The recent switch from analog to digital TV broadcasting around the world has led to the development of communications standards that consider the use of TV White Spaces (TVWS). One such standard is the IEEE 802.22 wireless regional area network (WRAN), which considers the use of TVWS to provide broadband wireless services over long transmission links, and therefore presents an opportunity to bring connectivity and data-based services from urban to rural areas. Services that could greatly benefit from the deployment of wireless broadband data links between urban and rural areas are those related to telemedicine and m-health. To enable proper telemedicine service delivery from urban (e.g. an urban hospital) to rural locations (e.g. a rural clinic) it is of paramount importance to provide a certain quality of service (QoS) level. In this context, QoS provisioning for telemedicine applications over wireless networks presents a major challenge that must be addressed to fulfill the potential that rural wireless telemedicine has to offer. In this paper, a cross-layer approach combining medium access control (MAC) and application (APP) layers is proposed with the aim of reducing blocking probability in teleconsulting services operating over IEEE802.22/WRANs. At the APP layer, a teleconsulting traffic profile based on utilization rates is defined. On the other hand, at the MAC layer, an Adaptive Bandwidth Management (ABM) mechanism is used to perform a QoS-based classification of teleconsulting services and then dynamically allocate the bandwidth requirements. Three teleconsulting services with different bandwidth requirements are considered in order to evaluate the performance of the proposed approach: high-resolution teleconsulting, medium-resolution teleconsulting, and audio-only teleconsulting. Simulation results demonstrate that the proposed approach is able to reduce blocking probability by using different criteria for service modes within the admission control scheme.

Ethernet passive optical network (EPON) is one of the energy-efficient access networks. Many studies have been done to reach maximum energy saving in the EPON. However, it is a trade-off between achieving maximum energy saving and guaranteeing QoS. In this paper, a predictive doze mode mechanism in an enhanced EPON architecture is proposed to achieve energy saving by using a logistic regression (LR) model. The optical line terminal (OLT) in the EPON employs an enhanced Doze Manager practicing the LR model to predict the doze periods of the optical network units (ONUs). The doze periods are estimated more accurately based on the historical high-priority traffic information, and logistic regression DBA (LR-DBA) performs dynamic bandwidth allocation accordingly. The proposed LR-DBA mechanism is compared with a scheme without energy saving (IPACT) and another scheme with energy saving (GDBA). Simulation results show that LR-DBA effectively improves the power consumption of ONUs in most cases, and the improvement can be up to 45% while it guarantees the QoS metrics, such as the high-priority traffic delay and jitter.

This work presents a low-complexity lossless electrocardiogram (ECG) compression ASIC for wireless sensors. Three linear predictors aiming for different signal characteristics are provided for prediction based on a history table that records of the optimum predictors for recent samples. And unlike traditional methods using a unified encoder, the prediction error is encoded by a hybrid Golomb encoder combining Exp-Golomb and Golomb-Rice and can adaptively configure the encoding scheme according to the predictor selection. The novel adaptive prediction and encoding scheme contributes to a compression rate of 2.77 for the MIT-BIH Arrhythmia database. Implemented in 40nm CMOS process, the design takes a small gate count of 1.82K with 37.6nW power consumption under 0.9V supply voltage.

A grammar compression is a restricted context-free grammar (CFG) that derives a single string deterministically. The goal of a grammar compression algorithm is to develop a smaller CFG by finding and removing duplicate patterns, which is simply a frequent pattern discovery process. Any frequent pattern can be obtained in linear time; however, a huge working space is required for longer patterns, and the entire string must be preloaded into memory. We propose an online algorithm to address this problem approximately within compressed space. For an input sequence of symbols, a1,a2,..., let Gi be a grammar compression for the string a1a2…ai. In this study, an online algorithm is considered one that can compute Gi+1 from (Gi,ai+1) without explicitly decompressing Gi. Here, let G be a grammar compression for string S. We say that variable X approximates a substring P of S within approximation ratio δ iff for any interval [i,j] with P=S[i,j], the parse tree of G has a node labeled with X that derives S[l,r] for a subinterval [l,r] of [i,j] satisfying |[l,r]|≥δ|[i,j]|. Then, G solves the frequent pattern discovery problem approximately within δ iff for any frequent pattern P of S, there exists a variable that approximates P within δ. Here, δ is called the approximation ratio of G for S. Previously, the best approximation ratio obtained by a polynomial time algorithm was Ω(1/lg2|P|). The main contribution of this work is to present a new lower bound Ω(1/<*|S|lg|P|) that is smaller than the previous bound when lg*|S|

In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.

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.

Microblog data contains rich information of real-world events with great commercial values, so microblog-oriented natural language processing (NLP) tasks have grabbed considerable attention of researchers. However, the performance of microblog-oriented Chinese Word Segmentation (CWS) based on deep neural networks (DNNs) is still not satisfying. One critical reason is that the existing microblog-oriented training corpus is inadequate to train effective weight matrices for DNNs. In this paper, we propose a novel active learning method to extend the scale of the training corpus for DNNs. However, due to a large amount of partially overlapped sentences in the microblogs, it is difficult to select samples with high annotation values from raw microblogs during the active learning procedure. To select samples with higher annotation values, parameter λ is introduced to control the number of repeatedly selected samples. Meanwhile, various strategies are adopted to measure the overall annotation values of a sample during the active learning procedure. Experiments on the benchmark datasets of NLPCC 2015 show that our λ-active learning method outperforms the baseline system and the state-of-the-art method. Besides, the results also demonstrate that the performances of the DNNs trained on the extended corpus are significantly improved.

In this paper we show a simple heuristic for constructing smaller automata for a set of regular expressions, based on suffix sorting: finding common prefixes and suffixes in regular expressions and merging them. It is an important problem in network security. We applied our approach to random and real-world regular expressions. Experimental results showed that our approach yields up to 12 times enhancement in throughput.

The rotating element electric field vector (REV) method is a classical measurement technique for phased array calibration. Compared with other calibration methods, it requires only power measurements. Thus, the REV method is more reliable for operating phased array calibration systems. However, since the phase of each element must be rotated from 0 to 2π, the conventional REV method requires a large number of measurements. Moreover, the power of composite electric field vector doesn't vary significantly because only a single element's phase is rotated. Thus, it can be easily degraded by the receiver noise. A simplified REV method combined with Hadamard group division is proposed in this paper. In the proposed method, only power measurements are required. All the array elements are divided into different groups according to the group matrix derived from the normalized Hadamard matrix. The phases of all the elements in the same group are rotated at the same time, and the composite electric field vector of this group is obtained by the simplified REV method. Hence, the relative electric fields of all elements can be obtained by a matrix equation. Compared with the conventional REV method, the proposed method can not only reduce the number of measurements but also improve the measurement accuracy under the particular range of signal to noise ratio(SNR) at the receiver, especially under low and moderate SNRs.

Previous research has shown that transcripts generated by automatic speech recognition (ASR) technologies can improve the listening comprehension of non-native speakers (NNSs). However, we still lack a detailed understanding of how ASR transcripts affect listening comprehension of NNSs. To explore this issue, we conducted two studies. The first study examined how the current presentation of ASR transcripts impacted NNSs' listening comprehension. 20 NNSs engaged in two listening tasks, each in different conditions: C1) audio only and C2) audio+ASR transcripts. The participants pressed a button whenever they encountered a comprehension problem, and explained each problem in the subsequent interviews. From our data analysis, we found that NNSs adopted different strategies when using the ASR transcripts; some followed the transcripts throughout the listening; some only checked them when necessary. NNSs also appeared to face difficulties following imperfect and slightly delayed transcripts while listening to speech - many reported difficulties concentrating on listening/reading or shifting between the two. The second study explored how different display methods of ASR transcripts affected NNSs' listening experiences. We focused on two display methods: 1) accuracy-oriented display which shows transcripts only after the completion of speech input analysis, and 2) speed-oriented display which shows the interim analysis results of speech input. We conducted a laboratory experiment with 22 NNSs who engaged in two listening tasks with ASR transcripts presented via the two display methods. We found that the more the NNSs paid attention to listening to the audio, the more they tended to prefer the speed-oriented transcripts, and vice versa. Mismatched transcripts were found to have negative effects on NNSs' listening comprehension. Our findings have implications for improving the presentation methods of ASR transcripts to more effectively support NNSs.

In this paper we propose the Zone-based Energy Aware data coLlection (ZEAL) protocol. ZEAL is designed to be used in agricultural applications for wireless sensor networks. In these type of applications, all data is often routed to a single point (named “sink” in sensor networks). The overuse of the same routes quickly depletes the energy of the nodes closer to the sink. In order to minimize this problem, ZEAL automatically creates zones (groups of nodes) independent from each other based on the trajectory of one or more mobile sinks. In this approach the sinks collects data queued in sub-sinks in each zone. Unlike existing protocols, ZEAL accomplish its routing tasks without using GPS modules for location awareness or synchronization mechanisms. Additionally, ZEAL provides an energy saving mechanism on the network layer that puts zones to sleep when there are no mobile sinks nearby. To evaluate ZEAL, it is compared with the Maximum Amount Shortest Path (MASP) protocol. Our simulations using the ns-3 network simulator show that ZEAL is able to collect a larger number of packets with significantly less energy in the same amount of time.

This paper presents experimental results of our proposed null-space expansion scheme for multiuser massive multiple-input multiple-output (MIMO) in time varying channels. Multiuser MIMO transmission with the proposed scheme can suppress the inter-user interference (IUI) caused by outdated channel state information (CSI). The excess degrees of freedom (DoFs) of massive MIMO is exploited to perform additional null-steering using past estimated CSI. The signal-to-interference power ratio (SIR) and spectral efficiency performances achieved by the proposed scheme that uses measured CSI is experimentally evaluated. It is confirmed that the proposed scheme shows performance superior to the conventional channel prediction scheme. In addition, IUI can be stably suppressed even in high mobility environments by further increasing the null-space dimension.

Three-dimensional (3D) field-programmable gate arrays (FPGAs) are expected to offer higher logic density as well as improved delay and power performance by utilizing 3D integrated circuit technology. However, because through-silicon-vias (TSVs) for conventional 3D FPGA interlayer connections have a large area overhead, there is an inherent tradeoff between connectivity and small size. To find a balance between cost and performance, and to explore 3D FPGAs with realistic 3D integration processes, we propose two types of 3D FPGA and construct design tool sets for architecture exploration. In previous research, we created a TSV-free 3D FPGA with a face-down integration method; however, this was limited to two layers. In this paper, we discuss the face-up stacking of several face-down stacked FPGAs. To minimize the number of TSVs, we placed TSVs peripheral to the FPGAs for 3D-FPGA with 4 layers. According to our results, a 2-layer 3D FPGA has reasonable performance when limiting the design to two layers, but a 4-layer 3D FPGA is a better choice when area is emphasized.

In this paper, we describe the direct learning of an end-to-end mapping between under-/over-exposed images and well-exposed images. The mapping is represented as a deep convolutional neural network (CNN) that takes multiple-exposure images as input and outputs a high-quality image. Our CNN has a lightweight structure, yet gives state-of-the-art fusion quality. Furthermore, we know that for a given pixel, the influence of the surrounding pixels gradually increases as the distance decreases. If the only pixels considered are those in the convolution kernel neighborhood, the final result will be affected. To overcome this problem, the size of the convolution kernel is often increased. However, this also increases the complexity of the network (too many parameters) and the training time. In this paper, we present a method in which a number of sub-images of the source image are obtained using the same CNN model, providing more neighborhood information for the convolution operation. Experimental results demonstrate that the proposed method achieves better performance in terms of both objective evaluation and visual quality.

To design antennas for ingestible capsule endoscope systems, the transmission factors of dipole and loop antennas placed in the torso-shaped phantom filled with deionized water or human body equivalent liquid (HBEL) are investigated by numerical and experimental study. The S-parameter method is used to evaluate transmission characteristics through a torso-shaped phantom in a broadband frequency range. Good agreement of S-parameters between measured results and numerical analysis is observed and the transmission factors for both cases are obtained. Comparison of the transmission factors between HBEL and deionized water is presented to explain the relation between conductivity and the transmission characteristics. Two types of antennas, dipole antenna and loop antenna are compared. In the case of a dipole antenna placed in deionized water, it is observed that the transmission factor decreases as conductivity increases. On the other hand, there is a local maximum in the transmission factor at 675 MHz in the case of HBEL. This phenomenon is not observed in the case of a loop antenna. The transmission factor of capsule dipole antenna and capsule loop antenna are compared and the guideline in designing capsule antennas by using transmission factor is also proposed.

This paper proposes a genetic algorithm (GA) based design method for arbitrarily-shaped resonant elements that offer enhanced reflectarray antenna performance. All elements have the specified phase property over the range of 360°, and also have dual-polarization and low cross-polarization properties for better reflectarray performance. In addition, the proposal is suitable for linear-to-circular polarization conversion elements. Thus, polarizer reflectarray elements are also presented in this paper. The proposed elements are validated using both numerical simulations and experiments.

This paper proposes a circuit modeling technique for electrically-very-small devices, e.g. electrodes for intrabody communications, coils for wireless power transfer systems, high-frequency transformers, etc. The proposed technique is based on the method of moments and can be regarded as an improved version of the partial element equivalent circuit method.

This letter presents a 12-bit column-parallel hybrid two-step successive approximation register/single-slope analog-to-digital converter (SAR/SS ADC) for CMOS image sensor (CIS). For achieving a high conversion speed, a simple SAR ADC is used in upper 6-bit conversion and a conventional SS ADC is used in lower 6-bit conversion. To reduce the power consumption, a comparator is shared in each column, and a 6-bit ramp generator is shared by all columns. This ADC is designed in SMIC 0.18µm CMOS process. At a clock frequency of 22.7MHz, the conversion time is 3.2µs. The ADC has a DNL of -0.31/+0.38LSB and an INL of -0.86/+0.8LSB. The power consumption of each column ADC is 89µW and the ramp generator is 763µW.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

Block compressed sensing with random permutation (BCS-RP) has been shown to be very effective for image Encryption-then-Compression (ETC) applications. However, in the BCS-RP scheme, the statistical information of the blocks is disclosed, because the encryption is conducted within each small block of the image. To solve this problem, a two-dimension compressed sensing (2DCS) with 2D random permutation (2DRP) strategy for image ETC applications is proposed in this letter, where the 2DRP strategy is used for encrypting the image and the 2DCS scheme is used for compressing the encrypted image. Compared with the BCS-RP scheme, the proposed approach has two benefits. Firstly, it offers better security. Secondly, it obtains a significant gain of peak signal-to-noise ratio (PSNR) of the reconstructed-images.