed. Secondly, the assumption of complete information of QoS of all NSPs is relaxed, and a learning algorithm is proposed for NSPs to achieve the Nash equilibrium of the game. Analytical and experimental results show that NSPs can benefit from TDP scheme, however, not only the competition effect but also the incomplete information among NSPs causes revenue loss for NSPs under the TDP scheme.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.

This paper studies the impact of integrating pricing with connection admission control (CAC) on the congestion management practices in contention-based wireless random access networks. Notably, when the network is free of charge, each self-interested user tries to occupy the channel as much as possible, resulting in the inefficient utilization of network resources. Pricing is therefore adopted as incentive mechanism to encourage users to choose their access probabilities considering the real-time network congestion level. A Stackelberg leader-follower game is formulated to analyze the competitive interaction between the service provider and the users. In particular, each user chooses the access probability that optimizes its payoff, while the self-interested service provider decides whether to admit or to reject the user's connection request in order to optimize its revenue. The stability of the Stackelberg leader-follower game in terms of convergence to the Nash equilibrium is established. The proposed CAC scheme is completely distributed and can be implemented by individual access points using only local information. Compared to the existing schemes, the proposed scheme achieves higher revenue gain, higher user payoff, and higher QoS performance.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms.

To reduce the inaccuracy caused by inappropriate time window, we propose two probabilistic fault localization schemes based on the idea of "extending time window." The global window extension algorithm (GWE) uses a window extension strategy for all candidate faults, while the on-demand window extension algorithm (OWE) uses the extended window only for a small set of faults when necessary. Both algorithms can increase the metric values of actual faults and thus improve the accuracy of fault localization. Simulation results show that both schemes perform better than existing algorithms. Furthermore, OWE performs better than GWE at the cost of a bit more computing time.

Due to network users' different time-preference, network traffic load usually significantly differs at different time. In traffic peak time, network congestion may happen, which make the quality of service for network users deteriorate. There are essentially two ways to improve the quality of services in this case: (1) Network service providers (NSPs) over-provision network capacity by investment; (2) NSPs use time-dependent pricing (TDP) to reduce the traffic at traffic peak time. However, over-provisioning network capacity can be costly. Therefore, some researchers have proposed TDP to control congestion as well as improve the revenue of NSP. But to the best of our knowledge, all of the literature related time-dependent pricing scheme only consider the monopoly NSP case. In this paper, a duopoly NSP case is studied. The NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSP by considering their own preference, congestion status in the networks and the price set by the NSPs. Analytical and experimental results show that the TDP benefits the NSPs, but the revenue improvement is limited due to the competition effect.

In recent years, the adoption of Software as a Service (SaaS) cloud services has surpassed that of Infrastructure as a Service (IaaS) cloud service and is now the focus of attention in cloud computing. The cloud market is becoming highly competitive owing to the increasing number of cloud service providers (CSPs), who are likely to exhibit different cloud capacities, i.e., the cloud market is heterogeneous. Moreover, as different users generally exhibit different Quality of Service (QoS) preferences, it is challenging to set prices for cloud services of good QoS. In this study, we investigate the price competition in the heterogeneous cloud market where two SaaS providers, denoted by CSP1 and CSP2, lease virtual machine (VM) instances from IaaS providers to offer cloud-based application services to users. We assume that CSP1 only has M/M/1 queue of VM instances owing to its limited cloud resources, whereas CSP2 has M/M/∞ queue of VM instances reflecting its adequate resources. We consider two price competition scenarios in which two CSPs engage in two games: one is a noncooperative strategic game (NSG) where the two CSPs set prices simultaneously and the other is a Stackelberg game (SG) where CSP2 sets the price first as the leader and is followed by CSP1, who sets the price in response to CSP2. Each user decides which cloud services to purchase (if purchases are to be made) based on the prices and QoS. The NSG scenario corresponds to the practical cloud market, where two CSPs with different cloud capacities begin to offer cloud services simultaneously; meanwhile, the SG scenario covers the instance where a more recent CSP plans to enter a cloud market whose incumbent CSP has larger cloud resources. Equilibrium is achieved in each of the scenarios. Numerical results are presented to verify our theoretical analysis.