In this paper, an integrated multimode battery charger in a Qi-compliant wireless power receiver is presented. The proposed wireless battery charger includes a synchronous rectifier circuit and a multi feedback low dropout regulator. The charging circuit automatically switches trickle current, constant-current, and constant-voltage mode corresponding to battery voltage. Through control of the target rectified voltage from the perspective of the wireless power receiver, the synchronous rectifier circuit can generate an adaptive rectified voltage to closely track the battery voltage, which significantly reduce the power loss in the charging circuit. The wireless battery charger was implemented with a TSMC 0.18 µm BCD 1P5M process and the experimental results show the charging current of constant-current mode is 1 A and the final voltage of the wireless battery charger is 4.2 V. The maximum efficiency of the overall system is 78%.

In this paper we discuss recovering two signals from their convolution in 3 dimensions. One of the signals is assumed to lie in a known subspace and the other one is assumed to be sparse. Various applications such as super resolution, radar imaging, and direction of arrival estimation can be described in this framework. We introduce a method to estimate parameters of a signal in a low-dimensional subspace which is convolved with another signal comprised of some impulses in time domain. We transform the problem to a convex optimization in the form of a positive semi-definite program using lifting and the atomic norm. We demonstrate that unknown parameters can be recovered by lowpass observations. Numerical simulations show excellent performance of the proposed method.

The use of dry electrodes is increasing rapidly. Since their impedance is high, there is a high impedance node at the connecting node between the electrode and amplifier. This leads to absorb powerline signal and high CMRR amplifiers are essential to eliminate this. In this article, we propose a low-power low-noise chopper-stabilized amplifier with high CMRR. In order to minimise the input-referred noise, an inverter-based differential amplifier is utilized. Meanwhile, a DC servo loop is designed to reject the DC offset of the electrode. Since all of the stages required a common-mode feedback, for each of the amplifiers a suitable circuit was used. Furthermore, a chopping spike filter is implemented at the final stage to attenuate the choppers’ spike. Finally, to eliminate the offset effect from the mismatch and post-layout, a DC offset rejection technique is used. The designed circuit is simulated in a standard 180 nm CMOS technology. The designed chopper amplifier consumes just 1.1 \(\upmu \hbox {W}\) at a 1.2 \(\hbox {V}\) supply. The mid-band gain is 40 dB while the bandwidth is from 0.5 to 200 Hz. The total input-referred noise is 1 \(\upmu \hbox {V}_{\mathrm{rms}}\) in its bandwidth. Thus the NEF and PEF of the designed circuit is 2.7 and 9.7, respectively. In order to analyse the performance of the proposed chopper amplifier against process and mismatch variation, Monte Carlo simulation is done. According to 200 Monte Carlo simulations, CMRR and PSRR are 124 dB with 6.9 dB standard deviation and 107 dB with 7.7 standard deviation, respectively. Ultimately, the total area consumption is 0.1 \(\hbox {mm}^2\) without pads.

Direction of arrival estimation is one of the most important issues in array signals processing. In this paper, a new method is presented for direction estimation of coherent wide-band signals. First of all, signal eigenvalues which have information of the direction of coherent signals, are extracted as primary vectors in each frequency bin. Afterwards, by constructing the innovative matrix H from signal eigenvalues, the de-correlation process is performed and the linear independent vectors of sources are extracted from SVD decomposition. Finally, by choosing transfer matrix, the signal subspace of the reference frequency is transferred to other frequency bins and the direction estimation process is performed by using TOPS algorithm. The proposed method does not need any knowledge of the number of sources and initial estimation of arrival angles. According to the simulation results, the CTOPS new method has a better performance than TOPS method in the presence of correlated sources.

This paper presents a 12-bit 120-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) with improved split capacitive DAC and low-noise dynamic comparator. A split DAC structure with parasitic capacitance depressed technique is introduced, the top-plate parasitic capacitances of MSB and LSB DAC arrays are both reduced, and the accuracy of split DAC is increased without calibration. Further, an optimized sampling method is used to provide a unit capacitance between the MSB and LSB DAC arrays, the match of DAC is improved. In addition, a high-speed dynamic comparator with input-referred noise reduction technique is proposed, an extra positive feedback loop is provided to reduce the comparison delay and the gain of the comparator is also increased to depress noise. To demonstrate the proposed techniques, a design of SAR ADC is fabricated in 65-nm CMOS technology, consuming 6 mW from 1.2 V power supply with a SNDR > 66.1 dB and SFDR > 81.5 dB. The proposed ADC core occupies an active area of 0.05 mm2, and the corresponding FoM is 30 fJ/conversion-step with Nyquist frequency.

Abstract The recent growth of World Wide Web (WWW) and development of the next-generation internet facilitates a huge amount of data being conveniently transmitted via wireless networks. The sensitive information transmitted is potentially vulnerable in the communication channel like wireless networks. Unauthorized users could potentially intercept and negatively exploit the true intent of the information being exchanged between legitimate users. The efficient steganography techniques are very useful to prevent such undesirable interception of information. In this work, we propose and evaluate an efficient image steganography using Fruit Fly Optimization hybridized Improved Seeker (FOIS) algorithm. The FOIS provides information security and safeguards the medical data to avoid medical related cybercrimes. FOIS efficiently determines the optimal locations of pixels adaptively in the spatial domain of the cover image. Initially, the cover image is divided into n blocks of \(8 \times 8\), on which a permutation combination is applied to find the number of blocks for further processing. This method improves the image quality and secures data. The secret messages are embedded in each block using optimal pixels selection and Least Significant Bit (LSB) of Discrete Cosine Transform coefficients. Moreover, in order to ensure seamless communication over an insecure communication channel, a dual cryptosystem model is developed which consist of the proposed steganography scheme and Rivest Cipher (RC4) cryptosystem. This work validates the security level of the stego image, and finally the performance is compared with state-of-the-art methods such as LSB, Particle Swarm Optimization and Genetic Algorithm. The performance assessment reveals that the proposed steganography model outperforms other optimization based approaches in terms of Peak Signal-to-Noise Ratio, embedding capacity and imperceptibility.

Abstract This article is dedicated to the stability of one-dimensioned spatially interconnected systems. More precisely, it focuses on systems which results of the interconnection of a possibly large number of cells (continuous subsystems). This note is restricted to the case where cells are just distributed along a line. The global system can then be seen as a mixed continuous–discrete 2D Roesser system but with implicit discrete dynamics along the space dimension. Recent results on the stability of 2D Roesser models are exploited and adapted to derive a sufficient condition for such a system to be stable. The condition seems to be close to necessity if not necessary. It is tractable since it is expressed in terms of linear matrix inequalities. The novelty clearly lies in the reduction of the conservatism of the proposed analysis.

This paper investigates how to factorize a class of multivariate polynomial matrices. We prove that an \(l\times m\) multivariate polynomial matrix admits a matrix factorization with respect to a given polynomial if the polynomial and all the \((l-1)\times (l-1)\) reduced minors of the matrix generate a unit ideal. This result is a generalization of a theorem in Liu et al. (Circuits Syst Signal Process 30(3):553–566, 2011). Based on three main theorems presented in the paper and a constructive algorithm proposed by Lin et al. (Circuits Syst Signal Process 20(6):601–618, 2001), we give an algorithm which can be used to factorize more multivariate polynomial matrices. In addition, an illustrative example is given to show the effectiveness of the proposed algorithm.

High-frequency oscillations (HFOs) in intracranial electroencephalograms of patients with epilepsy are regarded as promising biomarkers of epileptogenic zones. Their detection and classification can be achieved by visual assessment or automated approaches, although manual processing of large recordings can be laborious. As a result, an automated analysis scheme is indispensable to enable the clinical use of HFOs. In this paper, we present a two-stage strategy to detect and classify HFOs, which starts with a threshold-based approach to detect plausible HFO events followed by an event classification to discriminate different oscillations. Unlike existing approaches, the detection process in the proposed schemes starts by calculating various multi-channel features that allow interrelations among electrodes to be exploited for detection. On this basis, the detection thresholds are set epoch-by-epoch, relying on a two-component Gaussian mixture model to avoid threshold overestimation. The events deemed to be plausible HFOs are then subjected to classification. By simultaneously examining the raw data and time-frequency maps of these events, they are ultimately sorted into the following categories: HFOs, spikes, and spikes with HFOs, so that the oscillations solely caused by filtering sharp transients can be discriminated. Experimental results using simulated data and intracranial recordings from three epileptic patients demonstrate that our proposed schemes achieve promising sensitivity and precision, especially when the noise level is high.

Abstract In this paper, we mainly investigate the nonuniform sampling for random signals which are bandlimited in the linear canonical transform (LCT) domain. We show that the nonuniform sampling for a random signal bandlimited in the LCT domain is equal to the uniform sampling in the sense of second order statistic characters after a pre-filter in the LCT domain. Moreover, we propose an approximate recovery approach for nonuniform sampling of random signals bandlimited in the LCT domain. Furthermore, we study the mean square error of the nonuniform sampling. Finally, we do some simulations to verify the correctness of our theoretical results.

Generalized coprime structure decomposes the interleaved subarrays in the conventional coprime array by introducing a displacement and the resulting CADiS, i.e. coprime array with displaced subarrays, configuration can enlarge the minimum adjacent spacing between elements to multiples of half-wavelength, which is considerably attractive in alleviating mutual coupling effect. However, the difference co-array that CADiS yields is fractured, which greatly deteriorates direction of arrival (DOA) estimation performance and achievable degrees of freedom of the algorithms based on consecutive co-array, e.g. spatial smoothing technique and Toeplitz matrix method. In this paper, from the mutual coupling effect and difference co-array perspective, we propose an expanded coprime array (ECA) structure by two steps. The first step is to further augment the displacement between the subarrays of CADiS to suppress the mutual coupling effect. The second step is to relocate a proper number of rightmost elements to concatenate the dominant consecutive co-array generated by CADiS to enhance the consecutive difference co-array. Specifically, we provide the closed-form expressions of resulting consecutive difference co-array, the number of relocated elements and their positions. Furthermore, different from the spatial smoothing based methods, we employ Toeplitz matrix property to directly construct the full-rank covariance matrix of the received data from the consecutive co-array with a lower computational cost and present the Toeplitz-MUSIC algorithm to testify the effectiveness and superiority of the proposed ECA structure.

Algebraic structures and their hardware–software implementation gain considerable attention in the field of information security and coding theory. Research progress in the applications of arithmetic properties of algebraic structures is being frequently made. These structures are mostly useful in improvement of the cryptographic algorithms. A novel technique is given to design a cryptosystem responsible for lossless for image encryption. The proposed scheme is for the RGB image whose pixels are considered as 24 binary bits, accordingly a unique arrangement for the construction of S-boxes over a Galois field \( GF\left( {2^{9} } \right) \) is employed. Consequently, it generates multiple different S-boxes with excellent cryptographic characteristic and hence confusing process of the cryptosystem has been working. Whereas the diffusion process in this cryptosystem is based on Affine transformation over a unit elements of an integers modulo ring \( {\mathbb{Z}}_{n} \). The scrambling of the image data through the Affine transformation escalate the security asset, avoid computational effort and abbreviated the time complexity. In addition, the simulation test and comparative scrutinize illustrate that the proposed scheme is highly sensitive, large keyspace, excellent statistical properties and secure against differential attacks. Therefore, the proposed algorithm is valuable for confidential communication. Furthermore, due to the arithmetic properties of algebraic structures, the proposed scheme would be easily implemented, secure and fast enough to be utilized in real-world applications.

Abstract Wireless sensor networks (WSN) consists of dedicated sensors, which monitor and record various physical and environmental conditions like temperature, pollution levels, humidity etc. WSN is compatible with several applications related to environmental and healthcare monitoring. The sensor nodes have a limited battery life and are deployed in hostile environments. Recharging or replacement of the batteries in the sensor nodes are very difficult after deployment in inaccessible areas where energy is an important factor for continuous network operation. Energy efficiency is a major concern in the wireless sensor networks as it is important for maintaining network operation. In this paper, an energy efficient clustering algorithm based energy centroid and energy threshold has been proposed for wireless sensor networks. Here each cluster is designed to own 25% of the sensor nodes using distance centroid algorithm. Cluster head selection is based on the energy centroid of each cluster and energy threshold of the sensor nodes. Communication between the sink node and cluster head uses distance of separation as a parameter for reducing the energy consumption. The result obtained shows an average increase of 53% in energy conservation and network lifetime compared to Leach-B, Park Approach, EECPK-means Approach and MPST Approach.

Copy-move is one of the most common image forgeries, wherein one or more region are copied and pasted within the same image. The motivations of such forgery include hiding an element in the image or emphasizing a particular object. Copy-move image forgery is more challenging to detect than other types, such as splicing and retouching. Keypoint based copy-move forgery detection extracts image keypoints and uses local visual features to identify duplicated regions, which exhibits remarkable performance with respect to memory requirement and robustness against various attacks. However, these approaches fail to handle the cases when copy-move forgeries only involve small or smooth regions, where the number of keypoints is very limited. Also, they generally have higher time costs owing to complex feature descriptor and more error matching points. To tackle these challenges, we propose a fast and effective copy-move forgery detection method through adaptive keypoint extraction and processing, introducing fast robust invariant feature, and filtering out the wrong pairs. Firstly, the uniform distribution keypoints are extracted adaptively from the forged image by employing the fast approximated LoG filter and performing the uniformity processing. Then, the image keypoints are described using fast robust invariant feature and matched through the Rg2NN algorithm. Finally, the falsely matched pairs are removed by employing the segmentation based candidate clustering, and the duplicated regions are localized using optimized mean-residual normalized production correlation. We conduct extensive experiments to evaluate the performance of the proposed scheme, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.

Abstract Deep convolutional neural networks (CNNs) have demonstrated its extraordinary power on various visual tasks like object detection and classification. However, it is still challenging to deploy state-of-the-art models into real-world applications, such as autonomous vehicles, due to their expensive computation costs. In this paper, to accelerate the network inference, we introduce a novel pruning method named Drop-path to reduce model parameters of 2D deep CNNs. Given a trained deep CNN, pruning paths with different lengths is achieved by ordering the influence of neurons in each layer on the probably approximately correct (PAC) Bayesian boundary of the model. We believe that the invariance of PAC-Bayesian boundary is an important factor to guarantee the generalization ability of deep CNN under the condition of optimizing as much as possible. To the best of our knowledge, this is the first time to reduce model size based on the generalization error boundary. After pruning, we observe that the convolutional kernels themselves become sparse, rather than some being removed directly. In fact, Drop-path is generic and can be well generalized on multi-layer and multi-branch models, since parameter ranking criterion can be applied to any kind of layer and the importance scores can still be propagated. Finally, Drop-path is evaluated on two image classification benchmark datasets (ImageNet and CIFAR-10) with multiple deep CNN models, including AlexNet, VGG-16, GoogLeNet, and ResNet-34/50/56/110. Experimental results demonstrate that Drop-path achieves significant model compression and acceleration with negligible accuracy loss.

The applications of object-based image analysis (OBIA) in remote sensing studies have received a considerable amount of attention over the recent decade due to dramatically increasing of the spatial resolution of satellite imaging sensors for earth observation. In this study, an unsupervised methodology based on OBIA paradigm for the estimation of multi-scale training sets for land cover classification is proposed. The proposed method consists of selection of valid region of interests in an unsupervised way and its characterization using some attributes in order to form meaningful and reliable training sets for supervised classification of different land covers of a satellite image. Multi-scale image segmentation is a prerequisite step for estimation of multi-scale training sets. However, scale selection remains a challenge in multi-scale segmentation. In this work, we propose a method to determine the appropriate segmentation scale for each land cover with the help of prior knowledge in the form of in-situ data. The proposed method is further discussed and validated through multi-scale segmentation using quick shift and random forest algorithms on two multi-spectral images captured using Worldview-2 sensor. Experimental results indicate that the proposed method qualitatively and quantitatively outperforms three state-of-the-art methods.

Abstract Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective way to analyze multiset data. Thus there is a need for flexible JBSS algorithms for efficient data-driven feature extraction in the complex domain. Independent vector analysis (IVA) is a prominent recent extension of independent component analysis to multivariate sources, i.e., to perform JBSS, but its effectiveness is determined by how well the source models used match the true latent distributions and the optimization algorithm employed. The complex multivariate generalized Gaussian distribution (CMGGD) is a simple, yet effective parameterized family of distributions that account for full second- and higher-order statistics including noncircularity, a property that has been often omitted for convenience. In this paper, we marry IVA and CMGGD to derive, IVA-CMGGD, with a number of numerical optimization implementations including steepest descent, the quasi-Newton method Broyden–Fletcher–Goldfarb–Shanno (BFGS), and its limited-memory sibling limited-memory BFGS all in the complex-domain. We demonstrate the performance of our algorithm on simulated data as well as a 14-subject real-world complex-valued functional magnetic resonance imaging dataset against a number of competing algorithms.

Abstract This paper is concerned with source localization when path loss is taken into account. We modify multiple signal classification method to localize near-field sources whose received power is different in the sensors of the array due to path loss. Traditional methods fail to localize the sources, and they also fail to separate the bearing estimation and the range estimation of the sources, when path loss is considered. We suggest a T-shaped array avoiding multidimensional search to estimate source location parameters, separately. At the first step, the ranges of the signal sources are estimated, and then at the second step, the directions of arrival of the sources are estimated using the respective ranges determined at the first stage. The performance of the proposed method is assessed when mixed near-field and far-field sources coexist. Simulation results are presented to show the superior performance of the proposed algorithm compared to the existing techniques and the Cramer–Rao bound.

Abstract The prostate carcinoma is amongst the most commonly occurring cancers in Taiwanese males. Moreover, it is one of the chief reasons for cancer deaths among Taiwanese men, and early diagnosis of prostate cancer is vital for effective treatment. In this work, a diagnosis model for identifying the prostate carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. The urologists utilize the DCE-MRI as a support mechanism for better diagnosis of the carcinoma development in the prostate. Gadolinium is utilized as the contrast agent for the DCE-MRI data, and it was injected once and the time series data were captured at distinct time intervals of 0, 20, 60, and 100 s correspondingly. Primarily, after pre-processing the DCE-MRI information, the prostate data is segmented by employing the active contour model. Subsequently, 136 features are extracted from the segmented prostrate expanse of the DCE-MRI data, and the relative intensity change curve is computed. Afterward, Fisher’s discriminant ratio and sequential forward floating selection is deployed for choosing ten highly discriminative features. Lastly, the segmented prostate regions are classified into two groups, namely: tumor and normal classes by employing the support vector machine classifier. The experimental results elucidate that the proposed system is superior on the subject of accuracy, sensitivity, and specificity when compared with specific existing methods. Additionally, the proposed system also demonstrates a 94.75% accuracy. Moreover, this signifies the fact that the proposed method for analyzing the DCE data has shown prodigious prospects in the prostate carcinoma diagnosis.

The paper presents a content based image retrieval scheme based on feature extraction and weighing. Features are extracted using frequency adder based local binary pattern and blur detection metric which are then optimally combined using a weighing scheme. Simulations are performed on modified Wang and KTH-TIPS databases, which include images from four different classes of blur respectively. Comparison of simulation results with the state-of-the-art techniques show better retrieval precision and recall values for proposed technique.

Abstract Contactless detection of human beings via extracting vital sign features (VSF) is a perfect technology by employing an ultra-wideband radar. Only using Fourier transform, it is a challenging task to extract VSF in a complex environment, which can cause a lower signal to noise ratio (SNR) and significant errors due to the harmonics. This paper proposes an improved signal processing algorithm for VSF extraction via analyzing the skewness and standard deviation of the collected impulses. The discrete windowed Fourier transform technique is used to estimate the time of arrival of the pulses. The frequency of human breathing movements is obtained using an accumulation scheme in frequency domain, which can better cancel out the harmonics. The capabilities of removing clutters and improving SNR are validated compared with several well-known methods experimentally.

Abstract Image fusion plays a vital role in providing better visualization of remotely sensed image data. Most earth observation satellites have sensors that provide both high spatial resolution panchromatic (PAN) images and low resolution multispectral (MS) images. In this paper, we propose a new fusion algorithm that optimally combines spectral information from MS image and spatial information from the PAN image of the same scene to create a single comprehensive fused image. As the performance of the fusion scheme relies on the choice of fusion rule, the proposed algorithm is based on a weighted averaging fusion rule that uses optimal weights obtained from brain storm optimization (BSO) algorithm for the fusion of high frequency and low frequency coefficients obtained by applying Curvelet transform to the source images. The objective function in BSO is formulated with twin objectives of maximizing the entropy and minimizing the root mean square error. The fusion results are compared with the existing fusion techniques, such as Brovey, principal component analysis, discrete wavelet transform, non sub-sampled contourlet transform, and intensity hue saturation. From the experimental results and analysis, the proposed fusion algorithm gives a better fusion performance in terms of subjective and objective measures than the traditional algorithms. As a benefit, the proposed fusion scheme preserves spectral information of the MS image with increased spatial resolution and edge information.

Lung cancer is the leading cause of death among cancer-related death. Like other cancers, the finest solution for lung cancer diagnosis and treatment is early screening. Automatic CAD system of lung cancer screening from Computed Tomography scan mainly involves two steps: detect all suspicious pulmonary nodules and evaluate the malignancy of the nodules. Recently, there are many works about the first step, but rare about the second step. Since the presence of pulmonary nodules does not absolutely specify cancer, the morphology of nodules such as shape, size, and contextual information has a sophisticated relationship with cancer, the screening of lung cancer needs a careful investigation on each suspicious nodule and integration of information of all nodules. We propose deep CNN architecture which differs from those traditionally used in computer vision to solve this problem. First, the suspicious nodules are generated with the modified version of U-Net and then the generated nodules become an input data for our model. The proposed model is a multi-path CNN which exploits both local features as well as more global contextual features simultaneously to automatically detect lung cancer. To this end, the model used three paths, each path employed different receptive field size which helps to model distant dependencies (short and long-range dependencies of the neighboring pixels). Then, to further upgrade our model performance, we concatenate features from the three paths. This balance the receptive field size effect and makes our model more adaptable to the variability of shape, size, and contextual information among nodules. Finally, we also introduce a retraining phase system that permits us to tackle difficulties related to the imbalance of image labels. Experimental results on Kaggle Data Science Bowl 2017 challenge shows that our model is better adaptable to the described inconsistency among nodules size and shape, and also obtained better detection results compared to the recently published state of the art methods.

This paper is concerned with the output feedback stabilization of two-dimensional discrete fuzzy systems described by the Fornasini–Marchesini second model. Based on the fuzzy-basis-dependent Lyapunov function, a new criterion is proposed for the fuzzy static output feedback (SOF) controller, which is expressed as strict linear matrix inequalities and hence numerically tractable. The main advantage of the developed SOF control scheme is that no constraints are imposed on system matrices, which is expected to have a wider range of applications. The applicability and the advantage of the proposed results are shown through two numerical examples.

Abstract An encryption algorithm based on sparse coding and compressive sensing is proposed. Sparse coding is used to find the sparse representation of images as a linear combination of atoms from an overcomplete learned dictionary. The overcomplete dictionary is learned using K-SVD, utilizing non-overlapping patches obtained from a set of images. Compressed sensing is used to sample data at a rate below the Nyquist rate. A Gaussian measurement matrix compressively samples the plain image. As these measurements are linear, chaos based permutation and substitution operations are performed to obtain the cipher image. Bit-level scrambling and block substitution is done to confuse and diffuse the measurements. Simulation results verify the performance of the proposed technique against various statistical attacks.

Smartphones are increasingly becoming popular due to the wide range of capabilities, such as Wi-Fi connectivity, video acquisition, and navigation. Some of these applications require large computational power, memory, and long battery life. Sports entertainment applications executed on smartphones is the future paradigm shift that will be enabled by the mobile cloud computing environments. Many times mobile users request multiple mobile services in workflows to fulfill their complex requirements. To investigate such issues, we develop a mobile cloud based framework that detects and retrieves player statistics on a mobile phone during live cricket. The proposed framework is divided into several services and each service is either executed locally or on the cloud. Our approach considers the dependencies among different services and aims to optimize the execution time and energy consumption for executing the services. Due to the applied offloading strategy, the proposed framework turns the smartphones smarter by significantly reducing the execution burden and energy consumption of the smartphone. Experimental results are promising and show feasibility of the proposed framework to be deployed in several related applications using techniques of computer vision and machine learning.

Abstract The objective of this work is to achieve a compact wideband band-stop filter using complementary split ring resonators (CSRR) as the fundamental element. The relation between the geometry and resonances of the CSRR were studied analytically along with their field distribution to determine the factors governing coupling between the rings of the CSRR. The effects of the inner-outer ring orientation on resonances of the CSRR has been studied and the resulting properties have been used to design the proposed compact wideband band-stop filter prototype operating with a center frequency of 2.5 GHz and a bandwidth of 1 GHz. The area of the proposed filter is 0.078 λg2 with a fractional bandwidth of 39.76%. This structure has following advantages: more compact, wide bandwidth and occupies less area. The fabricated prototype was tested and the results were promising representing this works potential.

Abstract An analog-to-digital converter based on the time-interleaved delta-sigma modulator is a proper method for high-speed ADCs. Time-interleaved delta-sigma modulators (TIDSM) can be successfully implemented with the development of the block digital filtering (BDF) technique. In this method, M mutually cross-connection delta-sigma modulators are used, with each one operating at a sampling rate of \({\text{f}}_{\text{s}}\) hence, the effective sampling rate will be \({\text{M}}*{\text{f}}_{\text{s}}\) However, SNDR is approximately equal to the single path standard structure. In this paper, a new structure based on the Noise Coupled time-interleaved delta-sigma modulator is proposed to increase the overall noise transfer function order without any additional active element. This improvement is analytically verified and then validated using simulations. Also, some practical issues regarding the implementation of the proposed structure, such as, finite op-amp’s gain and mismatching effects are discussed. Also, analyzes and some practical solutions are presented. The results of the simulation at the system level show that the SNDR of the proposed first-order two-channel structure is 18 dB better than its BDF technique counterpart, for the second-order two-channel TIDSM, the SNDR of the proposed structure is 13 dB better than that of the BDF technique.

Emerging nanoelectronic memories such as PCRAM, STT-RAM, Ferroelectric FET Memory and Resistive Random Access Memory (RRAM) are proficient to substitute the traditional memory technologies such as SRAMs, DRAMs and flash memory in future computers. Among all these nanoelectronic memories RRAM (combines the merits of both RAM and Flash memories) is exceptionally fast, cost-effective which is capable of accomplishing the requirements of immense growth in data and storage. Despite the fact that it suffers by various faults, more specifically Write Disturbance Fault (WDF) and Read Disturbance Fault (RDF) has huge effect on system performance and reliability. Different algorithms are designed to identify specific kind of faults and the main drawback of all existing algorithm is redundancy (repeated March elements with write and read operations) it will increase the test complexity and time. Moreover, this repeated write and read operation will leads to endurance degradation. To circumvent this pitfall, a self-checking write circuit is adapted where the accuracy of every write operation is verified at the end of write cycle by means of a write verification signal in the write circuit (acts as in built read circuit). Hence, this self checking write method offers a combined “write and read” operation (wr) that detects the faulty write operation (main reason for read fault) and avoids the data corruption in memory system. To the best of our knowledge, we are the first one to propose March algorithm with “wr” element hence it is named as novel ‘March WR’ testing algorithm which detects WDF and RDF effectively with 4n test complexity which is very less than all other existing test algorithms. Thus it reduces 63.63% of the test time. Due to keen monitoring of each operation 100% fault coverage is achieved that results in enhanced performance and reliability.

Abstract A switched coupled transmission line (SCTL) method with high Q and flexible structure is proposed for mm-wave VCO to improve tuning range (TR), meanwhile maintaining low phase noise, low power dissipation and zero additional area. This TR extension method is also proved effective in a switched coupled coplanar waveguide (SCCPW) VCO. Fabricated in 45 nm SOI CMOS, the SCTL-VCO achieves 38.74% TR from 39.46 to 58.42 GHz, increased by 29.2% compared to the reference TL-VCO. The SCCPW-VCO achieves 38.83% TR from 38.69 to 57.33 GHz, increased by 31.9% compared to the reference CPW-VCO. The measured phase noise of SCTL-VCO and SCCPW-VCO over the entire frequency tuning range is from \(-\) 106 to \(-\) 116.8 and \(-\) 106.1 to \(-\) 116 dBc/Hz at 10 MHz offset, while the corresponding FOM\(_T\) is from \(-\) 181.7 to \(-\) 192.5 and \(-\) 181.6 to \(-\) 191.5 dBc/Hz, respectively. Each VCO dissipates 8.6–10.8 mW from 0.7 V power supply.

In this paper, a fully-integrated tunable grounded memristor emulator circuit based on voltage differencing transconductance amplifier (VDTA) has been proposed. The proposed memristor emulator circuit utilizes two VDTA active building blocks, two grounded resistors, a grounded capacitor and a four-quadrant analog multiplier. The working concept along with the detailed derivation of the mathematical model of the circuit has been discussed numerically and analytically to validate the operation of the proposed emulator. The operations of the proposed emulator circuit, as governed by the established model, have been verified by performing simulations in Cadence Virtuoso at 45 nm technology node. Robustness analyses performed, reveal significant process-variation tolerance at deep sub-micron technology node.

A wideband integrated delay chain chip with 5-bit delay control, maximum delay of 120 ps and 3.9 ps delay resolution, designed and fabricated in 0.18 \(\upmu \hbox {m}\) CMOS technology is presented. Second-order all pass networks (APN) are used as delay structures in this delay circuit. In the design of the two MSB bits of the fabricated chip, a new design approach is used which allows higher group delay to be achieved with fewer number of passive second-order APN circuits. This would in turn reduce insertion loss of the designed delay control chain. Measurement results of the fabricated delay chain show 12.6–20.5 dB insertion loss and less than 3.3 ps RMS delay error over the intended frequency band from 8 to 18 GHz. The fabricated chip occupies an area of \(1.2\times 2.7\) mm\(^{2}\) and has no DC power consumption.

Abstract A switching scheme to realize 2nd-order digital-to-analog converter (DAC) mismatch error shaping (MES) technique in the high-resolution noise shaping successive approximation register analogue-to-digital converters (NS SAR ADCs) is proposed. By feeding back the combination of the least significant bit (LSB) DAC mismatch errors in two previous cycles, the DAC mismatch error is 2nd-order shaped. The scheme only requires an extra reference voltage and triples LSB DAC, which leads to simple control logic. According to the simulation, the spurious-free dynamic range is improved 10 dB more than the 1st-order MES.

Abstract Quantum-dot cellular automata (QCA) is an emerging trend in nanotechnology and appropriate for the development of high performance and low power integrated circuit design. Dadda and Wallace tree multipliers are designed by employing CZBCO technique to overcome the crossover issues of geometric design complexity and alignment accuracy and also to achieve high device density. The proposed design of QCA-based Hybrid parallel multiplier consists of decomposing structure that adopts Dadda and Wallace algorithms to optimize the design. In this proposal, N-bit multiplier array is decomposed into four N/2-bit multiplier arrays that are easily constructed by employing both Wallace and Dadda multipliers. The Hybrid multiplier comprising dadda and Wallace tree multiplier uses less number of majority gates and inverters and hence minimizes area, cell count and delay. It has been observed that the QCA cost function of the proposed multiplier better than existing multiplier referred in the literature in terms of energy and speed. Furthermore, the proposed multiplier significantly achieves high device density, lessened clock delay, area and cell count and also to eliminate fabrication difficulty of crossover.

In this paper, a novel chaos-ring based dual entropy core TRNG architecture on FPGA with high operating frequency and high throughput has been performed and presented. The design of dual entropy core TRNG has been generated by uniting the chaotic system-based RNG and the RO-based RNG structures on FPGA. The chaotic oscillator structure as the basic entropy source has been implemented in VHDL using Euler numerical algorithm in 32-bit IQ-Math fixed point number standart on FPGA. The designed chaotic oscillator has been synthesized for the FPGA chip and the statistics related to chip resource consumption and clock frequencies of the units have been presented. The RO-based RNG structure has been designed as the second entropy source. Chaos-ring based dual entropy core novel TRNG unit have been created by combining of these two FPGA-based structures in the XOR function used at the post processing unit. The throughput of the designed dual entropy core TRNG unit ranges 464 Mbps. The output bit streams obtained from FPGA-based novel TRNG have been subjected to NIST 800-22 test suites.

Abstract In this paper, a procedure is proposed to implement a novel and effective Gaussian-shaped and Bell-shaped membership function. The circuit is designed in a current mode. Therefore, the power consumption has been decreased. Higher power supply rejection ratio is also achieved by the use of a differential structure. The most important aims are to design simple, accurate and low power consumption circuits. The proposed circuit operates in the saturation region. Therefore, high-accuracy, as well as the high-speed performance and independency to the temperature variations, are obtained. Programmability, power consumption and parameters variations of the proposed circuit are also presented. The simulations are done in 0.18 µm CMOS technology.

The phase modification of noisy speech signal plays a crucial role in speech enhancement (SE). In the recent past, many speech denoising algorithms have been proposed using the modification of phase information which depends on the scaling factor computed from the noise level. The performance measures of SE is significantly affected by this scaling factor and noise level estimation. However, in these algorithms, the parameters are not optimally tuned for the different noise conditions and also in some cases, the background noise is presumed to be stationary. Further, no earlier attempt has been made to obtain adaptive models which can establish the relationship between noise levels and scaling factor. Being motivated by these observations an attempt has been made in this paper to develop a neural network based model which is capable of properly estimating this scaling factor from the noise level. In the current work, a popular and efficient bio-inspired technique known as firefly algorithm is employed to determine the best possible scaling factor for each noise level. In addition, a relationship is established between noise level and scaling factor using trigonometric functional expansion based artificial neural network. An effective nonstationary noise estimation strategy is also incorporated in the proposed algorithm. Simulation-based experiments are performed to evaluate the effectiveness of the proposed SE algorithm and compared with other six standard SE algorithms using standard database. The analysis of the simulation results demonstrates that the proposed method outperforms the others in terms of both subjective and objective evaluation measures.

Abstract All adaptive algorithms suffer stability issues when employed for the impulsive noise control under the domain of active noise control (ANC) systems. There is a dire need of investigations to overcome this limitation for the impulsive noise, a robust adaptive algorithm is proposed in literature. In the first part of paper, this robust adaptive algorithm is tested for the first time under ANC environment for impulsive noise cancellation and thus, a new ANC algorithm named filtered-x least cosine hyperbolic (FxLCH) algorithm is presented. Simulations are carried out to validate the improved performance of proposed FxLCH algorithm where the impulsive noise realizations are generated by symmetric α-stable distributions. Moreover, the proposed solutions perform better than the standard filtered-x least mean square (FxLMS) algorithm including its variants, and it shows better stability and converges faster than its competitors. Robustness of the algorithm is a constraint in the presence of high impulsive noise. To overcome this problem and to enhance the robustness of proposed FxLCH algorithm, two modifications are suggested. First proposed modification clips the reference and error signals (CFxLCH algorithm), while the second modification integrates already reported normalized step size with FxLCH (MFxLCH) algorithm. The performance of suggested MFxLCH algorithm is validated by extensive simulations. The results exhibited that MFxLCH algorithm acts as a trade-off between FxLMS and filtered-x recursive least square (FxRLS) family algorithms. It has shown better convergence speed than that of FxLMS family algorithms and can approach steady state error as of FxRLS family with almost same computational complexity as of FxLMS family algorithms.

This paper implements a 125 level asymmetric cascaded multilevel inverter using fuzzy logic which is used in the dynamic voltage restorer. The inverter is designed with a reduced number of switches. The higher switching frequency is defined for the better performance of the multilevel inverter. The 125 level output is obtained in this proposed approach with only twelve switches and six voltage sources. By changing the switching frequency, the proposed output voltage level is obtained in the inverter. The paper is organized into two phases. In the first step the design of 125 level inverter is proposed, and in the second phase, the power quality improvement using the designed inverter is discussed. The proposed design of an inverter is implemented for dynamic voltage restorer solves the power quality problems such as are voltage sag, voltage swell, and harmonics. The power quality issue mitigation performance is increased with the proposed 125 level inverter. The enrichment of the proposed design is investigated using the comparison of existing works. The proposed work is implemented in the Matlab/Simulink environment.

A modified Dickson’s charge pump circuit with high output voltage and high pumping efficiency fabricated by IHP’s 130 nm SiGe BiCMOS process is proposed. Instead of traditional on-chip metal–insulator–metal capacitor, a modified vertical parallel plate capacitor is utilized as the pumping capacitor, which owns a breakdown voltage higher than 84 V and an improved capacitance density of 1.92 fF/μm2. Thus, the output voltage and chip size of charge pump circuit are not limited by the pumping capacitor. To further improve the voltage pumping efficiency and make the circuit suitable for low voltage operation, the threshold voltage and the body effect coefficient is eliminated by using a dynamic control to both the charge transfer switches and the MOSFETs body voltages. Simulated result of a 35-stage charge pump circuit with an output voltage higher than 100 V is demonstrated. A 7-stage charge pump circuit with an output voltage of 13.8 V and a pumping efficiency of 75%, higher than the traditional Dickson’s charge pump circuits, is fabricated and measured.

Abstract In this paper, a new memristive diode bridge-based RC hyperjerk circuit is proposed. This new memristive hyperjerk oscillator (MHO) is obtained from the autonomous 4-D hyperjerk circuit (Leutcho et al. in Chaos Solitons Fractals 107:67–87, 2018) by replacing the nonlinear component (formed by two antiparallel diodes) with a first order memristive diode bridge. The circuit is described by a fifth-order continuous time autonomous (‘elegant’) hyperjerk system with smooth nonlinearities. The dynamics of the system is investigated in terms of equilibrium points and stability, phase portraits, bifurcation diagrams and two-parameter Lyapunov exponents diagrams. The numerical analysis of the model reveals interesting behaviors such as period-doubling, chaos, offset boosting, symmetry recovering crisis, antimonotonicity (i.e. concurrent creation and destruction of periodic orbits) and several coexisting bifurcations as well. One of the most attractive features of the new MHO considered in this work is the presence of several coexisting attractors (e.g. coexistence of two, three, four, five, six, seven, or nine attractors) for some suitable sets of system parameters, depending on the choice of initial conditions. Accordingly, the distribution of initial conditions related to each coexisting attractor is computed to highlight different basins of attraction. Laboratory experimental measurements are carried out to verify the theoretical analysis.

Abstract This research paper reports a novel design for third order chaotic and hyperchaotic oscillator with cubic nonlinearity using single operational trans-resistance amplifier (OTRA) and few passive elements. The key nonlinear dynamical characteristics in terms of sensitivity, divergence, equilibrium point and Lyapunov exponent are recorded in this literature. The operational activity of the proposed oscillator based on OTRA is integrated using 0.25 µm TSMC CMOS parameter. For the generation of hyperchaotic oscillator, an external capacitor is added to the third order chaotic oscillator. To justify the theoretical nonlinear dynamics of proposed chaotic oscillator, PSPICE simulation by using CMOS based OTRA and experimental investigation using IC AD844 based OTRA are well implemented.

Abstract It is well known that timing jitter can degrade the bit error rate of receivers that recover the clock from input data. However, timing jitter can also result in an indefinite increase in the settling time of clock recovery circuits, particularly in low swing mesochronous systems. Mesochronous clock retiming circuits are required in repeaterless low swing on-chip interconnects. We first discuss how timing jitter can result in a large increase in the settling time of the clock recovery circuit. Next, the circuit is modelled as a Markov chain with absorbing states. The mean time to absorption of the Markov chain, which represents the mean settling time of the circuit, is determined. The model is validated through behavioural simulations of the circuit, the results of which match well with the model predictions. We consider circuits with (1) data dependent jitter, (2) random jitter, and (3) combination of both of them. We show that a mismatch between the strengths of up and down corrections of the retiming can reduce the settling time. In particular, a 10% mismatch can reduce the mean settling time by up to 40%. We leverage this fact toward improving the settling time performance, and propose useful techniques based on biased training sequences and mismatched charge pumps. We also present a coarse+fine clock retiming circuit, which can operate in coarse first mode, to reduce the settling time substantially. These fast settling retiming circuits are verified with circuit simulations.

This study presents a noise-canceled transimpedance amplifier (TIA) for optical receivers. The proposed structure consists of a shunt feedback common source amplifier as an input stage followed by two regulated cascodes (RGC) and finally a differential to the single-ended amplifier at the output stage. By exploiting the noise-canceling technique at the input stage, 31.8% of the total output noise is canceled. In addition, the auxiliary path’s RGC circuit, as it has a low input impedance, is utilized to cancel out the photodiode (PD) large parasitic capacitance at the input stage. The proposed TIA along with post amplifiers, including packaging components, are simulated in TSMC 90 nm RF CMOS technology at the post-layout level. The TIA average input-referred current noise is equal to \(9.5\;{\text{pA}}/\sqrt {\text{Hz}}\). The PD capacitance is considered as 325 fF for all simulations. The transimpedance gain is equal to 60 dBΩ and the 3-dB bandwidth is equal to 7 GHz. The power consumption of the proposed TIA is 3.6 mW from a 1.2 V supply voltage. The TIA occupies a chip area of 0.036 mm2.

In this paper, a peak efficiency tracking technique to improve the efficiency of switched capacitor (SC) DC–DC converters as the load varies is presented. A peak efficiency tracking circuit based on feedback control over the switching frequency is implemented for this scope. The basic idea of the proposed technique is to adjust the switching frequency according to the load. The technique is successfully implemented in a SC DC–DC converter to be embedded in detector pixels for the Large Hadron Collider (LHC) experiment at the Conseil Européen pour la Recherche Nucléaire (CERN) of Geneve. It is realized in 65 nm bulk CMOS technology with an occupied area of 1.31 mm2. This converter provides an 800 mV output voltage from a 1.2 V supply. The load of the DC–DC converters is modeled as a resistor, RLOAD, that has 4 Ω nominal value but it can range from 2.67 up to 10 Ω. At 10 Ω RLOAD, a 6% efficiency improvement is reached with respect to the typical approach consisting in keeping constant the switching frequency at the optimum value for RLOAD nominal value.

Abstract MEMS capacitive switches have longer lifetimes compared to other types of metal-to-metal switches, and when placed on the membrane on the transmission line, they can easily return to the up-state due to a dielectric layer. They also transmit the input signal with more power and frequency and therefore, they are better than metal-to-metal switches. In this paper, first three switches were considered as the basic structures. Then, in order to demonstrate the credibility and high quality of the simulations, the same switches were simulated. The obtained results are very close to the results of fabrication of these switches. In the next step, with the presentation of three new structures, stimulation voltage, stress, switching time and isolation were improved in four steps. The mechanical simulation of the switch was performed to determine the amount of displacement, the amount of stress and the resonant frequency using the COMSOL software. In addition, electrical simulation of the switch was performed to obtain the S-parameter using the HFSS software. The simulation results demonstrate that the isolation is 57–66 dB and the insertion loss is 0.3–2 dB in the desired frequency band (1–50 GHz). Using new spring structures, the actuation voltage was reduced from 4.8 V in basic structures (the smallest in three structures) to 2.4 V in new structures, which is considered excellent. In order to increase the lifetime of the switch, the stress in the new switches is reduced from 12 to 4.5 MPa compared to the basic switches.

Presents the table of contents for this issue of this publication.

Provides a listing of current staff, committee members and society officers.

The recipients of the 2019 IEEE Information Theory Society Paper Award are Emmanuel J. Candès, Xiaodong Li, and Mahdi Soltanolkotabi for the article “Phase Retrieval via Wirtinger Flow: Theory and Algorithms,” which appeared in the IEEE Transactions on Information Theory, vol. 61, no. 4, pp. 1985–2007, April 2015.

The recipients of the 2019 IEEE Communications Society and Information Theory Society Joint Paper Award are Yuyi Mao, Jun Zhang, and Khaled B. Letaief for the article “Dynamic computation offloading for mobile-edge computing with energy harvesting devices” which appeared in the IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 3590–3605, December 2016.

The task of manipulating correlated random variables in a distributed setting has received attention in the fields of both Information Theory and Computer Science. Often shared correlations can be converted, using a little amount of communication, into perfectly shared uniform random variables. Such perfect shared randomness, in turn, enables the solutions of many tasks. Even the reverse conversion of perfectly shared uniform randomness into variables with a desired form of correlation turns out to be insightful and technically useful. In this article, we describe progress-to-date on such problems and lay out pertinent measures, achievability results, limits of performance, and point to new directions.

This work investigates the general two-user compound Broadcast Channel (BC) in which an encoder wishes to transmit two private messages W 1 and W 2 to two receivers while being oblivious to the actual channel realizations controlling the communication. The focus is on the characterization of the largest achievable rate region by resorting to more involved encoding and decoding techniques than the usual coding schemes of the standard BC. Involved decoding schemes are first explored, and an achievable rate region is derived based on the principle of Interference Decoding (ID), in which each receiver decodes its intended message and chooses to (non-uniquely) decode, or not, the interfering non-itended message. This decoding scheme is shown to be capacity achieving for a class of non-trivial compound BEC/BSC broadcast channels while the worst-case of Marton’s inner bound—based on No Interference Decoding (NID)—fails to achieve the capacity region. Involved encoding schemes are later investigated, and an achievable rate region is derived based on Multiple Description (MD) coding wherin the encoder transmits a common description as well as multiple dedicated private descriptions to the many possible channel realizations of the users. It turns out that MD coding yields larger inner bounds than the single description scheme—Common Description (CD) coding—for a class of compound Multiple Input Single Output Broadcast Channels (MISO BC).

The $K$ -user discrete memoryless (DM) broadcast channel (BC) with two nested multicast messages is studied in which one common message is to be multicast to all receivers and the second private message to a subset of receivers. The receivers that must decode both messages are referred to as private receivers and the others that must decode only the common message as common receivers. For two nested multicast messages, we establish the capacity region for several classes of partially ordered DM BCs characterized by the respective associated sets of pair-wise relationships between and among the common and private receivers, each described by the well-known pair-wise more capable or less noisy condition. For three classes of partially ordered DM BCs, the capacity region is shown to be simply achieved by two-level superposition coding and the proofs of the converses rely on a recently found information inequality. The rate region achievable by two-level superposition coding is then enhanced through a multi-level superposition coding scheme after splitting the private message into as many parts as there are common receivers and indirect decoding. A closed-form two-dimensional polyhedral (polygonal) description is obtained for it for a given coding distribution in spite of the indeterminate number of split rates via a structured form of Fourier-Motzkin elimination. Through a converse result that relies on the Csiszar sum lemma and that information inequality, a specialization of this region that corresponds to splitting the private message into just two sub-messages is proved to be the capacity region for several classes of partially ordered DM BCs beyond those for which two-level superposition coding is capacity optimal, thereby underscoring the benefit of rate-splitting. All previously known capacity results for partially ordered DM BCs with two nested multicast messages for the two and three-receiver DM BCs as well as DM BCs with one private or one common receiver are subsumed in the general results obtained in this work.

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. In the enrollment phase, users’ data are stored into the database in two layers. In the identification phase an observer obtains an observation, which originates from an unknown user in the enrolled database through a memoryless channel. The observation is sent for processing in two stages. In the first stage, the observation is pre-processed, and the result is then used in combination with the stored first layer information in the database to output a list of compatible users to the second stage. Then the second step uses the information of users contained in the list from both layers and the original observation sequence to return the exact user identity and a corresponding reconstruction sequence. The rate-distortion regions are characterized for both discrete and Gaussian scenarios. Specifically, for a fixed list size and distortion level, the compression-identification trade-off in the Gaussian scenario results in three different operating cases characterized by three auxiliary functions. While the choice of the auxiliary random variable for the first layer information is essentially unchanged when the identification rate is varied, the second one is selected based on the dominant function within those three. Due to the presence of a mixture of discrete and continuous random variables, the proof for the Gaussian case is highly non-trivial, which makes a careful measure theoretic analysis necessary. In addition, we study a connection of the previous setting to a two observer identification and a related problem with a lower bound for the list size, where the latter is motivated from privacy concerns.

Consider the problem of guessing the realization of a random vector ${X}$ by repeatedly submitting queries (guesses) of the form “Is ${X}$ equal to ${x}$ ?” until an affirmative answer is obtained. In this setup, a key figure of merit is the number of queries required until the right vector is identified, a number that is termed the guesswork . Typically, one wishes to devise a guessing strategy which minimizes a certain guesswork moment. In this work, we study a universal, decentralized scenario where the guesser does not know the distribution of ${X}$ , and is not allowed to use a strategy which prepares a list of words to be guessed in advance, or even remember which words were already used. Such a scenario is useful, for example, if bots within a Botnet carry out a brute–force attack in order to guess a password or decrypt a message, yet cannot coordinate the guesses between them or even know how many bots actually participate in the attack. We devise universal decentralized guessing strategies, first, for memoryless sources, and then generalize them for finite–state sources. In each case, we derive the guessing exponent, and then prove its asymptotic optimality by deriving a compatible converse bound. The strategies are based on randomized guessing using a universal distribution. We also extend the results to guessing with side information. Finally, for all above scenarios, we design efficient algorithms in order to sample from the universal distributions, resulting in strategies which do not depend on the source distribution, are efficient to implement, and can be used asynchronously by multiple agents.

We introduce a new graphical framework for designing quantum error correction codes based on classical principles. A key feature of this graphical language, over previous approaches, is that it is closely related to that of factor graphs or graphical models in classical information theory and machine learning. It enables us to formulate the description of the recently-introduced ‘coherent parity check’ quantum error correction codes entirely within the language of classical information theory. This makes our construction accessible without requiring background in quantum error correction or even quantum mechanics in general. More importantly, this allows for a collaborative interplay where one can design new quantum error correction codes derived from classical codes.

A new bound on quantum version of Wielandt inequality for positive (not necessarily completely positive) maps has been established. Also bounds for entanglement breaking and PPT channels are put forward which are better bound than the previous bounds known. We prove that a primitive positive map $\mathcal {E}$ acting on $\mathcal {M}_{d}$ that satisfies the Schwarz inequality becomes strictly positive after at most $2(d-1)^{2}$ iterations. This is to say that after $2(d-1)^{2}$ iterations, such a map sends every positive semidefinite matrix to a positive definite one. This finding does not depend on the number of Kraus operators as the map may not admit any Kraus decomposition. The motivation of this work is to provide an answer to a question raised by Sanz-García-Wolf and Cirac in their work on quantum Wielandt bound.

We study the exact, non-deterministic conversion of multipartite pure quantum states into one-another via local operations and classical communication (LOCC) and asymptotic entanglement transformation under such channels. In particular, we consider the maximal number of copies of any given target state that can be extracted exactly from many copies of any given initial state as a function of the exponential decay in the success probability, known as the converse error exponent. We give a formula for the optimal rate presented as an infimum over the asymptotic spectrum of LOCC conversion. A full understanding of exact asymptotic extraction rates between pure states in the converse regime thus depends on a full understanding of this spectrum. We present a characterization of spectral points and use it to describe the spectrum in the bipartite case. This leads to a full description of the spectrum and thus an explicit formula for the asymptotic extraction rate between pure bipartite states, given a converse error exponent. This extends the result on entanglement concentration in [1] , where the target state is fixed as the Bell state. In the limit of vanishing converse error exponent, the rate formula provides an upper bound on the exact asymptotic extraction rate between two states, when the probability of success goes to 1. In the bipartite case, we prove that this bound holds with equality.

Let $q$ be a prime power and $\mathbb {F}_{q}$ be the finite field of size $q$ . In this paper we provide a Galois theoretical framework that allows to produce good polynomials for the Tamo and Barg construction of optimal locally recoverable codes (LRC). Using our approach we construct new good polynomials and therefore optimal LRCs with new parameters. The existing theory of good polynomials fits entirely in our new framework. The key advantage of our method is that we do not need to rely on arithmetic properties of the pair $(q,r)$ , where $r$ is the locality of the code.

The problem of exact-repair regenerating codes against eavesdropping attack is studied. The eavesdropping model we consider is that the eavesdropper has the capability to observe the data involved in the repair of a subset of $\ell $ nodes. An $(n,k,d,\ell )$ secure exact-repair regenerating code is an $(n,k,d)$ exact-repair regenerating code that is secure under this eavesdropping model. It has been shown that for some parameters $(n,k,d,\ell )$ , the associated optimal storage-bandwidth tradeoff curve, which has one corner point, can be determined. The focus of this paper is on characterizing such parameters. We establish a lower bound $\hat {\ell }$ on the number of wiretap nodes, and show that this bound is tight for the case $k = d = n-1$ .