Book review: Applied cryptography: Protocols, algorithms, and source code in C

Background 
Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established


Objective 
To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds This systematic review provides a complete summary of methods used in the literature to give a baseline for future works


Data sources 
A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases Additional articles were further obtained by references listed in the articles found Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification


Study selection 
Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated


Data extraction 
Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved


Data synthesis 
A total of 77 reports from the literature were included in this review 55 (7143%) of the studies focused on wheeze, 40 (5195%) on crackle, 9 (1169%) on stridor, 9 (1169%) on rhonchi, and 18 (2338%) on other sounds such as pleural rub, squawk, as well as the pathology Instrumentation used to collect data included microphones, stethoscopes, and accelerometers Several references obtained data from online repositories or book audio CD companions Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques Performance reported in the surveyed works were converted to accuracy measures for data synthesis


Limitations 
Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different A standard validation method has not been established, resulting in different works using different methods and performance measure definitions


Conclusion 
A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis The performance of recent studies showed a high agreement with conventional non-automatic identification This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases

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Automatic adventitious respiratory sound analysis: A systematic review.

A symmetrical image encryption scheme in wavelet and time domain

Due to the potential security problem about key management and distribution for the symmetric image encryption schemes, a novel asymmetric image encryption method is proposed in this paper, which is based on the elliptic curve ElGamal (EC-ElGamal) cryptography and chaotic theory. Specifically, the SHA-512 hash is first adopted to generate the initial values of a chaotic system, and a crossover permutation in terms of chaotic index sequence is used to scramble the plain-image. Furthermore, the generated scrambled image is embedded into the elliptic curve for the encrypted by EC-ElGamal which can not only improve the security but also can help solve the key management problems. Finally, the diffusion combined chaos game with DNA sequence is executed to get the cipher image. The experimental analysis and performance comparisons demonstrate that the proposed method has high security, good efficiency, and strong robustness against the chosen-plaintext attack which make it have potential applications for the image secure communications.

/pdf/an-image-encryption-method-based-on-elliptic-curve-elgamal-1ngs4swbun.pdf

An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems

A robust image encryption algorithm based on Chua's circuit and compressive sensing

Based on the spatiotemporal chaotic system, a novel algorithm for constructing a one-way hash function is proposed and analysed. The message is divided into fixed length blocks. Each message block is processed by the hash compression function in parallel. The hash compression is constructed based on the spatiotemporal chaos. In each message block, the ASCII code and its position in the whole message block chain constitute the initial conditions and the key of the hash compression function. The final hash value is generated by further compressing the mixed result of all the hash compression values. Theoretic analyses and numerical simulations show that the proposed algorithm presents high sensitivity to the message and key, good statistical properties, and strong collision resistance.

One-way hash function construction based on the spatiotemporal chaotic system

Most automatic speech recognition systems have concentrated exclusively on the acoustic speech signal, and therefore they are susceptible to acoustic noise. The benefits from visual speech cues have motivated significant interest in automatic lip-reading, which aims at improving automatic speech recognition by exploiting informative visual features of a speaker's mouth region, which means speaker lip motion stands out as the most linguistically visual feature. In this paper, we present a new improved robust lip location and tracking approach, aims at improving the lip-reading accuracy. Lip regions of interest are detected by a new method, combining with Intel Open source (OpenCV). In this new method, we analyze the distribution relationship between faces, eyes and mouth, and then the mouth region can be easily located. It can be proved as an effective method for lip tracking. In the subsequent step, color space is transferred to Lab from RGB color space, and a component of Lab color space is used for extracting lip segmentation and tracking lip region more accurately and efficiently from video sequences of a speaker's talking face in different lighting conditions, and with different lip shapes and head poses. Extensive experiments show that our proposed method can achieve superior performance to other similar lip tracking approaches, and then can be effectively integrated in lip-reading or visual speech recognition systems.

A real-time lip localization and tacking for lip reading

To ensure the security of a digital image, a new self-adapting encryption algorithm based on the spatiotemporal chaos and ergodic matrix is proposed in this paper. First, the plain-image is divided into different blocks of the same size, and each block is sorted in ascending order to obtain the corresponding standard ergodic matrix. Then each block is encrypted by the spatiotemporal chaotic system and shuffled according to the standard ergodic matrix. Finally, all modules are rearranged to acquire the final encrypted image. In particular, the plain-image information is used in the initial conditions of the spatiotemporal chaos and the ergodic matrices, so different plain-images will be encrypted to obtain different cipher-images. Theoretical analysis and simulation results indicate that the performance and security of the proposed encryption scheme can encrypt the image effectively and resist various typical attacks.

https://iopscience.iop.org/article/10.1088/1674-1056/22/8/080503/pdf

A self-adapting image encryption algorithm based on spatiotemporal chaos and ergodic matrix

Crackles are an important kind abnormal lung sound for detection in lung sound analysis. Focused on characteristic morphology of crackles in time-domain, a novel time-domain processing method is proposed to extract features of crackles based on the newly rising theories of Fractional Hilbert Transform. After applying the transformation of Fractional Hilbert Transform with various fractional values, exclusive timedomain features are merged and can be used as validated detection features. Experiments show great application feasibilities for such kind of wave detections. Later we use correlation functions to construct an elementary detection system, and system simulation results support the effectiveness of our work. Discussions on detection errors are followed.

A novel method for feature extraction of crackles in lung sound

A modified pitch period detection (MPPD) algorithm is proposed in this paper. It is based on the redefinition of the instantaneous energy density level of Hilbert-Huang transform, called as frequency weighted energy density FIE(t). Two examples are employed to illuminate the physical meaning of this new definition. MPPD are used in detection experiments with 78 normal and 70 abnormal vowel /a:/ voices, the experimental results show that MPPD provides more robust performance and makes better results in normal voice pitch detection

Du Minghui

Papers

One-way hash function construction based on the spatiotemporal chaotic system

A real-time lip localization and tacking for lip reading

A self-adapting image encryption algorithm based on spatiotemporal chaos and ergodic matrix

A novel method for feature extraction of crackles in lung sound

A Novel Pitch Period Detection Algorithm Bases on HHT with Application to Normal and Pathological Voice