Wavelet Multiresolution Analysis of High-Frequency FX Rates, Summer 1997

FX pricing processes are nonstationary and their frequency characteristics are time-dependent. Most do not conform to geometric Brownian motion, since they exhibit a scaling law with a Hurst exponent between zero and 0.5 and fractal dimensions between 1.5 and 2. This paper uses wavelet multiresolution analysis, with Haar wavelets, to analyze the nonstationarity (time-dependence) and self-similarity (scale-dependence) of intra-day Asian currency spot exchange rates.foreign exchange, anti-persistence, multi-resolution analysis, wavelets, Asia

Wavelet Multiresolution Analysis of High-Frequency Asian FX Rates, Summer 1997

FX pricing processes are nonstationary and their frequency characteristics are time-dependent. Most do not conform to geometric Brownian motion, since they exhibit a scaling law with a Hurst exponent between zero and 0.5 and fractal dimensions between 1.5 and 2. This paper uses wavelet multiresolution analysis, with Haar wavelets, to analyze the nonstationarity (time-dependence) and self-similarity (scale-dependence) of intra-day Asian currency spot exchange rates. These are the ask and bid quotes of the currencies of eight Asian countries (Japan, Hong Kong, Indonesia, Malaysia, Philippines, Singapore, Taiwan, Thailand), and of Germany for comparison, for the crisis period May 1, 1998 - August 31, 1997, provided by Telerate (U.S. dollar is the numeraire). Their time-scale dependent spectra, which are localized in time, are observed in wavelet based scalograms. The FX increments can be characterized by the irregularity of their singularities. This degrees of irregularity are measured by homogeneous Hurst exponents. These critical exponents are used to identify the fractal dimension, relative stability and long term dependence of each Asian FX series. The invariance of each identified Hurst exponent is tested by comparing it at varying time and scale (frequency) resolutions. It appears that almost all FX markets show anti-persistent pricing behavior. The anchor currencies of the D-mark and Japanese Yen are ultra-efficient in the sense of being most anti-persistent. The Taiwanese dollar is the most persistent, and thus unpredictable, most likely due to administrative control. FX markets exhibit these non- linear, non-Gaussian dynamic structures, long term dependence, high kurtosis, and high degrees of non-informational (noise) trading, possibly because of frequent capital flows induced by non-synchronized regional business cycles, rapidly changing political risks, unexpected informational shocks to investment opportunities, and, in particular, investment strategies synthesizing interregional claims using cash swaps with different duration horizons.foreign exchange markets, anti-persistence, long-term dependence, multi-resolution analysis, wavelets, time-scale analysis, scaling laws, irregularity analysis, randomness, Asia

FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS SENSOR NETWORK

Wireless Sensor Networks are to be widely deployed in the near future for data monitoring in commercial, industrial and military applications. Though much research has focused on making these networks feasible and useful security has received very little attention. Sensor networks are exposed to variety of attacks like eavesdropping, message tampering, selective forward, gray hole attack, and Wormhole and Sybil attacks. Watchdog is a kind of behaviour monitoring mechanism which is the base of many trust systems in Ad hoc and Wireless Sensor Network. Current watchdog mechanism only evaluates its next-hop’s behaviour and propagates the evaluation result to other nodes by broadcasting, which is neither energy efficient nor attack resilient. The fundamental problem of secure neighbour discovery is studied which is importunate in protecting the network from different forms of attacks. In this paper an improved watchdog monitoring mechanism is proposed by using the process of change point detection. By implementing this change point detection algorithm in watchdog mechanism, the limitations of the existing watchdog mechanism are overcome. From this the exact malicious node can be found out and the data will be routed through a secure path bypassing the malicious node. Finally to analyze the efficiency of this algorithm, the results obtained from the proposed algorithm and the existing algorithms are compared

LEVEL PARTITIONING OF NODES TO ENHANCE THE NETWORK LIFETIME DURING INTRUSION DETECTION IN WIRELESS SENSOR NETWORKS

A Wireless Sensor Network (WSN) consists of many sensor nodes with low cost and power capability Based on the deployment, in the sensing coverage of a sensor node, typically more nodes are covered. A major challenge in constructing a WSN is to enhance the network life time. Nodes in a WSN are usually highly energy-constrained and expected to operate for long periods from limited on-board energy reserves. To permit this, nodes and the embedded software that they execute – must have energy-aware operation. Because of this, continued developments in energy-efficient operation are paramount, requiring major advances to be made in energy hardware, power management circuitry and energy aware algorithms znd protocols. During Intrusion Detection in sensor networks, some genuine nodes need to communicate with the Cluster Head to inform about the details of malicious nodes. For such applications in sensor networks, a large number of sensor nodes that are deployed densely in specific sensing environment share the same sensing tasks. Due to this, the individual nodes might waste their energy in sensing data that are not destined to it and as a result the drain in the energy of the node is more resulting in much reduced network life time. In this paper, a novel algorithm is developed to avoid redundancy in sensing the data thereby enhancing the life time of the network. The concept of Power Factor bit is proposed while a node communicates with the Cluster Head. The simulation results show that the network life time is greatly enhanced by the proposed method

A novel energy-efficient sybil node detection algorithm for intrusion detection system in wireless sensor networks

A Wireless Sensor Network (WSN) is vulnerable to different types of security attacks where the attackers could easily intrude into the network and could cause inexplicable destruction by disrupting the expected functionalities of the network. Severe drainage of battery may occur due to the attacks and as a result, the lifetime of the network may decrease drastically. In this paper, an energy-efficient integrated Intrusion Detection System (IDS) is proposed to detect network layer Sybil attack. Our scheme spots out accurately and purges out the Sybil node which may falsely behave as a genuine node. The experimental results show that the critical factor in WSN, energy is conserved more efficiently by the proposed scheme than the existing alternative methods. Also, accurate detection of the malicious node is possible spending relatively less energy

Risk factors of musculoskeletal disorders (MSD) among palm oil mill workers

The palm oil industry is the fourth largest contributor to the Malaysian Gross National Income (GNI) but the importance of this industry to the nation’s economy does not preclude them from the risk of being affected by the occurrence of musculoskeletal disorders (MSD) among the workers in palm oil mills. Previous studies carried out have not properly explained the exact reasons for the occurrence of musculoskeletal disorders (MSD) among the palm oil mill workers. Thus, this study aim to determine the association between risk factors and MSD among palm oil mill workers. A total of 120 male workers were consecutively enrolled in this crosssectional study. Information on sociodemographic, working, lifestyle, health and injury factors were collected via questionnaire and face-to-face interviews. A high lifetime prevalence of MSD (71.7 percent) among palm oil mill workers was found. However, the one-year prevalence of MSD among the same population was only 50.8 percent. Logistic regression analysis adjusted for age, Body Mass Index (BMI) and smoking revealed that the risk factors: history of previous injury [2.35, 95 percent CI 1.12-4.93], Osteoarthritis [3.9, 95 percent CI 1.568-9.708] and perception of exertion [8.09, 95 percent CI 1.358-48.170] was found to be significantly associated with MSD symptoms reported in the past 12 months. As a conclusion, exposure to the combination of these risk factors may lead to an increased risk of developing MSD among palm oil mill workers

BOILED WATER TEMPERATURE MEASUREMENT SYSTEM USING PIC MICROCONTROLLER

ABSTRACT: The measurement system for temperature of boiled water is a critical task in industry. In this paper we designed and implemented a PIC micro controller based boiled water temperature measurement system using PIC 18F452 and national semiconductors LM35 temperature sensor. The designing system is used to measure the tank I boiled water temperature value. If the temperature value reaches the set value high temperature relay board becomes ON to control the solenoid valve. The high temperature of the boiled water which flows through the metal pipe I is lowered by spraying the cool water using control valves. The valves are controlled by manually depends upon the requirement of temperature. Thus the output is obtained by reducing high temperature. Low temperature water is collected in tank III. Here, after comparing the reduced temperature value water with programming of temperature set value, the collected cooling water is ready for supplying to various purposes in the industry plants

Molecular Cloning and Docking of speB Gene Encoding Cysteine Protease With Antibiotic Interaction in Streptococcus pyogenes NBMKU12 From the Clinical Isolates

Streptococcus pyogenes causes a variety of diseases ranging from mild diseases to severe invasive infections which result in significant morbidity and mortality. This study focuses on the antibiotic resistance of S. pyogenes and their interaction with cysteine protease. Around 36 beta-hemolytic isolates were collected from the clinical lab, of which seven isolates (19.4%) were identified as Streptococcus pyogenes. One of the seven isolates was collected from a urinary tract infection, which was identified by antibody agglutination and MALTI-TOF-MS, and it is designated as S. pyogenes NBMKU12. Around 8.3 to 66.6 % of the isolates were found to be resistant to one or more antimicrobial agents, especially, penicillin-G resistance was exhibited by 29.1% of the isolates. In the NBMKU12 isolate, the beta lactem (TEM) gene was detected among the 13 antibiotic genes for which it was tested. Furthermore, when analysis for presence of 13 virulence genes were carried out in NBMKU12 isolate, only speJ and speB were detected. The speB (streptococcal pyrogenic exotoxin B) encoding cysteine protease gene was cloned. This was followed by performing DNA sequencing to understand the putative cysteine protease interaction with antibiotics, inhibitors, and substrate. The speB gene consists of 1197 nucleotides and encodes a protein with multiple domains, including a signal peptide (aa 1–22), an inhibitor region (aa 27–156), and a catalytic cysteine domain (aa 160–367). The signal peptide cleavage site is predicted between Ala22 and Asn23. The putative 398 amino acid residues were found to have a theoretical pI of 8.76 and a molecular mass of 43,204.36 Da. The tested culture supernatants of NBMKU12 isolate exhibited the proteolytic activity against casein, papaya and pineapple used as substrates. The proteolytic activity suggests the expression of speB gene. Molecular docking analysis of cysteine protease showed that erythromycin (bond length 2.41 Å), followed by chloramphenicol (2.51 Å), exhibited a strong interaction; while penicillin-G (3.24 Å) exhibited a weak interaction, and this factor could be considered as a cause for penicillin-G resistance. The present study contributes to a better understanding of speB gene encoding cysteine protease, antibiotic resistance, and their interaction in the isolate, S. pyogenes NBMKU12. The antibiotics and cysteine protease interaction study confirms the resistance or sensitivity of S. pyogenes. Hence, it could be hypothesized that the isolate NBMKU12 is resistant to most of the tested antibiotics, and this resistance might be a cause for mutation