This study looks at how an e-Health System can reduce morbidity (poor health) in unreached communities. The e-Health system combines affordable sensors and Body Area Networking technology with mobile health concepts and is called a Portable Health Clinic. The health clinic is portable because all the medical devices fit inside a briefcase and are carried to unreached communities by a healthcare assistants. Patient morbidity is diagnosed using software stratification algorithm and categorized according to triage color-coding scheme within the briefcase. Morbid patients are connected to remote doctor in a telemedicine call center using the mobile network coverage. Electronic Health Records (EHR) are used for the medical consultancy and e-Prescription is generated. The effectiveness of the portable health clinic system to target morbidity was tested on 8690 patients in rural and urban areas of Bangladesh during September 2012 to January 2013. There were two phases to the experiment: the first phase identified the intensity of morbidity and the second phase re-examined the morbid patients, two months later. The experiment results show a decrease in patients to identify as morbid among those who participated in telemedicine process.

This letter deals with a new cell clustering problem subject to signal-to-interference-plus-noise-ratio (SINR) constraints in uplink network MIMO systems, where multiple base stations (BSs) cooperate for joint processing as forming a cluster. We first prove that the SINRs of users in a certain cluster always increase monotonically as the cluster size increases when the receiver filter that maximizes the SINR is used. Using this result, we propose an efficient clustering algorithm to minimize the maximum number of cooperative BSs in a cluster. Simulation results show that the maximum number of cooperative BSs minimized by the proposed method is close to that minimized by the exhaustive search and the proposed scheme outperforms the conventional one in terms of the outage probability.

As the use of information technology (IT) is explosively spreading, reducing the power consumption of IT devices such as servers has become an important social challenge. Nevertheless, while the efficiency of the power supply modules integrated into computers has recently seen significant improvements, their overall efficiency generally depends on load rates. This is especially true under low power load conditions, where it is known that efficiency decreases drastically. Recently, power-saving techniques that work by controlling the power module configuration under low power load conditions have been considered. Based on such techniques, further efficiency improvements can be expected by an adaptive efficiency controls which interlocks the real-time data processing load status with the power supply configuration control. In this study, the performance counters built into the processor of a computer are used to predict power load variations and an equation that predicts the power consumption levels is defined. In a server application experiment utilizing prototype computer hardware and regression analysis, it is validated that the equation could precisely predict processor power consumption. The evaluation shows that significant power supply efficiency improvements could be achieved especially for light load condition. The dependency of the efficiency improvement and operation period is investigated and preferable time scale of the adaptive control is proposed.

2-time slot cooperative relay can be used to increase the cell-edge throughput. Adaptive data modulation further improves the throughput. In this paper, we introduce adaptive modulation to single-carrier (SC) cooperative decode-and-forward (DF) relay. The best modulation combination for mobile-terminal (MT)-relay station (RS) and RS-base station (BS) links is determined for the given local average signal-to-noise power ratios (SNRs) of MT-BS, MT-RS and RS-BS links. According to the modulation combination, the ratio of time slot length of the MT-RS link (first time slot) and the RS-BS link (second time slot) is changed. It is shown by computer simulation that the use of adaptive modulation can achieve higher throughput than fixed modulation and reduces by about 9dB the required normalized total transmit SNR for a 10%-outage throughput of 0.8 bps/Hz compared to direct transmission.

This paper derives the balanced realizations of second-order analog filters directly from the transfer function. Second-order analog filters are categorized into the following three cases: complex conjugate poles, distinct real poles, and multiple real poles. For each case, simple formulas are derived for the synthesis of the balanced realizations of second-order analog filters. As a result, we obtain closed form expressions of the balanced realizations of second-order analog filters.

In a cognitive radio (CR) network, the channel sensing scheme used to detect the existence of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to the inefficiency of the sensing scheme. This may yield primary user interference and low system performance. In this paper, we present a learning-based scheme for channel sensing in CR networks. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. Simulation results show the effectiveness and efficiency of our proposed scheme.

Many discrete functions are often compactly represented by Decision Diagrams (DD). The main problem in the construction of decision diagrams is the space and time requirements. While constructing a decision diagram the memory requirement may grow exponentially with the function. Also, large numbers of temporary nodes are created while constructing the decision diagram for a function. Here the problem of reducing the number of temporary nodes is addressed with respect to the PLA specification format of a function, where the function is represented using a set of cubes. Usually a DD is constructed by recursively processing the input cubes in the PLA specification. The DD, representing a sub function, is specified by a single cube. This DD is merged with a master DD, which represents the entire previously processed cubes. Thus the master DD is constructed recursively, until all the cubes in the input cube set are processed. In this paper, an efficient method is proposed, which reorders and also partitions the cube set into unequal number of cubes per subset, in such a way that, the number of temporary nodes created and the number of logical operations done, during the merging of cubes with the master DD are reduced. This results in the reduction of space and time required for the construction of DDs to a remarkable extent.

As a promising lamination-loss-free fabrication technique, diffusion bonding of etched thin metal plates is used to realize double-layer waveguide slot antennas. Alternating-phase feed is adopted in this paper to reduce the number of laminated plates to simplify fabrication as well as to reduce cost. A 20 × 20-element double-layer waveguide slot antenna with a bottom partially-corporate feed circuit is designed for 39GHz band operation as an example. The adjacent radiating waveguides as well as the 2 × 2 sub-arrays fed in an alternating-phase manner eliminate the need for complete electrical contact in the top layer. However, the feed circuit in the bottom layer has to be completely diffusion-bonded. These two layers are simply assembled by screws. An antenna laminated by only diffusion bonding is also fabricated and evaluated for comparison. The comparison proved that the simply fabricated antenna is comparable in performance to the fully diffusion-bonded one.

This letter describes a design methodology for an arithmetic logic unit (ALU) incorporating reconfigurability based on double-gate carbon nanotube field-effect transistors (DG-CNTFETs). The design of a DG-CNTFET with an ambipolar-property-based reconfigurable static logic circuit is simple and straightforward using an ambipolar binary decision diagram (Am-BDD), which represents the cornerstone for the automatic pass transistor logic (PTL) synthesis flows of ambipolar devices. In this work, an ALU with 16 functions is synthesized by the design methodology of a DG-CNTFET-based reconfigurable static logic circuit. Furthermore, it is shown that the proposed ALU is much more flexible and practical than a conventional DG-CNTFET-based reconfigurable ALU.

Space-time block code (STBC) with complex orthogonal designs achieves full diversity with a simple maximum-likelihood (ML) decoding, however, do not achieve a full transmission rate for more than two antennas. To attain a higher transmission rate, STBC with quasi-orthogonal designs were proposed, whereas there are interference terms caused by relaxing the orthogonality. It has an impact on decoding complexity because a receiver needs to decode two symbols at a time. Moreover, QO-STBC does not achieve full diversity. In this paper, we propose a scheme which makes possible to decode symbols one by one, and two schemes which gain full transmission diversity by upsetting the balance of the transmit power and rotating constellation.

In modern hardware security applications, silicon physical unclonable functions (PUFs) are of interest for their potential use as a unique identity or secret key that is generated from inherent characteristics caused by process variations. However, arbiter-based PUFs utilizing the relative delay-time difference between equivalent paths have a security issue in which the generated challenge-response pairs (CRPs) can be predicted by a machine learning attack. We previously proposed the RG-DTM PUF, in which a response is decided from divided time domains allocated to response 0 or 1, to improve the uniqueness of the conventional arbiter-PUF in a small circuit. However, its resistance against machine learning attacks has not yet been studied. In this paper, we evaluate the resistance against machine learning attacks by using a support vector machine (SVM) and logistic regression (LR) in both simulations and measurements and compare the RG-DTM PUF with the conventional arbiter-PUF and with the XOR arbiter-PUF, which strengthens the resistance by using XORing output from multiple arbiter-PUFs. In numerical simulations, prediction rates using both SVM and LR were above 90% within 1,000 training CRPs on the arbiter-PUF. The machine learning attack using the SVM could never predict responses on the XOR arbiter-PUF with over six arbiter-PUFs, whereas the prediction rate eventually reached 95% using the LR and many training CRPs. On the RG-DTM PUF, when the division number of the time domains was over eight, the prediction rates using the SVM were equal to the probability by guess. The machine learning attack using LR has the potential to predict responses, although an adversary would need to steal a significant amount of CRPs. However, the resistance can exponentially be strengthened with an increase in the division number, just like with the XOR arbiter-PUF. Over one million CRPs are required to attack the 16-divided RG-DTM PUF. Differences between the RG-DTM PUF and the XOR arbiter-PUF relate to the area penalty and the power penalty. Specifically, the XOR arbiter-PUF has to make up for resistance against machine learning attacks by increasing the circuit area, while the RG-DTM PUF is resistant against machine learning attacks with less area penalty and power penalty since only capacitors are added to the conventional arbiter-PUF. We also attacked RG-DTM PUF chips, which were fabricated with 0.18-µm CMOS technology, to evaluate the effect of physical variations and unstable responses. The resistance against machine learning attacks was related to the delay-time difference distribution, but unstable responses had little influence on the attack results.

We describe a novel idea to improve machine translation by combining multiple candidate translations and extra translations. Without manual work, extra translations can be generated by identifying and hybridizing the syntactic equivalents in candidate translations. Candidate and extra translations are then combined on sentence level for better general translation performance.

Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.

Combining the strong anti-interference advantages of OFDM technology and the time-frequency analysis features of fractional Fourier transform (FFT), we apply OFDM as the coding modulation technology for digital watermarking. Based on the Arnold scrambling and OFDM coding, an innovative DFRFT digital watermarking algorithm is proposed. First, the watermark information is subjected to the Arnold scrambling encryption and OFDM coding transform. Then it is embedded into the FFT domain amplitude. The three parameters of scrambling iterations number, t, FFT order, p, and the watermark information embedded position, L, are used as keys, so that the algorithm has high safety. A simulation shows that the algorithm is highly robust against noise, filtering, compression, and other general attacks. The algorithm not only has strong security, but also makes a good balance between invisibility and robustness. But the possibility of using OFDM technique in robust image watermarking has drawn a very little attention.

The traditional reinforcement learning (RL) methods can solve Markov Decision Processes (MDPs) online, but these learning methods cannot effectively use a priori knowledge to guide the learning process. The exploration of the optimal policy is time-consuming and does not employ the information about specific issues. To tackle the problem, this paper proposes heuristic function negotiation (HFN) as an online learning framework. The HFN framework extends MDPs and introduces heuristic functions. HFN changes the state-action dual layer structure of traditional RL to the triple layer structure, in which multiple heuristic functions can be set to meet the needs required to solve the problem. The HFN framework can use different algorithms to let the functions negotiate to determine the appropriate action, and adjust the impact of each function according to the rewards. The HFN framework introduces domain knowledge by setting heuristic functions and thus speeds up the problem solving of MDPs. Furthermore, user preferences can be reflected in the learning process, which improves the flexibility of RL. The experiments show that, by setting reasonable heuristic functions, the learning results of the HFN framework are more efficient than traditional RL. We also apply HFN to the air combat simulation of unmanned aerial vehicles (UAVs), which shows that different function settings lead to different combat behaviors.

Recently, small cell systems such as femto cell are being considered as a good alternative that can support the increasing demand for mobile data traffic because they can significantly enhance network capacity by increasing spatial reuse. In this paper, we analyze the coverage and capacity of a femto cell when it is deployed in a hotspot to reduce the traffic loads of neighboring macro base stations (BSs). Our analysis results show that the coverage and capacity of femto cell are seriously affected by surrounding signal environment and they can be greatly enhanced by adapting power allocation for channels to the surrounding environment. Thus, we propose an adaptive power partitioning scheme where power allocation for channels can be dynamically adjusted to suit the environment surrounding the femto cell. In addition, we numerically derive the optimal power allocation ratio for channels to optimize the performance of the femto cell in the proposed scheme. It is shown that the proposed scheme with the optimal channel power allocation significantly outperforms the conventional scheme with fixed power allocation for channels.

An effective interlaced-to-progressive scanning format conversion method is presented for the interpolation of interlaced images. On the basis of the weight assignment algorithm, the proposed method is composed of three stages: (1) straightforward interpolation with pre-determined six-tap filter, (2) fuzzy metric-based weight assignment, (3) updating the interpolation results. We first deinterlace the missing line with six-tap filter in the working window. Then we compute the local weight among the adjacent pixels with a fuzzy metric. Finally we deinterlace the missing pixels using the proposed interpolator. Comprehensive simulations conducted on different images and video sequences have proved the effectiveness of the proposed method, with significant improvement over conventional methods.

Coupled with the discrete wavelet transform, SPIHT (set partitioning in hierarchical trees) is a highly efficient image compression technique that allows for progressive transmission. One problem, however, is that its decoding can be extremely sensitive to bit errors in the code sequence. In this paper, we address the issue of transmitting SPIHT-encoded images via noisy channels, wherein errors are inevitable. The communication scenario assumed in this paper is that the transmitter cannot get any acknowledgement from the receiver. In our scheme, the original SPIHT code sequence is first segmented into packets. Each packet is classified as either a CP (critical packet) or an RP (refinement packet). For error control, cyclic redundancy check (CRC) is incorporated into each packet. By checking the CRC check sum, the receiver is able to tell whether a packet is correctly received or not. In this way, the noisy channel can be effectively modeled as an erasure channel. For unequal error protection (UEP), each of those packets are repeatedly transmitted for a few times, as determined by a process called diversity allocation (DA). Two DA algorithms are proposed. The first algorithm produces a nearly optimal decoded image (as measured in the expected signal-to-noise ratio). However, its computation cost is extremely high. The second algorithm works in a progressive fashion and is naturally compatible with progressive transmission. Its computation complexity is extremely low. Nonetheless, its decoded image is nearly as good. Experimental results show that the proposed scheme significantly improves the decoded images. They also show that making distinction between CP and RP results in wiser diversity allocation to packets and thus produces higher quality in the decoded images.

We implemented a scalar multiplication method over elliptic curves using division polynomials. We adapt an algorithm for computing elliptic nets proposed by Stange. According to our experimental results, the scalar multiplication method using division polynomials is faster than the binary method in an affine coordinate system.

This paper presents two types of cryptanalysis on a Merkle-Damgård hash based MAC, which computes a MAC value of a message M by Hash(K||l||M) with a shared key K and the message length l. This construction is often called LPMAC. Firstly, we present a distinguishing-H attack against LPMAC instantiated with any narrow-pipe Merkle-Damgård hash function with O(2n/2) queries, which indicates the incorrectness of the widely believed assumption that LPMAC instantiated with a secure hash function should resist the distinguishing-H attack up to 2n queries. In fact, all of the previous distinguishing-H attacks considered dedicated attacks depending on the underlying hash algorithm, and most of the cases, reduced rounds were attacked with a complexity between 2n/2 and 2n. Because it works in generic, our attack updates these results, namely full rounds are attacked with O(2n/2) complexity. Secondly, we show that an even stronger attack, which is a powerful form of an almost universal forgery attack, can be performed on LPMAC. In this setting, attackers can modify the first several message-blocks of a given message and aim to recover an internal state and forge the MAC value. For any narrow-pipe Merkle-Damgård hash function, our attack can be performed with O(2n/2) queries. These results show that the length prepending scheme is not enough to achieve a secure MAC.