We propose a novel LSB-BMSE method that enhances LSB audio steganography. It uses an innovative mechanism, Binaries of Message Size Encoding (BMSE), to embed a secret message after hiding its size in random samples. First, the secret message is compressed using Huffman coding, then encrypted by AES-128. The audio cover is split into number of blocks depending on secret message size. A secure key BMSE , output from the BMSE mechanism, is used to embed the secret message in random blocks and bytes adaptively according to its size. It is implemented using MATLAB and standard parameters: Perceptual Evaluation of Speech Quality and NIST Statistical Test Suite were used to measure imperceptibility between cover & stego audios and randomness of BMSE mechanism respectively. The fidelity was tested using Mean Square Error, Peak Signal to Noise Ratio and Signal-to-Noise Ratio. Comprehensive experiments on widely used metrics demonstrate that LSB-BMSE significantly surpasses existing methods in terms of hiding capacity and imperceptibility. Moreover, LSB-BMSE shows resistance to brute force attacks and statistical analysis. Although it was robust towards re-sampling attacks, nevertheless was not vigorous towards noise nor LSB attacks. The key BMSE complies with Kerckhoff's principle and exhibits avalanche criteria. The tested proposed LSB-BMSE proclaimed its effectiveness.

No abstract

Object detection of autonomous vehicles presents a big challenge for researchers due to the requirements of accuracy and precision in real-time. This work presents a deep learning approach based on a dual architecture design of the network. A highly accurate multi-class network of convolutional neural networks (CNN) is presented for input data classification. A Region-Based Convolutional Neural Networks (Faster R-CNN) network with a modified Feature Pyramid Networks (FPN) is used for better detection of tiny objects and You Only Look Once (YOLOv3) network is used for general detection. Each network independently detects the existence of an object. The decision maps are then fused and compared to decide whether an object is present or not. Faster R-CNN with FPN model reported a higher intersection over Union (IoU) and mean average precision (mAP) than the YOLOv3. This approach is reliable demonstrating an upgrade on the existing state-of-the-art methods of fully connected networks. Index Terms— autonomous driving, computer vision, deep learning, object detection

Audio steganography hides a secret message into an audio. Existing techniques are lacking in achieving high payload, imperceptibility in addition to robustness at the same time. They also suffer from choosing the samples and even LSBs in an unpredictable fashion. Moreover, few adaptive techniques exist, besides not many embed in higher LSBs. Hence, a novel LSB PW LCM method that ameliorates LSB audio steganography is proposed. It uses piecewise linear chaotic map (PWLCM) to embed a secret message in random samples, besides selecting one of the 4-LSBs in an unsystematic way. It is noteworthy that each time a distinct sample and hence a differed 4-LSB is chosen as per different generated PWLCM. At first, Huffman coding is used to lessen the secret message size. Thereafter, to ameliorate the security of the onetime pad, random numbers are generated using PWLCM as an input key. This gives the proposed method a dual protection by amalgamating steganography with enhanced secure one-time pad. MATLAB is used to implement the proposed LSB PW LCM method and evaluate the imperceptibility between cover and stego audios against standard parameters viz. Perceptual Evaluation of Speech Quality (PESQ) and Perceptual Evaluation of Audio Quality (PEAQ). Furthermore, its imperceptibility was tested using Mean Square Error, Peak Signal to Noise Ratio, Signal-to-Noise Ratio, Percentage Root Mean Square Difference and Audio Fidelity. Exhaustive experiments on vastly used metrics affirm that the proposed method LSB PW LCM excel prevailing methods regarding hiding capacity and imperceptibility. Furthermore, it is resistant to brute force attacks having a large key space besides its dependency on the secret message size. In addition, it effectively withstood statistical analysis, specifically histogram attack and fourth first moments. Albeit it was vigorous towards re-sampling attacks, yet it was not very robust against LSB attacks nor noise. Assuredly, it prevails over existing methods and beyond comparison when juxtaposed with them affirming its efficacy.

No abstract

The current work aimed to evaluate the effect of some micronutrients (Fe, Mn, Zn and Cu) in mineral (sulphates) and chelated (-amino acids and -EDTA) forms added to soil in solely or in combined treatments with both organic composts (wheat residues and cattle wastes) and sulphur on grain and straw yields as well as their contents of such micronutrients for wheat-maize cropping sequence in a calcareous soil, with special reference to the effects of these treatments on available soil contents of these micronutrients. To achieve this target two field experiments were conducted on a calcareous soil located at the eastern edge of Tamia district, El Fayoum Governorate, and cultivated with winter wheat (Sakha 69) followed by summer maize (single cross 10 hybrid) to verify the results obtained during growing season of 2003/2004 under surface irrigation system.The data obtained reveal that the experimental soil is characterized by secondary calcic formations in compacted phase, especially in the uppermost layer, and micronutrient deficient. The soil is classified at the family level as Typic Haplocalcids, clayey, mixed, hyperthermic. Also, its capability was evaluated as marginally suitable (S3ws), with a moderate intensity degree (rating = 60-85) for all the identified soil limitations (wetness, soil texture, soil depth and CaCO 3 content). The results showed an improvement occurred in available micronutrient contents (Fe, Mn, Zn and Cu) in the studied soil as a result of the applied treatments, with different magnitudes depend on their effective roles, nature of chemical composition, as shown in the following descending order: mineral micronutrients + organic composts > micronutrients + sulphur > chelated micronutrients > organic composts + sulphur > mineral micronutrients > organic composts > sulphur.The favourable conditions of the combined treatments with organic composts or sulphur are commonly achieved by lowering soil pH and forming organo-metalic compounds. The chelated micronutrients (-amino acids and -EDTA) represented the next superior form due to a higher portion of these compounds still in maintained active forms for uptake by plant roots.The beneficial effects of the studied treatments were actually reflected on increasing the grain and straw yields of wheat and also extended to the next cultivated maize. In addition, the positive effects of the studied treatments are more attributed to improve the efficiency of micronutrients uptake according to their effective roles. Moreover, the micronutrients response of Fe, Mn, Zn and Cu to accumulate in the grain and straw tissues showed a closely relationship to their corresponding available contents in the treated soils.