Lab report Essay Example | Topics and Well Written Essays - 750 words - 5

Lab report - Essay Example In this case, a stationary body can obtain kinetic energy from a moving. On the contrary, potential energy is totally not transferable to other body, but it can be converted to kinetic energy. Potential energy is directly connected with forces. If the work done on a body by a force that moves from point A to B is independent of the path between the two points, then the work done by this force is assigns a scalar value on each point in space and referred to as a scalar potential field. This means that the integral equation drawn from the line representing the change of force between these two points can be defined as the negative of the vector gradient and it gives the potential field. This potential field is the equivalent of the change in potential energy between the two points. This explains why the spring’s potential energy is given as a negative value. The negative sign denotes the convection that work done by a force field increase the PE while work applied against the force field reduces the potential energy It is important to note that work is required to either reduce or increase the potential energy of a body. In this case, a change in potential energy principally reflects the work done on the object. Therefore, the integral derivative of a PE function will give the amount of work done. Again the value is given as a negative figure to denote that the work done has reduced the PE possession of the body. 1. A normal pendulum with a few modifications can be used to achieve similar objectives. In this case, a zero position for the pendulum is identified. Since many labs are done on tabletops, the table top is assigned to be the zero height (mean) position. If the tabletop is designated the zero position, then the PE of an object is dependent on its relative height from the tabletop. Therefore, by obtaining the mass of the pendulum and its relative height from the table top, the gravitational

Communication class Assignment Example | Topics and Well Written Essays - 500 words

Communication class - Assignment Example In the long run, such power produces dysfunctional behavior. The film The Lion King is replete with scenes that exhibit the use of coercive power. This is evident in the way Simba forcefully grabs the throne of Pride Lands and uses coercive power in his rule. Following the death of Musafa, Scar takes over the throne of Pride Lands. Under his leadership, he exhibits a high degree of coercive power. For instance, Zazu is confined to a bone cage singing while Scar lazily lies about chewing on bones ("Internet Movie Database").when Zazu complains of his predicament and mentions that he never experienced the same under Mufasa, Scar scolds him and reminds him that the law requires them never to mention Mufasa’s name. Meanwhile, as Shenzi, Banzai and Ed complain about scarcity of food and water as well as the refusal of lionesses to hunt, Scar solution to them is to eat Zulu. Thus, it is evident that coercive power results in an atmosphere of insecurity and fear. When Scar confronts and asks Sarabi why the lionesses had refused to hunt, Sarabi answers that the herds had opted to leave Pride Rock. She then compares him to Mufasa. This angers Scar, who cruelly hits Sarabi. This typifies the fact that coercive power reduces people’s satisfaction with their jobs and therefore leads to lack of commitment and general withdrawal. Another instance where coercive power is manifested in the movie is the scene of Simba’s arrives in the Pride Land to take his rightful throne. On his arrival, Simba confronts Scar, and demands that he steps down from the throne or fight. The use of the threat of violence clearly depicts the use of coercive power. Even so, Scar retreats back by prompting Simba to confess who was responsible for Mufasa’s death ("Internet Movie Database"). In this regard, Simba confessed that he was responsible for Mufasa’s death, though it was accidental. This prompts Mufasa to use coercive power so as to maintain the throne. Thus, he accuses

National Food Security Bill 2013 Essay Example for Free

National Food Security Bill 2013 Essay Only three percent of Indians pay income tax; our tax-GDP ratio is among the lowest in the world. This must change. Our elites must realise that India’s poverty has damaging consequences for them, and that they can help decrease it. The food security bill, with all its limitations, will hopefully contribute to generating such awareness, says Praful Bidwai. After vacillating for years over taking any pro-people measures, the United Progressive Alliance finally did something bold and worthy by having the National Food Security Bill passed in Parliament a promise made in the UPA’s â€Å"first 100 days† agenda after its return to power in 2009. The Bill won a resounding victory in the Lok Sabha, with a margin exceeding 100, because non-UPA parties including the Janata Dal-United, the Dravida Munnetra Kazhagam and even the Shiv Sena felt they had no choice but to support it. It sailed through the Rajya Sabha too. The stage was set by a rare, spirited speech by Congress president Sonia Gandhi, in which she described the legislation as India’s chance to ‘make history’ by abolishing hunger and malnutrition, and emphasised that India cannot afford not to have the law: â€Å"The question is not whether we can [raise the resources] or not. We have to do it.† The NFSB has invested meaning, public purpose and a degree of legitimacy into the UPA’s otherwise corruption-ridden, shoddy and often appalling performance in government under an increasingly right-leaning leadership. This at once put the Bharatiya Janata Party on the defensive. Its leaders were reduced to opposing a measure that represents genuine social progress, and making thoughtless statements about the Bill being about ‘vote security’, not food security. The BJP now has nothing to offer to the nation but obscurantist programmes like building a temple at Ayodhya, and parochial, and predatory pro-corporate agendas under Narendra Modi’s rabidly communal leadership. The Bill is open to the criticism that it doesn’t go far enough. Instead of universalising subsidised food provision, it confines it to two-thirds of the population, and truncates it further by limiting the food quota to five kilos of grain per capita per month instead of the 35 kg per family demanded by right-to-food campaigners. The per capita quota puts small households, such as those headed by widows and single women, at a disadvantage. A universalised Public Distribution System, covering the entire population, has been proved to be more effective and less prone to leakage than one targeted at ‘below-poverty-line’ groups in Kerala, Tamil Nadu and even poor, backward Chhattisgarh. The relatively well-off won’t stand in queues at ration shops; they select themselves out of a universal PDS. Besides, a large proportion even of those officially defined as poor don’t possess BPL ration cards. The ratio can be as high as 40 percent in some highly deprived states. The latest National Sample Survey reveals that 51 percent of rural people possessing less than one-hundredth of a hectare of land have no ration cards of any kind; less than 23 percent have BPL cards. The problem of identifying the poor remains unresolved. Nevertheless, the broader coverage proposed under the NFSB and the simple, attractive formula of rice at Rs 3 per kg, wheat at Rs 2, and coarse grains at Re 1 marks a definite improvement over the current situation. It creates a right or entitlement for the poor, which can go some way in reducing acute hunger. However, right-wing commentators, including neo-liberal economists, credit-rating agencies, multinational and Indian big business, and writers/anchors in the media, have vitriolically attacked the NFSB as an instance of reckless â€Å"populism†. Some claim it will do to little to relieve malnutrition among Indian children, almost one-half of whom suffer from it. Yet others contend that the poor don’t want or deserve subsidies; they aspire to work, earn more and eat better. And almost all of them say the NFSB will entail excessive wasteful expenditure of Rs 1.25 lakh crores. This will aggravate India’s growing fiscal crisis and further depress already faltering GDP growth, now down to four-five percent. Eventually, this will work against the poor. Besides, if investment and growth are to be revived, India can’t spend so much on food security.

User Behavior Mining in Software as a Service Environment

User Behavior Mining in Software as a Service Environment Abstract—Software as a Service (SaaS) provides software application vendors a Web based delivery model to serve large number of clients with multi-tenancy based infrastructure and application sharing architecture. With the growing of the SaaS business, data mining in the environment becomes achallenging area. In this paper, we suggest a new metric along with a few existing metrics for customer analysis in a Software as a Service environment. Keywords: Software as a Service, SaaS, Customer Behavior analysis, Data mining in SaaS Environment I. Introduction With the rapid development of Internet Technology and the application software usage, SaaS (Software as a Service) as a complete innovative model of software application delivery model is attracting more and more customers to use SaaS for reducing the software purchase and maintenance costs as it can provide on-demand application software, and the users can adjust the functions provided by services to meet changes in demand. SaaS is gaining speed with the considerable increase in the number of vendors moving into this space[1]. The SaaS model is different from a regular website model. In a regular website model, users of the software directly interact with the software application. But in the case of a SaaS model, users interact with the application through the service provider. The difference between a regular website model and a SaaS model can be shown in figure 1. Figure: 1 II. Motivation Software as a Service (SaaS) is being adopted by more and more software application vendors and enterprises [2].SaaS is beneficial for the customers in such a way that, a customer can unsubscribe from the services whenever he wants which makes it a challenge to manage customer relationships. One of the characteristics of the SaaS business model is that one SaaS service needs to serve a large number of customers, among which considerable portion are customers for whom services are offered on trial basis. As there is competition in the market, both trial and paying customers may move their business to another service provider based on their requirements. It is essential for a service provider to retain the customers from migrating to another service provider. Previous studies show that a small increase in retention rate would lead to a considerable increment in the new present value of the customers. To withstand the competition in the market, a service provider should satisfy the cust omers by understanding their current behavior and predicting their next move like if they are having any problems in using the services, how much are the customers satisfied based on the seriousness and activeness of the customers. III. Related Works A lot of work has been done in the area of analyzing the customers’ behavior on website model. Various methodologies are stated by various authors on various processes in mining the web. In [3] Sindhu P Menon and Nagaratna P Hegde, analyzed the views and methodologies stated by various authors on various processes in web mining. In [4] R. Suguna and D. Sharmila listed out work done by various authors in the web usage mining area. In [5] the authors Jiehui Ju. Et.al, gives a quick survey on SaaS. It covers key technologies in SaaS, difference between Application Software Provider and Software as a Service Provider, SaaS architecture and SaaS maturity model and the advantages that SaaS offers to small businesses. In [6], the authors Espadas et. al, presents the analysis of the impart of a set of requirements and proposes guidelines to be applied for application deployment in Software as a Service (SaaS) Environment. In [7], the authors Ning Duan, et. al, proposed an algorithm and two metrics which work with the collaboration among the users of a customer in a Software as a Service environment. IV. Problem Definition In a SaaS Environment, an effective relationship with the customer depends on how much the status of each customer is understood. In order to understand the status of a customer, it is necessary to study the behavior of ehte customer form time to time. It is necessary to predict the customers’ seriousness and activeness in using the service. This prediction may help the service providers in improving their business strategies. In a business to customer website model, the mining is done based on selected metrics like visit frequency, average depth, average stay time etc. In the case of SaaS model, there is another level of users who actually use the service. So, regular user behavior metrics may not yield accurate results in the case of SaaS model. If individual customer’s user’s behavior is studied, then the difference between the customers may be identified. A lot of research is done on user behavior analysis in regular website model but those methods used for user behavior analysis may not guarantee accurate predictions. So an extra parameter or metric is to be considered. As in the SaaS model, a tenant is the direct customer of the service provider and the actual users of the service are the users of the customers, one way to study the behavior of the customers may be by summing up the individual user’s metrics of a customer to evaluate the customer’s behavior. But this way ignore the individual differences of the behaviors of the users of a customer. In addition to these regular web usage mining metrics if collaboration among the users is also considered in the analysis of customer behavior, it may yield better results than just using the regular metrics. But previous works done in user behavior analysis in SaaS uses only collaboration metrics in the analysis which ignores almost half of the analysis data. The experiment done aims at using collaboration metrics along with another metric which works with the data not considered in the collaboration metric calculation so that all the available data is considered in user behavior prediction. V. Experiment The experiment is done in two phases, namely Data Collection Phase and Data Processing Phase. In the Data collection phase, the necessary data (like server log files, transaction history, etc) are collected. In the second phase i.e. in Data Processing phase, the actual analysis takes place. This phase is further divided into individual modules like preprocessing, pattern discovery, and pattern analysis. Preprocessing is a process of refining the sever log data and transaction history removing noise in data (if any) and populating database for further use in next modules. It includes data cleaning, user identification, session identification, transaction identification. Pattern Discovery is the process of discovering the usage patterns from the cleaned raw log data. As in this experiment, it is not regular usage patterns that are to be considered, collaboration patterns are to be considered. Regular usage patterns are the sequences of activities that are performed by the users individually. But, collaboration patterns are those that are performed by users by interaction. Collaboration patterns are not the transaction patterns rather they are the patterns of users that collaborate to perform a transaction. Definition of Collaboration: Collaboration is defined to happen when different users of a customer work on the same business object during a certain period of time. For example, in a Human Resource management SaaS service, the vacation request is submitted by a regular employee user of a customer and then is approved/rejected by manager user of the same customer. Here two users of a customer are involved in the process of granting a leave. This is called collaboration. After the raw data is cleansed, the data used for collaboration discovery may contain details of the transactions performed by the users of any tenant with tenant id(tid), user id (uid), transaction id (transaction_id) (may also be called business object id), date, time, service id (sid). In this table more than one user may be involved in the completing of a transaction. Algorithm: Collaboration User Set Identification Input: Table 1 that consists of the transaction details Output: Collaboration_Table with collaboration transaction details Initially Collaboration_Table is empty Get first record from Table 1 Insert details into Collaboration_Table While end of table 1 not reached Get next record from table 1 Search for transaction_id in collaboration_Table If found, update collaboration user set and no_of_users Else Add details to collaboration_table as new record Table 1: Sample table showing the contents of Table 1 Table 2: Sample Collaboration Table Pattern analysis plays vital role in the experiment. This module deals with the behavior analysis based on the collaboration patterns extracted above. From [7], there are two type of collaboration. They are random collaboration and repeated collaboration by certain group of users. The first type of collaboration can indicate the activeness of the customer no matter which users are involved in the collaboration process. It can be called as Active Collaboration Index (ACI). The second type of collaboration can be described by the usage patterns among the users of a customer. It can be called Patterned Collaboration Index (PCI). A high ACI value tells that a customer is actively using the SaaS service and if such a customer is still a trial customer, it probably shall be the high priority target to get it converted into paying customer. A high PCI value tells that a tenant is seriously using the SaaS service with relatively strong loyalty, cross-selling or up-selling opportunity can be explored for such a customer. The formula to calculate ACI and PCI are as follows The AppCNorm is the normalizing factor indicating collaboration characteristic of SaaS service. While some SaaS service are rich with collaborations and others may not be. In order to balance the difference among different SaaS services, this normalization factor is employed. Where Pni denotes the collaboration pattern i of customer n, N is the total number of customers, and m is the total number of patterns in customer n. supp(pni) is the support value of pattern Pni, and len(Pni) is the length of the pattern. These collaboration metrics works only with the collaboration data and neglects the remaining data which is almost half of the data. Hence another metric can be added along with the above metrics which considers the non-collaboration transactions. As the new metric is for non-collaboration transactions of a tenant, it can be called Average Usage Index (AUI). This can be calculated using the formula This AUI increases the accuracy of prediction of activeness of the customer along with ACI. VI. RESULTS For this experiment, the data created is for 100 customers of a Software as a Service provider who is providing 6 different components of an application as different services. Among these 100 customers, first 50 are taken as paid customers and the other 50 are taken as trial customers. Table 3: Summary of transactions Table 4: Sample pattern list Table 5: Sample Calculated Metrics From the above calculated values, we can observe that though T0 is a paid customer, less ACI and PCI values indicate that this customer is not using the services to the full and hence revenue generated from this particular customer is not appreciable. Rather, this customer may be planning to unsubscribe from the service and hence is an important target for the service provider to retain the customer. In the case of T45, it has high ACI value, high AUI value indicating active usage of the services and high PCI indicating that this customer is completely migrating his business onto the SaaS service generating the service provider more revenue. Among the sample trial calculated values, customer T50 is active and serious and hence, there is a high probability for this customer to convert into paid customer. On the other hand, customer T89 is not very active and is not serious indicating that he may be facing technical difficulties in using the services and hence should be helped with or is thinking to unsubscribe from the services. Table 6: Summary of Calculated metrics From the above table, for any tenant to be considered active in using the services, minimum ACI and AUI values needed are 1 and 1 respectively and minimum PCI value needed is 2. VII. Conclusion The metrics ACI and PCI are introduced in previous works done by Ning Daun, et. al in [7] which works with collaboration data and leaving the non collaboration data. In our work, a new metric is introduced AUI which considers the non collaboration data also in customer behavior analysis. Still further, frequent pattern analysis can be applied on this non collaboration data to get usage patterns and so the analysis can be further improved. VIII. References [1] Wei Sun, Xing Zhang, Chang Jie Gou, Pei Sun, Hui Su, IBM China Research Lab, Beiing 100094, â€Å"Software as a Service: Configuration and Customization Perspective† IEEE Congress on Services Part II, IEEE 2008. [2] E. Knorr, â€Å" Software as a Service: The Next Big Thing†, http://www. infoworld.com/article/06/03/20/76103_12FEsaas_1.html† [3] Sindhu P Menon, Nagaratna P Hegde, â€Å"Requisite for Web Usage Mining – A Survey†, Special Issue of International Journal of Computer Science Informatics: 2231-5292, Vol-II, Issue-1, 2, pp. 209-215. [4] R. Suguna, D. Sharmila â€Å"An Overview of Web Usage Mining†, International Conference of Computer Applications (0975 – 8887), Vol. 39, No, 13, February 2012, pp. 11 – 13. [5] Jiehui, et. al, â€Å"Research on Key Technologu=ies in SaaS†, International Conference on Intelligent Computing and Cognitive Informatics, 2010, pp. 384-387. [6] Espadas et. al, â€Å"Application Development over Software-as-a-Service platforms†, The Third International Conference on Software Engineering Advances, 2008, pp. 87-104. [7] Ning Duan, et. al, â€Å"Tenant Behavior analysis in Software as a Service Environment†: Service Operations, Logistics and Informatics (SOLI), 2011 IEEE International Conference, pp 132-137, July 2011.

social science :: essays research papers

I knew then that I wanted to devote my studies to learning how body mechanisms react to varying chemicals. Witnessing innovative pharmaceutical research had only intensified my passion for biochemistry, a subject I had become fascinated with in high school; it had intrigued me because it integrated my love of chemistry with my desire to learn more about biological processes. My A-level studies provided me with a solid introduction to biochemistry; I now seek a greater academic focus and more extensive research opportunities by pursuing a university degree. Throughout high school, my extracurricular activities sharpened skills I will need in my biochemistry course-even if the activities often involved dance and music rather than science. Each week, I spent the majority of my spare time participating in {List school-related music and dance activities}. I also participated in my local parish's band and was elected Band Leader by the other members. Serving in this leadership position has allowed me to shape a community music program and taught me just how much I have learned about time-management and commitment. My busy schedule has required me to carefully divide my time among my academics, extracurricular interests, family and friends; throughout it all, I have prioritised my academics while remaining firmly committed to my outside pursuits. During my biochemistry studies, this balancing act will prove extremely useful as I seriously dedicate myself to my academics while also maintaining time for my hobbies and relationships. Five years ago, I had the opportunity to visit Birmingham, England-and I loved every minute of it. The people, the culture and the location all sparked my interest in one day living in England.

Imagery and Themes in the Epic of Gilgamesh Essay -- Epic Gilgamesh es

Historical Context - Imagery and Themes Rosenberg notes that Gilgamesh is probably the world's first human hero in literature (27). The Epic of Gilgamesh is based on the life of a probably real Sumerian king named Gilgamesh, who ruled about 2600 B.C.E. We learned of the Gilgamesh myth when several clay tablets written in cuneiform were discovered beginning in 1845 during the excavation of Nineveh (26). We get our most complete version of Gilgamesh from the hands of an Akkadian priest, Sin-liqui-unninni. It is unknown how much of the tale is the invention of Sin-liqui-unninni, and how much is the original tale. The flood story, which appears in the Sin-liqui-unninni version, is probably based on an actual flood that occurred in Mesopotamia around 2900 B.C.E. (26). The Sumerian culture influenced the entire Near East (Swisher 13). The success of their culture was dependent on the agricultural viability of the area. Every year there were floods which provided rich silt for successful farming that encouraged the people to stay in the same area year after year instead of migrating to find new areas for crops (19). There are indications that the Sumerians were composed of two different peoples which mingled in the same area. The Semites are believed to have mixed with the Highlanders. The Semites were patriarchal hunters and more warlike than the Highlanders. The Highlanders were matriarchal and peaceful. Swisher suggests that there is evidence of both social groups and that the combination of the two led to changes in the perception of the roles of the gods and goddess as well as the men and women (21). Sumer was originally small groups of people that eventually grew to form cities. As a country it included 13 ... ...der to receive eternal life. The apparent belief in an afterlife which is indicated by the burial with useful objects may show that eternal life is achieved after physical death. The Flood - recounted by Utanapishtim is representative of the purification of human life by the gods. Their transgressions are swept away (with most of the population) and they are reborn into a fresh, new world and relationship with the gods. Ark - the symbol of the gods' love of the humans and their interest in preserving the human race. We also identified five themes in the Epic of Gilgamesh: Conflict between chaos and order, represented by nature and civilization; Man's quest for immortality and knowledge; Dealing with loss; Male bonding/brotherhood; Heroism (man's victory over nature).

Mw Transmission Basics

Microwave Transmission Basics Microwave Transmission Basics Table of Contents †¢ MW Advantages †¢ MW Frequency bands †¢ MW Link elements †¢ Antenna Radiation Pattern †¢ MW Alarms Microwave Transmission Basics Features Advantages ? Rapid Deployment – A microwave link can be installed in as little as one day No right-of-way issues – Radio spans all obstacles such as roads, railways, etc. , avoiding any requirement to seek permissions that inevitably are costly and introduce time delays. Flexibility – The capacity of a microwave link can be easily increased at minimal or even no cost. Radios can also be redeployed if network needs change or as a result of customer churn. Losing customers does not mean assets are lost like in the case of fiber build. ? ? Microwave Transmission Basics Features Advantages †¢ †¢ †¢ †¢ Easily crosses city terrain – In many metropolitan and city authorities, street digging to install fiber is either extremely restricted, prohibitively expensive or is even banned outright. Operator-owned infrastructure – no reliance on competitors. Low start-up capital costs, which are independent of the link distance. Minimal recurring operational costs. †¢ †¢ Radio infrastructure already exists for many networks in the form of rooftops, cellular masts and existing radio transmission towers. Microwave radio is not susceptible to common catastrophic failure of cable systems caused by cable cuts, and can be repaired in minutes instead of hours or days. †¢ Limitations †¢ †¢ †¢ Transmission Capacity Limits (i. e. PDH, STM-1) Competitive Transmission Media Optical Fiber (i. e. STM-1 up to STM-N) Microwave Transmission Basics Common M/W Frequency Bands According to ITU-R recommendations ? ? ? ? ? ? ? ? 7 GHz 8 GHz 11 GHz 13 GHz 15 GHz 18 GHz 23 GHz 28 GHz 38 GHz Note: Hop distance is depending on frequency. High frequency coincides with shorter transmission hop distance. Systems on 23 – 38 GHz are prone to rain attenuation. Microwave Transmission Basics M/W Channel Arrangement Lower Band F0 Upper Band F1 F2 F3 F4 F5 F6 F1’ F2’ F3’ F4’ F5â €™ F6’ RF channel arrangement acc. ITU-R Microwave Transmission Basics Table of Contents M/W Link Elements? ? ? ? ? ? ? ? IDU ODU Antenna— Functions Specifications Types Pressurization Equipments Microwave Transmission Basics Elements of a MW link Antenna ODU Free Space Antenna ODU IDU IF Cable IF Cable IDU Microwave Transmission Basics Spur Link (Low Capacity Link) 7 – 38GHz Systems, generally used for PDH Spur Links Outdoor Unit (ODU, consists of RF-transmitter and antenna) IF-interconnection cable Indoor Unit (IDU) ? ? ? ? IDU Functions: Multiplexing /Demulteplexing of Tributaries. Modulation/Demodulation with the Carrier. IF conversion. Microwave Transmission Basics IDU BLOCK DIAGRAM: Power Supply 155Mbps Main Channel MUX/ DEMUX Modulator IF Card Cable Combi-ner 2 Mbps Wayside Channel 64 Kbps User Channel Demodulator Coax Cable NMS/Ethernet External Alarms IDU Controller Microwave Transmission Basics †¢ ODU BLOCK DIAGRAM: Power Supply AGC Voltage Reading 350MHz TX Converter 3-4GHz Coax Cable AGC Cable Combiner X Band Synthesizer RX Converter Trans receiver Converter Amplifier 140 MHz 3-4GHz IDU Controller Microwave Transmission Basics ? †¢ †¢ Antenna Functions: Works as an amplifier to overcome the fade margin and give desired AGC levels. Antennas: Wire carrying HF Current is surrounded by Electric and Magnetic Fields Voltage Standing Wave radiation 2 Microwave Transmission Basics ? Antennas: Voltage Standing Wave radiation Microwave Transmission Main Specification Basics Antenna Radiation Pattern Omni Antenna Lobe Point-to-Multipoint Directional Antenna Lobe Point-to-Point Microwave Transmission Basics Antenna Gain: Directive Gain ? Power Density radiated by antenna ? Power Density Radiated by Isotropic Antenna ? ? ? * Power Density measured at same distance * Both Antenna radiate radiate the same total power Bandwidth: — Operating Frequency bands — Possible to tune antenna for slightly different frequency range while retaining the same characteristics †¢ VSWR: Is the Guaranteed peak Voltage Standing wave ratio within the operating band Microwave Transmission Basics Antenna Specifications: Gain: Stated in dBi( Decibels over an isotropic radiator). Primirarly a function of antenna size ? Front to ba ck Ratio: In dB. Denotes highest radiation relative to main beam at 180+/-40 degree across the band. ? Half Power Beam Width: Nominal total width of the main beam at -3dB points ? Polarization: Can be single or dual polarised ? ? ? Isolation: For Dual polarised antennas. Refers to isolation between each polarised beam Microwave Transmission Basics Antenna Polarization ? Two main Types of Polarization: ? Vertical Polarization ? Horizontal Polarization Microwave Transmission Basics Antenna Polarization Vertical Polarization Horizontal Polarization Gridpak Low Back Lobe Focal Plane High Performance Dual Beam Angle Diversity Microwave Transmission Basics Antenna Feeder Types Air Dielectric Antenna Cables require Pressurization when used outdoors