Side-channel Attack Using Word Embedding and Long Short Term Memories

Side-channel attack (SCA) based on machine learning has proved to be a valid technique in cybersecurity, especially subjecting to the symmetric-key crypto implementations in serial operation. At the same time, parallel-encryption computing based on Field Programmable Gate Arrays (FPGAs) grows into a new influencer, but the attack results using machine learning are exiguous. Research on the traditional SCA has been mostly restricted to pre-processing: Signal Noisy Ratio (SNR) and Principal Component Analysis (PCA), etc. In this work, firstly, we propose to replace Points of Interests (POIs) and dimensionality reduction by utilizing word embedding, which converts power traces into sensitive vectors. Secondly, we combined sensitive vectors with Long Short Term Memories (LSTM) to execute SCA based on FPGA crypto-implementations. In addition, compared with traditional Template Attack (TA), Multiple Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). The result shows that the proposed model can not only reduce the manual operation, such as parametric assumptions and dimensionality setting, which limits their range of application, but improve the effectiveness of side-channel attacks as well.

DEVELOPMENT OF A SMART OCEAN RADIATION MONITORING SYSTEM

Ocean radiation monitoring systems (ORMSs) are an essential component in the radiation early warning network that monitors radiation exposure and estimates radioactive propagation induced by nuclear activities or nuclear accidents in the sea. Numerous systems have been developed and installed in the radiation warning network in different countries. However, there is not any similar product that has been studied and developed in Vietnam. This paper presents a complete process in designing and manufacturing a marine buoy integrated with a radiation sensor. The radiation detector can measure both dose rate and radiological spectrum. The ORMS also combines multimodal data transmission and various programmed software for data processing, signal transmission, and system control. Therefore, the proposed configuration system has potential application in terms of performance and maintenance.

Halobacterium salinarum and Haloferax volcanii Comparative Transcriptomics Reveals Conserved Transcriptional Processing Sites

Post-transcriptional processing of messenger RNA is an important regulatory strategy that allows relatively fast responses to changes in environmental conditions. In halophile systems biology, the protein perspective of this problem (i.e., ribonucleases which implement the cleavages) is generally more studied than the RNA perspective (i.e., processing sites). In the present in silico work, we mapped genome-wide transcriptional processing sites (TPS) in two halophilic model organisms, Halobacterium salinarum NRC-1 and Haloferax volcanii DS2. TPS were established by reanalysis of publicly available differential RNA-seq (dRNA-seq) data, searching for non-primary (monophosphorylated RNAs) enrichment. We found 2093 TPS in 43% of H. salinarum genes and 3515 TPS in 49% of H. volcanii chromosomal genes. Of the 244 conserved TPS sites found, the majority were located around start and stop codons of orthologous genes. Specific genes are highlighted when discussing antisense, ribosome and insertion sequence associated TPS. Examples include the cell division gene ftsZ2, whose differential processing signal along growth was detected and correlated with post-transcriptional regulation, and biogenesis of sense overlapping transcripts associated with IS200/IS605. We hereby present the comparative, transcriptomics-based processing site maps with a companion browsing interface.

On full spark frames via Cauchy matrices

Full spark frames have been widely applied in sparse signal processing, signal reconstruction with erasures and phase retrieval. Since testing whether a given frame is full spark is hard for NP under randomized polynomial-time reductions, hence the deterministic full spark (DFS) frames are particularly significant. However, the degree of freedom of choices of DFS frames is not enough in practical applications because the DFS frames are well known as Vandermonde frames and harmonic frames. In this paper, we focus on the deterministic constructions of full spark frames. We present a new and effective method to construct DFS frames by using Cauchy matrices. We also construct the DFS frames by using Cauchy-Vandermonde matrices. Finally, we show that full spark tight frames can be constructed from generalized Cauchy matrices.

Development of Detection Equipment for a Polymerase Chain Reaction with a Loop-Mediated Isothermal Amplification Reaction

In this research, low-cost detection equipment intended to carry out a polymerase chain reaction (PCR) through a loop-mediated isothermal amplification (LAMP) reaction is presented. We designed the internal structure with SolidWorks and AutoCAD. The equipment comprised a Raspberry Pi development board, a temperature control module, and a fluorescent optical detection module. The main program, temperature control, florescent signal processing, signal analysis, and screen display were programmed with Java. We applied the digital temperature controller module to obtain precise temperature control of the equipment. The experimental results showed that the heating rate of the testing equipment could reach 65°C within 4 minutes and could be accurately controlled to within 1°C. The duration of the LAMP PCR experiment was found to be significantly shorter than that of the conventional PCR. The results also revealed that with LAMP PCR, the temperature could be accurately controlled within a specific range, and the designed heating tasks could be completed within 15 minutes to one hour, depending on the specimen. The equipment could also correctly read both the positive and negative reactions with fluorescent signals. Thus, the proposed LAMP PCR detection equipment is more sensitive, more stable, and more cost-effective than other conventional alternatives and can be used in numerous clinical applications.

Sensing Occupancy through Software: Smart Parking Proof of Concept

In order to detect the vehicle presence in parking slots, different approaches have been utilized, which range from image recognition to sensing via detection nodes. The last one is usually based on getting the presence data from one or more sensors (commonly magnetic or IR-based), controlled and processed by a micro-controller that sends the data through radio interface. Consequently, given nodes have multiple components, adequate software is required for its control and state-machine to communicate its status to the receiver. This paper presents an alternative, cost-effective beacon-based mechanism for sensing the vehicle presence. It is based on the well-known effect that, once the metallic obstacle (i.e., vehicle) is on top of the sensing node, the signal strength will be attenuated, while the same shall be recognized at the receiver side. Therefore, the signal strength change conveys the information regarding the presence. Algorithms processing signal strength change at the receiver side to estimate the presence are required due to the stochastic nature of signal strength parameters. In order to prove the concept, experimental setup based on LoRa-based parking sensors was used to gather occupancy/signal strength data. In order to extract the information of presence, the Hidden Markov Model (HMM) was employed with accuracy of up to 96%, while the Neural Network (NN) approach reaches an accuracy of up to 97%. The given approach reduces the costs of the sensor production by at least 50%.

Challenges in Sparse Image Reconstruction

Handling huge amount of data from different sources more so in the images is the latest challenge. One of the solutions to this is sparse representation. The idea of sparsity has been receiving much attention recently from many researchers in the areas such as satellite image processing, signal processing, medical image processing, microscopy image processing, pattern recognition, neuroscience, seismic imaging, etc. Many algorithms have been developed for various areas of sparse representation. The main objective of this paper is to provide a comprehensive study and highlight the challenges in the area of sparse representation which will be helpful for researchers. Also, the current challenges and opportunities of applying sparsity to image reconstruction, namely, image super-resolution, image denoising and image restoration are discussed. This survey on sparse representation categorizes the existing methods into three groups: dictionary learning approach, greedy strategy approximation approach and deep learning approach.

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.

Application of Stochastic Process in Signal Processing

In this paper introduction about birth and death Poisson process basic result of the markovian application in queuing theory used in signal processing, signal transfer from some to passion based on the intermediate node, each intermediate node are transformed from signal strength S is directly proportional to 1/√p based on the formula using the internal communication a dependent can be characterised by the Gilbert model. Two state Markov model signals, distance when signal strength is greater the distance should be reduced. Bayesian inference is used, few numerical examples are studied.