<title>Motion-Adaptive TV Data Compression</title>

With the large-scale integration of distributed photovoltaic systems into the power grid, the multi-component and highly dynamic characteristics of these systems have led to a significant increase in power data volume. Traditional fixed dictionary compression methods are difficult to adapt to load fluctuations and changes in time-frequency features, making it urgent to develop adaptive compression schemes that balance efficiency and data integrity. This paper discloses a power data compression method based on adaptive dictionary learning of the load characteristics of distributed photovoltaic systems, aiming to enhance the compression efficiency of power data, reduce storage and transmission burdens, and ensure data integrity. The method mainly consists of the following steps: signal acquisition, time-frequency feature extraction, adaptive dictionary learning, sparse coding and data compression, as well as data reconstruction and recovery. Dynamic features of the photovoltaic system load signal are extracted through time-frequency analysis, and adaptive dictionary learning technology is used to dynamically optimize the dictionary structure, achieving sparse representation of the signal. The compressed data generated by sparse coding is reconstructed at the main station, which can retain key features, remove redundant information, and significantly reduce the amount of transmitted data. Compared with traditional compression methods, this method has the advantages of real-time performance and strong adaptability, effectively coping with the complex dynamic changes of photovoltaic systems. It is widely applicable to scenarios such as photovoltaic system monitoring, data transmission, and fault diagnosis.

Network-on-Chip (NoC) links consume a significant fraction of the total NoC power. We present Selective Packet Interleaving (SPI), a flit transmission scheme that reduces power consumption in NoC links. SPI decreases the number of bit transitions in the links by exploiting the multiplicity of virtual channels in a NoC router. SPI multiplexes flits to the router's output link so as to minimize the number of bit transitions from the previously transmitted flit. Analysis and simulations demonstrate a reduction of up to 55% in the number of bit transitions and up to 40% savings in power consumed on the link. SPI benefits grow with the number of virtual channels. SPI works better for links with a small number of bits in parallel. While SPI compares favorably against bus inversion, combining both schemes helps to further reduce bit transitions.

We consider decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth-constrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varying sensor quality and inhomogeneous sensing environment. The classical best linear unbiased estimator (BLUE) linearly combines the real-valued sensor observations to minimize the mean square error (MSE). Unfortunately, such a scheme cannot be implemented in a practical bandwidth-constrained sensor network due to its requirement to transmit real-valued messages. In this paper, we construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signal-to-noise ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that each sensor compression scheme requires only the knowledge of local SNR, rather than the noise probability distribution functions (pdf), while the final fusion step is also independent of the local noise pdfs. We show that the MSE of the proposed DES is within a constant factor of 25/8 of that achieved by the classical centralized BLUE estimator.

Transform coding techniques for image transmission are examined in detail and compared to the DCT (discrete cosine transform) under the same conditions, e.g. data rate, type of quantization of coefficients, number of operations needed, suitability for real-time execution, and quality of the received image. The quality is measured by objective (normalized mean square error) and subjective criteria. The images used in the experiments represent different situations which may arise in practice (buildings, head and shoulder, aerial images). These investigations show that the LOT (lapped orthogonal transform) has some advantages at data rates about 0.5 b/pixel, although DCT is better regarding computing speed and ringing effects. >

A lot of attentions has been paid by the researchers for lossy image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality, is still a research issue. In this paper, we proposed an image compression using adaptive scanning, in which DWT(discrete wavelet transform) is performed to source image, DCT(discrete cosine transform) is performed to obtained DWT elements. Next, non zero quantized DCT (discrete cosine transform) coefficients are represented in less number of bits. First, the source image is divided into \(16\times 16\) grids and then DWT transformed, later the obtained elements are divided into \(8\times 8\) grids and then DCT is performed, quantized, differential encoded and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore the indexed vector is divided into two indexed vectors namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.

Exhaustive self-testing of combinational circuitry within the framework of the level-sensitive scan design (LSSD) discipline requires that every output node depend on a small number of input nodes. We present here efficient algorithms that take an arbitrary block of combinational logic and add to it the smallest number of bits of new LSSD registers necessary to: (1) partition the logic so that no output depends on more thank inputs, and (2) maintain timing within the block (so that all input-to-output paths encounter the same number of bits of register). Our partitioning algorithms conform to two different design constraints. We also show that the unconstrained partitioning problem is NP-complete.

A steganographic technique inspired by rook is presented in this paper to ensure privacy and secrecy. In this approach, the cover image is partitioned into n × 1 pixel blocks and converted equivalent n × 8 binary bit planes. Then the functional output of each block is calculated on the basis of the number of rook positions, which are attacked by opponent rooks. The rook is a chess piece that moves only forward and backward in a straight line. In binary bit plane, 0 and 1 are considered as a black and white opponent rook, respectively. The secret information is considered as stream of binary bits. The binary bits of secret information are compared with the functional output of the corresponding block. If it is equal to the functional output of the corresponding block, then nothing needs to be done. In case of inequality, the small number of bits needs to be flipped in such a way that the functional output of the corresponding block becomes equal to the corresponding secret binary bits and the distortion of the cover is minimized.

The defining characteristic of a networked control system (NCS) is having a feedback loop that passes through a local area computer network. This paper considers nonlinear systems controlled in this manner, and demonstrates that for sufficiently high transmission rates, the network may be considered transparent. Three methods of scheduling data packets are compared: a static scheduler (token ring), the try-once-discard (maximum-error-first) scheduler with continuous priority levels, and the try-once-discard scheduler with discrete priority levels. The third method is of particular interest when only a small number of bits are available for collision resolution. Asymptotic stability is guaranteed in the first two cases, and ultimate uniform boundedness in the third. In the final section, simulations demonstrate the theoretical results. The contributions of this paper are two-fold: first, it extends the earlier results on NCS to nonlinear systems, and second, it allows for finite word-length message identifiers.

This paper introduces a best approach for Least Significant Bit (LSB) based on image steganography that enhances the existing LSB substitution techniques to improve the security level of hidden information. It is a new approach to substitute LSB of RGB true color image. The new security conception hides secret information within the LSB of image where a secret key encrypts the hidden information to protect it from unauthorized users. In general, in LSB methods, hidden information is stored into a specific position of LSB of image. For this reason, knowing the retrieval methods, anyone can extract the hidden information. In our paper, hidden information is stored into different position of LSB of image depending on the secret key. As a result, it is difficult to extract the hidden information knowing the retrieval methods. We have used the Peak Signal-to-Noise Ratio (PSNR) to measure the quality of the stego images. The value of PSNR gives better result because our proposed method changes very small number of bits of the image. The obtained results show that the proposed method results in LSB based image steganography using secret key which provides good security issue and PSNR value than general LSB based image steganography methods.

Halftoning is a process that deliberately injects noise into the original image in order to obtain visually pleasing output images with a smaller number of bits per pixel for displaying or printing purposes. In this paper, a novel inverse halftoning method is proposed to restore a continuous tone image from the given halftone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space domain projection is introduced assuming the halftoning is performed with error diffusion, and the error diffusion filter kernel is known. The space domain, frequency domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.

This paper presents an efficient method to block unknowns for temporal compactors. The proposed blocking logic can reduce data volume required to control the blocking logic and increase the number of scan cells that are observed by temporal compactors. Control patterns, which specify values required at the control signals of the blocking logic, are compressed by linear feedback shift register reseeding. In this paper, the blocking logic gates for some scan chains that do not capture unknowns are bypassed. Since all scan cells in these scan chains can be observed without specifying the corresponding bits in control patterns, more scan cells are observed while a smaller number of bits are required to be specified. The seed size is further reduced by reducing the numbers of specified bits in the densely specified control patterns. The proposed method can always achieve the same fault coverage that can be achieved by directly observing scan chains without any output compaction. Experiments with large industrial designs clearly demonstrate that the proposed method is scalable to large circuits. Hardware overhead for the proposed unknown blocking scheme is very low.

We propose an AVF-driven parity selection method for protecting modern microprocessor in-core memory arrays against MBUs. As MBUs constitute more than 50% of the upsets in latest technologies, error correcting codes or physical interleaving are typically employed to effectively protect out-of-core memory structures, such as caches. However, such methods are not applicable to high-performance in-core arrays, due to computational complexity, high delay and area overhead. To this end, we revisit parity as an effective mechanism to detect errors and we resort to pipeline flushing and checkpointing for correction. We demonstrate that optimal parity tree construction for MBU detection is a computationally complex problem, which we then formulate as an integer-linear-program (ILP). Experimental results on Alpha 21264 and Intel P6 in-core memory arrays demonstrate that optimal parity tree selection can achieve great vulnerability reduction, even when a small number of bits are added to the parity trees, compared to simple heuristics. Furthermore, the ILP formulation allows us to find better solutions by effectively exploring the solution space in the presence of multiple parity trees; results show that the presence of 2 parity trees offers a vulnerability reduction of more than 50% over a single parity tree.

Adversarial attacks on Neural Network weights, such as the progressive bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by flipping a very small number of bits. Furthermore, PBFA can be conducted at run time on the weights stored in DRAM main memory. In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA. We organize weights that are interspersed in a layer into groups and employ a checksum-based algorithm on weights to derive a 2-bit signature for each group. At run time, the 2-bit signature is computed and compared with the securely stored golden signature to detect the bit-flip attacks in a group. After successful detection, we zero out all the weights in a group to mitigate the accuracy drop caused by malicious bit-flips. The proposed scheme is embedded in the inference computation stage. For the ResNet-18 ImageNet model, our method can detect 9.6 bit-flips out of 10 on average. For this model, the proposed accuracy recovery scheme can restore the accuracy from below 1% caused by 10 bit flips to above 69%. The proposed method has extremely low time and storage overhead. System-level simulation on gem5 shows that RADAR only adds <1% to the inference time, making this scheme highly suitable for run-time attack detection and mitigation.

We consider the problem of constructing randomness extractors that are locally computable; that is, read only a small number of bits from their input. As recently shown by Lu locally computable extractors directly yield secure private-key cryptosystems in Maurer’s bounded-storage model. We suggest a general “sample-then-extract” approach to constructing locally computable extractors: use essentially any randomness-efficient sampler to select bits from the input and then apply any extractor to the selected bits. Plugging in known sampler and extractor constructions, we obtain locally computable extractors, and hence cryptosystems in the bounded-storage model, whose parameters improve upon previous constructions. We also provide lower bounds showing that the parameters we achieve are nearly optimal. The correctness of the sample-then-extract approach follows from a fundamental lemma of Nisan and Zuckerman, which states that sampling bits from a weak random source roughly preserves the min-entropy rate. We also present a refinement of this lemma, showing that the min-entropy rate is preserved up to an arbitrarily small additive loss, whereas the original lemma loses a logarithmic factor.

Many applications, including program optimizations, debugging tools, and event loggers, rely on calling context to gain additional insight about how a program behaves during execution. One common strategy for determining calling contexts is to use compiler instrumentation at each function call site and return sites to encode the call paths and store them in a designated area of memory. While recent works have shown that this approach can generate precise calling context encodings with low overhead, the encodings can grow to hundreds or even thousands of bytes to encode a long call path, for some applications. Such lengthy encodings increase the costs associated with storing, detecting, and decoding call path contexts, and can limit the effectiveness of this approach for many usage scenarios.