To help student nurses learn to transfer patients from a bed to a wheelchair, this paper proposes a system for automatic skill evaluation in nurses' training for this task. Multiple Kinect sensors were employed, in conjunction with colored markers attached to the trainee's and patient's clothing and to the wheelchair, in order to measure both participants' postures as they interacted closely during the transfer and to assess the correctness of the trainee's movements and use of equipment. The measurement method involved identifying body joints, and features of the wheelchair, via the colors of the attached markers and calculating their 3D positions by combining color and depth data from two sensors. We first developed an automatic segmentation method to convert a continuous recording of the patient transfer process into discrete steps, by extracting from the raw sensor data the defining features of the movements of both participants during each stage of the transfer. Next, a checklist of 20 evaluation items was defined in order to evaluate the trainee nurses' skills in performing the patient transfer. The items were divided into two types, and two corresponding methods were proposed for classifying trainee performance as correct or incorrect. One method was based on whether the participants' relevant body parts were positioned in a predefined spatial range that was considered ‘correct’ in terms of safety and efficacy (e.g., feet placed appropriately for balance). The second method was based on quantitative indexes and thresholds for parameters describing the participants' postures and movements, as determined by a Bayesian minimum-error method. A prototype system was constructed and experiments were performed to assess the proposed approach. The evaluation of nurses' patient transfer skills was performed successfully and automatically. The automatic evaluation results were compared with evaluation by human teachers and achieved an accuracy exceeding 80%.

In this paper, a simple yet efficient texture representation is proposed for texture classification by exploring the joint statistics of local quantized patterns (jsLQP). In order to combine information of different domains, the Gaussian derivative filters are first employed to obtain the multi-scale gradient responses. Then, three feature maps are generated by encoding the local quantized binary and ternary patterns in the image space and the gradient space. Finally, these feature maps are hybridly encoded, and their joint histogram is used as the final texture representation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art LBP based and even learning based methods for texture classification.

This letter proposes a fast intra prediction method to reduce encoding time for the high-efficiency video coding (HEVC) standard, which involves an increase in the number of intra-modes. The proposed intra-mode coding method uses correlation between intra-modes and adjacent pixels. The proposed method skips half of the intra-modes in certain blocks, specifically those that satisfy predetermined conditions. Using the half of intra-modes reduces number of bits for intra-mode coding and offsets a decline of coding performance caused by mode skipping. The experimental results show that the proposed method achieved 5.87% reduction in encoding time compared to the HEVC test model 7.1 encoder with almost no loss in coding performance.

The implementation security of the RSA cryptosystem, under the threat of side-channel analysis, has attracted the attentions of many researchers. Boer et al. had proposed the MRED-DPA attack on RSA-CRT by choosing ciphertexts of equi-distant data. Their attack can be applied to RSA-OAEP decryption but not RSA-PSS signing because of the PSS random padding. We propose a new DPA attack on an implementation of RSA-CRT, with the Montgomery reduction. The proposed attack assumes only known ciphertexts, and can be applied to both RSA-OAEP decryption and RSA-PSS signing even if a random padding technique is used in practice. This study also presents experimental results to verify the proposed attack. Finally, this study proposes a CRT-based message blinding technique as a low-cost DPA countermeasure.

In this paper, a priority control problem between uplink and downlink flows in IEEE 802.11 wireless LANs is considered. The minimum contention window size (CWmin) has a nonnegative integer value. CWmin control scheme is one of the solutions for priority control to achieve the fairness between links. However, it has the problem that CWmin control scheme cannot achieve precise priority control when the CWmin values become small. As the solution of this problem, this paper proposes a new CWmin control method called a virtual continuous CWmin control (VCCC) scheme. The key concept of this method is that it involves the use of small and large CWmin values probabilistically. The proposed scheme realizes the expected value of CWmin as a nonnegative real number and solves the precise priority control problem. Moreover, we proposed a theoretical analysis model for the proposed VCCC scheme. Computer simulation results show that the proposed scheme improves the throughput performance and achieves fairness between the uplink and the downlink flows in an infrastructure mode of the IEEE 802.11 based wireless LAN. Throughput of the proposed scheme is 31% higher than that of a conventional scheme when the number of wireless stations is 18. The difference between the theoretical analysis results and computer simulation results of the throughput is within 1% when the number of STAs is less than 10.

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.

This paper presents differential-based distinguishers against double-branch compression functions and applies them to ISO standard hash functions RIPEMD-128 and RIPEMD-160. A double-branch compression function computes two branch functions to update a chaining variable and then merges their outputs. For such a compression function, we observe that second-order differential paths will be constructed by finding a sub-path in each branch independently. This leads to 4-sum attacks on 47 steps (out of 64 steps) of RIPEMD-128 and 40 steps (out of 80 steps) of RIPEMD-160. Then new properties called a (partial) 2-dimension sum and a q-multi-second-order collision are considered. The partial 2-dimension sum is generated on 48 steps of RIPEMD-128 and 42 steps of RIPEMD-160, with complexities of 235 and 236, respectively. Theoretically, the 2-dimension sum is generated faster than the brute force attack up to 52 steps of RIPEMD-128 and 51 steps of RIPEMD-160, with complexities of 2101 and 2158, respectively. The results on RIPEMD-128 can also be viewed as q-multi-second-order collision attacks. The practical attacks have been implemented and examples are presented. We stress that our results do not impact to the security of full RIPEMD-128 and RIPEMD-160 hash functions.

This paper presents a chosen-IV (Initial Vector) correlation power analysis on the international standard stream cipher KCipher-2 together with an effective countermeasure. First, we describe a power analysis technique which can reveal the secret key (initial key) of KCipher-2 and then evaluate the validity of the CPA with experiments using both FPGA and ASIC implementations of KCipher-2 processors. This paper also proposes a masking-based countermeasure against the CPA. The concept of the proposed countermeasure is to mask intermediate data which pass through the non-linear function part including integer addition, substitution functions, and internal registers L1 and L2. We design two types of masked integer adders and two types of masked substitution circuits in order to minimize circuit area and delay, respectively. The effectiveness of the countermeasure is demonstrated through an experiment on the same FPGA platform. The performance of the proposed method is evaluated through the ASIC fabricated by TSMC 65nm CMOS process technology. In comparison with the conventional design, the design with the countermeasure can be achieved by the area increase of 1.6 times at most.

We proposes a method for determining the frequency for monitoring the activities of a malware download site used for malware attacks on websites. In recent years, there has been an increase in attacks exploiting vulnerabilities in web applications for infecting websites with malware and maliciously using those websites as attack platforms. One scheme for countering such attacks is to blacklist malware download sites and filter out access to them from user websites. However, a malware download site is often constructed through the use of an ordinary website that has been maliciously manipulated by an attacker. Once the malware has been deleted from the malware download site, this scheme must be able to unblacklist that site to prevent normal user websites from being falsely detected as malware download sites. However, if a malware download site is frequently monitored for the presence of malware, the attacker may sense this monitoring and relocate that malware on a different site. This means that an attack will not be detected until the newly generated malware download site is discovered. In response to these problems, we clarify the change in attack-detection accuracy caused by attacker behavior. This is done by modeling attacker behavior, specifying a state-transition model with respect to the blacklisting of a malware download site, and analyzing these models with synthetically generated attack patterns and measured attack patterns in an operation network. From this analysis, we derive the optimal monitoring frequency that maximizes the true detection rate while minimizing the false detection rate.

CLEFIA is a 128-bit block cipher proposed by Shirai et al. at FSE2007. It has been reported that CLEFIA has a 9-round saturation characteristic, in which 32bits of the output of 9-th round 112-th order differential equals to zero. By using this characteristic, a 14-round CLEFIA with 256-bit secret key is attacked with 2113 blocks of chosen plaintext and 2244.5 times of data encryption. In this paper, we focused on a higher order differential of CLEFIA. This paper introduces two new concepts for higher order differential which are control transform for the input and observation transform for the output. With these concepts, we found a new 6-round saturation characteristic, in which 24bits of the output of 6-th round 9-th order differential equals to zero. We also show a new 9-round saturation characteristic using 105-th order differential which is a 3-round extension of the 6-round one. If we use it, instead of 112-th order differential, using the meet-in-the-middle attack technique for higher order differential table, the data and computational complexity for the attack to 14-round CLEFIA can be reduced to around 2-5, 2-34 of the conventional attack, respectively.

To characterize an antenna, the acquisition of its three-dimensional radiation pattern is the fundamental requirement. Spherical antenna measurement is a practical approach to measuring antenna patterns in spherical geometry. However, due to the limitations of measurement range and measurement time, the measured samples may either be incomplete on scanning sphere, or be inadequate in terms of the sampling interval. Therefore there is a need to extrapolate and interpolate the measured samples. Spherical wave expansion, whose band-limited property is derived from the sampling theorem, provides a good tool for reconstructing antenna patterns. This research identifies the limitation of the conventional algorithm when reconstructing the pattern of an antenna which is not located at the coordinate origin of the measurement set-up. A novel algorithm is proposed to overcome the limitation by resampling between the unprimed and primed (where the antenna is centred) coordinate systems. The resampling of measured samples from the unprimed coordinate to the primed coordinate can be conducted by translational phase shift, and the resampling of reconstructed pattern from the primed coordinate back to the unprimed coordinate can be accomplished by rotation and translation of spherical waves. The proposed algorithm enables the analytical and continuous pattern reconstruction, even under the severe sampling condition for deviated AUT. Numerical investigations are conducted to validate the proposed algorithm.

PEiD is a packer identification tool widely used for malware analysis but its accuracy is becoming lower and lower recently. There exist two major reasons for that. The first is that PEiD does not provide a way to create signatures, though it adopts a signature-based approach. We need to create signatures manually, and it is difficult to catch up with packers created or upgraded rapidly. The second is that PEiD utilizes exact matching. If a signature contains any error, PEiD cannot identify the packer that corresponds to the signature. In this paper, we propose a new automated packer identification method to overcome the limitations of PEiD and report the results of our numerical study. Our method applies string-kernel-based support vector machine (SVM): it can measure the similarity between packed programs without our operations such as manually creating signature and it provides some error tolerant mechanism that can significantly reduce detection failure caused by minor signature violations. In addition, we use the byte sequence starting from the entry point of a packed program as a packer's feature given to SVM. That is, our method combines the advantages from signature-based approach and machine learning (ML) based approach. The numerical results on 3902 samples with 26 packer classes and 3 unpacked (not-packed) classes shows that our method achieves a high accuracy of 99.46% outperforming PEiD and an existing ML-based method that Sun et al. have proposed.

We propose an adaptive reversible data hiding method with superior visual quality and capacity in which an adaptive generalized difference expansion (AGDE) method is applied to an integer-to-integer subband transform (I2I-ST). I2I-ST performs the reversible subband transform and the AGDE method is a state-of-the-art method of reversible data hiding. The results of experiments we performed objectively and perceptually show that the proposed method has better visual quality than conventional methods at the same embedding rate due to low variance in the frequency domain.

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.

Computation integrity is difficult to verify when mass data processing is outsourced. Current integrity protection mechanisms and policies verify results generated by participating nodes within a computing environment of service providers (SP), which cannot prevent the subjective cheating of SPs. This paper provides an analysis and modeling of computation integrity for mass data processing services. A third-party sampling-result verification method, named TS-TRV, is proposed to prevent lazy cheating by SPs. TS-TRV is a general solution of verification on the intermediate results of common MapReduce jobs, and it utilizes the powerful computing capability of SPs to support verification computing, thus lessening the computing and transmission burdens of the verifier. Theoretical analysis indicates that TS-TRV is effective on detecting the incorrect results with no false positivity and almost no false negativity, while ensuring the authenticity of sampling. Intensive experiments show that the cheating detection rate of TS-TRV achieves over 99% with only a few samples needed, the computation overhead is mainly on the SP, while the network transmission overhead of TS-TRV is only O(log N).

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.

In this paper, we investigate how to achieve call admission control (CAC) for guaranteeing call dropping probability QoS which is caused by handoff timeout in cognitive radio (CR) networks. When primary user (PU) appears, spectrum handoff should be initiated to maintain secondary user (SU)'s link. We propose a novel virtual queuing (VQ) scheme to schedule spectrum handoff requests sent by multiple SUs. Unlike the conventional first-come-first-served (FCFS) scheduling, resuming transmission in the original channel has higher priority than switching to another channel. It costs less because it avoids the cost of signaling frequent spectrum switches. We characterize the handoff delay on the effect of PU's behavior and the number of SUs in CR networks. And user capacity under certain QoS requirement is derived as a guideline for CAC. The analytical results show that call dropping performance can be greatly improved by CAC when a large amount of SUs arrives fast as well as the VQ scheme is verified to reduce handoff cost compared to existing methods.

A macroscopic structure was analyzed for a system comprising multiple elements in which the dynamics is affected by their distribution. First, a nonlinear Boltzmann equation, which has an integration term with respect to the distribution of the elements, was derived. Next, the moment vector equation (MVE) for the Boltzmann equation was derived. The average probability density function (pdf) in a steady state was derived using eigen analysis of the coefficient matrix of the MVE. The macroscopic structure of the system and the mechanism that provides the average pdf and the transient response were then analyzed using eigen analysis. Evaluation of the average pdf and transient response showed that using eigen analysis is effective for analyzing not only the transient and stationary properties of the system but also the macroscopic structure and the mechanism providing the properties.

The results of fishery investigations are used to estimate the catch size, times fish are caught, and future stock in the fish culture industry. In Tokoro, Japan, scallop farms are located on gravel and sand seabed. Seabed images are necessary to visually estimate the number of scallops of a particular farm. However, there is no automatic technology for measuring resources quantities and so the current investigation technique is the manual measurement by experts. We propose a method to extract scallop areas from images of sand seabed. In the sand field, we can see only the shelly rim because the scallop is covered with sand and opens and closes its shell while it is alive and breathing. We propose a method to extract the shelly rim areas under varying illumination, extract the scallop areas using the shelly rims based on professional knowledge of the sand field, explain the results, and evaluate the method's effectiveness.

The second generation digital terrestrial broadcasting system (DVB-T2) is the first broadcasting system employing MISO (Multiple-Input Single-Output) algorithms. The potential MISO gain of this system has been roughly predicted through simulations and field tests. Of course, the potential MISO SFN gain (MISO-SFNG) differs according to the simulation conditions, test methods, and measurement environments. In this paper, network gains of SISO-SFN and MISO-SFN are theoretically derived. Such network gains are also analyzed with respect to the receive power imbalance and coverage distances of SISO and MISO SFN. From the analysis, it is proven that MISO-SFNG is always larger than SISO SFN gain (SISO-SFNG) in terms of the achievable SNR. Further, both MISO-SFNG and SISO-SFNG depend on the power imbalance, but the network gains are constant regardless of the modulation order. Once the field strength of the complete SFN is obtained by coverage planning tools or field measurements, the SFN service coverage can be precisely calibrated by applying the closed-form SFNG formula.